what is population or universe in research

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How to design a research project in communication > Universe and samples > Universe or population

Another important decision is what is our universe or population, composed of people (in a study of electoral behavior, for example, the set of voters; in a study of audiences, people who follow a particular medium or program) or objects (the set of episodes of a television series that constitutes our object of study, or of the news on a specific subject that we must analyze). In the technique of content analysis it is also called corpus and its manufacture requires a series of steps that we will explain in due course. Once the problem has been defined, the hypotheses have been formulated and the variables have been identified, it is necessary to delimit what is part of the universe or population. If we can observe all the units that make up the universe (= a census) or if it is a part of the universe (= the sample). The census is a feasible option for small universes.

The hypothetical universe is the totality of the set of elements, beings or objects that are tried to investigate. It includes all existing populations on which research could be conducted.

The population is the set of all cases coinciding with a series of specifications. Your units must be of the same nature as the sample. The sample, therefore, is a subgroup of the population.

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‘Defining the universe’ is essential when writing about survey data

The answer to almost any survey question depends on who you ask.

At Pew Research Center, we conduct surveys in the United States and dozens of other countries on topics ranging from politics and religion to science and technology. Given the wide range of people we speak to for our polls – and the many issues we ask them about – it’s important to be as clear as possible in our writing about exactly who says what .

In research circles, this practice is sometimes called “defining the universe” – that is, clearly identifying the population whose attitudes we’re studying, whether those people are police officers in the U.S. , Christians in Western Europe or some other specific group. This kind of clarification can go a long way toward ensuring that readers interpret survey results correctly.

In many cases, the universe of survey respondents we’re talking about is relatively straightforward. If we say that nearly two-thirds of U.S. adults think the national economic situation is good, we’re referring to the views of Americans ages 18 and older. But in other cases, additional clarification and context may be needed, particularly for readers who aren’t schooled in the art of interpreting poll results.

Consider one of our recent survey findings about Facebook users. The survey found that 74% of adult Facebook users in the U.S. have taken at least one of the following three steps in the past 12 months: adjusted their privacy settings, taken a break from checking the platform for several weeks or more, or deleted the app from their phone. The survey drew a lot of attention because it suggested – correctly – that adult Facebook users in the U.S. have a complex relationship with one of the major social platforms in their lives.

But there are some caveats to bear in mind when considering this finding. First, not all U.S. adults use Facebook. Pew Research Center’s most recent estimate is that 68% of American adults use the platform. In other words, it’s not accurate to say that 74% of all U.S. adults have adjusted their Facebook privacy settings, taken a break from the platform or deleted the app from their phone in the past 12 months. It is accurate to say that 74% of U.S. adults who currently use Facebook – that is, 74% of that 68% – have taken one of these three steps in the past year.

It’s also crucial to remember that this finding only refers to adult Facebook users in one country. Worldwide, Facebook claims more than 2 billion monthly active users , and our survey did not include respondents from countries outside the U.S. It also doesn’t reflect U.S. Facebook users who are younger than 18 – a substantial population. How substantial? We know from a separate Pew Research Center survey earlier this year that 51% of U.S. teens ages 13 to 17 use Facebook. And there may be many other U.S. Facebook users who are younger than 13.

Another example of a survey universe that’s not always easily understood concerns U.S. politics. Pew Research Center and other polling organizations frequently conduct polls to find out Americans’ views on political topics. But these views can vary depending on whether the universe of interest is all U.S. adults, registered voters, or – in some cases – an even more narrowly defined group, likely voters.

Only about two-thirds of adults are registered to vote, and these adults tend to be more politically engaged than those who are not registered. That can translate into differences in attitudes.

Some polling organizations also report on the subset of the public they deem likely to vote, particularly for surveys conducted in the run-up to an election. This determination is complicated, but might be made by asking respondents questions about their voting behavior and intentions, or by using people’s past records of voting, among other things.

Since different polling organizations use different definitions of the term “likely voters,” it’s important to understand who is and isn’t included in the definition being used. Only a third of U.S. adults cast ballots in the last midterm election, so a report based on likely midterm voters is aimed at understanding the views of that subset of the adult population.

Survey data can be tricky for non-experts to follow, as we’ve noted in prior blog posts. It can be confusing for some readers to understand why 51% may not represent a “majority,” or how a survey of 1,000 people can tell you what an entire country thinks . One way to help readers interpret the many poll findings they encounter is to be as clear as possible about the group of people whose answers are being described.

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John Gramlich is an associate director at Pew Research Center .

Key things to know about U.S. election polling in 2024

National public opinion reference survey (npors), how pew research center uses its national public opinion reference survey (npors), q&a: what is the american trends panel, and how has it changed, how do people in the u.s. take pew research center surveys, anyway, most popular.

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Statistics By Jim

Making statistics intuitive

Populations, Parameters, and Samples in Inferential Statistics

By Jim Frost 25 Comments

However, to gain these benefits, you must understand the relationship between populations, subpopulations, population parameters, samples, and sample statistics.

In this blog post, learn the differences between population vs. sample, parameter vs. statistic, and how to obtain representative samples using random sampling.

Related post : Difference between Descriptive and Inferential Statistics

Populations

Populations can include people, but other examples include objects, events, businesses, and so on. In statistics, there are two general types of populations.

Populations can be the complete set of all similar items that exist. For example, the population of a country includes all people currently within that country. It’s a finite but potentially large list of members.

However, a population can be a theoretical construct that is potentially infinite in size. For example, quality improvement analysts often consider all current and future output from a manufacturing line to be part of a population.

Populations share a set of attributes that you define. For example, the following are populations:

• Stars in the Milky Way galaxy.
• Parts from a production line.
• Citizens of the United States.

Before you begin a study, you must carefully define the population that you are studying. These populations can be narrowly defined to meet the needs of your analysis. For example, adult Swedish women who are otherwise healthy but have osteoporosis.

Population vs Sample

It’s virtually impossible to measure a whole population completely because they tend to be extremely large. Consequently, researchers must measure a subset of the population for their study. These subsets are known as samples.

Typically, a researcher’s goal is to draw a representative sample from their target population. A representative sample mirrors the properties of the population. Using this approach, researchers can generalize the results from their sample to the population. Performing valid inferential statistics requires a strong relationship between the population and a sample.

In a later section, you’ll learn about the importance of representative samples and how to obtain them.

A statistical inference is when you use a sample to infer the properties of the entire population from which it was drawn. Learn more about making Statistical Inferences .

Learn more in-depth about Populations vs. Samples: Uses and Examples  and Sample Mean vs. Population Mean .

Subpopulations can Improve Your Analysis

Subpopulations share additional attributes. For instance, the population of the United States contains the subpopulations of men and women. You can also subdivide it in other ways such as region, age, socioeconomic status, and so on. Different studies that involve the same population can divide it into different subpopulations depending on what makes sense for the data and the analyses.

Understanding the subpopulations in your study helps you grasp the subject matter more thoroughly. They can also help you produce statistical models that fit the data better. Subpopulations are particularly important when they have characteristics that are systematically different than the overall population. When you analyze your data, you need to be aware of these deeper divisions. In fact, you can treat the relevant subpopulations as additional factors in later analyses.

For example, if you’re analyzing the average height of adults in the United States, you’ll improve your results by including male and female subpopulations because their heights are systematically different. I’ll cover that example in depth later in this post!

Parameter vs Statistic

A parameter is a value that describes a characteristic of an entire population, such as the population mean. Because you can almost never measure an entire population, you usually don’t know the real value of a parameter. In fact, parameter values are nearly always unknowable. While we don’t know the value, it definitely exists.

For example, the average height of adult women in the United States is a parameter that has an exact value—we just don’t know what it is!

The population mean and standard deviation are two common parameters. In statistics, Greek symbols usually represent population parameters, such as μ (mu) for the mean and σ (sigma) for the standard deviation.

A statistic is a characteristic of a sample. If you collect a sample and calculate the mean and standard deviation, these are sample statistics. Inferential statistics allow you to use sample statistics to make conclusions about a population. However, to draw valid conclusions, you must use particular sampling techniques. These techniques help ensure that samples produce unbiased estimates. Biased estimates are systematically too high or too low. You want unbiased estimates because they are correct on average.

In inferential statistics, we use sample statistics to estimate population parameters. For example, if we collect a random sample of adult women in the United States and measure their heights, we can calculate the sample mean and use it as an unbiased estimate of the population mean. We can also perform hypothesis testing on the sample estimate and create confidence intervals to construct a range that the actual population value likely falls within. Learn more about Parameters vs Statistics .

The law of large numbers states that as the sample size grows, sample statistics will converge on the population parameters. Additionally, the standard error of the mean mathematically describes how larger samples produce more precise estimates.

Related posts : Measures of Central Tendency and Measures of Variability

Representative Sampling and Simple Random Samples

In statistics, sampling refers to selecting a subset of a population. After drawing the sample, you measure one or more characteristics of all items in the sample, such as height, income, temperature, opinion, etc. If you want to draw conclusions about these characteristics in the whole population, it imposes restrictions on how you collect the sample. If you use an incorrect methodology, the sample might not represent the population, which can lead you to erroneous conclusions. Learn more about Representative Samples .

The most well-known method to obtain an unbiased, representative sample is simple random sampling. With this method, all items in the population have an equal probability of being selected. This process helps ensure that the sample includes the full range of the population. Additionally, all relevant subpopulations should be incorporated into the sample and represented accurately on average. Simple random sampling minimizes the bias and simplifies data analysis.

I’ll discuss sampling methodology in more detail in a future blog post, but there are several crucial caveats about simple random sampling. While this approach minimizes bias, it does not indicate that your sample statistics exactly equal the population parameters. Instead, estimates from a specific sample are likely to be a bit high or low, but the process produces accurate estimates on average. Furthermore, it is possible to obtain unusual samples with random sampling—it’s just not the expected result.

Procedures for collecting a representative sample include the following probability sampling methods :

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Systematic sampling

Additionally, random sampling might sound a bit haphazard and easy to do—both of which are not true. Simple random sampling assumes that you systematically compile a complete list of all people or items that exist in the population. You then randomly select subjects from that list and include them in the sample. It can be a very cumbersome process.

Random sampling can increase the internal and external validity of your study. Learn more about internal and external validity .

Conversely, convenience sampling does not tend to obtain representative samples. These samples are much easier to collect but the results are minimally useful.

Let’s bring these concepts to life!

Related post : Sample Statistics Are Always Wrong (to Some Extent)!

Example of a Population with Important Subpopulations

Suppose we’re studying the height of American citizens and let’s further assume that we don’t know much about the subject. Consequently, we collect a random sample, measure the heights in centimeters, and calculate the sample mean and standard deviation. Here is the CSV data file: Heights .

Because we gathered a random sample, we can assume that these sample statistics are unbiased estimates of the population parameters.

Now, suppose we learn more about the study area and include male and female as subpopulations. We obtain the following results.

Notice how the single broad distribution has been replaced by two narrower distributions? The distribution for each gender has a smaller standard deviation than the single distribution for all adults, which is consistent with the tighter spread around the means for both men and women in the graph. These results show how the mean provides more precise estimates when we assess heights by gender. In fact, the mean for the entire population does not equal the mean for either subpopulation. It’s misleading!

During this process, we learn that gender is a crucial subpopulation that relates to height and increases our understanding of the subject matter. In future studies about height, we can include gender as a predictor variable.

This example uses a categorical grouping variable (Gender) and a continuous outcome variable (Heights). When you want to compare distributions of continuous values between groups like this example, consider using box plots . This plot become more useful as the number of groups increases.

This example is intentionally easy to understand but imagine a study about a less obvious subject. This process helps you gain new insights and produce better statistical models.

Using your knowledge of populations, subpopulations, parameters, sampling, and sample statistics, you can draw valuable conclusions about large populations by using small samples. For more information about how you can test hypotheses about populations, read my Overview of Hypothesis Tests .

When you take measurements, ensure that your measurement instruments and test scores are valid. To learn more, read my post Validity .

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Reader Interactions

August 1, 2023 at 9:16 am

Thanks for your thoughtful response, Jim – I appreciate you taking the time.

That makes a lot of sense. To be crystal clear, and using the M&A hypothetical as an example, am I right in thinking that I would therefore need to use a hypothesis test in order to demonstrate any statistically significant year-on-year change in frequency/observations/count? i.e. establish that the annual samples are likely to come from different populations (say, rates of M&A activity) and are not different merely due to randomness/variation?

I think perhaps the all-too-common sensationalist interpretations in the financial media of year-on-year changes in activity without statistical testing have contributed to my confusion! Are there any grounds at all to compare a 2021 “population” of complete observations vs a 2022 population of observations, say, and coming up with a definite conclusion without statistical testing? Or are the media wrong to do so?

Thank you again – I appreciate my initial questions have spilled over into a second set, but this would really help clear things up for me! In the meantime, I’ve purchased a couple of your books, which look fantastic, and will enjoy improving my understanding in due course.

Best regards, Kerry

July 30, 2023 at 6:57 am

Thanks for the great site.

With reference to the following statement, I was hoping you could kindly advise your position on the examples below.

“However, a population can be a theoretical construct that is potentially infinite in size. For example, quality improvement analysts often consider all current and future output from a manufacturing line to be part of a population.”

Ex1: How would you treat the performances of an athlete or sports team in any given year? Assuming all recorded performances are available, do they constitute a population or are they merely a sample of the athlete’s/team’s inherent ability?

Ex2: If we count all instances of an event in a given year (for example, the number of public M&A transactions worldwide), does this constitute a complete population, or merely a sample of all M&A transactions that could theoretically exist now and in the future?

Thank you very much in advance for considering my request.

All the best, Kerry

July 31, 2023 at 10:12 pm

Thank you for your question and for the kind words about the site.

Ex1: In the case of an athlete or sports team’s performances, it depends on the context. If you’re considering the entirety of an athlete’s or team’s career, then the performance in a single year could be viewed as a sample because it does not encompass all the performances that the athlete or team has or will have. It’s a subset of a larger ‘population’ (the athlete’s or team’s overall career). However, if you are specifically interested in performance within a given year, then those performances could constitute a ‘population’ for that particular context or research question.

Ex2: Regarding the number of public M&A transactions in a given year, again it depends on your research question. If you’re studying M&A transactions within a specific year, then the transactions that occur during that year can be considered a ‘population’. However, if your scope of interest includes M&A transactions across multiple years or indefinitely into the future, then the transactions in a given year would be a sample of that larger, potentially infinite population.

In both cases, your sample or population is defined by the scope of your research question or area of interest. The distinction between a sample and a population isn’t a fixed, objective attribute of a set of data, but rather a perspective that depends on the particular context and research goals.

I hope this provides some clarity on your inquiry. Please feel free to ask if you have any further questions.

Best Regards.

August 7, 2021 at 3:16 pm

hello jim, will you explain this statement?

“”The parameter is drawn by the measurements of units in the sample, and statistics is drawn by the measurements of the population”.

August 7, 2021 at 11:24 pm

I *think* I know that statement is trying to say but it’s not totally clear. The first part explains that you can use a sample to estimate the parameters of a population, such as the mean and standard deviation. Remember, inferential statistics will use a sample to infer the properties of a population. I write about that in this post. So, read that to understand that portion. The second part I’m not totally clear on what it’s trying to say. Typically, when you talk about “a statistic,” we’re referring to a value computed from a sample. Contrary to what the statement says, a statistic is NOT a measure from a population. Also, the use of the word “drawn” is unusual in that statement.

To understand what I believe this statement is trying to explain in a confusing and at least partially incorrect manner, read this post and also read my post about descriptive and inferential statistics .

February 9, 2021 at 12:28 am

how do u know if the standard deviation is stat or parameter?

February 11, 2021 at 4:55 pm

If you’re using a sample drawn from a population (i.e., you didn’t measure the entire population), then you have a sample statistic, which is also known as a parameter estimate. However, if you measure the entire population (almost always impossible), then the value is the parameter itself. For example, if you measure the heights of everyone in population, the mean height is the population parameter.

However, you almost always work with sample statistics (parameter estimates) because you generally cannot measure the entire population.

January 1, 2021 at 1:29 am

population vs. sample, and the terms parameter vs. statistic which a researcher almost always use, and why?

January 1, 2021 at 6:51 pm

Hi Hamthal,

Read this article more carefully! It’s clear in this article that researchers will almost never know the population parameters. In fact, they are usually unknowable. Instead, researchers use sample statistics to estimate the parameter values.

September 20, 2020 at 10:39 am

Hi Jim, if the only sampling method that we can use is convenience sampling ,or samples that are obtained by voluntary response (which are biased), should we still proceed with our research?

September 22, 2020 at 10:35 pm

That’s a tricky situation. Often researchers will have samples that aren’t truly random. The question then becomes understanding the implications of the nonrandomness for your sample.

Are you talking about data that aren’t random but used a systematic technique such as a stratified or clustered sample? These methods approximate random sampling but use some intentional differences. I talk about these methods in my Introduction to Statistics ebook . There are techniques that can handle these types of samples.

Or, do you mean a convenience sample? In this case, you need to understand the ways in which your sample is different than your study population. Your results might be biased on way or another. There’s no firm answer I can give here because it depends on the specifics of how your sample is different from the population. It weakens your evidence undoubtedly. You can’t really trust the p-values and confidence intervals. Effects can biased. How much these issues affect your results depends on your sample. How different is your sample from a random sample? You need to understand that. A place to start would be to look at the various properties of your sample and compare those properties to published values of the population you are studying. Are there any striking differences?

I hope that helps!

August 14, 2020 at 11:35 am

Hello Sir Does the value of statistic necessarily equal parameter and why?

August 15, 2020 at 3:29 pm

It can be surprising, but no, the sample statistic doesn’t necessarily equal the population parameter. In fact, the sample statistic is almost always at least a little different from the parameter. That difference between statistics and parameter is sampling error. A key goal of inferential statistics is estimating the size of sampling error so you can understand how good your estimate is. Sampling error occurs because your sample, even with appropriate random sampling methodology, won’t exactly represent the full population.

For more on this topic, read my post about how sample statistics are always wrong (to some extent) .

July 14, 2020 at 6:15 am

How bias influences the estimation of a population parameter

June 30, 2020 at 10:22 am

Such a helpful content ,understood the topic very clearly ,thanks uh so much sir for providing this kind of explanation Extremely grateful !

–trusha

June 17, 2020 at 5:52 am

Hi therre. Is a population parameter a value or the characteristic? i.e. is the population parameter ‘the proportion of faulty items in a production batch’ or ‘5% of items in a production batch are faulty’ Thank you 🙂

June 18, 2020 at 5:26 pm

Hi Allissa,

A parameter is a value that describes a characteristic of a population. For example, the mean height of all women in the United States is a parameter. It has a specific value, we just don’t know what it is. That value is for a specific characteristic (height). So, it’s a value that uses units relevant to the characteristic (such as CM).

For your example, the parameter is the proportion of faulty items. The actual parameter value is a proportion for the entire population. Of course, we’ll never know it exactly. You mention “5% of a batch.” Now that is a sample estimate of the parameter, not the parameter itself. Usually, the best we can do is estimate a parameter.

So, parameters are values but we never know those values exactly. However, we can estimate them.

February 13, 2020 at 12:00 pm

I am trying to understand the importance of parameters in drawing conclusions when an exact value can be calculated. Can you explain this for me?

February 8, 2020 at 8:52 am

What are population parameters and how can they be use for estimation

February 8, 2020 at 3:18 pm

This post answers your question. Read the section titled “Population Parameters versus Sample Statistics” more closely!

November 25, 2019 at 9:22 am

Hope you are doing well, I want to ask a clarification when your time permit, please throw some light on it.

Which is the best way to estimate the (population) parameter?

1. Calculate the required sample size by defining Z-score (95%, 1-96), error (example 0, 03), and p (say .5 for maximum sample size) then estimate the sample statistic (example sample proportion). Then we say the calculated sample proportion is an unbiased estimator of the population proportion and 95% confidence the population proportion lies within plus or minus 0.03 (this value was used for calculating sample size) of the sample proportion. That is,

p- 0.03=< P <= p + 0.03

We take a small sample (not calculate sample size statistically, say 40) due to limitation but using sampling techniques (srs, cluster or ..) while selecting a sample, then calculate the sample proportion after that and its variance (using statistical techniques). Finally, we say population proportion-P lies between p + – Z SE(p). That is,

p- Z[SE(p)] =< P <= p + Z [SE(p)]

Please clarify it, when your time permits.

November 26, 2019 at 5:03 pm

I’m not sure exactly what you’re asking. There are established power analysis methods for estimating samples sizes require to obtain statistical power that you specify. And other procedures for determining the precision of the estimate. For those types of procedures, you’ll need to enter information such as estimated effect size and estimated standard deviations. Read the post I link to for more information.

I hope this helps, Jim

September 2, 2019 at 4:26 am

Content of this blog is awesome, quick absorb-able , Thank You

September 2, 2019 at 2:20 pm

You’re very welcome, Sudarshan! I happy to hear that it was helpful!

July 23, 2018 at 2:58 am

Hello sir! I m always enjoying ur e-lectures. I hv a query. I m conducting research on gender based and type of school management based sample. Target population is secondary school students. I employed d probability sampling of randomization Cud u tell me wheth i shud adopt straitified or simple random sampling technique m how. Remember d population is itself large but finite here

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Sampling is the statistical process of selecting a subset—called a ‘sample’—of a population of interest for the purpose of making observations and statistical inferences about that population. Social science research is generally about inferring patterns of behaviours within specific populations. We cannot study entire populations because of feasibility and cost constraints, and hence, we must select a representative sample from the population of interest for observation and analysis. It is extremely important to choose a sample that is truly representative of the population so that the inferences derived from the sample can be generalised back to the population of interest. Improper and biased sampling is the primary reason for the often divergent and erroneous inferences reported in opinion polls and exit polls conducted by different polling groups such as CNN/Gallup Poll, ABC, and CBS, prior to every US Presidential election.

The sampling process

As Figure 8.1 shows, the sampling process comprises of several stages. The first stage is defining the target population. A population can be defined as all people or items ( unit of analysis ) with the characteristics that one wishes to study. The unit of analysis may be a person, group, organisation, country, object, or any other entity that you wish to draw scientific inferences about. Sometimes the population is obvious. For example, if a manufacturer wants to determine whether finished goods manufactured at a production line meet certain quality requirements or must be scrapped and reworked, then the population consists of the entire set of finished goods manufactured at that production facility. At other times, the target population may be a little harder to understand. If you wish to identify the primary drivers of academic learning among high school students, then what is your target population: high school students, their teachers, school principals, or parents? The right answer in this case is high school students, because you are interested in their performance, not the performance of their teachers, parents, or schools. Likewise, if you wish to analyse the behaviour of roulette wheels to identify biased wheels, your population of interest is not different observations from a single roulette wheel, but different roulette wheels (i.e., their behaviour over an infinite set of wheels).

The second step in the sampling process is to choose a sampling frame . This is an accessible section of the target population—usually a list with contact information—from where a sample can be drawn. If your target population is professional employees at work, because you cannot access all professional employees around the world, a more realistic sampling frame will be employee lists of one or two local companies that are willing to participate in your study. If your target population is organisations, then the Fortune 500 list of firms or the Standard & Poor’s (S&P) list of firms registered with the New York Stock exchange may be acceptable sampling frames.

Note that sampling frames may not entirely be representative of the population at large, and if so, inferences derived by such a sample may not be generalisable to the population. For instance, if your target population is organisational employees at large (e.g., you wish to study employee self-esteem in this population) and your sampling frame is employees at automotive companies in the American Midwest, findings from such groups may not even be generalisable to the American workforce at large, let alone the global workplace. This is because the American auto industry has been under severe competitive pressures for the last 50 years and has seen numerous episodes of reorganisation and downsizing, possibly resulting in low employee morale and self-esteem. Furthermore, the majority of the American workforce is employed in service industries or in small businesses, and not in automotive industry. Hence, a sample of American auto industry employees is not particularly representative of the American workforce. Likewise, the Fortune 500 list includes the 500 largest American enterprises, which is not representative of all American firms, most of which are medium or small sized firms rather than large firms, and is therefore, a biased sampling frame. In contrast, the S&P list will allow you to select large, medium, and/or small companies, depending on whether you use the S&P LargeCap, MidCap, or SmallCap lists, but includes publicly traded firms (and not private firms) and is hence still biased. Also note that the population from which a sample is drawn may not necessarily be the same as the population about which we actually want information. For example, if a researcher wants to examine the success rate of a new ‘quit smoking’ program, then the target population is the universe of smokers who had access to this program, which may be an unknown population. Hence, the researcher may sample patients arriving at a local medical facility for smoking cessation treatment, some of whom may not have had exposure to this particular ‘quit smoking’ program, in which case, the sampling frame does not correspond to the population of interest.

The last step in sampling is choosing a sample from the sampling frame using a well-defined sampling technique. Sampling techniques can be grouped into two broad categories: probability (random) sampling and non-probability sampling. Probability sampling is ideal if generalisability of results is important for your study, but there may be unique circumstances where non-probability sampling can also be justified. These techniques are discussed in the next two sections.

Probability sampling

Probability sampling is a technique in which every unit in the population has a chance (non-zero probability) of being selected in the sample, and this chance can be accurately determined. Sample statistics thus produced, such as sample mean or standard deviation, are unbiased estimates of population parameters, as long as the sampled units are weighted according to their probability of selection. All probability sampling have two attributes in common: every unit in the population has a known non-zero probability of being sampled, and the sampling procedure involves random selection at some point. The different types of probability sampling techniques include:

Stratified sampling. In stratified sampling, the sampling frame is divided into homogeneous and non-overlapping subgroups (called ‘strata’), and a simple random sample is drawn within each subgroup. In the previous example of selecting 200 firms from a list of 1,000 firms, you can start by categorising the firms based on their size as large (more than 500 employees), medium (between 50 and 500 employees), and small (less than 50 employees). You can then randomly select 67 firms from each subgroup to make up your sample of 200 firms. However, since there are many more small firms in a sampling frame than large firms, having an equal number of small, medium, and large firms will make the sample less representative of the population (i.e., biased in favour of large firms that are fewer in number in the target population). This is called non-proportional stratified sampling because the proportion of the sample within each subgroup does not reflect the proportions in the sampling frame—or the population of interest—and the smaller subgroup (large-sized firms) is oversampled . An alternative technique will be to select subgroup samples in proportion to their size in the population. For instance, if there are 100 large firms, 300 mid-sized firms, and 600 small firms, you can sample 20 firms from the ‘large’ group, 60 from the ‘medium’ group and 120 from the ‘small’ group. In this case, the proportional distribution of firms in the population is retained in the sample, and hence this technique is called proportional stratified sampling. Note that the non-proportional approach is particularly effective in representing small subgroups, such as large-sized firms, and is not necessarily less representative of the population compared to the proportional approach, as long as the findings of the non-proportional approach are weighted in accordance to a subgroup’s proportion in the overall population.

Cluster sampling. If you have a population dispersed over a wide geographic region, it may not be feasible to conduct a simple random sampling of the entire population. In such case, it may be reasonable to divide the population into ‘clusters’—usually along geographic boundaries—randomly sample a few clusters, and measure all units within that cluster. For instance, if you wish to sample city governments in the state of New York, rather than travel all over the state to interview key city officials (as you may have to do with a simple random sample), you can cluster these governments based on their counties, randomly select a set of three counties, and then interview officials from every office in those counties. However, depending on between-cluster differences, the variability of sample estimates in a cluster sample will generally be higher than that of a simple random sample, and hence the results are less generalisable to the population than those obtained from simple random samples.

Matched-pairs sampling. Sometimes, researchers may want to compare two subgroups within one population based on a specific criterion. For instance, why are some firms consistently more profitable than other firms? To conduct such a study, you would have to categorise a sampling frame of firms into ‘high profitable’ firms and ‘low profitable firms’ based on gross margins, earnings per share, or some other measure of profitability. You would then select a simple random sample of firms in one subgroup, and match each firm in this group with a firm in the second subgroup, based on its size, industry segment, and/or other matching criteria. Now, you have two matched samples of high-profitability and low-profitability firms that you can study in greater detail. Matched-pairs sampling techniques are often an ideal way of understanding bipolar differences between different subgroups within a given population.

Multi-stage sampling. The probability sampling techniques described previously are all examples of single-stage sampling techniques. Depending on your sampling needs, you may combine these single-stage techniques to conduct multi-stage sampling. For instance, you can stratify a list of businesses based on firm size, and then conduct systematic sampling within each stratum. This is a two-stage combination of stratified and systematic sampling. Likewise, you can start with a cluster of school districts in the state of New York, and within each cluster, select a simple random sample of schools. Within each school, you can select a simple random sample of grade levels, and within each grade level, you can select a simple random sample of students for study. In this case, you have a four-stage sampling process consisting of cluster and simple random sampling.

Non-probability sampling

Non-probability sampling is a sampling technique in which some units of the population have zero chance of selection or where the probability of selection cannot be accurately determined. Typically, units are selected based on certain non-random criteria, such as quota or convenience. Because selection is non-random, non-probability sampling does not allow the estimation of sampling errors, and may be subjected to a sampling bias. Therefore, information from a sample cannot be generalised back to the population. Types of non-probability sampling techniques include:

Convenience sampling. Also called accidental or opportunity sampling, this is a technique in which a sample is drawn from that part of the population that is close to hand, readily available, or convenient. For instance, if you stand outside a shopping centre and hand out questionnaire surveys to people or interview them as they walk in, the sample of respondents you will obtain will be a convenience sample. This is a non-probability sample because you are systematically excluding all people who shop at other shopping centres. The opinions that you would get from your chosen sample may reflect the unique characteristics of this shopping centre such as the nature of its stores (e.g., high end-stores will attract a more affluent demographic), the demographic profile of its patrons, or its location (e.g., a shopping centre close to a university will attract primarily university students with unique purchasing habits), and therefore may not be representative of the opinions of the shopper population at large. Hence, the scientific generalisability of such observations will be very limited. Other examples of convenience sampling are sampling students registered in a certain class or sampling patients arriving at a certain medical clinic. This type of sampling is most useful for pilot testing, where the goal is instrument testing or measurement validation rather than obtaining generalisable inferences.

Quota sampling. In this technique, the population is segmented into mutually exclusive subgroups (just as in stratified sampling), and then a non-random set of observations is chosen from each subgroup to meet a predefined quota. In proportional quota sampling , the proportion of respondents in each subgroup should match that of the population. For instance, if the American population consists of 70 per cent Caucasians, 15 per cent Hispanic-Americans, and 13 per cent African-Americans, and you wish to understand their voting preferences in an sample of 98 people, you can stand outside a shopping centre and ask people their voting preferences. But you will have to stop asking Hispanic-looking people when you have 15 responses from that subgroup (or African-Americans when you have 13 responses) even as you continue sampling other ethnic groups, so that the ethnic composition of your sample matches that of the general American population.

Non-proportional quota sampling is less restrictive in that you do not have to achieve a proportional representation, but perhaps meet a minimum size in each subgroup. In this case, you may decide to have 50 respondents from each of the three ethnic subgroups (Caucasians, Hispanic-Americans, and African-Americans), and stop when your quota for each subgroup is reached. Neither type of quota sampling will be representative of the American population, since depending on whether your study was conducted in a shopping centre in New York or Kansas, your results may be entirely different. The non-proportional technique is even less representative of the population, but may be useful in that it allows capturing the opinions of small and under-represented groups through oversampling.

Expert sampling. This is a technique where respondents are chosen in a non-random manner based on their expertise on the phenomenon being studied. For instance, in order to understand the impacts of a new governmental policy such as the Sarbanes-Oxley Act, you can sample a group of corporate accountants who are familiar with this Act. The advantage of this approach is that since experts tend to be more familiar with the subject matter than non-experts, opinions from a sample of experts are more credible than a sample that includes both experts and non-experts, although the findings are still not generalisable to the overall population at large.

Snowball sampling. In snowball sampling, you start by identifying a few respondents that match the criteria for inclusion in your study, and then ask them to recommend others they know who also meet your selection criteria. For instance, if you wish to survey computer network administrators and you know of only one or two such people, you can start with them and ask them to recommend others who also work in network administration. Although this method hardly leads to representative samples, it may sometimes be the only way to reach hard-to-reach populations or when no sampling frame is available.

Statistics of sampling

In the preceding sections, we introduced terms such as population parameter, sample statistic, and sampling bias. In this section, we will try to understand what these terms mean and how they are related to each other.

When you measure a certain observation from a given unit, such as a person’s response to a Likert-scaled item, that observation is called a response (see Figure 8.2). In other words, a response is a measurement value provided by a sampled unit. Each respondent will give you different responses to different items in an instrument. Responses from different respondents to the same item or observation can be graphed into a frequency distribution based on their frequency of occurrences. For a large number of responses in a sample, this frequency distribution tends to resemble a bell-shaped curve called a normal distribution , which can be used to estimate overall characteristics of the entire sample, such as sample mean (average of all observations in a sample) or standard deviation (variability or spread of observations in a sample). These sample estimates are called sample statistics (a ‘statistic’ is a value that is estimated from observed data). Populations also have means and standard deviations that could be obtained if we could sample the entire population. However, since the entire population can never be sampled, population characteristics are always unknown, and are called population parameters (and not ‘statistic’ because they are not statistically estimated from data). Sample statistics may differ from population parameters if the sample is not perfectly representative of the population. The difference between the two is called sampling error . Theoretically, if we could gradually increase the sample size so that the sample approaches closer and closer to the population, then sampling error will decrease and a sample statistic will increasingly approximate the corresponding population parameter.

If a sample is truly representative of the population, then the estimated sample statistics should be identical to the corresponding theoretical population parameters. How do we know if the sample statistics are at least reasonably close to the population parameters? Here, we need to understand the concept of a sampling distribution . Imagine that you took three different random samples from a given population, as shown in Figure 8.3, and for each sample, you derived sample statistics such as sample mean and standard deviation. If each random sample was truly representative of the population, then your three sample means from the three random samples will be identical—and equal to the population parameter—and the variability in sample means will be zero. But this is extremely unlikely, given that each random sample will likely constitute a different subset of the population, and hence, their means may be slightly different from each other. However, you can take these three sample means and plot a frequency histogram of sample means. If the number of such samples increases from three to 10 to 100, the frequency histogram becomes a sampling distribution. Hence, a sampling distribution is a frequency distribution of a sample statistic (like sample mean) from a set of samples , while the commonly referenced frequency distribution is the distribution of a response (observation) from a single sample . Just like a frequency distribution, the sampling distribution will also tend to have more sample statistics clustered around the mean (which presumably is an estimate of a population parameter), with fewer values scattered around the mean. With an infinitely large number of samples, this distribution will approach a normal distribution. The variability or spread of a sample statistic in a sampling distribution (i.e., the standard deviation of a sampling statistic) is called its standard error . In contrast, the term standard deviation is reserved for variability of an observed response from a single sample.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• v.90(4); 2012 Dec

Who and What Is a “Population”? Historical Debates, Current Controversies, and Implications for Understanding “Population Health” and Rectifying Health Inequities

The idea of “population” is core to the population sciences but is rarely defined except in statistical terms. Yet who and what defines and makes a population has everything to do with whether population means are meaningful or meaningless, with profound implications for work on population health and health inequities.

In this article, I review the current conventional definitions of, and historical debates over, the meaning(s) of “population,” trace back the contemporary emphasis on populations as statistical rather than substantive entities to Adolphe Quetelet's powerful astronomical metaphor, conceived in the 1830s, of l’homme moyen (the average man), and argue for an alternative definition of populations as relational beings. As informed by the ecosocial theory of disease distribution, I then analyze several case examples to explore the utility of critical population-informed thinking for research, knowledge, and policy involving population health and health inequities.

Four propositions emerge: (1) the meaningfulness of means depends on how meaningfully the populations are defined in relation to the inherent intrinsic and extrinsic dynamic generative relationships by which they are constituted; (2) structured chance drives population distributions of health and entails conceptualizing health and disease, including biomarkers, as embodied phenotype and health inequities as historically contingent; (3) persons included in population health research are study participants, and the casual equation of this term with “study population” should be avoided; and (4) the conventional cleavage of “internal validity” and “generalizability” is misleading, since a meaningful choice of study participants must be in relation to the range of exposures experienced (or not) in the real-world societies, that is, meaningful populations, of which they are a part.

Conclusions

To improve conceptual clarity, causal inference, and action to promote health equity, population sciences need to expand and deepen their theorizing about who and what makes populations and their means.

Population sciences, whether focused on people or the plenitude of other species with which we inhabit this world, rely on a remarkable, almost alchemical, feat that nevertheless now passes as commonplace: creating causal and actionable knowledge via the transmutation of data from unique individuals into population distributions, dynamics, and rates. In the case of public health, a comparison of population data—especially rates and averages of traits—sets the basis for not only elucidating etiology but also identifying and addressing health, health care, and health policy inequities manifested in differential outcomes caused by social injustice ( Davis and Rowland 1983 ; Irwin et al. 2006 ; Krieger 2001 , 2011 ; Svensson 1990 ; Whitehead 1992 ; WHO 2008 , 2011 ).

But who are these “populations,” and why should their means be meaningful? Might some instead be meaningless, the equivalent of fool's gold or, worse, dangerously misleading?

Because “population” is such a fundamental term for so many sciences that analyze population data—for example, epidemiology, demography, sociology, ecology, and population biology and population genetics, not to mention statistics and biostatistics (see, e.g., Desrosières 1998 ; Gaziano 2010 ; Greenhalgh 1996 ; Hey 2011 ; Kunitz 2007 ; Mayr 1988 ; Pearce 1999 ; Porter 1986 ; Ramsden 2002 ; Stigler 1986 ; Weiss and Long 2009 )—presumably it would be reasonable to posit that the meaning of “population” is clear-cut and needs no further discussion.

As I document in this article, the surprise instead is that although the idea of “population” is core to the population sciences, it is rarely defined, especially in sciences dealing with people, except in abstract statistical terms. Granted, the “fuzziness” of concepts sometimes can be useful, especially when their empirical content is still being worked out, as illustrated by the well-documented contested history of the meanings of the “gene” as variously an abstract, functional, or physical entity, extending from before and still continuing well after the mid-twentieth-century discovery of DNA ( Burian and Zallen 2009 ; Falk 2000 ; Keller 2000 ; Morange 2001 ). Nevertheless, such fuzziness can also be a major problem, especially if the lack of clear definition or a conflation of meanings distorts causal analysis and accountability.

In this article, I accordingly call for expanding and deepening what I term “critical population-informed thinking.” Such thinking is needed to reckon with, among other things, claims of “population-based” evidence, principles for comparing results across “populations” (and their “subpopulations”), terminology regarding “study participants” (vs. “study population”), and assessing the validity (and not just the generalizability) of results. Addressing these issues requires clearly differentiating between (1) the dominant view that populations are (statistical) entities composed of component parts defined by innate attributes and (2) the alternative that I describe, in which populations are dynamic beings constituted by intrinsic relationships both among their members and with the other populations that together produce their existence and make meaningful casual inference possible.

To make my case, I review current conventional definitions of, and historical debates over, the meaning(s) of “population” and then offer case examples involving population health and health inequities. Informing my argument is the ecosocial theory of disease distribution and its focus on how people literally biologically embody their societal and ecological context, at multiple levels, across the life course and historical generations ( Krieger 1994 , 2001 , 2011 ), thereby producing population patterns of health, disease, and well-being.

Who and What Is a Population?

Conventional definitions.

Who and what determines who and what counts as a “population”? Table 1 lists conventional definitions culled from several contemporary scholarly reference texts. As quickly becomes apparent, the meaning of this term has expanded over time to embrace a variety of concepts. Tracing its etymology to the word's Latin roots, the Oxford English Dictionary ( OED 2010 ), for example, notes that “population” originally referred to the people living in (i.e., populating) a particular place, and this remains its primary meaning. Even so, as the OED 's definitions also make clear, “population” has come to acquire a technical meaning. In statistics, it refers to “a (real or hypothetical) totality of objects or individuals under consideration, of which the statistical attributes may be estimated by the study of a sample or samples drawn from it.” In genetics (or, really, biology more broadly), the OED defines “population” as “a group of animals, plants, or humans, within which breeding occurs.” Likewise, atoms, subatomic particles, stars, and other “celestial objects” are stated as sharing certain properties allowing them to be classed together in “populations” (even though the study of inanimate objects typically falls outside the purview of the “population sciences”).

Definitions of “Population” from Scholarly Reference Texts

Mirroring the OED 's definitions are those provided in diverse “population sciences” dictionaries and encyclopedias. Four such texts, whose definitions are echoed in key works in population health ( Evans, Barer, and Marmor 1994 ; Rose 1992 , 2008 ; Rothman, Greenland, and Lash 2008 ; Young 2005 ), are worth noting: A Dictionary of Epidemiology ( Porta 2008 ), A Dictionary of Sociology ( Scott and Marshall 2005 ), and the two entries from the International Encyclopedia of the Social & Behavioral Sciences that offer a definition of “population,” one focused on “human evolutionary genetics” ( Mountain 2001 ) and the other on “generalization: conceptions in the social sciences” ( Cook 2001 ). A fifth resource, the Encyclopedia of Life Sciences , interestingly does not include any articles specifically on defining “population.” However, of the 396 entries located with the search term “population” and sorted by “relevance,” the first 25 focus on populations principally in relation to genetics, reproduction, and natural selection ( Clarke et al. 2000 –2011).

Among these four texts, all germane to population sciences that study people, the first two briefly define “population” in relation to inhabitants of an area but notably remain mum on the myriad populations appearing in the public health literature not linked to geographic locale (e.g., the “elderly population,” the “white population,” or the “lesbian/gay/bisexual/transgender population”). Most of their text is instead devoted to the idea of “population” in relation to statistical sampling ( Porta 2008 ; Scott and Marshall 2005 ). By contrast, the third text invokes biology (with no mention of statistics) and defines a “population” to be a “mating pool” ( Mountain 2001 , 6985), albeit observing that “groups of humans rarely, if ever, meet this definition,” so that “in practice … human evolutionary geneticists delineate populations along linguistic, geographic, socio-political, and/or cultural boundaries. A population might include, for example, all speakers of a particular Bantu language, all inhabitants of a river valley in Italy, or all members of a caste group in India.”

The fourth text avers that in the social sciences, “population” has two meanings: as a theory-dependent hypothetical “construct” (whose basis is not defined) and as an empirically defined “universe” (used as a sampling frame) ( Cook 2001 ). A telling example illustrates that for people, geographical location, nationality, and ancestry need not neatly match, as in the case of an illegal immigrant or a legal citizen of one country legally residing in a different country ( table 1 ). Consequently, apart from specifying that entities comprising a population individually possess some attribute qualifying them to be a member of that population, none of the conventional definitions offers systematic criteria by which to decide, in theoretical or practical terms, who and what is a population, let alone whether and, if so, why their mean value or rate (or any statistical parameter) might have any substantive meaning.

Meet the “Average Man”: Quetelet's 1830s Astronomical Metaphor Amalgamating “Population” and “Statistics”

The overarching emphasis on “populations” as technical statistical entities and the limited discussion as to what defines them, especially for the human populations, is at once remarkable and unsurprising. It is remarkable because “population” stands at the core, conceptually and empirically, of any and all population sciences. It is unsurprising, given the history and politics of how, in the case of people, “population” and “sample” first were joined ( Krieger 2011 ).

In brief, and as recounted by numerous historians of statistics ( Daston 1987 ; Desrosières 1998 ; Hacking 1975 , 1990 ; Porter 1981 , 1986 , 1995 , 2002 , 2003 ; Stigler 1986 , 2002 ; Yeo 2003 ), during the early 1800s the application of quantitative methods and laws of probability to the study of people in Europe took off, a feat that required reckoning with such profound issues as free will, God's will, and human fate. To express the mind shift involved, a particularly powerful metaphor took root: that of the “l’homme moyen” (the average man), which, in the convention of the day, included women ( figure 1 ). First used in 1831 in an address given by Adolphe Quetelet (1796–1874), the Belgian astronomer-turned-statistician-turned-sociologist-turned-nosologist ( Hankins 1968 ; Stigler 2002 ), the metaphor gained prominence following the publication in 1835 of Quetelet's enormously influential opus, Sur l’homme et le development de ses facultés, ou essai de physique sociale ( Quetelet 1835 ). Melding the ideas of essential types, external influences, and random errors, the image of the “average man” solidified a view of populations, particularly human populations, as innately defined by their intrinsic qualities. Revealing these innate qualities, according to Quetelet, was a population's on-average traits, whether pertaining to height and weight, birth and death rates, intellectual faculties, moral properties, and even propensity to commit crime ( Quetelet 1835 , 1844 ).

What is the meaning of means and errors?—Adolphe Quetelet (1796–1874) and the astronomical metaphor animating his 1830s “I'homme moyen” (“the average man”).

Source: Illustration of normal curve from Quetelet 1844 .

The metaphor animating Quetelet's “average man” was inspired by his background in astronomy and meteorology. Shifting his gaze from the heavens to the earth, Quetelet arrived at his idea of “the average man” by inverting the standard approach his colleagues used to fix the location of stars, in which the results of observations from multiple observatories (each with some degree of error) were combined to determine a star's most likely celestial coordinates ( Porter 1981 ; Stigler 1986 , 2002 ). Reasoning by analogy, Quetelet ingeniously, if erroneously, argued that the distribution of a population's characteristics served as a guide to its true (inherent) value ( Quetelet 1835 , 1844 ). From this standpoint, the observed “deviations” or “errors” arose from the imperfect variations of individuals, each counting as an “observation-with-error” akin to the data produced by each observatory. The impact of these “errors” was effectively washed out by the law of large numbers. Attesting to the power of metaphor in science and more generally ( Krieger 1994 , 2011 ; Martin and Harré 1982 ; Ziman 2000 ), Quetelet's astronomical “average man” simultaneously enabled a new way to see and study population variation even as it erased a crucial distinction. For a star, the location of the mean referred to the location of a singular real object, whereas for a population, the location of its population mean depended on how the population was defined.

To Quetelet, this new conception of population meant that population means, based on sufficiently large samples, could be meaningfully compared to determine if the populations’ essential characteristics truly differed. The contingent causal inference was that if the specified populations differed in their means, this would mean that they either differed in their essence (if subject to the same external forces) or else were subject to different external forces (assuming the same internal essence). Reflecting, however, the growing pressure for nascent social scientists to be seen as “objective,” Quetelet's discussion of external forces steered clear of politics. Concretely, this translated to not challenging mainstream religious or economic beliefs, including the increasingly widespread individualistic philosophies then linked to the rapid ascendance of the liberal free-market economy ( Desrosières 1998 ; Hacking 1990 ; Heilbron, Magnusson, and Wittrock 1998 ; Porter 1981 , 1986 , 1995 , 2003 ; Ross 2003 ). For example, although Quetelet conceded that “the laws and principles of religion and morality” could act as “influencing causes” ( Quetelet 1844 , xvii), in his analyses he treated education, occupation, and the propensity to commit crime as individual attributes no different from height and weight. The net result was that a population's essence—crucial to its success or failure—was conceptualized as an intrinsic property of the individuals who comprised the population; the corollary was that population means and rates were a result and an expression of innate individual characteristics.

Or so the argument went. At the time, others were not convinced and contended that Quetelet's means were simply arbitrary arithmetic contrivances resulting from declaring certain groups to be populations ( Cole 2000 ; Desrosières 1998 ; Porter 1981 ; Stigler 1986 , 2002 ). As Quetelet himself acknowledged, the national averages and rates defining a country's “average man” coexisted with substantial regional and local variation. Hence, data for one region of France would yield one mean, and for another region it would be something else. If the two were combined, a third mean would result—and who was to say which, if any, of these means was meaningful, let alone reflective of an intrinsic essence (or, for that matter, external influences)?

Quetelet's tautological answer was to differentiate between what he termed “true means” versus mere “arithmetical averages” ( Porter 1981 ; Quetelet 1844 ). The former could be derived only from “true” populations, whose distribution by definition expressed the “law of errors” (e.g., the normal curve). In such cases, Quetelet argued, the mean reflected the population's true essence. By contrast, any disparate lot of objects measured by a common metric could yield a simple “average” (e.g., average height of books or of buildings), but the meaningless nature of this parameter, that is, its inability to be informative about any innate “essence,” would be revealed by the lack of a normal distribution.

And so the argument continued until the terms were changed in a radically different way by Darwin's theory of evolution, presented in Origin of Species , published in 1859 (Darwin [1859] [ 2004 ]). The central conceptual shift was from “errors” to “variation” ( Eldredge 2005 ; Hey 2011 ; Hodge 2009 ; Mayr 1988 ). This variation, thought to reflect inheritable characteristics passed on from parent to progeny, was in effect a consequence of who survived to reproduce, courtesy of “natural selection.” No longer were species, that is, the evolving biological populations to which these individuals belonged, either arbitrary or constant. Instead, they were produced by reproducing organisms and their broader ecosystem. Far from being either Platonic “ideal types” ( Hey 2011 ; Hodge 2009 ; Mayr 1988 ; Weiss and Long 2009 ), per Quetelet's notion of fixed essence plus error, or artificially assembled aggregates capable of yielding only what Quetelet would term meaningless mere “averages,” “populations” were newly morphed into temporally dynamic and mutable entities arising by biological descent. From this standpoint, variation was vital, and variants that were rare at one point in time could become the new norm at another.

Nevertheless, even though the essence of biological populations was now impermanent, what substantively defined “populations” remained framed as fundamentally endogenous. In the case of biological organisms, this essence resided in whatever material substances were transmitted by biological reproduction. Left intact was an understanding of population, population traits, and their variability as innately defined, with this variation rendered visible through a statistical analysis of appropriate population samples. The enduring result was to (1) collapse the distinctions between populations as substantive beings versus statistical objects and (2) imply that population characteristics reflect and are determined by the intrinsic essence of their component parts. Current conventional definitions of “population” say as much and no more ( table 1 ).

Conceptual Criteria for Defining Meaningful Populations for Public Health

Framing and Contesting “Population” through an Epidemiologic Lens . In the 150 years since these initial features of populations were propounded, they have become deeply entrenched, although not entirely uncontested. Figure 2 is a schematic encapsulation of mid-nineteenth to early twentieth-century notions of populations, with the entries emphasizing population statistics and population genetics because of their enduring influence, even now, on conceptions of populations in epidemiology and other population sciences. During this period, myriad disciplines in the life, social, and physical sciences embraced a statistical understanding of “population” ( Desrosières 1998 ; Hey 2011 ; Porter 1981 , 1986 , 2002 , 2003 ; Ross 2003 ; Schank and Twardy 2009 ; Yeo 2003 ). Eugenic thinking likewise became ascendant, espoused by leading scientists and statisticians, especially the newly named “biometricians,” who held that individuals and populations were determined and defined by their heredity, with the role of the “environment” being negligible or nil ( Carlson 2001 ; Davenport 1911 ; Galton 1904 ; Kevels 1985 ; Mackenzie 1982 ; Porter 2003 ; Tabery 2008 ).

A schematic cross-disciplinary genealogy of mid-nineteen to early twentieth-century “population” thinking and current impact.

Sources : Carver 2003 ; Crow 1990 , 1994 ; Dale and Katz 2011 ; Darwin 1859 ; Daston 1987 ; Desrosières 1998 ; Eldredge 2005 ; Galton 1889 , 1904 ; Hacking 1975 , 1990 ; Hey 2011 ; Hodge 2009 ; Hogben 1933 ; Keller 2010 ; Mackenzie 1982 ; Marx 1845 ; Mayr 1988 ; Porter 1981 , 1986 , 2002 , 2003 ; Quetelet 1835 , 1844 ; Sarkar 1996 ; Schank and Twardy 2009 ; Stigler 1986 , 1997 ; Tabery 2008 ; Yeo 2003 .

It was also during the early twentieth century that the nascent academic discipline of epidemiology advanced its claims about being a population science, as part of distinguishing both the knowledge it generated and its methods from those used in the clinical and basic sciences ( Krieger 2000 , 2011 ; Lilienfeld 1980 ; Rosen [1958] [ 1993 ]; Susser and Stein 2009 ; Winslow et al. 1952 ). In 1927 and in 1935, for example, the first professors of epidemiology in the United States and the United Kingdom—Wade Hampton Frost (1880–1938) at the Johns Hopkins School of Hygiene and Public Health in 1921 ( Daniel 2004 ; Fee 1987 ), and Major Greenwood (1880–1949) at the London School of Hygiene and Tropical Medicine in 1928 ( Butler 1949 ; Hogben 1950 )—urged that epidemiology clearly define itself as the science of the “mass phenomena” of disease, Frost in his landmark essay “Epidemiology” (Frost [1927] [ 1941 ], 439) and Greenwood in his discipline-defining book Epidemics and Crowd Diseases: An Introduction to the Study of Epidemiology ( Greenwood 1935 , 125). Neither Frost nor Greenwood, however, articulated what constituted a “population,” other than the large numbers required to make a “mass.”

Also during the 1920s and 1930s, two small strands of epidemiologic work—each addressing different aspects of the inherent dual engagement of epidemiology with biological and societal phenomena ( Krieger 1994 , 2001 , 2011 )—began to challenge empirically and conceptually the dominant view of population characteristics as arising solely from individuals' intrinsic properties. The first thread was metaphorically inspired by chemistry's law of “mass action,” referring to the likelihood that two chemicals meeting and interacting in, say, a beaker, would equal the product of their spatial densities ( Heesterbeek 2005 ; Mendelsohn 1998 ). Applied to epidemiology, the law of “mass action” spurred novel efforts to model infectious disease dynamics arising from interactions between what were termed the “host” and the “microbial” populations, taking into account changes in the host's characteristics (e.g., from susceptible to either immune or dead) and also the population size, density, and migration patterns (Frost [ 1928 ] 1976; Heesterbeek 2005 ; Hogben 1950 ; Kermack and McKendrick 1927 ; Mendelsohn 1998 ).

The second thread was articulated in debates concerning eugenics and also in response to the social crises and economic depression precipitated by the 1929 stock market crash. Its focus concerned how societal conditions could drive disease rates, not only by changing individuals’ economic position, but also through competing interests. Explicitly stating this latter point was the 1933 monograph Health and Environment ( Sydenstricker 1933 ), prepared for the U.S. President's Research Committee on Social Trends by Edgar Sydenstricker (1881–1936), a leading health researcher and the first statistician to serve in the U.S. Public Health Service ( Krieger 2011 ; Krieger and Fee 1996 ; Wiehl 1974 ). In this landmark text, which explicitly delineated diverse aspects of what he termed the “social environment” alongside the physical environment, Sydenstricker argued (1933, 16, italics in original):

Economic factors in the conservation or waste of health, for example, are not merely the rate of wages; the hours of labor; the hazard of accident, of poisonous substances, or of deleterious dusts; they include also the attitude consciously taken with respect to the question of the relative importance of large capitalistic profits versus maintenance of the workers’ welfare.

In other words, social relations, not just individual traits, shape population distributions of health.

Influenced by and building on both Greenwood's and Sydenstricker's work, in 1957 Jeremy Morris (1910–2009) published his highly influential and pathbreaking book Uses of Epidemiology ( Morris 1957 ), which remains a classic to this day ( Davey Smith and Morris 2004 ; Krieger 2007a ; Smith 2001 ). Going beyond Frost and Greenwood, Morris emphasized that “the unit of study in epidemiology is the population or group , not the individual ” ( Morris 1957 , 3, italics in original) and also went further by newly defining epidemiology in relational terms, as “ the study of health and disease of populations and of groups in relation to their environment and ways of living ” ( Morris 1957 , 16, italics in original). As a step toward defining “population,” Morris noted that “the ‘population’ may be of a whole country or any particular and defined sector of it” ( Morris 1957 , 3), as delimited by people's “environment, their living conditions, and special ways of life” ( Morris 1957 , 61). He also, however, recognized that better theorizing about populations was needed and hence called for a greater “understanding of the properties of individuals which they have in virtue of their group membership” ( Morris 1957 , 120, italics in original). But this appeal went largely unheeded, as it directly contradicted the era's prevailing framework of methodological individualism ( Issac 2007 ; Krieger 2011 ; Ross 2003 ).

Morris's insights notwithstanding, the dominant view has remained what is presented in table 1 . Even the recent influential work of Geoffrey Rose (1926–1993), crucial to reframing individual risk in population terms, theorized populations primarily in relation to their distributional, not substantive, properties ( Rose 1985 , 1992 , 2008 ). Rose's illuminating analyses thus emphasized that (1) within a population, most cases arise from the proportionately greater number of persons at relatively low risk, as opposed to the much smaller number of persons at high risk; (2) determinants of risk within populations may not be the same as determinants of risk between populations; and (3) population norms shape where both the tails and the mean of a distribution occur. Rose thus cogently clarified that to change populations is to change individuals, and vice versa, implying that the two are mutually constitutive, but he left unspecified who and what makes meaningful populations and when they can be meaningfully compared.

Current Challenges to Conventional Views of “Population.” A new wave of work contesting the still reigning idea of “the average man” can currently be found in recent theoretical and empirical work in the social and biological sciences attempting to analyze population phenomena in relation to dynamic causal processes that encompass multiple levels and scales, from macro to micro ( Biersack and Greenberg 2006 ; Eldredge 1999 ; Eldredge and Grene 1992 ; Gilbert and Epel 2009 ; Grene and Depew 2004 ; Harraway 2008 ; Illari, Russo, and Williamson 2011 ; Krieger 2011 ; Lewontin 2000 ; Turner 2005 ). Also germane is research on system properties in the physical and information sciences ( Kuhlmann 2011 ; Mitchell 2009 ; Strevens 2003 ).

Applicable to the question of who and what makes a population, one major focus of this alternative thinking is on processes that generate, maintain, transform, and lead to the demise of complex entities. This perspective builds on and extends a long history of critiques of reductionism ( Grene and Depew 2004 ; Harré 2001 ; Illari, Russo, and Williamson 2011 ; Lewontin 2000 ; Turner 2005 ; Ziman 2000 ), which together aver that properties of a complex “whole” cannot be reduced to, and explained solely by, the properties of its component “parts.” The basic two-part argument is that (a) new (emergent) properties can arise out of the interaction of the “parts” and (b) properties of the “whole” can transform the properties of their parts. Thus, to use one well-known example, a brain can think in ways that a neuron cannot. Taking this further in regard to the generative causal processes at play, what a brain thinks can affect neuron connections within the brain, and it also is affected by the ecological context and experiences of the organism, of which the brain is a part ( Fox, Levitt, and Nelson 2010 ; Gibson 1986 ; Harré 2001 ; Stanley, Phelps, and Banaji 2008 ). The larger claim is that the causal processes that give rise to complex entities can both structure and transform the characteristics of both the whole and its parts.

What might it look like for public health to bring this alternative perspective to the question of defining, substantively, who and what makes a population? Let me start with a conceptual answer, followed by some concrete public health propositions and examples.

Populations as Relational Beings: An Alternative Causal Conceptualization

In brief, I argue that a working definition of “populations” for public health (or any field concerned with living organisms) would, in line with Sydenstricker (1933) and Morris (1957) and the other contemporary theorists just cited, stipulate that populations are first and foremost relational beings, not “things.” They are active agents, not simply statistical aggregates characterized by distributions.

Specifically, as tables 2 and ​ and3 3 show, the substantive populations that populate our planet

Conceptual Criteria for Defining Meaningful Populations for Population Sciences, Guided by the Ecosocial Theory of Disease Distribution

Source: Krieger 1994 , 2001 , and 2011 , 214–15.

Defining Features of Populations of Living Beings, Including Humans, Relevant to Public Health and Population Sciences

• Are animate, self-replicating, and bounded complex entities, generated by systemic causal processes.
• Arise from and are constituted by relationships of varying strengths, both externally (with and as bounded by other populations) and internally (among their component beings).
• Are inherently constituted by, and simultaneously influence the characteristics of, the varied individuals who comprise its members and their population-defined and -defining relationships.

It is these relationships and their underlying causal processes (both deterministic and probabilistic), not simply random samples derived from large numbers, that make it possible to make meaningful substantive and statistical inferences about population characteristics, as well as meaningful causal inferences about observed associations.

Accordingly, as summarized by Richard A. Richards, a philosopher of biology (who was writing about species, one type of population), populations have “well-defined beginnings and endings, and cohesion and causal integration” ( Richards 2001 ). They likewise necessarily exhibit historically contingent distributions in time and space, by virtue of the dynamic interactions intrinsically occurring between (and within) their unique individuals and with other equally dynamic codefining populations and also their changing abiotic environs. Underscoring this point, even a population of organisms cloned from a single source organism will exhibit variation and distributions as illustrated by the phenomenon of developmental “noise,” an idea presaged by early twentieth-century observations of chance differences in coat color among litter mates of pure-bred populations raised in identical circumstances ( Davey Smith 2011 ; Lewontin 2000 ; Wright 1920 ).

As for the inherent relationships characterizing populations, both internally and externally, I suggest that four key types stand out, as informed by the ecosocial theory of disease distribution ( Krieger 1994 , 2001 , 2011 ); the collaborative writing of Niles Eldredge, an evolutionary biologist, and Marjorie Grene, a philosopher of biology ( Eldredge and Grene 1992 ); as well as works from political sociology, political ecology, and political geography ( Biersack and Greenberg 2006 ; Harvey 1996 ; Nash and Scott 2001 ). As tables 2 and ​ and3 3 summarize, these four kinds of relationships are (1) genealogical , that is, relationships by biological descent; (2) internal and economical, in the original sense of the term, referring to relationships essential to the daily activities of whatever is involved in maintaining life (in ancient Greece, oikos , the root of the “eco” in both “ecology” and “economics,” referred to a “household,” conceptualized in relation to the activities and interactions required for its existence [ OED 2010]); (3) external and ecological , referring to relationships between populations and with the environs they coinhabit; and (4) in the case of people (and likely other species as well), teleological , that is, by design, with some conscious purpose in mind (e.g., citizenship criteria). Spanning from mutually beneficial (e.g., symbiotic) to exploitative (benefiting one population at the expense of the other), these relationships together causally shape the characteristics of populations and their members.

What are some concrete examples of animate populations that exemplify these points? Table 3 provides four examples. Two pertain to human populations: the “U.S. population” ( Foner 1997 ; Zinn 2003 ) and “social classes” ( Giddens and Held 1982 ; Wright 2005 ). The third considers microbial populations within humans ( Dominguez-Bello and Blaser 2011 ; Pflughoeft and Versalovic 2012 ; Walter and Ley 2011 ), and the fourth concerns a plant population, a species of tree, the poplar, whose genus name ( Populus ) derives from the same Latin root as “population” ( Braatne, Rood, and Heillman 1996 ; Fergus 2005 ; Frost et al. 2007 ; Jansson and Douglas 2007 ). Together, these examples clarify what binds—as well as distinguishes—each of these dynamic populations and their component individuals. They likewise underscore that contrary to common usage, “population” and “individual” are not antonyms. Instead, they hark back to the original meaning of “individual”—that is, “individuum,” or what is indivisible, referring to the smallest unit that retained the properties of the whole to which it intrinsically belonged ( OED 2010; Williams 1985 ). Thus, although it is analytically possible to distinguish between “populations” and “individuals,” in reality these phenomena occur and are lived simultaneously. A person is not an individual on one day and a member of a population on another. Rather, we are both, simultaneously. This joint fact is fundamental and is essential to keep in mind if analysis of either individual or population phenomena is to be valid.

The importance of considering the intrinsic relationships—both internal and external—that are the integuments of living populations, themselves active agents and composed of active agents, is further illuminated through contrast to the classic case of a hypothetical population: the proverbial jar of variously colored marbles, used in many classes to illustrate the principles of probability and sampling. Apart from having been manufactured to be of a specific size, density, and color, there are no intrinsic relationships between the marbles as such. Spill such a jar, and see what happens.

As this thought experiment makes clear, the marbles will not reconstitute themselves into any meaningful relationships in space or time. They will just roll to wherever they do, and that will be the end of it, unless someone with both energy and a plan scoops them up and puts them back in the jar. Nor will a sealed jar of marbles change its color composition (i.e., the proportion of marbles of a certain color), or an individual marble change its color, unless someone opens the jar and replaces, adds, or removes some marbles or treats them with a color-changing agent. Hence, a purely statistical understanding of “populations,” however necessary for sharpening ideas about causal inference, study design, and empirical estimation, is by itself insufficient for defining and analyzing real-life populations, including “population health.”

That said, marbles do have their uses. In particular, they can help us visualize how causal determinants can structure population distributions of the risks of random individuals via what I term “structured chances.”

Populations and Structured Chances

One long-standing conundrum in population sciences is their ability to identify and use data on population regularities to elucidate causal pathways, even though they cannot predict which individuals in the population will experience the outcome in question ( Daston 1987 ; Desrosières 1998 ; Hacking 1990 ; Illari, Russo, and Williamson 2011 ; Porter 1981 , 2002 , 2003 ; Quetelet 1835 ; Stigler 1986 ; Strevens 2003 ). This incommensurability of population and individual data has been a persistent source of tension between epidemiology and medicine (Frost [1927] [ 1941 ]; Greenwood 1935 ; Morris 1957 ; Rose 1992 , 2008 ). Epidemiologic research, for example, routinely uses aggregated data obtained from individuals to gain insight into both disease etiology and why population rates vary, and does so with the understanding that such research cannot predict which individual will get the disease in question ( Coggon and Martyn 2005 ). By contrast, medical research remains bent on using just these sorts of data to predict an individual's risk, as exemplified in its increasingly molecularized quest for “personalized medicine” ( Davey Smith 2011 ).

Where marbles enter the picture is that they can, through the use of a physical model, demonstrate the importance of how population distributions are simultaneously shaped by both structure (arising from causal processes) and randomness (including truly stochastic events, not just “randomness” as a stand-in for “ignorance” of myriad deterministic events too complex to model). As Stigler has recounted (1997), perhaps the first person to propose using physical models to understand probability was Sir Francis Galton (1822–1911), a highly influential British scientist and eugenicist ( figure 2 ), who himself coined the term “eugenics” and who held that heredity fundamentally trumped “environment” for traits influencing the capacity to thrive, whether physical, like health status, or mental, like “intelligence” ( Carlson 2001 ; Cowan 2004 ; Galton 1889 , 1904 ; Keller 2010 ; Kevels 1985 ; Stigler 1997 ). In his 1889 opus Natural Inheritance ( Galton 1889 ), Galton sketched ( figure 3 ) “an apparatus … that mimics in a very pretty way the conditions on which Deviation depends” ( Galton 1889 , 63), whereby gun shots (i.e., marble equivalents) would be poured through a funnel down a board whose surface was studded with carefully placed pins, off which each pellet would ricochet, to be collected in evenly spaced bins at the bottom.

Producing population distributions: structured chances as represented by physical models.

Sources: Galton's Quincunx, Galton 1889 , 63; physical models, Limpert, Stahel, and Abbt 2001 (reproduced with permission).

Galton termed his apparatus, which he apparently never built ( Stigler 1997 ), the “Quincunx” because the pattern of the pins used to deflect the shot was like a tree-planting arrangement of that name, which at the time was popular among the English aristocracy ( Stigler 1997 ). The essential point was that although each presumably identical ball had the same starting point, depending on the chance interplay of which pins it hit during its descent at which angle, it would end up in one or another bin. The accumulation of balls in any bin in turn would reflect the number of possible pathways (i.e., likelihood) leading to its ending up in that bin. Galton designed the pin pattern to yield a normal distribution. He concluded that his device revealed ( Galton 1889 , 66)

a wonderful form of cosmic order expressed by the “Law of Frequency of Error.” The law would have been personified by the Greeks and deified, if they had known of it. It reigns with serenity and in complete self-effacement amidst the wildest confusion. The huger the mob, and the greater the apparent anarchy the more perfect is its sway … each element, as it is sorted into place, finds, as it were, a pre-ordained niche, accurately adapted to fit it.

In other words, in accord with Quetelet's view of “l’homme moyen,” Galton saw the order produced as the property of each “element,” in this case, the gun shot.

However, a little more than a century later, some physicists not only built Galton's “Quincunx,” as others have done ( Stigler 1997 ), but went one further ( Limpert, Stahel, and Abbt 2001 ): they built two, one designed to generate the normal distribution and the other to generate the log normal distribution (a type of distribution skewed on the normal scale, but for which the natural logarithm of the values displays a normal distribution) ( figure 3 ). As their devices clearly show, what structures the distribution is not the innate qualities of the “elements” themselves but the features of both the funnel and the pins—both their shape and placement. Together, these structural features determine which pellets can (or cannot) pass through the pins and, for those that do, their possible pathways.

The lesson is clear: altering the structure can change outcome probabilities, even for identical objects, thereby creating different population distributions. For the population sciences, this insight permits understanding how there can simultaneously be both chance variation within populations (individual risk) and patterned differences between population distributions (rates). Such an understanding of “structured chances” rejects explanations of population difference premised solely on determinism or chance and also brings Quetelet's astronomical “l’homme moyen” and its celestial certainties of fixed stars back down to earth, grounding the study of populations instead in real-life, historically contingent causal processes, including those structured by human agency.

Rethinking the Meaning and Making of Means: The Utility of Critical Population-Informed Thinking

How might a more critical understanding of the substantive nature of real-life populations benefit research on, knowledge about, and policies regarding population health and health inequities? Drawing on table 2 's conceptual criteria for defining who and what makes populations, table 4 offers four sets of critical public health propositions about “populations” and “study populations,” whose salience I assess using examples of breast cancer, a disease increasingly recognized as a major cause of morbidity and mortality in both the global South and the global North ( Althuis et al. 2005 ; Bray, McCarron, and Parkin 2004 ; Parkin and Fernández 2006 ) and one readily revealing that the problem of meaningful means is as vexing for “the average woman” as for “the average man.”

Four Propositions to Improve Population Health Research, Premised on Critical Population-Informed Thinking

Propositions 1 and 2: Critically Parsing Population Rates and Their Comparisons

Consider, first, three illustrative cases pertaining to analyses of population rates of breast cancer:

• A recent high-profile analysis of the global burden of breast cancer ( Briggs 2011 ; Forouzanafar et al. 2011 ; IHME 2011; Jaslow 2011 ), which estimated and compared rates across countries, accompanied by interpretative text, with the article stating, for example, that Colombia and Venezuela “… have very different trends, despite sharing many of the same lifestyle and demographic factors,” followed by the inference that the “explanation of these divergent trends may lie in the interaction between genes and individual risk factors.” (IHME 2011, 24)
• Typical reviews of the global epidemiology of breast cancer, which contain such statements as “Population-based statistics show that globally, when compared to whites, women of African ancestry (AA) tend to have more aggressive breast cancers that present more frequently as estrogen receptor negative (ERneg) tumors” ( Dunn et al. 2010 , 281); and “early onset ER negative tumors also develop more frequently in Asian Indian and Pakistani women and in women from other parts of Asia, although not as prevalent as it is in West Africa.” ( Wallace, Martin, and Ambs 2011 , 1113)
• The headline-making news that the U.S. breast cancer incidence rate in 2003 unexpectedly dropped by 10 percent, a huge decrease ( Kolata 2006 , 2007 ; Ravdin et al. 2006 , 2007 ).

What these three commonplace examples have in common is an uncritical approach to presenting and interpreting population data, premised on the dominant assumption that population rates are statistical phenomena driven by innate individual characteristics. Cautioning against accepting these claims at face value are propositions 1 and 2, with their emphases, respectively, on (1) critically appraising who constitutes the populations whose means are at issue and (2) critically considering the dynamic relationships that give rise to population patterns of health, including health inequities.

From the standpoint of proposition 1, the first relevant fact is that as a consequence of global disparities in resources ( Klassen and Smith 2011 ) arising from complex histories of colonialism and underdevelopment ( Birn, Pillay, and Holtz 2009 ), only 16 percent of the world's population is covered by cancer registries, with coverage of less than 10 percent within the world's most populous regions (Africa, Asia [other than Japan], Latin America, and the Caribbean), versus 99 percent in North America ( Parkin and Fernández 2006 ). Put in national terms, among the 184 countries for which the International Agency on Cancer (IARC) reports estimated rates, only 33 percent—almost all located in the global North—have reliable national incidence data ( GLOBOCAN 2012 ). These data limitations are candidly acknowledged both by IARC ( GLOBOCAN 2012 ) and in the scientific literature, including that on breast cancer ( Althuis et al. 2005 ; Bray, McCarron, and Parkin 2004 ; Ferlay et al. 2012 ; Krieger, Bassett, and Gomez 2012 ; Parkin and Fernández 2006 ). To generate estimates of incidence in countries lacking national cancer registry data, the IARC transparently employs several modeling approaches, based on, for example, a country's national mortality data combined with city-specific or regional cancer registry data (if they do exist, albeit typically not including the rural poor) or, when no credible national data are available, estimating rates based on data from neighboring countries ( GLOBOCAN 2012 ).

A critical analysis of the population claims asserted in examples 1 and 2 starts by questioning whether the means at issue can bear the weight of meaningful comparisons and inference. Thus, relevant to example 1, Colombia has only one city-based cancer registry (in Cali), and Venezuela has no cancer registries at all ( GLOBOCAN 2012 ). Moreover, the rates compared ( Forouzanafar et al. 2011 ; IHME 2011 ) were generated by nontransparent modeling methods ( Krieger, Bassett, and Gomez 2012 ) that have empirically been shown not to estimate accurately the actually observed rates in the “gold-standard” Nordic countries, known for their excellent cancer registration data ( Ferlay et al. 2012 ). Second, relevant to the countries and geographic regions listed in example 2, the cancer incidence rates estimated by IARC are based (a) for Pakistan, solely on the weighted average for observed rates in south Karachi, (b) for India, on a complex estimation scheme for urban and rural rates in different Indian states and data from cancer registries in several cities, and (c) for western Africa, on the weighted average of data for sixteen countries, of which ten have incidence rates estimated based on those of neighboring countries, another five rely on data extrapolated from cancer registry data from one city (or else city-based cancer registries in neighboring countries), and only one of which has a national cancer registry ( GLOBOCAN 2012 ). Critical thinking about who and what makes a population thus prompts questions about whether the data presented in examples 1 and 2 can provide insight into either alleged individual innate characteristics or into what the true on-average rate would be if everyone were counted (let alone what the variability in rates might be across social groups and regions). There is nothing mundane about a mean.

Proposition 2 in turn calls attention to structured chance in relation to the dynamic intrinsic and extrinsic relationships constituting national populations, with table 2 illustrating what types of relationships are at play using the example of the United States. It thus spurs critical queries as to whether observed national and racial/ethnic differences (if real, and not an artifact of inaccurate data) arise from innate (i.e., genetic) differences between “populations,” as posed by examples 1 and 2. Two lines of evidence alternatively suggest these population differences could instead be embodied inequalities ( Krieger 1994 , 2000 , 2005 , 2011 ; Krieger and Davey Smith 2004 ) that arise from structured chances. The first line pertains to well-documented links among national, racial/ethnic, and socioeconomic inequalities in breast cancer incidence, survival, and mortality ( Klassen and Smith 2011 ; Krieger 2002 ; Vona-Davis and Rose 2009 ). The second line stems from research that evaluates claims of intrinsic biological difference by examining their dynamics, as illustrated by the first investigation to test statistically for temporal trends in the white/black odds ratio for ER positive breast cancer between 1992 and 2005, which revealed that in the United States, the age-adjusted odds ratio rose between 1992 and 2002 and then leveled off (and actually fell among women aged fifty to sixty-nine) ( Krieger, Chen, and Waterman 2011 ).

Relevant to example 3, these findings of dynamic, not fixed, black/white risk differences for breast cancer ER status likely reflect the socially patterned abrupt decline in hormone therapy use following the July 2002 release of results from the U.S. Women's Health Initiative (WHI) ( Rossouw et al. 2002 ). This was the first large randomized clinical trial of hormone therapy, despite its having been widely prescribed since the mid-1960s ( Krieger 2008 ). The WHI found that contrary to what was expected, hormone therapy did not decrease (and may have raised) the risk of cardiovascular disease, and at the same time, the WHI confirmed prior evidence that long-term use of hormone therapy increased the risk of breast cancer (especially ER+). Thus, before the initiative, hormone therapy use in the United States was highest among white women with health insurance who could afford, and were healthy enough, to take the medication without any contraindications ( Brett and Madans 1997 ; Friedman-Koss et al. 2002 ). Population-informed thinking would thus predict that any drops in breast cancer incidence would occur chiefly among those sectors of women most likely to have used hormone therapy. Subsequent global research has borne out these predictions ( Zbuk and Anand 2012 ), including the sole U.S. study that systematically explored socioeconomic differentials both within and across racial/ethnic groups, which found that the observed breast cancer decline was restricted to white non-Hispanic women with ER+ tumors residing in more affluent counties ( Krieger, Chen, and Waterman 2010 ). These results counter the widely disseminated and falsely reassuring impression that breast cancer risk was declining for everyone ( Kolata 2006 , 2007 ). They accordingly provide better guidance to public health agencies, clinical providers, and breast cancer advocacy groups regarding trends in breast cancer occurrence among the real-life populations they serve.

Together, these examples illuminate why proposition 2's corollary 2.2 proposes conceptualizing the jointly lived experience of population rates and individual manifestations of health, disease, and well-being as what I would term “embodied phenotype.” Inherently dynamic and relational, this proposed construct meaningfully links the macro and micro, and populations and individuals, through the play of structured chance. It also is consonant with new insights emerging from the fast-growing field of ecological evolutionary developmental biology (“eco-evo-devo”) into the profound and dynamic links among environmental exposures, gene expression, development, speciation, and the flexibility of organisms’ phenotypes across the life span ( Gilbert and Epel 2009 ; Piermsa and van Gils 2011 ; West-Eberhard 2003 ). Only just beginning to be integrated into epidemiologic theorizing and research ( Bateson and Gluckman 2012 ; Davey Smith 2011 , 2012 ; Gilbert and Epel 2009 ; Kuzawa 2012 ; Relton and Davey Smith 2012 ), eco-evo-devo's historical and relational approach to biological expression affirms the need for critical population-informed thinking.

Propositions 3 and 4: Study Participants, Study Populations, and Causal Inference

Finally, a population-informed approach helps clarify, in accordance with propositions 3 and 4, why improving our understanding of “study populations,” and thus study participants, matters for causal inference. Consider, for example, the 1926 pathbreaking epidemiologic study of breast cancer conducted by the British physician and epidemiologist Janet Elizabeth Lane-Claypon (1877–1967) ( Lane-Claypon 1926 ), the first study to identify systematically what were then called “antecedents” of breast cancer (today termed “risk factors”) and now also widely acknowledged to be the first epidemiologic case-control study, as well as the first epidemiologic study to publish its questionnaire ( Press and Pharoah 2010 ; Winkelstein 2004 ). Quickly replicated in the United States in 1931 by Wainwright ( Wainwright 1931 ), these two studies have recently been reanalyzed, using current statistical methods. The results show that their estimates of risk associated with major reproductive risk factors (e.g., early age at first birth, parity, lactation, and early age at menopause) are consistent with the current evidence ( Press and Pharoah 2010 ).

Not addressed in the reanalysis, however, are the two studies’ different results for occupational class, defined in relation to the women's employment before marriage. When these occupational data are recoded into the meaningful categories of professional, working-class nonmanual, and working-class manual ( Krieger, Williams, and Moss 1997 ; Rose and Pevalin 2003 ), the data quickly reveal why the studies had discrepant results. Thus, Lane-Claypon concluded there was no “appreciable difference” in breast cancer risk by social class ( Lane-Claypon 1926 , 12) (χ 2 = 1.833; p = 0.4), whereas in the U.S. study risk was lower among the working-class manual women (χ 2 = 9.305; p = 0.01). Why? In brief, a far higher proportion of the British women were working-class manual (78.7% cases, 84.2% controls vs. the U.S. women: 48.8% cases, 62.5% controls), and a far lower proportion were professionals (6.5% cases, 4.2% controls, vs. the U.S. women: 23.8% cases, 20.7% controls). Just as Rose famously observed that if everyone smoked, smoking would not be identified as a cause of lung cancer ( Rose 1985 , 1992 ), when most study participants are from only one social class, socioeconomic inequalities in health cannot and will not be detected ( Krieger 2007b ). The net result is erroneous causal inferences about the relevance of social class to structuring the risk of disease, thereby distorting the evidence base informing efforts to address health inequities.

Critical population-informed thinking therefore would question the dominant conventional cleavage, in both the population health and the social sciences, between “internal validity” and “generalizability” (or “external validity”) and the related endemic language of “study population”—routinely casually equated with study participants—and “general population” ( Broadbent 2011 ; Cartwright 2011 ; Cook 2001 ; Kincaid 2011 ; Kukuall and Ganguli 2012 ; Porta 2008 ; Rothman, Greenland, and Lash 2008 ). One critical determinant of a study's ability to provide valid tests of exposure-outcome hypotheses is the range of exposure encompassed ( Chen and Rossi 1987 ; Schlesselman and Stadel 1987 ); another is the extent to which participants’ selection into a study is associated with important unmeasured determinants of the outcome ( Pizzi et al. 2011 ). Given the social structuring of the vast majority of exposures, as evidenced by the virtually ubiquitous and dynamic societal patternings of disease ( Birn, Pillay, and Holtz 2009 ; Davey Smith 2003 ; Krieger 1994 , 2011 ; WHO 2008), meaningful research requires that the range of exposures experienced (or not) by study participants needs to capture the etiologically relevant range experienced in the real-world societies, that is, meaningful populations, of which they are a part. The point is not that ideal study participants should be a random sample of some “general population”; instead, it is that their location in the intrinsic and extrinsic relationships creating their population membership cannot be ignored.

Highlighting the need for critical population-informed thinking is advice provided in the widely used and highly influential textbook Modern Epidemiology ( Rothman, Greenland, and Lash 2008 ). Although the text correctly states that “the pursuit of representativeness can defeat the goal of validly identifying causal relations,” it further asserts that “one would want to select study groups for homogeneity with respect to important confounders, for highly cooperative behavior, and for availability of accurate information, rather than attempt to be representative of a natural population” (p. 146). “Classic examples” of the populations fulfilling these criteria are stated to be “the British Physicians’ Study of smoking and health and the Nurses’ Health Study, neither of which were remotely representative of the general population with respect to sociodemographic factors” ( Rothman, Greenland, and Lash 2008 , 146–47).

Of course, studies need accurate data, but the advice here raises more questions than it answers. First, just who and what is a “natural population”?—and, related, who is that “general population”? Second, might there be drawbacks to, not just benefits from, preferentially studying predominantly white health professionals and others with the resources to be “highly cooperative” and possess “accurate information”? Stated another way, what might be the adverse consequences on scientific knowledge and policymaking of discounting people that mainstream research already routinely and problematically calls “hard-to-reach” populations ( Crosby et al. 2010 ; Shaghaghi, Bhopal, and Sheik 2011 )? These populations include the disempowered and dispossessed, whose adverse social and physical circumstances mean that their range of exposures almost invariably differ, in both level and type, from those encountered by the effectively “easy-to-reach.” Might it not also be critical for researchers to develop more inclusive approaches that could yield accurate etiologic and policy-relevant data on the distributions and determinants of disease among those who bear the brunt of health inequities ( Smylie et al. 2012 )?—a scientific task that necessarily requires contrasts in both exposures and outcomes between the social groups defined by the inequitable societal relationships at issue, whether involving social class, racism, gender, or other forms of social inequality ( Krieger 2007b ).

Reflecting on how who is studied determines what can be learned, the eminent British biologist Lancelot Hogben (1895–1975) ( figure 2 ; Bud 2004 ; Werskey 1988 ), in his lucid and prescient 1933 book titled Nature and Nurture ( Hogben 1933 , 106), cogently observed:

Differences to which members of the same family or different families living at one and the same social level are exposed may be very much less than differences to which individuals belonging to families taken from different social levels are exposed. Experiment shows that ultra-violet light has a considerable influence on growth in mammals. In Great Britain, some families live continuously in the sooty atmosphere of an industrial area. Others spend their winters on the Riviera.

In other words, critical population-informed thinking is vital to good science.

Conclusion: Meaningful Means, Embodied Phenotypes, and the Structural Determinants of Populations and the People's Health

In conclusion, to improve causal inference and policies and action based on this knowledge, the population sciences need to expand and deepen theorizing about who and what makes populations and their means. At a time when the topic of causality in the sciences remains hotly debated by philosophers and researchers alike, all parties nevertheless agree that “the question of how probabilistic accounts of causality can mesh with mechanistic accounts of causality desperately needs answering” ( Illari, Russo, and Williamson 2011 , 20). As my article makes clear, the idea and reality of “population” reside at the nexus of this question. Clarifying the substantive defining features of populations, including who and what structures the dynamic and emergent distributions of their characteristics and components, is thus crucial to both analyzing and altering causal processes. For public health, this means sharpening our thinking about how structured chances, structured by the political and economic relationships constituting the societal determinants of health ( Birn, Pillay, and Holtz 2009 ; Irwin et al. 2006 ; Krieger 1994 , 2011 ), generate the embodied phenotypes that are the people's health.

As should be evident, the challenges to developing critical population-informed thinking are not purely conceptual; they are also political, because these ideas necessarily engage with issues involving not only the distribution of people but also the distribution of power and property and the societal relationships that bind individuals and populations, for good and for bad ( Krieger 2011 ). Nearly two hundred years after Quetelet introduced his “l’homme moyen,” the countervailing call for routinely measuring and tracking population health inequities, and not just on-average population rates of health, is only now gaining traction globally (WHO 2008, 2011). This is coincident with the ever-accelerating aforementioned genomic quest for “personalized medicine” ( Davey Smith 2011 ), as well as the continued economic, social, political, and public health reverberations of the 2008 global economic crash ( Benatar, Gill, and Bakker 2011 ; Stiglitz 2010 ). In such a context, clarity regarding who and what populations are, and the making and meaning of their means, is vital to population sciences, population health, and the promotion of health equity.

Acknowledgments

No funding supported this work.

• Althuis MD, Dozier JM, Anderson WF, Devesa SS, Brinton LA. Global Trends in Breast Cancer Incidence and Mortality 1973–1999. International Journal of Epidemiology. 2005; 34 :405–12. [ PubMed ] [ Google Scholar ]
• Bateson P, Gluckman P. Plasticity and Robustness in Development and Evolution. International Journal of Epidemiology. 2012; 41 :219–23. [ PubMed ] [ Google Scholar ]
• Benatar SR, Gill S, Bakker I. Global Health and the Global Economic Crisis. American Journal of Public Health. 2011; 101 :646–53. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Biersack A, Greenberg JB. Reimagining Political Ecology. Durham, NC: Duke University Press; 2006. [ Google Scholar ]
• Birn AE, Pillay Y, Holtz TM. Textbook of International Health: Global Health in a Dynamic World. 3rd ed. New York: Oxford University Press; 2009. [ Google Scholar ]
• Braatne JH, Rood SB, Heillman PE. Life History, Ecology, and Conservation of Riparian Cottonwoods in North America. In: Stettler RF, Bradshaw HD Jr, Heilman PE, Hinckley TM, editors. Biology of Populus and Its Implications for Management and Conservation. Ottawa: National Research Council of Canada, NRC Research Press; 1996. pp. 57–85. [ Google Scholar ]
• Bray F, McCarron P, Parkin DM. The Changing Global Patterns of Female Breast Cancer Incidence and Mortality. Breast Cancer Research. 2004; 6 :229–39. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Brett KM, Madans JH. Difference in Use of Postmenopausal Hormone Replacement Therapy by Black and White Women. Menopause. 1997; 4 :66–70. [ Google Scholar ]
• Briggs H. Women's Cancers Reach Two Million. 2011. BBC News Health, September 14. Available at http://www.bbc.co.uk/news/health-14917284 (accessed June 17, 2012)
• Broadbent A. Inferring Causation in Epidemiology: Mechanisms, Black Boxes, and Contrasts. In: Illari PM, Russo F, Williamson J, editors. Causality in the Sciences. Oxford: Oxford University Press; 2011. pp. 45–69. [ Google Scholar ]
• Bud R. Oxford Dictionary of National Biography. Oxford: Oxford University Press; 2004. Hogben, Lancelot Thomas (1895–1975) Available at http://www.oxforddnb.com.ezp-prod1.hul.harvard.edu/view/article/31244?docPos=1 (accessed June 17, 2012) [ Google Scholar ]
• Burian RM, Zallen DT. Genes. In: Bowler PJ, Pickstone JV, editors. The Modern Biological and Earth Sciences. Cambridge: Cambridge University Press; 2009. Cambridge Histories Online. DOI: 10.1017/CHOL9780521572019.024 . [ Google Scholar ]
• Butler AHB. Obituary: Major Greenwood. Journal of the Royal Statistical Society: Series A (General) 1949; 112 :487–89. [ Google Scholar ]
• Carlson EA. The Unfit: A History of a Bad Idea. Cold Spring Harbor, NY: Cold Spring Harbor Press; 2001. [ Google Scholar ]
• Cartwright N. Predicting “It Will Work for Us”: (Way) beyond Statistics. In: Illari PM, Russo F, Williamson J, editors. Causality in the Sciences. Oxford: Oxford University Press; 2011. pp. 750–68. [ Google Scholar ]
• Carver T. Marx and Marxism. In: Porter TM, Ross D, editors. The Modern Social Sciences. Cambridge: Cambridge University Press; 2003. Cambridge Histories Online. DOI: 10.1017/CHOL9780521594424.013 . [ Google Scholar ]
• Chen H-T, Rossi PH. The Theory-Driven Approach to Validity. Evaluation and Program Planning. 1987; 10 :95–103. [ Google Scholar ]
• Clarke A, Agrò AF, Zheng Y, Tickle C, Jansson R, Kehrer-Sawatzki H, Cooper DN, Delves P, Battista J, Melino G, Perkel DJ, Hetherington AM, Bynum WF, Valpuesta JM, Harper D. Encyclopedia of Life Sciences. Chichester: Wiley; 2000. –2011. Available at http://www.els.net/WileyCDA/ (accessed September 6, 2011) [ Google Scholar ]
• Coggon DIW, Martyn CN. Time and Chance: The Stochastic Nature of Disease Causation. The Lancet. 2005; 365 :1434–37. [ PubMed ] [ Google Scholar ]
• Cole J. The Power of Large Numbers: Populations, Politics, and Gender in Nineteenth-Century France. Ithaca, NY: Cornell University Press; 2000. [ Google Scholar ]
• Cook TD. Generalization: Conceptions in the Social Sciences. In: Smelser NJ, Baltes PB, editors. International Encyclopedia of the Social & Behavioral Sciences. Oxford: Pergamon; 2001. pp. 6037–43. DOI: 10.1016/B0-08-043076-7/00698-7 . [ Google Scholar ]
• Cowan RS. Oxford Dictionary of National Biography. Oxford: Oxford University Press; 2004. Galton, Sir Francis (1822–1911) Available at http://www.oxforddnb.com.ezp-prod1.hul.harvard.edu/view/article/33315 (accessed June 17, 2012) [ Google Scholar ]
• Crosby RA, Salazar LF, DiClemente RJ, Lang DL. Balancing Rigor against the Inherent Limitations of Investigating Hard-to-Reach Populations. Health Education Research. 2010; 25 :1–5. [ PubMed ] [ Google Scholar ]
• Crow JF. R.A. Fisher: A Centennial View. Genetics. 1990; 124 :204–11. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Crow JF. Sewall Wright (1889–1988): A Biographical Memoir. Washington, DC: National Academy of Science; 1994. [ Google Scholar ]
• Daintith J, Martin E, editors. A Dictionary of Science. 5th ed. Oxford: Oxford University Press; 2005. [ Google Scholar ]
• Dale AI, Katz S. Arthur L. Bowley: A Pioneer in Modern Statistics and Economics. London: World Scientific Publishing; 2011. [ Google Scholar ]
• Daniel TM. Wade Hampton Frost: Pioneer Epidemiologist 1880–1938. Rochester, NY: University of Rochester Press; 2004. [ Google Scholar ]
• Darwin C. Origin of Species. Edison, NJ: Castle Books; 2004. (1859) [ Google Scholar ]
• Daston LJ. Rational Individuals versus Laws of Society: From Probability to Statistics. In: Krüger L, Daston LJ, Heidelberger M, editors. The Probabilistic Revolution. Vol. 1 , Ideas in History. Cambridge, MA: MIT Press; 1987. pp. 295–304. [ Google Scholar ]
• Davenport CB. Heredity in Relation to Eugenics. New York: Henry Holt; 1911. [ Google Scholar ]
• Davey Smith G. Health Inequalities: Lifecourse Approaches. Bristol: Policy Press; 2003. [ Google Scholar ]
• Davey Smith G. Epidemiology, Epigenetics and the “Gloomy Prospect”: Embracing Randomness in Population Health Research and Practice. International Journal of Epidemiology. 2011; 40 :537–62. [ PubMed ] [ Google Scholar ]
• Davey Smith G. Epigenesis for Epidemiologists: Does Evo-Devo Have Implications for Population Health Research and Practice. International Journal of Epidemiology. 2012; 41 :236–47. [ PubMed ] [ Google Scholar ]
• Davey Smith G, Morris J. A Conversation with Jerry Morris. Epidemiology. 2004; 15 :770–73. [ PubMed ] [ Google Scholar ]
• Davis K, Rowland D. Uninsured and Underserved: Inequities in Health Care in the United States. The Milbank Quarterly. 1983; 61 :149–76. [ PubMed ] [ Google Scholar ]
• Desrosières A. The Politics of Large Numbers: A History of Statistical Reasoning. Cambridge, MA: Harvard University Press; 1998. Trans. Camille Naish. [ Google Scholar ]
• Dominguez-Bello MG, Blaser MJ. The Human Microbiota as a Marker for Migrations of Individuals and Populations. Annual Review of Anthropology. 2011; 40 :451–74. [ Google Scholar ]
• Dunn BK, Agurs-Collins T, Browne D, Lubet R, Johnson KA. Health Disparities in Breast Cancer: Biology Meets Socioeconomic Status. Breast Cancer Research and Treatment. 2010; 121 :281–92. [ PubMed ] [ Google Scholar ]
• Eldredge N. The Pattern of Evolution. New York: Freeman; 1999. [ Google Scholar ]
• Eldredge N. Darwin: Discovering the Tree of Life. New York: Norton; 2005. [ Google Scholar ]
• Eldredge N, Grene M. Interactions: The Biological Context of Social Systems. New York: Columbia University Press; 1992. [ Google Scholar ]
• Evans RG, Barer ML, Marmor TR. Why Are Some People Healthy and Others Not? The Determinants of Health of Populations. New York: De Gruyter; 1994. [ Google Scholar ]
• Falk R. The Gene—A Concept in Tension: A Critical Overview. In: Beurton PJ, Falk R, Rehinberger H-J, editors. The Concept of the Gene in Development and Evolution: Historical and Epistemological Perspectives. Cambridge: Cambridge University Press; 2000. pp. 317–49. [ Google Scholar ]
• Fee E. Disease and Discovery: A History of the Johns Hopkins School of Hygiene and Public Health, 1916–1939. Baltimore: Johns Hopkins University Press; 1987. [ Google Scholar ]
• Fergus C. Trees of New England: A Natural History. Guildford, CT: FalconGuide; 2005. [ Google Scholar ]
• Ferlay J, Forman D, Mathers CD, Bray F. Re: “Breast and Cervical Cancer in 187 Countries between 1980 and 2010” The Lancet. 2012; 379 :1390–91. [ PubMed ] [ Google Scholar ]
• Foner E, editor. The New American History. Rev. and expanded ed. Philadelphia: Temple University Press; 1997. [ Google Scholar ]
• Forouzanafar MH, Foreman KJ, Delossantos AM, Lozano R, Lopez AD, Murray CJ, Naghanvi M. Breast and Cervical Cancer in 187 Countries between 1980 and 2010: A Systematic Analysis. The Lancet. 2011; 378 :1461–84. [ PubMed ] [ Google Scholar ]
• Fox SE, Levitt P, Nelson CA., III How the Timing and Quality of Early Experiences Influence the Development of Brain Architecture. Child Development. 2010; 81 :28–40. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Friedman-Koss D, Crespo CJ, Bellantoni MF, Andersen RE. The Relationship of Race/Ethnicity and Social Class to Hormone Replacement Therapy: Results from the Third National Health and Nutrition Examination Survey 1988–1994. Menopause. 2002; 9 :264–72. [ PubMed ] [ Google Scholar ]
• Frost C, Appel H, Carlson J, De Moraes CM, Mescher M, Schultz JC. Within-Plant Signaling by Volatiles Overcomes Vascular Constraints on Systemic Signaling and Primes Responses against Herbivores. Ecology Letters. 2007; 10 :490–98. [ PubMed ] [ Google Scholar ]
• Frost WH. In: Epidemiology. Maxcy KF, editor. New York: Commonwealth Fund; 1941. pp. 439–52. (1927) In Papers of Wade Hampton Frost, M.D . [ Google Scholar ]
• Frost WH. 1976. Some Conceptions of Epidemics in General. American Journal of Epidemiology. 1928; 103 :141–51. [ PubMed ] [ Google Scholar ]
• Galton F. Natural Inheritance. London: Macmillan; 1889. [ Google Scholar ]
• Galton F. Eugenics: Its Definition, Scope, and Aims. Nature. 1904; 70 :82. [ Google Scholar ]
• Gaziano JM. The Evolution of Population Science: Advent of the Mega Cohort. JAMA. 2010; 304 :2288–89. [ PubMed ] [ Google Scholar ]
• Gibson JJ. The Ecological Approach to Visual Perception. Hillsdale, NJ: Erlbaum; 1986. [ Google Scholar ]
• Giddens A, Held D, editors. Classes, Power, and Conflict: Classical and Contemporary Debates. Berkeley: University of California Press; 1982. [ Google Scholar ]
• Gilbert SF, Epel D. Ecological Developmental Biology: Integrating Epigenetics, Medicine, and Evolution. Sunderland, MA: Sinaeur Associates; 2009. [ Google Scholar ]
• GLOBOCAN. 2012. Data Sources and Methods. International Agency for Research on Cancer, World Health Organization. Available at http://globocan.iarc.fr/ (accessed June 17, 2012)
• Greenhalgh S. The Social Construction of Population Science: An Intellectual, Institutional, and Political History of Twentieth-Century Demography. Comparative Studies Society History. 1996; 38 :26–66. [ Google Scholar ]
• Greenwood M. Epidemics and Crowd Diseases: An Introduction to the Study of Epidemiology. London: Williams & Norgate; 1935. [ Google Scholar ]
• Grene M, Depew D. The Philosophy of Biology. Cambridge: Cambridge University Press; 2004. [ Google Scholar ]
• Hacking I. The Emergence of Probability. Cambridge: Cambridge University Press; 1975. [ Google Scholar ]
• Hacking I. The Taming of Chance. Cambridge: Cambridge University Press; 1990. [ Google Scholar ]
• Hankins FH. Adolphe Quetelet as Statistician. New York: Arno Press; 1968. [ Google Scholar ]
• Harraway DJ. When Species Meet. Minneapolis: University of Minnesota Press; 2008. [ Google Scholar ]
• Harré R. Individual/Society: History of the Concept. In: Smelser NJ, Baltes PB, editors. International Encyclopedia of the Social & Behavioral Sciences. Oxford: Pergamon; 2001. pp. 7306–10. DOI: 10.1016/B0-08-043076-7/00125-X . [ Google Scholar ]
• Harvey D. Justice, Nature, and the Geography of Difference. Cambridge, MA: Blackwell; 1996. [ Google Scholar ]
• Heesterbeek H. The Law of Mass-Action in Epidemiology: A Historical Perspective. In: Cuddington K, Beisner BE, editors. Ecological Paradigms Lost: Routes of Theory Change. Burlington, MA: Elsevier Academic Press; 2005. pp. 81–106. [ Google Scholar ]
• Heilbron J, Magnusson L, Wittrock B, editors. The Rise of the Social Sciences and the Formation of Modernity: Conceptual Change in Context, 1750–1850. Dordrecht: Kluwer Academic Publishers; 1998. [ Google Scholar ]
• Hey J. Regarding the Confusion between the Population Concept and Mayr's “Population Thinking.” Quarterly Review of Biology. 2011; 86 :253–64. [ PubMed ] [ Google Scholar ]
• Hodge J. Evolution. In: Bowler PJ, Pickstone JV, editors. The Modern Biological and Earth Sciences. Cambridge: Cambridge University Press; 2009. Cambridge Histories Online. DOI: 10.1017/CHOL9780521572019.015 . [ Google Scholar ]
• Hogben L. Nature and Nurture. London: Williams & Norgate; 1933. [ Google Scholar ]
• Hogben L. Major Greenwood: 1880–1949. Obituary Notices of Fellows of the Royal Society. 1950; 7 :138–54. [ Google Scholar ]
• IHME (Institute for Health Metrics and Evaluation) The Challenge Ahead: Progress and Setbacks in Breast and Cervical Cancer. Seattle: 2011. [ Google Scholar ]
• Illari PM, Russo F, Williamson J. Why Look at Causality in the Sciences? A Manifesto. In: Illari PM, Russo F, Williamson J, editors. Causality in the Sciences. Oxford: Oxford University Press; 2011. pp. 3–22. [ Google Scholar ]
• Irwin A, Valentine N, Brown C, Loewenson R, Solar O, Brown H, Koller T, Vega J. The Commission on the Social Determinants of Health: Tackling the Social Roots of Health Inequities. PLoS Medicine. 2006; 3 (6):e106. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Issac J. The Human Sciences in Cold War America. Historical Journal. 2007; 50 :725–46. [ Google Scholar ]
• Jansson S, Douglas CJ. Populus: A Model System for Plant Biology. Annual Review of Plant Biology. 2007; 58 :435–458. [ PubMed ] [ Google Scholar ]
• Jaslow R. CBS News. 2011. Breast, Cervical Cancer Rates Rising around World: Why? September 15, 2011. Available at http://www.cbsnews.com/8301-504763_162-20106719-10391704.html (accessed June 17, 2012) [ Google Scholar ]
• Keller EF. The Century of the Gene. Cambridge, MA: Harvard University Press; 2000. [ Google Scholar ]
• Keller EF. The Mirage of a Space between Nature and Nurture. Durham, NC: Duke University Press; 2010. [ Google Scholar ]
• Kermack WO, McKendrick AG. Contributions to the Mathematical Theory of Epidemics, Part I. Proceedings of the Royal Society Series A. 1927; 115 :700–721. [ Google Scholar ]
• Kevels D. In the Name of Eugenics: Genetics and the Uses of Human Heredity. New York: Knopf; 1985. [ Google Scholar ]
• Kincaid H. Causal Modeling, Mechanisms, and Probability in Epidemiology. In: Illari PM, Russo F, Williamson J, editors. Causality in the Sciences. Oxford: Oxford University Press; 2011. pp. 70–90. [ Google Scholar ]
• Klassen AC, Smith KC. The Enduring and Evolving Relationship between Social Class and Breast Cancer Burden: A Review of the Literature. Cancer Epidemiology. 2011; 35 :217–34. [ PubMed ] [ Google Scholar ]
• Kolata G. New York Times. 2006. Reversing Trend, Big Drop Is Seen in Breast Cancer. December 15. Available at http://www.nytimes.com/2006/12/15/health/15breast.html?pagewanted=all (accessed June 17, 2012) [ Google Scholar ]
• Kolata G. New York Times. 2007. Sharp Drop in Rates of Breast Cancer Holds. April 19. Available at http://query.nytimes.com/gst/fullpage.html?res=9a03e6d91e3ff93aa25757c0a9619c8b63 (accessed June 17, 2012) [ Google Scholar ]
• Krieger N. Epidemiology and the Web of Causation: Has Anyone Seen the Spider. Social Science & Medicine. 1994; 39 :887–903. [ PubMed ] [ Google Scholar ]
• Krieger N. Epidemiology and Social Sciences: Towards a Critical Reengagement in the 21st Century. Epidemiology Review. 2000; 11 :155–63. [ PubMed ] [ Google Scholar ]
• Krieger N. Theories for Social Epidemiology in the 21st Century: An Ecosocial Perspective. International Journal of Epidemiology. 2001; 30 :668–77. [ PubMed ] [ Google Scholar ]
• Krieger N. Breast Cancer: A Disease of Affluence, Poverty, or Both?—The Case of African American Women. American Journal of Public Health. 2002; 92 :611–13. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Krieger N. Embodiment: A Conceptual Glossary for Epidemiology. Journal of Epidemiology & Community Health. 2005; 59 :350–55. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Krieger N. Ways of Asking and Ways of Living: Reflections on the 50th Anniversary of Morris’ Ever-Useful Uses of Epidemiology. International Journal of Epidemiology. 2007a; 36 :1173–80. [ PubMed ] [ Google Scholar ]
• Krieger N. Why Epidemiologists Cannot Afford to Ignore Poverty. Epidemiology. 2007b; 18 :658–63. [ PubMed ] [ Google Scholar ]
• Krieger N. Hormone Therapy and the Rise and Perhaps Fall of US Breast Cancer Incidence Rates: Critical Reflections. International Journal of Epidemiology. 2008; 37 :627–37. [ PubMed ] [ Google Scholar ]
• Krieger N. Epidemiology and the People's Health: Theory and Context. New York: Oxford University Press; 2011. [ Google Scholar ]
• Krieger N, Bassett M, Gomez S. Re: “Breast and Cervical Cancer in 187 Countries between 1980 and 2010.” The Lancet. 2012; 379 :1391–92. [ PubMed ] [ Google Scholar ]
• Krieger N, Chen JT, Waterman PD. Decline in US Breast Cancer Rates after the Women's Health Initiative: Socioeconomic and Racial/Ethnic Differentials. American Journal of Public Health. 2010; 100 :S132–S139. erratum, 972. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Krieger N, Chen JT, Waterman PD. Temporal Trends in the Black/White Breast Cancer Case Ratio for Estrogen Receptor Status: Disparities Are Historically Contingent, Not Innate. Cancer Causes and Control. 2011; 22 :511–14. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Krieger N, Davey Smith G. Bodies Count & Body Counts: Social Epidemiology & Embodying Inequality. Epidemiology Review. 2004; 26 :92–103. [ PubMed ] [ Google Scholar ]
• Krieger N, Fee E. Measuring Social Inequalities in Health in the United States: An Historical Review, 1900–1950. International Journal of Health Services. 1996; 26 :391–418. [ PubMed ] [ Google Scholar ]
• Krieger N, Williams D, Moss N. Measuring Social Class in US Public Health Research: Concepts, Methodologies and Guidelines. Annual Review of Public Health. 1997; 18 :341–78. [ PubMed ] [ Google Scholar ]
• Kuhlmann M. Mechanisms in Dynamically Complex Systems. In: Illari PM, Russo F, Williamson J, editors. Causality in the Sciences. Oxford: Oxford University Press; 2011. pp. 880–906. [ Google Scholar ]
• Kukuall WA, Ganguli M. Generalizability: The Trees, the Forest, and the Low-Hanging Fruit. Neurology. 2012; 78 :1886–91. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kunitz SJ. The Health of Populations: General Theories and Particular Realities. New York: Oxford University Press; 2007. [ Google Scholar ]
• Kuzawa C. Why Evolution Needs Development, and Medicine Needs Evolution. International Journal of Epidemiology. 2012; 41 :223–29. [ PubMed ] [ Google Scholar ]
• Lane-Claypon JE. A Further Report on Cancer of the Breast with Special Reference to Its Associated Antecedent Conditions. Reports on Public Health and Medical Subjects no. 32. London: HMSO; 1926. [ Google Scholar ]
• Lewontin R. The Triple Helix: Gene, Organism, and Environment. Cambridge, MA: Harvard University Press; 2000. [ Google Scholar ]
• Lilienfeld AM, editor. Times, Places, and Persons: Aspects of the History of Epidemiology. Baltimore: Johns Hopkins University Press; 1980. [ Google Scholar ]
• Limpert E, Stahel WA, Abbt M. Log-Normal Distributions across the Sciences: Keys and Clues. BioSci. 2001; 51 :341–52. [ Google Scholar ]
• Mackenzie D. Statistics in Britain, 1865–1930: The Social Construction of Scientific Knowledge. Edinburgh: Edinburgh University Press; 1982. [ Google Scholar ]
• Martin J, Harré R. Metaphor in Science. In: Miall DS, editor. Metaphor: Problems and Perspectives. Sussex, NJ: Harvester Press; 1982. pp. 89–105. [ Google Scholar ]
• Marx K. In: Theses on Feuerbach. Dietz JHW, editor. Stuttgart: 1845. 1888. First published, in an edited version, as an appendix to Engels F. Ludwig Feuerbach und der Ausgang der klassischen deutschen Philosophie. Mit Anghard: Karl Marx über Feuerbach von Jarhe 1845 . Available at http://www.marxists.org/archive/marx/works/1845/theses/index.htm (2002 trans. by Cyril Smith) (accessed June 17, 2012) [ Google Scholar ]
• Mayr E. Towards a New Philosophy of Biology: Observations of an Evolutionist. Cambridge, MA: Harvard University Press; 1988. [ Google Scholar ]
• Mendelsohn JA. From Eradication to Equilibrium: How Epidemics Became Complex after World War I. In: Lawrence C, Weisz G, editors. Greater Than the Parts: Holism in Biomedicine, 1920–1950. New York: Oxford University Press; 1998. pp. 303–31. [ Google Scholar ]
• Mitchell M. Complexity: A Guided Tour. Oxford: Oxford University Press; 2009. [ Google Scholar ]
• Morange M. The Misunderstood Gene. Cambridge, MA: Harvard University Press; 2001. [ Google Scholar ]
• Morris JN. Uses of Epidemiology. Edinburgh: E. & S. Livingston; 1957. [ Google Scholar ]
• Mountain JL. Human Evolutionary Genetics. In: Smelser NJ, Baltes PB, editors. International Encyclopedia of the Social & Behavioral Sciences. Oxford: Pergamon, Oxford; 2001. pp. 6984–91. DOI: 10.1016/B0-08-043076-7/03088-6 . [ Google Scholar ]
• Nash K, Scott A, editors. The Blackwell Companion to Political Sociology. Malden, MA: Blackwell; 2001. [ Google Scholar ]
• OED (Oxford English Dictionary) online. 2010. Draft revision June. Available at http://dictionary.oed.com.ezp-prod1.hul.harvard.edu/ (accessed June 17, 2012)
• Parkin DM, Fernández LMG. Use of Statistics to Assess the Global Burden of Breast Cancer. Breast Journal. 2006; 12 (suppl. 1):S70– S80. [ PubMed ] [ Google Scholar ]
• Pearce N. Epidemiology as a Population Science. International Journal of Epidemiology. 1999; 28 :S1015–S18. [ PubMed ] [ Google Scholar ]
• Pflughoeft KJ, Versalovic J. Human Microbiome in Health and Disease. Annual Review of Pathology: Mechanisms of Disease. 2012; 7 :99–122. [ PubMed ] [ Google Scholar ]
• Piermsa T, van Gils JA. The Flexible Phenotype: A Body-Centered Integration of Ecology, Physiology, and Behavior. New York: Oxford University Press; 2011. [ Google Scholar ]
• Pizzi C, De Stavola B, Merletti F, Bellocco R, dos Santos Silva I, Pearce N, Richiardi L. Sample Selection and Validity of Exposure-Disease Association Estimates in Cohort Studies. Journal of Epidemiology & Community Health. 2011; 65 :407–11. [ PubMed ] [ Google Scholar ]
• Porta M, editor. A Dictionary of Epidemiology. 5th ed. Oxford: Oxford University Press; 2008. [ Google Scholar ]
• Porter TM. A Statistical Survey of Gases: Maxwell's Social Physics. Historical Studies in the Physical Sciences. 1981; 12 :77–116. [ Google Scholar ]
• Porter TM. The Rise of Statistical Thinking, 1820–1900. Princeton, NJ: Princeton University Press; 1986. [ Google Scholar ]
• Porter TM. Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton, NJ: Princeton University Press; 1995. [ Google Scholar ]
• Porter TM. Statistics and Physical Theories. In: Nye MJ, editor. The Modern Physical and Mathematical Sciences. Cambridge: Cambridge University Press; 2002. Cambridge Histories Online. DOI: 10.1017/CHOL9780521571999.027 . [ Google Scholar ]
• Porter TM. Statistics and Statistical Methods. In: Porter TM, Ross D, editors. The Modern Social Sciences. Cambridge: Cambridge University Press; 2003. Cambridge Histories Online. DOI: 10.1017/CHOL9780521594424.015 . [ Google Scholar ]
• Press DJ, Pharoah P. Risk Factors for Breast Cancer: A Reanalysis of Two Case-Control Studies from 1926 and 1931. Epidemiology. 2010; 21 :566–72. [ PubMed ] [ Google Scholar ]
• Quetelet A. In: Sur l’homme et le development des ses facultés, ou essai de physique sociale. Knox R, translator. 1835. Paris. For a translation, see Quetelet, A. (1842) 1968. A Treatise on Man and the Development of His Faculties . Reprint, New York: Burt Franklin. [ Google Scholar ]
• Quetelet A. Recherches statistiques. Brussels: M. Hayez (Imprimeur de la Commission centrale de statistique); 1844. [ Google Scholar ]
• Ramsden E. Carving Up Population Science: Eugenics, Demography and the Controversy over the “Biological Law” of Population Growth. Social Studies of Science. 2002; 32 :857–99. [ Google Scholar ]
• Ravdin PM, Cronin KA, Howlader N, Berg CD, Chlebowski RT, Feuer EJ, Edwards BK, Berry DA. The Decrease in Breast-Cancer Incidence in 2003 in the United States. New England Journal of Medicine. 2007; 356 :1670–74. [ PubMed ] [ Google Scholar ]
• Ravdin PM, Cronin KA, Howlader N, Chlebowski RT, Berry DA. A Sharp Decrease in Breast Cancer Incidence in the United States in 2003. Breast Cancer Research and Treatment. 2006; 100 (suppl) S2 (abstract) [ Google Scholar ]
• Relton CL, Davey Smith G. Is Epidemiology Ready for Epigenetics? International Journal of Epidemiology. 2012; 41 :5–9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Richards RA. Encyclopedia of Life Sciences. New York: Wiley; 2001. Species Problem—A Philosophical Analysis. (online 2007). DOI: 10.1002/9780470015902.a0003456 . [ Google Scholar ]
• Rose D, Pevalin DJ, editors. A Researcher's Guide to the National Statistics Socio-economic Classification. London: Sage; 2003. [ PubMed ] [ Google Scholar ]
• Rose GA. Sick Individuals and Sick Populations. International Journal of Epidemiology. 1985; 14 :32–38. [ PubMed ] [ Google Scholar ]
• Rose GA. The Strategy of Preventive Medicine. Oxford: Oxford University Press; 1992. [ Google Scholar ]
• Rose GA. Rose's Strategy of Preventive Medicine: The Complete Original Text, with a Commentary by Kay-Tee Khaw and Michael Marmot. Oxford: Oxford University Press; 2008. [ Google Scholar ]
• Rosen G. A History of Public Health. Baltimore: Johns Hopkins University Press; 1993. (1958) Expanded ed. Introduction by E. Fee; biographical essay and new bibliography by E.T. Morman. [ Google Scholar ]
• Ross D. Changing Contours of the Social Science Disciplines. In: Porter TM, Ross D, editors. The Modern Social Sciences. Cambridge: Cambridge University Press; 2003. pp. 275–305. [ Google Scholar ]
• Rossouw JE, Anderson GL, Prentice RL, LaCroix AZ, Kooperberg C, Stefanick ML, Jackson RD, Beresford SA, Howard BV, Johnson KC, Kotchen JM, Ockene J, Writing Group for the Women's Health Initiative Investigators Risk and Benefits of Estrogen plus Progestin in Healthy Postmenopausal Women: Principal Results from the Women's Health Initiative Randomized Controlled Trial. JAMA. 2002; 288 :321–33. [ PubMed ] [ Google Scholar ]
• Rothman KJ, Greenland S, Lash TL. Modern Epidemiology. 3rd ed. Philadelphia: Lippincott Williams & Wilkins; 2008. [ Google Scholar ]
• Sarkar S. Lancelot Hogben, 1895–1975. Genetics. 1996; 142 :655–60. [ Google Scholar ]
• Schank JC, Twardy C. Mathematical Models. In: Bowler PJ, Pickstone JV, editors. The Modern Biological and Earth Sciences. Cambridge: Cambridge University Press; 2009. Cambridge Histories Online. DOI: 10.1017/CHOL9780521572019.023 . [ Google Scholar ]
• Schlesselman JJ, Stadel BV. Exposure Opportunity in Epidemiologic Studies. American Journal of Epidemiology. 1987; 125 :174–78. [ PubMed ] [ Google Scholar ]
• Scott J, Marshall G, editors. A Dictionary of Sociology. 3rd ed. Oxford: Oxford University Press; 2005. [ Google Scholar ]
• Shaghaghi A, Bhopal RJ, Sheik A. Approaches to Recruiting “Hard-to-Reach” Populations in Research: Review of the Literature. Health Promotion Perspectives. 2011; 1 (2):1–9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Smith GD. The Uses of “Uses of Epidemiology.” International Journal of Epidemiology. 2001; 30 :1146–55. [ PubMed ] [ Google Scholar ]
• Smylie J, Lofters A, Firestone M, O’Campo P. Population-Based Data and Community Empowerment. In: O’Campo P, Dunn JR, editors. Rethinking Social Epidemiology: Towards a Science of Change. Dordrecht: Springer Science+Business Media B.V; 2012. pp. 68–92. [ Google Scholar ]
• Stanley D, Phelps AE, Banaji MR. The Neural Basis of Implicit Attitudes. Current Directions in Psychological Science. 2008; 17 :165–70. [ Google Scholar ]
• Steinman E. Sovereigns and Citizens? The Contested Status of American Indian Tribal Nations and Their Members. Citizenship Studies. 2011; 15 :57–74. [ Google Scholar ]
• Stigler SM. The History of Statistics: The Measurement of Uncertainty before 1900. Cambridge, MA: Belknap Press /Harvard University Press; 1986. [ Google Scholar ]
• Stigler SM. Regression towards the Mean, Historically Considered. Statistical Methods in Medical Research. 1997; 6 :103–14. [ PubMed ] [ Google Scholar ]
• Stigler SM. The Average Man Is 168 Years Old. In: Stigler SM, editor. Statistics on the Table: The History of Statistical Concepts and Methods. Cambridge, MA: Harvard University Press; 2002. pp. 51–65. [ Google Scholar ]
• Stiglitz J. Freefall: America, Free Markets, and the Sinking World Economy. New York: Norton; 2010. [ Google Scholar ]
• Strevens M. Bigger Than Chaos: Understanding Complexity through Probability. Cambridge, MA: Harvard University Press; 2003. [ Google Scholar ]
• Susser M, Stein Z. Eras in Epidemiology: The Evolution of Ideas. New York: Oxford University Press; 2009. [ Google Scholar ]
• Svensson P-G. Special Issue: Health Inequities in Europe. Social Science & Medicine. 1990; 31 :225–27. [ PubMed ] [ Google Scholar ]
• Sydenstricker E. Health and Environment. New York: McGraw-Hill; 1933. [ Google Scholar ]
• Tabery J. R.A. Fisher, Lancelot Hogben, and the Origin(s) of Genotype-Environment Interaction. Journal of the History of Biology. 2008; 41 :717–61. [ PubMed ] [ Google Scholar ]
• Turner JH. A New Approach for Theoretically Integrating Micro and Macro Analyses. In: Calhoun C, Rojek C, Turner B, editors. The Sage Handbook of Sociology. Thousand Oaks, CA: Sage; 2005. pp. 405–22. [ Google Scholar ]
• U.S. Citizenship and Immigration Services. 2012. Citizenship. Available at http://www.uscis.gov/portal/site/uscis/ (accessed June 17, 2012)
• Vona-Davis L, Rose DP. The Influence of Socioeconomic Disparities on Breast Cancer Tumor Biology and Prognosis: A Review. Journal of Women's Health. 2009; 18 :883–93. [ PubMed ] [ Google Scholar ]
• Wainwright JM. A Comparison of Conditions Associated with Breast Cancer in Great Britain and America. American Journal of Cancer. 1931; 15 :2610–45. [ Google Scholar ]
• Wallace TA, Martin DN, Ambs S. Interactions among Genes, Tumor Biology and the Environment in Cancer Health Disparities: Examining the Evidence on a National and Global Scale. Carcinogenesis. 2011; 32 :1107–21. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Walter J, Ley R. The Human Gut Microbiome: Ecology and Recent Evolutionary Changes. Annual Review of Microbiology. 2011; 65 :411–29. [ PubMed ] [ Google Scholar ]
• Weiss KM, Long JC. Non-Darwinian Estimation: My Ancestors, My Genes’ Ancestors. Genome Research. 2009; 19 :703–10. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Werskey G. In: The Visible College: A Collective Biography of British Scientists and Socialists of the 1930s. Young RM, editor. London: Free Association Books; 1988. Foreword by. [ Google Scholar ]
• West-Eberhard MT. Developmental Plasticity and Evolution. New York: Oxford University Press; 2003. [ Google Scholar ]
• Whitehead M. The Concepts and Principles of Equity and Health. International Journal of Health Services. 1992; 22 :429–45. [ PubMed ] [ Google Scholar ]
• WHO (World Health Organization) Closing the Gap in a Generation: Health Equity through Action on the Social Determinants of Health. 2008. Commission on the Social Determinants of Health—Final Report. Geneva. Available at http://www.who.int/social_determinants/thecommission/finalreport/en/index.html (accessed June 17, 2012) [ PubMed ] [ Google Scholar ]
• WHO (World Health Organization) 2011. Rio Political Declaration on Social Determinants of Health. Rio de Janeiro, October 21. Available at http://www.who.int/sdhconference/declaration/en/index.html (accessed June 17, 2012)
• Wiehl DG. Edgar Sydenstricker: A Memoir. In: Kasius RV, editor. The Challenge of the Facts: Selected Public Health Papers of Edgar Sydenstricker. New York: Prodist, for the Milbank Memorial Fund; 1974. pp. 1–17. [ Google Scholar ]
• Williams R. Keywords: A Vocabulary of Culture and Society. Rev. ed. New York: Oxford University Press; 1985. [ Google Scholar ]
• Wimmer A, Schiller NG. Methodological Nationalism and Beyond: Nation-State, Migration, and the Social Sciences. Global Networks. 2002; 4 :301–34. [ Google Scholar ]
• Winkelstein W., Jr . Oxford Dictionary of National Biography. Oxford: Oxford University Press; 2004. Claypon, Janet Elizabeth Lane- [married name Janet Elizabeth Forber, Lady Forber] (1877–1967) Available at http://www.oxforddnb.com.ezp-prod1.hul.harvard.edu/view/article/61714 (accessed June 17, 2012) [ Google Scholar ]
• Winslow C-EA, Smillie WG, Doull JA, Gordon JE. In: The History of American Epidemiology. Top FH, editor. Mosby; 1952. Sponsored by the Epidemiology Section, American Public Health Association. St. Louis. [ Google Scholar ]
• Wright EO, editor. Approaches to Class Analysis. Cambridge: Cambridge University Press; 2005. [ Google Scholar ]
• Wright S. The Relative Importance of Heredity and Environment in Determining the Pie-Bald Pattern of Guinea-Pigs. 1920; 6 :320–32. Proceedings of the National Academy of Sciences . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Yeo EJ. Social Surveys in the Eighteenth and Nineteenth Centuries. In: Porter TM, Ross D, editors. The Modern Social Sciences. Cambridge: Cambridge University Press; 2003. Cambridge Histories Online. DOI: 10.1017/CHOL9780521594424.007 . [ Google Scholar ]
• Young TK. Population Health: Concepts and Methods. 2nd ed. New York: Oxford University Press; 2005. [ Google Scholar ]
• Zbuk K, Anand SS. Declining Incidence of Breast Cancer after Decreased Use of Hormone-Replacement Therapy: Magnitude and Time Lags in Different Countries. Journal of Epidemiology & Community Health. 2012; 66 :1–7. [ PubMed ] [ Google Scholar ]
• Ziman J. Real Science: What It Is and What It Means. Cambridge: Cambridge University Press; 2000. [ Google Scholar ]
• Zinn H. A People's History of the United States: 1492–Present. New York: HarperCollins; 2003. [ Google Scholar ]

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Research Population

All research questions address issues that are of great relevance to important groups of individuals known as a research population.

This article is a part of the guide:

• Non-Probability Sampling
• Convenience Sampling
• Random Sampling
• Stratified Sampling
• Systematic Sampling

Browse Full Outline

• 1 What is Sampling?
• 2.1 Sample Group
• 2.2 Research Population
• 2.3 Sample Size
• 2.4 Randomization
• 3.1 Statistical Sampling
• 3.2 Sampling Distribution
• 3.3.1 Random Sampling Error
• 4.1 Random Sampling
• 4.2 Stratified Sampling
• 4.3 Systematic Sampling
• 4.4 Cluster Sampling
• 4.5 Disproportional Sampling
• 5.1 Convenience Sampling
• 5.2 Sequential Sampling
• 5.3 Quota Sampling
• 5.4 Judgmental Sampling
• 5.5 Snowball Sampling

A research population is generally a large collection of individuals or objects that is the main focus of a scientific query. It is for the benefit of the population that researches are done. However, due to the large sizes of populations, researchers often cannot test every individual in the population because it is too expensive and time-consuming. This is the reason why researchers rely on sampling techniques .

A research population is also known as a well-defined collection of individuals or objects known to have similar characteristics. All individuals or objects within a certain population usually have a common, binding characteristic or trait.

Usually, the description of the population and the common binding characteristic of its members are the same. "Government officials" is a well-defined group of individuals which can be considered as a population and all the members of this population are indeed officials of the government.

Relationship of Sample and Population in Research

A sample is simply a subset of the population. The concept of sample arises from the inability of the researchers to test all the individuals in a given population. The sample must be representative of the population from which it was drawn and it must have good size to warrant statistical analysis.

The main function of the sample is to allow the researchers to conduct the study to individuals from the population so that the results of their study can be used to derive conclusions that will apply to the entire population. It is much like a give-and-take process. The population “gives” the sample, and then it “takes” conclusions from the results obtained from the sample.

Two Types of Population in Research

Target population.

Target population refers to the ENTIRE group of individuals or objects to which researchers are interested in generalizing the conclusions. The target population usually has varying characteristics and it is also known as the theoretical population.

Accessible Population

The accessible population is the population in research to which the researchers can apply their conclusions. This population is a subset of the target population and is also known as the study population. It is from the accessible population that researchers draw their samples.

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3. Populations and samples

Populations, unbiasedness and precision, randomisation, variation between samples, standard error of the mean.

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Population is a statistical term that designates the pool from which a sample is drawn for a study. Any selection grouped by a common feature can be considered a population. A sample is a statistically significant portion of a population.

Key Takeaways

• A population is the entire group on which data is being gathered and analyzed.
• It's generally difficult in terms of cost and time to gather the data needed on an entire population so samples are often used to make inferences.
• A sample of a population must be randomly selected for the results of the study to accurately reflect the whole.
• A valid statistic can be drawn from either a sample or a study of an entire population.

Statisticians , scientists, and analysts prefer to know the characteristics of every entity in a population to draw the most precise conclusions possible. This is impossible or impractical most of the time, however, because population sets tend to be quite large.

A sample of a population must usually be taken because the characteristics of every individual in a population can't be measured due to constraints of time, resources, and accessibility.

The term "individual" doesn't always mean a person in statistics. An individual is a single entity in the group being studied.

There's no real way to gather data on all the great white sharks in the ocean, which is a population. Finding and tagging each one isn't feasible. Marine biologists instead tag the great whites they can as a sample. They begin collecting information on them to make inferences about the entire population of great whites. This is a random sampling approach because the initial encounters with tagged great whites are entirely random.

A valid statistic can be drawn from either a sample or a study of an entire population. The objective of a random sample is to avoid bias in the results. A sample is random when every member of the whole population has an equal chance to be selected to participate.

How to Measure a Population

The difficulty in measuring a population lies in whatever you're attempting to analyze and what you're trying to accomplish. Data must be collected through surveys, measurements, observation, or other methods. Gathering the data on a large population generally isn't done because of the costs, time, and resources required to obtain it.

All the doctors with patients who could use Drug XYZ in the U.S. likely weren't contacted to confirm this if you see an advertisement that claims, "62% of doctors recommend XYZ for their patients!" Rather 62% of the doctors who responded to the several hundred or thousands of surveys that were sent out responded that they would recommend XYZ. This is a population sample.

A parameter is a characteristic of a population. A statistic is a characteristic of a sample and samples can only result in inferences about a population characteristic. Inferential statistics allow you to make an educated guess about a population parameter based on a statistic computed from a sample randomly drawn from that population.

Statistics such as averages or means and  standard deviations are referred to as population parameters when they're taken from populations. Many such as a population's mean and standard deviation are represented by Greek letters like µ (mu) and σ (sigma). These statistics are inferential in nature much of the time because samples are used rather than populations.

You don't have to use statistical inference if you have all the data for the population being studied because you won't have to use a sample of the population.

Market and investment analysts use statistics to analyze investment data and make inferences about the market, a specific investment, or an index. Financial analysts can evaluate an entire population in some cases because price data has been recorded for decades. The price of every publicly traded stock could be analyzed for a total market evaluation because the prices are recorded. This is a population in terms of investment analysis.

An analyst can calculate parameters with all this data but the parameters used by analysts are only occasionally used in the same way that statisticians and scientists use them.

Some of the parameters you might see used by investment analysts, statisticians, and scientists and their differences are:

Alpha : The excess returns of an asset compared to a benchmark

Standard Deviation : Average amount of variability in prices, used to measure volatility and risk

Moving Average : Used to smooth out short-term price fluctuations to indicate trends

Beta : Measures the performance of an investment/portfolio against the market as a whole

Alpha : The probability of making a Type I error, or rejecting the null hypothesis when it is true

Standard Deviation : Average amount of variability in data

Moving Average : Smooths out short-term fluctuations in data values

Beta : The probability of making a Type II error, or incorrectly failing to reject the null hypothesis

What Is a Population Mean?

A population mean is the average of whatever value you're measuring in a given population.

What Are Two Examples of Population?

An example of a population might be all green-eyed children in the U.S. under age 12. Another could be all the great white sharks in the ocean.

What Is the Best Example of a Population?

Imagine you're a teacher trying to see how well your fifth-grade math class did on a standardized test compared to all fifth-graders in the U.S. The population would be all fifth-grade math scores in the country.

A population is the statistical pool being studied from which data is extracted. Populations can be difficult to gather data on, especially if the studied topic is expansive and widely dispersed. Studying humans is an excellent example. There's no way to gather data on every brown-eyed person in the world so random sampling is the only way to infer anything about that population.

Populations in investment analysis are generally specific types of assets being analyzed. These data sets are generally small in statistical terms and easy to acquire because they've been recorded, unlike data on living organisms which is much more difficult to obtain.

CliffsNotes. " Populations, Samples, Parameters, and Statistics ."

CUEMATH. " Inferential Statistics ."

Statistics How To. " Population Mean Definition ."

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Methodology

• Population vs. Sample | Definitions, Differences & Examples

Population vs. Sample | Definitions, Differences & Examples

Published on May 14, 2020 by Pritha Bhandari . Revised on June 21, 2023.

A population is the entire group that you want to draw conclusions about.

A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.

In research, a population doesn’t always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organizations, countries, species, organisms, etc.

Table of contents

Collecting data from a population, collecting data from a sample, population parameter vs. sample statistic, practice questions : populations vs. samples, other interesting articles, frequently asked questions about samples and populations.

Populations are used when your research question requires, or when you have access to, data from every member of the population.

Usually, it is only straightforward to collect data from a whole population when it is small, accessible and cooperative.

For larger and more dispersed populations, it is often difficult or impossible to collect data from every individual. For example, every 10 years, the federal US government aims to count every person living in the country using the US Census. This data is used to distribute funding across the nation.

However, historically, marginalized and low-income groups have been difficult to contact, locate and encourage participation from. Because of non-responses, the population count is incomplete and biased towards some groups, which results in disproportionate funding across the country.

In cases like this, sampling can be used to make more precise inferences about the population.

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When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sample. With statistical analysis , you can use sample data to make estimates or test hypotheses about population data.

Ideally, a sample should be randomly selected and representative of the population. Using probability sampling methods (such as simple random sampling or stratified sampling ) reduces the risk of sampling bias and enhances both internal and external validity .

For practical reasons, researchers often use non-probability sampling methods. Non-probability samples are chosen for specific criteria; they may be more convenient or cheaper to access. Because of non-random selection methods, any statistical inferences about the broader population will be weaker than with a probability sample.

Reasons for sampling

• Necessity : Sometimes it’s simply not possible to study the whole population due to its size or inaccessibility.
• Practicality : It’s easier and more efficient to collect data from a sample.
• Cost-effectiveness : There are fewer participant, laboratory, equipment, and researcher costs involved.
• Manageability : Storing and running statistical analyses on smaller datasets is easier and reliable.

When you collect data from a population or a sample, there are various measurements and numbers you can calculate from the data. A parameter is a measure that describes the whole population. A statistic is a measure that describes the sample.

You can use estimation or hypothesis testing to estimate how likely it is that a sample statistic differs from the population parameter.

Sampling error

A sampling error is the difference between a population parameter and a sample statistic. In your study, the sampling error is the difference between the mean political attitude rating of your sample and the true mean political attitude rating of all undergraduate students in the Netherlands.

Sampling errors happen even when you use a randomly selected sample. This is because random samples are not identical to the population in terms of numerical measures like means and standard deviations .

Because the aim of scientific research is to generalize findings from the sample to the population, you want the sampling error to be low. You can reduce sampling error by increasing the sample size.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

A sampling error is the difference between a population parameter and a sample statistic .

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Unraveling Research Population and Sample: Understanding their role in statistical inference

Research population and sample serve as the cornerstones of any scientific inquiry. They hold the power to unlock the mysteries hidden within data. Understanding the dynamics between the research population and sample is crucial for researchers. It ensures the validity, reliability, and generalizability of their findings. In this article, we uncover the profound role of the research population and sample, unveiling their differences and importance that reshapes our understanding of complex phenomena. Ultimately, this empowers researchers to make informed conclusions and drive meaningful advancements in our respective fields.

Table of Contents

What Is Population?

The research population, also known as the target population, refers to the entire group or set of individuals, objects, or events that possess specific characteristics and are of interest to the researcher. It represents the larger population from which a sample is drawn. The research population is defined based on the research objectives and the specific parameters or attributes under investigation. For example, in a study on the effects of a new drug, the research population would encompass all individuals who could potentially benefit from or be affected by the medication.

When Is Data Collection From a Population Preferred?

In certain scenarios where a comprehensive understanding of the entire group is required, it becomes necessary to collect data from a population. Here are a few situations when one prefers to collect data from a population:

1. Small or Accessible Population

When the research population is small or easily accessible, it may be feasible to collect data from the entire population. This is often the case in studies conducted within specific organizations, small communities, or well-defined groups where the population size is manageable.

2. Census or Complete Enumeration

In some cases, such as government surveys or official statistics, a census or complete enumeration of the population is necessary. This approach aims to gather data from every individual or entity within the population. This is typically done to ensure accurate representation and eliminate sampling errors.

3. Unique or Critical Characteristics

If the research focuses on a specific characteristic or trait that is rare and critical to the study, collecting data from the entire population may be necessary. This could be the case in studies related to rare diseases, endangered species, or specific genetic markers.

4. Legal or Regulatory Requirements

Certain legal or regulatory frameworks may require data collection from the entire population. For instance, government agencies might need comprehensive data on income levels, demographic characteristics, or healthcare utilization for policy-making or resource allocation purposes.

5. Precision or Accuracy Requirements

In situations where a high level of precision or accuracy is necessary, researchers may opt for population-level data collection. By doing so, they mitigate the potential for sampling error and obtain more reliable estimates of population parameters.

What Is a Sample?

A sample is a subset of the research population that is carefully selected to represent its characteristics. Researchers study this smaller, manageable group to draw inferences that they can generalize to the larger population. The selection of the sample must be conducted in a manner that ensures it accurately reflects the diversity and pertinent attributes of the research population. By studying a sample, researchers can gather data more efficiently and cost-effectively compared to studying the entire population. The findings from the sample are then extrapolated to make conclusions about the larger research population.

What Is Sampling and Why Is It Important?

Sampling refers to the process of selecting a sample from a larger group or population of interest in order to gather data and make inferences. The goal of sampling is to obtain a sample that is representative of the population, meaning that the sample accurately reflects the key attributes, variations, and proportions present in the population. By studying the sample, researchers can draw conclusions or make predictions about the larger population with a certain level of confidence.

Collecting data from a sample, rather than the entire population, offers several advantages and is often necessary due to practical constraints. Here are some reasons to collect data from a sample:

1. Cost and Resource Efficiency

Collecting data from an entire population can be expensive and time-consuming. Sampling allows researchers to gather information from a smaller subset of the population, reducing costs and resource requirements. It is often more practical and feasible to collect data from a sample, especially when the population size is large or geographically dispersed.

2. Time Constraints

Conducting research with a sample allows for quicker data collection and analysis compared to studying the entire population. It saves time by focusing efforts on a smaller group, enabling researchers to obtain results more efficiently. This is particularly beneficial in time-sensitive research projects or situations that necessitate prompt decision-making.

3. Manageable Data Collection

Working with a sample makes data collection more manageable . Researchers can concentrate their efforts on a smaller group, allowing for more detailed and thorough data collection methods. Furthermore, it is more convenient and reliable to store and conduct statistical analyses on smaller datasets. This also facilitates in-depth insights and a more comprehensive understanding of the research topic.

4. Statistical Inference

Collecting data from a well-selected and representative sample enables valid statistical inference. By using appropriate statistical techniques, researchers can generalize the findings from the sample to the larger population. This allows for meaningful inferences, predictions, and estimation of population parameters, thus providing insights beyond the specific individuals or elements in the sample.

5. Ethical Considerations

In certain cases, collecting data from an entire population may pose ethical challenges, such as invasion of privacy or burdening participants. Sampling helps protect the privacy and well-being of individuals by reducing the burden of data collection. It allows researchers to obtain valuable information while ensuring ethical standards are maintained .

Key Steps Involved in the Sampling Process

Sampling is a valuable tool in research; however, it is important to carefully consider the sampling method, sample size, and potential biases to ensure that the findings accurately represent the larger population and are valid for making conclusions and generalizations. While the specific steps may vary depending on the research context, here is a general outline of the sampling process:

1. Define the Population

Clearly define the target population for your research study. The population should encompass the group of individuals, elements, or units that you want to draw conclusions about.

2. Define the Sampling Frame

Create a sampling frame, which is a list or representation of the individuals or elements in the target population. The sampling frame should be comprehensive and accurately reflect the population you want to study.

3. Determine the Sampling Method

Select an appropriate sampling method based on your research objectives, available resources, and the characteristics of the population. You can perform sampling by either utilizing probability-based or non-probability-based techniques. Common sampling methods include random sampling, stratified sampling, cluster sampling, and convenience sampling.

4. Determine Sample Size

Determine the desired sample size based on statistical considerations, such as the level of precision required, desired confidence level, and expected variability within the population. Larger sample sizes generally reduce sampling error but may be constrained by practical limitations.

5. Collect Data

Once the sample is selected using the appropriate technique, collect the necessary data according to the research design and data collection methods . Ensure that you use standardized and consistent data collection process that is also appropriate for your research objectives.

6. Analyze the Data

Perform the necessary statistical analyses on the collected data to derive meaningful insights. Use appropriate statistical techniques to make inferences, estimate population parameters, test hypotheses, or identify patterns and relationships within the data.

Population vs Sample — Differences and examples

While the population provides a comprehensive overview of the entire group under study, the sample, on the other hand, allows researchers to draw inferences and make generalizations about the population. Researchers should employ careful sampling techniques to ensure that the sample is representative and accurately reflects the characteristics and variability of the population.

Research Study: Investigating the prevalence of stress among high school students in a specific city and its impact on academic performance.

Population: All high school students in a particular city

Sampling Frame: The sampling frame would involve obtaining a comprehensive list of all high schools in the specific city. A random selection of schools would be made from this list to ensure representation from different areas and demographics of the city.

Sample: Randomly selected 500 high school students from different schools in the city

The sample represents a subset of the entire population of high school students in the city.

Research Study: Assessing the effectiveness of a new medication in managing symptoms and improving quality of life in patients with the specific medical condition.

Population: Patients diagnosed with a specific medical condition

Sampling Frame: The sampling frame for this study would involve accessing medical records or databases that include information on patients diagnosed with the specific medical condition. Researchers would select a convenient sample of patients who meet the inclusion criteria from the sampling frame.

Sample: Convenient sample of 100 patients from a local clinic who meet the inclusion criteria for the study

The sample consists of patients from the larger population of individuals diagnosed with the medical condition.

Research Study: Investigating community perceptions of safety and satisfaction with local amenities in the neighborhood.

Population: Residents of a specific neighborhood

Sampling Frame: The sampling frame for this study would involve obtaining a list of residential addresses within the specific neighborhood. Various sources such as census data, voter registration records, or community databases offer the means to obtain this information. From the sampling frame, researchers would randomly select a cluster sample of households to ensure representation from different areas within the neighborhood.

Sample: Cluster sample of 50 households randomly selected from different blocks within the neighborhood

The sample represents a subset of the entire population of residents living in the neighborhood.

To summarize, sampling allows for cost-effective data collection, easier statistical analysis, and increased practicality compared to studying the entire population. However, despite these advantages, sampling is subject to various challenges. These challenges include sampling bias, non-response bias, and the potential for sampling errors.

To minimize bias and enhance the validity of research findings , researchers should employ appropriate sampling techniques, clearly define the population, establish a comprehensive sampling frame, and monitor the sampling process for potential biases. Validating findings by comparing them to known population characteristics can also help evaluate the generalizability of the results. Properly understanding and implementing sampling techniques ensure that research findings are accurate, reliable, and representative of the larger population. By carefully considering the choice of population and sample, researchers can draw meaningful conclusions and, consequently, make valuable contributions to their respective fields of study.

Now, it’s your turn! Take a moment to think about a research question that interests you. Consider the population that would be relevant to your inquiry. Who would you include in your sample? How would you go about selecting them? Reflecting on these aspects will help you appreciate the intricacies involved in designing a research study. Let us know about it in the comment section below or reach out to us using  #AskEnago  and tag  @EnagoAcademy  on  Twitter ,  Facebook , and  Quora .

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What is the difference between population and sampling frame?

I'm working on work for my Statistics course, and I am confused on what the difference between population and sampling frame is.

• terminology

3 Answers 3

A simplified view may be as follows:

Suppose the objective of the survey is to estimate the per household income of $abc$ national in a city. Then the all the households of $abc$ nationals is the the target population . It is the collection of items from which a sample has to be taken.

A sampling frame is a list of the items of the population from which a sample is to be obtained. Suppose a household list of the city is available. This list of households become the sampling frame.

This list may contain households of other nationals. These households are not eligible items for being members of the population. They need to removed before a sample is made.

The sampling frame may not contain all the households of the $abc$ nationals. In that case, some eligible items of the population are left out from sampling.

When contacted, some households may refuse to provide information.

The remaining households in the sampling frame become the actual sampled population .

I an ideal situation, the population and the sampling frame are same.

• 1 $\begingroup$ What do you mean by "abc national" and "abc nationals"? Do you mean nationality ? If so, I would suggest phrasing it as "Suppose the objective of the survey is to estimate the per household income of a particular nationality, such as the citizens of Turkey." $\endgroup$ –  David J. Commented Dec 9, 2019 at 4:20

population is the all people or objects to which you wishes to generalize the findings of your study, for instance if your study is about pregnant teenagers , all of the pregnant tens are your target population. Sample frame is a subset of the population and the people or object that you have access to them. For instance, the number of observations that you had about pregnant teens.

There is a big difference actually. A "frame" is just a group that you have selected to analyze, i.e. population, BUT with specific characteristics listed and identified. It's the "frame" (basically way of identifying) put on a section of a larger population. But it is not the actual population just a way of finding that "group" out of the infinite masses of potential groups. So it's a way to identify your population and later your sample.

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• Published: 31 August 2024

Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023

• Xianru Shang   ORCID: orcid.org/0009-0000-8906-3216 1 ,
• Zijian Liu 1 ,
• Chen Gong 1 ,
• Zhigang Hu 1 ,
• Yuexuan Wu 1 &
• Chengliang Wang   ORCID: orcid.org/0000-0003-2208-3508 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1115 ( 2024 ) Cite this article

Metrics details

• Science, technology and society

The rapid expansion of information technology and the intensification of population aging are two prominent features of contemporary societal development. Investigating older adults’ acceptance and use of technology is key to facilitating their integration into an information-driven society. Given this context, the technology acceptance of older adults has emerged as a prioritized research topic, attracting widespread attention in the academic community. However, existing research remains fragmented and lacks a systematic framework. To address this gap, we employed bibliometric methods, utilizing the Web of Science Core Collection to conduct a comprehensive review of literature on older adults’ technology acceptance from 2013 to 2023. Utilizing VOSviewer and CiteSpace for data assessment and visualization, we created knowledge mappings of research on older adults’ technology acceptance. Our study employed multidimensional methods such as co-occurrence analysis, clustering, and burst analysis to: (1) reveal research dynamics, key journals, and domains in this field; (2) identify leading countries, their collaborative networks, and core research institutions and authors; (3) recognize the foundational knowledge system centered on theoretical model deepening, emerging technology applications, and research methods and evaluation, uncovering seminal literature and observing a shift from early theoretical and influential factor analyses to empirical studies focusing on individual factors and emerging technologies; (4) moreover, current research hotspots are primarily in the areas of factors influencing technology adoption, human-robot interaction experiences, mobile health management, and aging-in-place technology, highlighting the evolutionary context and quality distribution of research themes. Finally, we recommend that future research should deeply explore improvements in theoretical models, long-term usage, and user experience evaluation. Overall, this study presents a clear framework of existing research in the field of older adults’ technology acceptance, providing an important reference for future theoretical exploration and innovative applications.

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Introduction.

In contemporary society, the rapid development of information technology has been intricately intertwined with the intensifying trend of population aging. According to the latest United Nations forecast, by 2050, the global population aged 65 and above is expected to reach 1.6 billion, representing about 16% of the total global population (UN 2023 ). Given the significant challenges of global aging, there is increasing evidence that emerging technologies have significant potential to maintain health and independence for older adults in their home and healthcare environments (Barnard et al. 2013 ; Soar 2010 ; Vancea and Solé-Casals 2016 ). This includes, but is not limited to, enhancing residential safety with smart home technologies (Touqeer et al. 2021 ; Wang et al. 2022 ), improving living independence through wearable technologies (Perez et al. 2023 ), and increasing medical accessibility via telehealth services (Kruse et al. 2020 ). Technological innovations are redefining the lifestyles of older adults, encouraging a shift from passive to active participation (González et al. 2012 ; Mostaghel 2016 ). Nevertheless, the effective application and dissemination of technology still depends on user acceptance and usage intentions (Naseri et al. 2023 ; Wang et al. 2023a ; Xia et al. 2024 ; Yu et al. 2023 ). Particularly, older adults face numerous challenges in accepting and using new technologies. These challenges include not only physical and cognitive limitations but also a lack of technological experience, along with the influences of social and economic factors (Valk et al. 2018 ; Wilson et al. 2021 ).

User acceptance of technology is a significant focus within information systems (IS) research (Dai et al. 2024 ), with several models developed to explain and predict user behavior towards technology usage, including the Technology Acceptance Model (TAM) (Davis 1989 ), TAM2, TAM3, and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al. 2003 ). Older adults, as a group with unique needs, exhibit different behavioral patterns during technology acceptance than other user groups, and these uniquenesses include changes in cognitive abilities, as well as motivations, attitudes, and perceptions of the use of new technologies (Chen and Chan 2011 ). The continual expansion of technology introduces considerable challenges for older adults, rendering the understanding of their technology acceptance a research priority. Thus, conducting in-depth research into older adults’ acceptance of technology is critically important for enhancing their integration into the information society and improving their quality of life through technological advancements.

Reviewing relevant literature to identify research gaps helps further solidify the theoretical foundation of the research topic. However, many existing literature reviews primarily focus on the factors influencing older adults’ acceptance or intentions to use technology. For instance, Ma et al. ( 2021 ) conducted a comprehensive analysis of the determinants of older adults’ behavioral intentions to use technology; Liu et al. ( 2022 ) categorized key variables in studies of older adults’ technology acceptance, noting a shift in focus towards social and emotional factors; Yap et al. ( 2022 ) identified seven categories of antecedents affecting older adults’ use of technology from an analysis of 26 articles, including technological, psychological, social, personal, cost, behavioral, and environmental factors; Schroeder et al. ( 2023 ) extracted 119 influencing factors from 59 articles and further categorized these into six themes covering demographics, health status, and emotional awareness. Additionally, some studies focus on the application of specific technologies, such as Ferguson et al. ( 2021 ), who explored barriers and facilitators to older adults using wearable devices for heart monitoring, and He et al. ( 2022 ) and Baer et al. ( 2022 ), who each conducted in-depth investigations into the acceptance of social assistive robots and mobile nutrition and fitness apps, respectively. In summary, current literature reviews on older adults’ technology acceptance exhibit certain limitations. Due to the interdisciplinary nature and complex knowledge structure of this field, traditional literature reviews often rely on qualitative analysis, based on literature analysis and periodic summaries, which lack sufficient objectivity and comprehensiveness. Additionally, systematic research is relatively limited, lacking a macroscopic description of the research trajectory from a holistic perspective. Over the past decade, research on older adults’ technology acceptance has experienced rapid growth, with a significant increase in literature, necessitating the adoption of new methods to review and examine the developmental trends in this field (Chen 2006 ; Van Eck and Waltman 2010 ). Bibliometric analysis, as an effective quantitative research method, analyzes published literature through visualization, offering a viable approach to extracting patterns and insights from a large volume of papers, and has been widely applied in numerous scientific research fields (Achuthan et al. 2023 ; Liu and Duffy 2023 ). Therefore, this study will employ bibliometric methods to systematically analyze research articles related to older adults’ technology acceptance published in the Web of Science Core Collection from 2013 to 2023, aiming to understand the core issues and evolutionary trends in the field, and to provide valuable references for future related research. Specifically, this study aims to explore and answer the following questions:

RQ1: What are the research dynamics in the field of older adults’ technology acceptance over the past decade? What are the main academic journals and fields that publish studies related to older adults’ technology acceptance?

RQ2: How is the productivity in older adults’ technology acceptance research distributed among countries, institutions, and authors?

RQ3: What are the knowledge base and seminal literature in older adults’ technology acceptance research? How has the research theme progressed?

RQ4: What are the current hot topics and their evolutionary trajectories in older adults’ technology acceptance research? How is the quality of research distributed?

Methodology and materials

Research method.

In recent years, bibliometrics has become one of the crucial methods for analyzing literature reviews and is widely used in disciplinary and industrial intelligence analysis (Jing et al. 2023 ; Lin and Yu 2024a ; Wang et al. 2024a ; Xu et al. 2021 ). Bibliometric software facilitates the visualization analysis of extensive literature data, intuitively displaying the network relationships and evolutionary processes between knowledge units, and revealing the underlying knowledge structure and potential information (Chen et al. 2024 ; López-Robles et al. 2018 ; Wang et al. 2024c ). This method provides new insights into the current status and trends of specific research areas, along with quantitative evidence, thereby enhancing the objectivity and scientific validity of the research conclusions (Chen et al. 2023 ; Geng et al. 2024 ). VOSviewer and CiteSpace are two widely used bibliometric software tools in academia (Pan et al. 2018 ), recognized for their robust functionalities based on the JAVA platform. Although each has its unique features, combining these two software tools effectively constructs mapping relationships between literature knowledge units and clearly displays the macrostructure of the knowledge domains. Particularly, VOSviewer, with its excellent graphical representation capabilities, serves as an ideal tool for handling large datasets and precisely identifying the focal points and hotspots of research topics. Therefore, this study utilizes VOSviewer (version 1.6.19) and CiteSpace (version 6.1.R6), combined with in-depth literature analysis, to comprehensively examine and interpret the research theme of older adults’ technology acceptance through an integrated application of quantitative and qualitative methods.

Data source

Web of Science is a comprehensively recognized database in academia, featuring literature that has undergone rigorous peer review and editorial scrutiny (Lin and Yu 2024b ; Mongeon and Paul-Hus 2016 ; Pranckutė 2021 ). This study utilizes the Web of Science Core Collection as its data source, specifically including three major citation indices: Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI). These indices encompass high-quality research literature in the fields of science, social sciences, and arts and humanities, ensuring the comprehensiveness and reliability of the data. We combined “older adults” with “technology acceptance” through thematic search, with the specific search strategy being: TS = (elder OR elderly OR aging OR ageing OR senile OR senior OR old people OR “older adult*”) AND TS = (“technology acceptance” OR “user acceptance” OR “consumer acceptance”). The time span of literature search is from 2013 to 2023, with the types limited to “Article” and “Review” and the language to “English”. Additionally, the search was completed by October 27, 2023, to avoid data discrepancies caused by database updates. The initial search yielded 764 journal articles. Given that searches often retrieve articles that are superficially relevant but actually non-compliant, manual screening post-search was essential to ensure the relevance of the literature (Chen et al. 2024 ). Through manual screening, articles significantly deviating from the research theme were eliminated and rigorously reviewed. Ultimately, this study obtained 500 valid sample articles from the Web of Science Core Collection. The complete PRISMA screening process is illustrated in Fig. 1 .

Presentation of the data culling process in detail.

Data standardization

Raw data exported from databases often contain multiple expressions of the same terminology (Nguyen and Hallinger 2020 ). To ensure the accuracy and consistency of data, it is necessary to standardize the raw data (Strotmann and Zhao 2012 ). This study follows the data standardization process proposed by Taskin and Al ( 2019 ), mainly executing the following operations:

(1) Standardization of author and institution names is conducted to address different name expressions for the same author. For instance, “Chan, Alan Hoi Shou” and “Chan, Alan H. S.” are considered the same author, and distinct authors with the same name are differentiated by adding identifiers. Diverse forms of institutional names are unified to address variations caused by name changes or abbreviations, such as standardizing “FRANKFURT UNIV APPL SCI” and “Frankfurt University of Applied Sciences,” as well as “Chinese University of Hong Kong” and “University of Hong Kong” to consistent names.

(2) Different expressions of journal names are unified. For example, “International Journal of Human-Computer Interaction” and “Int J Hum Comput Interact” are standardized to a single name. This ensures consistency in journal names and prevents misclassification of literature due to differing journal names. Additionally, it involves checking if the journals have undergone name changes in the past decade to prevent any impact on the analysis due to such changes.

(3) Keywords data are cleansed by removing words that do not directly pertain to specific research content (e.g., people, review), merging synonyms (e.g., “UX” and “User Experience,” “aging-in-place” and “aging in place”), and standardizing plural forms of keywords (e.g., “assistive technologies” and “assistive technology,” “social robots” and “social robot”). This reduces redundant information in knowledge mapping.

Bibliometric results and analysis

Distribution power (rq1), literature descriptive statistical analysis.

Table 1 presents a detailed descriptive statistical overview of the literature in the field of older adults’ technology acceptance. After deduplication using the CiteSpace software, this study confirmed a valid sample size of 500 articles. Authored by 1839 researchers, the documents encompass 792 research institutions across 54 countries and are published in 217 different academic journals. As of the search cutoff date, these articles have accumulated 13,829 citations, with an annual average of 1156 citations, and an average of 27.66 citations per article. The h-index, a composite metric of quantity and quality of scientific output (Kamrani et al. 2021 ), reached 60 in this study.

Trends in publications and disciplinary distribution

The number of publications and citations are significant indicators of the research field’s development, reflecting its continuity, attention, and impact (Ale Ebrahim et al. 2014 ). The ranking of annual publications and citations in the field of older adults’ technology acceptance studies is presented chronologically in Fig. 2A . The figure shows a clear upward trend in the amount of literature in this field. Between 2013 and 2017, the number of publications increased slowly and decreased in 2018. However, in 2019, the number of publications increased rapidly to 52 and reached a peak of 108 in 2022, which is 6.75 times higher than in 2013. In 2022, the frequency of document citations reached its highest point with 3466 citations, reflecting the widespread recognition and citation of research in this field. Moreover, the curve of the annual number of publications fits a quadratic function, with a goodness-of-fit R 2 of 0.9661, indicating that the number of future publications is expected to increase even more rapidly.

A Trends in trends in annual publications and citations (2013–2023). B Overlay analysis of the distribution of discipline fields.

Figure 2B shows that research on older adults’ technology acceptance involves the integration of multidisciplinary knowledge. According to Web of Science Categories, these 500 articles are distributed across 85 different disciplines. We have tabulated the top ten disciplines by publication volume (Table 2 ), which include Medical Informatics (75 articles, 15.00%), Health Care Sciences & Services (71 articles, 14.20%), Gerontology (61 articles, 12.20%), Public Environmental & Occupational Health (57 articles, 11.40%), and Geriatrics & Gerontology (52 articles, 10.40%), among others. The high output in these disciplines reflects the concentrated global academic interest in this comprehensive research topic. Additionally, interdisciplinary research approaches provide diverse perspectives and a solid theoretical foundation for studies on older adults’ technology acceptance, also paving the way for new research directions.

Knowledge flow analysis

A dual-map overlay is a CiteSpace map superimposed on top of a base map, which shows the interrelationships between journals in different domains, representing the publication and citation activities in each domain (Chen and Leydesdorff 2014 ). The overlay map reveals the link between the citing domain (on the left side) and the cited domain (on the right side), reflecting the knowledge flow of the discipline at the journal level (Leydesdorff and Rafols 2012 ). We utilize the in-built Z-score algorithm of the software to cluster the graph, as shown in Fig. 3 .

The left side shows the citing journal, and the right side shows the cited journal.

Figure 3 shows the distribution of citing journals clusters for older adults’ technology acceptance on the left side, while the right side refers to the main cited journals clusters. Two knowledge flow citation trajectories were obtained; they are presented by the color of the cited regions, and the thickness of these trajectories is proportional to the Z-score scaled frequency of citations (Chen et al. 2014 ). Within the cited regions, the most popular fields with the most records covered are “HEALTH, NURSING, MEDICINE” and “PSYCHOLOGY, EDUCATION, SOCIAL”, and the elliptical aspect ratio of these two fields stands out. Fields have prominent elliptical aspect ratios, highlighting their significant influence on older adults’ technology acceptance research. Additionally, the major citation trajectories originate in these two areas and progress to the frontier research area of “PSYCHOLOGY, EDUCATION, HEALTH”. It is worth noting that the citation trajectory from “PSYCHOLOGY, EDUCATION, SOCIAL” has a significant Z-value (z = 6.81), emphasizing the significance and impact of this development path. In the future, “MATHEMATICS, SYSTEMS, MATHEMATICAL”, “MOLECULAR, BIOLOGY, IMMUNOLOGY”, and “NEUROLOGY, SPORTS, OPHTHALMOLOGY” may become emerging fields. The fields of “MEDICINE, MEDICAL, CLINICAL” may be emerging areas of cutting-edge research.

Main research journals analysis

Table 3 provides statistics for the top ten journals by publication volume in the field of older adults’ technology acceptance. Together, these journals have published 137 articles, accounting for 27.40% of the total publications, indicating that there is no highly concentrated core group of journals in this field, with publications being relatively dispersed. Notably, Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction each lead with 15 publications. In terms of citation metrics, International Journal of Medical Informatics and Computers in Human Behavior stand out significantly, with the former accumulating a total of 1,904 citations, averaging 211.56 citations per article, and the latter totaling 1,449 citations, with an average of 96.60 citations per article. These figures emphasize the academic authority and widespread impact of these journals within the research field.

Research power (RQ2)

Countries and collaborations analysis.

The analysis revealed the global research pattern for country distribution and collaboration (Chen et al. 2019 ). Figure 4A shows the network of national collaborations on older adults’ technology acceptance research. The size of the bubbles represents the amount of publications in each country, while the thickness of the connecting lines expresses the closeness of the collaboration among countries. Generally, this research subject has received extensive international attention, with China and the USA publishing far more than any other countries. China has established notable research collaborations with the USA, UK and Malaysia in this field, while other countries have collaborations, but the closeness is relatively low and scattered. Figure 4B shows the annual publication volume dynamics of the top ten countries in terms of total publications. Since 2017, China has consistently increased its annual publications, while the USA has remained relatively stable. In 2019, the volume of publications in each country increased significantly, this was largely due to the global outbreak of the COVID-19 pandemic, which has led to increased reliance on information technology among the elderly for medical consultations, online socialization, and health management (Sinha et al. 2021 ). This phenomenon has led to research advances in technology acceptance among older adults in various countries. Table 4 shows that the top ten countries account for 93.20% of the total cumulative number of publications, with each country having published more than 20 papers. Among these ten countries, all of them except China are developed countries, indicating that the research field of older adults’ technology acceptance has received general attention from developed countries. Currently, China and the USA were the leading countries in terms of publications with 111 and 104 respectively, accounting for 22.20% and 20.80%. The UK, Germany, Italy, and the Netherlands also made significant contributions. The USA and China ranked first and second in terms of the number of citations, while the Netherlands had the highest average citations, indicating the high impact and quality of its research. The UK has shown outstanding performance in international cooperation, while the USA highlights its significant academic influence in this field with the highest h-index value.

A National collaboration network. B Annual volume of publications in the top 10 countries.

Institutions and authors analysis

Analyzing the number of publications and citations can reveal an institution’s or author’s research strength and influence in a particular research area (Kwiek 2021 ). Tables 5 and 6 show the statistics of the institutions and authors whose publication counts are in the top ten, respectively. As shown in Table 5 , higher education institutions hold the main position in this research field. Among the top ten institutions, City University of Hong Kong and The University of Hong Kong from China lead with 14 and 9 publications, respectively. City University of Hong Kong has the highest h-index, highlighting its significant influence in the field. It is worth noting that Tilburg University in the Netherlands is not among the top five in terms of publications, but the high average citation count (130.14) of its literature demonstrates the high quality of its research.

After analyzing the authors’ output using Price’s Law (Redner 1998 ), the highest number of publications among the authors counted ( n  = 10) defines a publication threshold of 3 for core authors in this research area. As a result of quantitative screening, a total of 63 core authors were identified. Table 6 shows that Chen from Zhejiang University, China, Ziefle from RWTH Aachen University, Germany, and Rogers from Macquarie University, Australia, were the top three authors in terms of the number of publications, with 10, 9, and 8 articles, respectively. In terms of average citation rate, Peek and Wouters, both scholars from the Netherlands, have significantly higher rates than other scholars, with 183.2 and 152.67 respectively. This suggests that their research is of high quality and widely recognized. Additionally, Chen and Rogers have high h-indices in this field.

Knowledge base and theme progress (RQ3)

Research knowledge base.

Co-citation relationships occur when two documents are cited together (Zhang and Zhu 2022 ). Co-citation mapping uses references as nodes to represent the knowledge base of a subject area (Min et al. 2021). Figure 5A illustrates co-occurrence mapping in older adults’ technology acceptance research, where larger nodes signify higher co-citation frequencies. Co-citation cluster analysis can be used to explore knowledge structure and research boundaries (Hota et al. 2020 ; Shiau et al. 2023 ). The co-citation clustering mapping of older adults’ technology acceptance research literature (Fig. 5B ) shows that the Q value of the clustering result is 0.8129 (>0.3), and the average value of the weight S is 0.9391 (>0.7), indicating that the clusters are uniformly distributed with a significant and credible structure. This further proves that the boundaries of the research field are clear and there is significant differentiation in the field. The figure features 18 cluster labels, each associated with thematic color blocks corresponding to different time slices. Highlighted emerging research themes include #2 Smart Home Technology, #7 Social Live, and #10 Customer Service. Furthermore, the clustering labels extracted are primarily classified into three categories: theoretical model deepening, emerging technology applications, research methods and evaluation, as detailed in Table 7 .

A Co-citation analysis of references. B Clustering network analysis of references.

Seminal literature analysis

The top ten nodes in terms of co-citation frequency were selected for further analysis. Table 8 displays the corresponding node information. Studies were categorized into four main groups based on content analysis. (1) Research focusing on specific technology usage by older adults includes studies by Peek et al. ( 2014 ), Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ), who investigated the factors influencing the use of e-technology, smartphones, mHealth, and smart wearables, respectively. (2) Concerning the development of theoretical models of technology acceptance, Chen and Chan ( 2014 ) introduced the Senior Technology Acceptance Model (STAM), and Macedo ( 2017 ) analyzed the predictive power of UTAUT2 in explaining older adults’ intentional behaviors and information technology usage. (3) In exploring older adults’ information technology adoption and behavior, Lee and Coughlin ( 2015 ) emphasized that the adoption of technology by older adults is a multifactorial process that includes performance, price, value, usability, affordability, accessibility, technical support, social support, emotion, independence, experience, and confidence. Yusif et al. ( 2016 ) conducted a literature review examining the key barriers affecting older adults’ adoption of assistive technology, including factors such as privacy, trust, functionality/added value, cost, and stigma. (4) From the perspective of research into older adults’ technology acceptance, Mitzner et al. ( 2019 ) assessed the long-term usage of computer systems designed for the elderly, whereas Guner and Acarturk ( 2020 ) compared information technology usage and acceptance between older and younger adults. The breadth and prevalence of this literature make it a vital reference for researchers in the field, also providing new perspectives and inspiration for future research directions.

Research thematic progress

Burst citation is a node of literature that guides the sudden change in dosage, which usually represents a prominent development or major change in a particular field, with innovative and forward-looking qualities. By analyzing the emergent literature, it is often easy to understand the dynamics of the subject area, mapping the emerging thematic change (Chen et al. 2022 ). Figure 6 shows the burst citation mapping in the field of older adults’ technology acceptance research, with burst citations represented by red nodes (Fig. 6A ). For the ten papers with the highest burst intensity (Fig. 6B ), this study will conduct further analysis in conjunction with literature review.

A Burst detection of co-citation. B The top 10 references with the strongest citation bursts.

As shown in Fig. 6 , Mitzner et al. ( 2010 ) broke the stereotype that older adults are fearful of technology, found that they actually have positive attitudes toward technology, and emphasized the centrality of ease of use and usefulness in the process of technology acceptance. This finding provides an important foundation for subsequent research. During the same period, Wagner et al. ( 2010 ) conducted theory-deepening and applied research on technology acceptance among older adults. The research focused on older adults’ interactions with computers from the perspective of Social Cognitive Theory (SCT). This expanded the understanding of technology acceptance, particularly regarding the relationship between behavior, environment, and other SCT elements. In addition, Pan and Jordan-Marsh ( 2010 ) extended the TAM to examine the interactions among predictors of perceived usefulness, perceived ease of use, subjective norm, and convenience conditions when older adults use the Internet, taking into account the moderating roles of gender and age. Heerink et al. ( 2010 ) adapted and extended the UTAUT, constructed a technology acceptance model specifically designed for older users’ acceptance of assistive social agents, and validated it using controlled experiments and longitudinal data, explaining intention to use by combining functional assessment and social interaction variables.

Then the research theme shifted to an in-depth analysis of the factors influencing technology acceptance among older adults. Two papers with high burst strengths emerged during this period: Peek et al. ( 2014 ) (Strength = 12.04), Chen and Chan ( 2014 ) (Strength = 9.81). Through a systematic literature review and empirical study, Peek STM and Chen K, among others, identified multidimensional factors that influence older adults’ technology acceptance. Peek et al. ( 2014 ) analyzed literature on the acceptance of in-home care technology among older adults and identified six factors that influence their acceptance: concerns about technology, expected benefits, technology needs, technology alternatives, social influences, and older adult characteristics, with a focus on differences between pre- and post-implementation factors. Chen and Chan ( 2014 ) constructed the STAM by administering a questionnaire to 1012 older adults and adding eight important factors, including technology anxiety, self-efficacy, cognitive ability, and physical function, based on the TAM. This enriches the theoretical foundation of the field. In addition, Braun ( 2013 ) highlighted the role of perceived usefulness, trust in social networks, and frequency of Internet use in older adults’ use of social networks, while ease of use and social pressure were not significant influences. These findings contribute to the study of older adults’ technology acceptance within specific technology application domains.

Recent research has focused on empirical studies of personal factors and emerging technologies. Ma et al. ( 2016 ) identified key personal factors affecting smartphone acceptance among older adults through structured questionnaires and face-to-face interviews with 120 participants. The study found that cost, self-satisfaction, and convenience were important factors influencing perceived usefulness and ease of use. This study offers empirical evidence to comprehend the main factors that drive smartphone acceptance among Chinese older adults. Additionally, Yusif et al. ( 2016 ) presented an overview of the obstacles that hinder older adults’ acceptance of assistive technologies, focusing on privacy, trust, and functionality.

In summary, research on older adults’ technology acceptance has shifted from early theoretical deepening and analysis of influencing factors to empirical studies in the areas of personal factors and emerging technologies, which have greatly enriched the theoretical basis of older adults’ technology acceptance and provided practical guidance for the design of emerging technology products.

Research hotspots, evolutionary trends, and quality distribution (RQ4)

Core keywords analysis.

Keywords concise the main idea and core of the literature, and are a refined summary of the research content (Huang et al. 2021 ). In CiteSpace, nodes with a centrality value greater than 0.1 are considered to be critical nodes. Analyzing keywords with high frequency and centrality helps to visualize the hot topics in the research field (Park et al. 2018 ). The merged keywords were imported into CiteSpace, and the top 10 keywords were counted and sorted by frequency and centrality respectively, as shown in Table 9 . The results show that the keyword “TAM” has the highest frequency (92), followed by “UTAUT” (24), which reflects that the in-depth study of the existing technology acceptance model and its theoretical expansion occupy a central position in research related to older adults’ technology acceptance. Furthermore, the terms ‘assistive technology’ and ‘virtual reality’ are both high-frequency and high-centrality terms (frequency = 17, centrality = 0.10), indicating that the research on assistive technology and virtual reality for older adults is the focus of current academic attention.

Research hotspots analysis

Using VOSviewer for keyword co-occurrence analysis organizes keywords into groups or clusters based on their intrinsic connections and frequencies, clearly highlighting the research field’s hot topics. The connectivity among keywords reveals correlations between different topics. To ensure accuracy, the analysis only considered the authors’ keywords. Subsequently, the keywords were filtered by setting the keyword frequency to 5 to obtain the keyword clustering map of the research on older adults’ technology acceptance research keyword clustering mapping (Fig. 7 ), combined with the keyword co-occurrence clustering network (Fig. 7A ) and the corresponding density situation (Fig. 7B ) to make a detailed analysis of the following four groups of clustered themes.

A Co-occurrence clustering network. B Keyword density.

Cluster #1—Research on the factors influencing technology adoption among older adults is a prominent topic, covering age, gender, self-efficacy, attitude, and and intention to use (Berkowsky et al. 2017 ; Wang et al. 2017 ). It also examined older adults’ attitudes towards and acceptance of digital health technologies (Ahmad and Mozelius, 2022 ). Moreover, the COVID-19 pandemic, significantly impacting older adults’ technology attitudes and usage, has underscored the study’s importance and urgency. Therefore, it is crucial to conduct in-depth studies on how older adults accept, adopt, and effectively use new technologies, to address their needs and help them overcome the digital divide within digital inclusion. This will improve their quality of life and healthcare experiences.

Cluster #2—Research focuses on how older adults interact with assistive technologies, especially assistive robots and health monitoring devices, emphasizing trust, usability, and user experience as crucial factors (Halim et al. 2022 ). Moreover, health monitoring technologies effectively track and manage health issues common in older adults, like dementia and mild cognitive impairment (Lussier et al. 2018 ; Piau et al. 2019 ). Interactive exercise games and virtual reality have been deployed to encourage more physical and cognitive engagement among older adults (Campo-Prieto et al. 2021 ). Personalized and innovative technology significantly enhances older adults’ participation, improving their health and well-being.

Cluster #3—Optimizing health management for older adults using mobile technology. With the development of mobile health (mHealth) and health information technology, mobile applications, smartphones, and smart wearable devices have become effective tools to help older users better manage chronic conditions, conduct real-time health monitoring, and even receive telehealth services (Dupuis and Tsotsos 2018 ; Olmedo-Aguirre et al. 2022 ; Kim et al. 2014 ). Additionally, these technologies can mitigate the problem of healthcare resource inequality, especially in developing countries. Older adults’ acceptance and use of these technologies are significantly influenced by their behavioral intentions, motivational factors, and self-management skills. These internal motivational factors, along with external factors, jointly affect older adults’ performance in health management and quality of life.

Cluster #4—Research on technology-assisted home care for older adults is gaining popularity. Environmentally assisted living enhances older adults’ independence and comfort at home, offering essential support and security. This has a crucial impact on promoting healthy aging (Friesen et al. 2016 ; Wahlroos et al. 2023 ). The smart home is a core application in this field, providing a range of solutions that facilitate independent living for the elderly in a highly integrated and user-friendly manner. This fulfills different dimensions of living and health needs (Majumder et al. 2017 ). Moreover, eHealth offers accurate and personalized health management and healthcare services for older adults (Delmastro et al. 2018 ), ensuring their needs are met at home. Research in this field often employs qualitative methods and structural equation modeling to fully understand older adults’ needs and experiences at home and analyze factors influencing technology adoption.

Evolutionary trends analysis

To gain a deeper understanding of the evolutionary trends in research hotspots within the field of older adults’ technology acceptance, we conducted a statistical analysis of the average appearance times of keywords, using CiteSpace to generate the time-zone evolution mapping (Fig. 8 ) and burst keywords. The time-zone mapping visually displays the evolution of keywords over time, intuitively reflecting the frequency and initial appearance of keywords in research, commonly used to identify trends in research topics (Jing et al. 2024a ; Kumar et al. 2021 ). Table 10 lists the top 15 keywords by burst strength, with the red sections indicating high-frequency citations and their burst strength in specific years. These burst keywords reveal the focus and trends of research themes over different periods (Kleinberg 2002 ). Combining insights from the time-zone mapping and burst keywords provides more objective and accurate research insights (Wang et al. 2023b ).

Reflecting the frequency and time of first appearance of keywords in the study.

An integrated analysis of Fig. 8 and Table 10 shows that early research on older adults’ technology acceptance primarily focused on factors such as perceived usefulness, ease of use, and attitudes towards information technology, including their use of computers and the internet (Pan and Jordan-Marsh 2010 ), as well as differences in technology use between older adults and other age groups (Guner and Acarturk 2020 ). Subsequently, the research focus expanded to improving the quality of life for older adults, exploring how technology can optimize health management and enhance the possibility of independent living, emphasizing the significant role of technology in improving the quality of life for the elderly. With ongoing technological advancements, recent research has shifted towards areas such as “virtual reality,” “telehealth,” and “human-robot interaction,” with a focus on the user experience of older adults (Halim et al. 2022 ). The appearance of keywords such as “physical activity” and “exercise” highlights the value of technology in promoting physical activity and health among older adults. This phase of research tends to make cutting-edge technology genuinely serve the practical needs of older adults, achieving its widespread application in daily life. Additionally, research has focused on expanding and quantifying theoretical models of older adults’ technology acceptance, involving keywords such as “perceived risk”, “validation” and “UTAUT”.

In summary, from 2013 to 2023, the field of older adults’ technology acceptance has evolved from initial explorations of influencing factors, to comprehensive enhancements in quality of life and health management, and further to the application and deepening of theoretical models and cutting-edge technologies. This research not only reflects the diversity and complexity of the field but also demonstrates a comprehensive and in-depth understanding of older adults’ interactions with technology across various life scenarios and needs.

Research quality distribution

To reveal the distribution of research quality in the field of older adults’ technology acceptance, a strategic diagram analysis is employed to calculate and illustrate the internal development and interrelationships among various research themes (Xie et al. 2020 ). The strategic diagram uses Centrality as the X-axis and Density as the Y-axis to divide into four quadrants, where the X-axis represents the strength of the connection between thematic clusters and other themes, with higher values indicating a central position in the research field; the Y-axis indicates the level of development within the thematic clusters, with higher values denoting a more mature and widely recognized field (Li and Zhou 2020 ).

Through cluster analysis and manual verification, this study categorized 61 core keywords (Frequency ≥5) into 11 thematic clusters. Subsequently, based on the keywords covered by each thematic cluster, the research themes and their directions for each cluster were summarized (Table 11 ), and the centrality and density coordinates for each cluster were precisely calculated (Table 12 ). Finally, a strategic diagram of the older adults’ technology acceptance research field was constructed (Fig. 9 ). Based on the distribution of thematic clusters across the quadrants in the strategic diagram, the structure and developmental trends of the field were interpreted.

Classification and visualization of theme clusters based on density and centrality.

As illustrated in Fig. 9 , (1) the theme clusters of #3 Usage Experience and #4 Assisted Living Technology are in the first quadrant, characterized by high centrality and density. Their internal cohesion and close links with other themes indicate their mature development, systematic research content or directions have been formed, and they have a significant influence on other themes. These themes play a central role in the field of older adults’ technology acceptance and have promising prospects. (2) The theme clusters of #6 Smart Devices, #9 Theoretical Models, and #10 Mobile Health Applications are in the second quadrant, with higher density but lower centrality. These themes have strong internal connections but weaker external links, indicating that these three themes have received widespread attention from researchers and have been the subject of related research, but more as self-contained systems and exhibit independence. Therefore, future research should further explore in-depth cooperation and cross-application with other themes. (3) The theme clusters of #7 Human-Robot Interaction, #8 Characteristics of the Elderly, and #11 Research Methods are in the third quadrant, with lower centrality and density. These themes are loosely connected internally and have weak links with others, indicating their developmental immaturity. Compared to other topics, they belong to the lower attention edge and niche themes, and there is a need for further investigation. (4) The theme clusters of #1 Digital Healthcare Technology, #2 Psychological Factors, and #5 Socio-Cultural Factors are located in the fourth quadrant, with high centrality but low density. Although closely associated with other research themes, the internal cohesion within these clusters is relatively weak. This suggests that while these themes are closely linked to other research areas, their own development remains underdeveloped, indicating a core immaturity. Nevertheless, these themes are crucial within the research domain of elderly technology acceptance and possess significant potential for future exploration.

Discussion on distribution power (RQ1)

Over the past decade, academic interest and influence in the area of older adults’ technology acceptance have significantly increased. This trend is evidenced by a quantitative analysis of publication and citation volumes, particularly noticeable in 2019 and 2022, where there was a substantial rise in both metrics. The rise is closely linked to the widespread adoption of emerging technologies such as smart homes, wearable devices, and telemedicine among older adults. While these technologies have enhanced their quality of life, they also pose numerous challenges, sparking extensive research into their acceptance, usage behaviors, and influencing factors among the older adults (Pirzada et al. 2022 ; Garcia Reyes et al. 2023 ). Furthermore, the COVID-19 pandemic led to a surge in technology demand among older adults, especially in areas like medical consultation, online socialization, and health management, further highlighting the importance and challenges of technology. Health risks and social isolation have compelled older adults to rely on technology for daily activities, accelerating its adoption and application within this demographic. This phenomenon has made technology acceptance a critical issue, driving societal and academic focus on the study of technology acceptance among older adults.

The flow of knowledge at the level of high-output disciplines and journals, along with the primary publishing outlets, indicates the highly interdisciplinary nature of research into older adults’ technology acceptance. This reflects the complexity and breadth of issues related to older adults’ technology acceptance, necessitating the integration of multidisciplinary knowledge and approaches. Currently, research is primarily focused on medical health and human-computer interaction, demonstrating academic interest in improving health and quality of life for older adults and addressing the urgent needs related to their interactions with technology. In the field of medical health, research aims to provide advanced and innovative healthcare technologies and services to meet the challenges of an aging population while improving the quality of life for older adults (Abdi et al. 2020 ; Wilson et al. 2021 ). In the field of human-computer interaction, research is focused on developing smarter and more user-friendly interaction models to meet the needs of older adults in the digital age, enabling them to actively participate in social activities and enjoy a higher quality of life (Sayago, 2019 ). These studies are crucial for addressing the challenges faced by aging societies, providing increased support and opportunities for the health, welfare, and social participation of older adults.

Discussion on research power (RQ2)

This study analyzes leading countries and collaboration networks, core institutions and authors, revealing the global research landscape and distribution of research strength in the field of older adults’ technology acceptance, and presents quantitative data on global research trends. From the analysis of country distribution and collaborations, China and the USA hold dominant positions in this field, with developed countries like the UK, Germany, Italy, and the Netherlands also excelling in international cooperation and research influence. The significant investment in technological research and the focus on the technological needs of older adults by many developed countries reflect their rapidly aging societies, policy support, and resource allocation.

China is the only developing country that has become a major contributor in this field, indicating its growing research capabilities and high priority given to aging societies and technological innovation. Additionally, China has close collaborations with countries such as USA, the UK, and Malaysia, driven not only by technological research needs but also by shared challenges and complementarities in aging issues among these nations. For instance, the UK has extensive experience in social welfare and aging research, providing valuable theoretical guidance and practical experience. International collaborations, aimed at addressing the challenges of aging, integrate the strengths of various countries, advancing in-depth and widespread development in the research of technology acceptance among older adults.

At the institutional and author level, City University of Hong Kong leads in publication volume, with research teams led by Chan and Chen demonstrating significant academic activity and contributions. Their research primarily focuses on older adults’ acceptance and usage behaviors of various technologies, including smartphones, smart wearables, and social robots (Chen et al. 2015 ; Li et al. 2019 ; Ma et al. 2016 ). These studies, targeting specific needs and product characteristics of older adults, have developed new models of technology acceptance based on existing frameworks, enhancing the integration of these technologies into their daily lives and laying a foundation for further advancements in the field. Although Tilburg University has a smaller publication output, it holds significant influence in the field of older adults’ technology acceptance. Particularly, the high citation rate of Peek’s studies highlights their excellence in research. Peek extensively explored older adults’ acceptance and usage of home care technologies, revealing the complexity and dynamics of their technology use behaviors. His research spans from identifying systemic influencing factors (Peek et al. 2014 ; Peek et al. 2016 ), emphasizing familial impacts (Luijkx et al. 2015 ), to constructing comprehensive models (Peek et al. 2017 ), and examining the dynamics of long-term usage (Peek et al. 2019 ), fully reflecting the evolving technology landscape and the changing needs of older adults. Additionally, the ongoing contributions of researchers like Ziefle, Rogers, and Wouters in the field of older adults’ technology acceptance demonstrate their research influence and leadership. These researchers have significantly enriched the knowledge base in this area with their diverse perspectives. For instance, Ziefle has uncovered the complex attitudes of older adults towards technology usage, especially the trade-offs between privacy and security, and how different types of activities affect their privacy needs (Maidhof et al. 2023 ; Mujirishvili et al. 2023 ; Schomakers and Ziefle 2023 ; Wilkowska et al. 2022 ), reflecting a deep exploration and ongoing innovation in the field of older adults’ technology acceptance.

Discussion on knowledge base and thematic progress (RQ3)

Through co-citation analysis and systematic review of seminal literature, this study reveals the knowledge foundation and thematic progress in the field of older adults’ technology acceptance. Co-citation networks and cluster analyses illustrate the structural themes of the research, delineating the differentiation and boundaries within this field. Additionally, burst detection analysis offers a valuable perspective for understanding the thematic evolution in the field of technology acceptance among older adults. The development and innovation of theoretical models are foundational to this research. Researchers enhance the explanatory power of constructed models by deepening and expanding existing technology acceptance theories to address theoretical limitations. For instance, Heerink et al. ( 2010 ) modified and expanded the UTAUT model by integrating functional assessment and social interaction variables to create the almere model. This model significantly enhances the ability to explain the intentions of older users in utilizing assistive social agents and improves the explanation of actual usage behaviors. Additionally, Chen and Chan ( 2014 ) extended the TAM to include age-related health and capability features of older adults, creating the STAM, which substantially improves predictions of older adults’ technology usage behaviors. Personal attributes, health and capability features, and facilitating conditions have a direct impact on technology acceptance. These factors more effectively predict older adults’ technology usage behaviors than traditional attitudinal factors.

With the advancement of technology and the application of emerging technologies, new research topics have emerged, increasingly focusing on older adults’ acceptance and use of these technologies. Prior to this, the study by Mitzner et al. ( 2010 ) challenged the stereotype of older adults’ conservative attitudes towards technology, highlighting the central roles of usability and usefulness in the technology acceptance process. This discovery laid an important foundation for subsequent research. Research fields such as “smart home technology,” “social life,” and “customer service” are emerging, indicating a shift in focus towards the practical and social applications of technology in older adults’ lives. Research not only focuses on the technology itself but also on how these technologies integrate into older adults’ daily lives and how they can improve the quality of life through technology. For instance, studies such as those by Ma et al. ( 2016 ), Hoque and Sorwar ( 2017 ), and Li et al. ( 2019 ) have explored factors influencing older adults’ use of smartphones, mHealth, and smart wearable devices.

Furthermore, the diversification of research methodologies and innovation in evaluation techniques, such as the use of mixed methods, structural equation modeling (SEM), and neural network (NN) approaches, have enhanced the rigor and reliability of the findings, enabling more precise identification of the factors and mechanisms influencing technology acceptance. Talukder et al. ( 2020 ) employed an effective multimethodological strategy by integrating SEM and NN to leverage the complementary strengths of both approaches, thus overcoming their individual limitations and more accurately analyzing and predicting older adults’ acceptance of wearable health technologies (WHT). SEM is utilized to assess the determinants’ impact on the adoption of WHT, while neural network models validate SEM outcomes and predict the significance of key determinants. This combined approach not only boosts the models’ reliability and explanatory power but also provides a nuanced understanding of the motivations and barriers behind older adults’ acceptance of WHT, offering deep research insights.

Overall, co-citation analysis of the literature in the field of older adults’ technology acceptance has uncovered deeper theoretical modeling and empirical studies on emerging technologies, while emphasizing the importance of research methodological and evaluation innovations in understanding complex social science issues. These findings are crucial for guiding the design and marketing strategies of future technology products, especially in the rapidly growing market of older adults.

Discussion on research hotspots and evolutionary trends (RQ4)

By analyzing core keywords, we can gain deep insights into the hot topics, evolutionary trends, and quality distribution of research in the field of older adults’ technology acceptance. The frequent occurrence of the keywords “TAM” and “UTAUT” indicates that the applicability and theoretical extension of existing technology acceptance models among older adults remain a focal point in academia. This phenomenon underscores the enduring influence of the studies by Davis ( 1989 ) and Venkatesh et al. ( 2003 ), whose models provide a robust theoretical framework for explaining and predicting older adults’ acceptance and usage of emerging technologies. With the widespread application of artificial intelligence (AI) and big data technologies, these theoretical models have incorporated new variables such as perceived risk, trust, and privacy issues (Amin et al. 2024 ; Chen et al. 2024 ; Jing et al. 2024b ; Seibert et al. 2021 ; Wang et al. 2024b ), advancing the theoretical depth and empirical research in this field.

Keyword co-occurrence cluster analysis has revealed multiple research hotspots in the field, including factors influencing technology adoption, interactive experiences between older adults and assistive technologies, the application of mobile health technology in health management, and technology-assisted home care. These studies primarily focus on enhancing the quality of life and health management of older adults through emerging technologies, particularly in the areas of ambient assisted living, smart health monitoring, and intelligent medical care. In these domains, the role of AI technology is increasingly significant (Qian et al. 2021 ; Ho 2020 ). With the evolution of next-generation information technologies, AI is increasingly integrated into elder care systems, offering intelligent, efficient, and personalized service solutions by analyzing the lifestyles and health conditions of older adults. This integration aims to enhance older adults’ quality of life in aspects such as health monitoring and alerts, rehabilitation assistance, daily health management, and emotional support (Lee et al. 2023 ). A survey indicates that 83% of older adults prefer AI-driven solutions when selecting smart products, demonstrating the increasing acceptance of AI in elder care (Zhao and Li 2024 ). Integrating AI into elder care presents both opportunities and challenges, particularly in terms of user acceptance, trust, and long-term usage effects, which warrant further exploration (Mhlanga 2023 ). These studies will help better understand the profound impact of AI technology on the lifestyles of older adults and provide critical references for optimizing AI-driven elder care services.

The Time-zone evolution mapping and burst keyword analysis further reveal the evolutionary trends of research hotspots. Early studies focused on basic technology acceptance models and user perceptions, later expanding to include quality of life and health management. In recent years, research has increasingly focused on cutting-edge technologies such as virtual reality, telehealth, and human-robot interaction, with a concurrent emphasis on the user experience of older adults. This evolutionary process demonstrates a deepening shift from theoretical models to practical applications, underscoring the significant role of technology in enhancing the quality of life for older adults. Furthermore, the strategic coordinate mapping analysis clearly demonstrates the development and mutual influence of different research themes. High centrality and density in the themes of Usage Experience and Assisted Living Technology indicate their mature research status and significant impact on other themes. The themes of Smart Devices, Theoretical Models, and Mobile Health Applications demonstrate self-contained research trends. The themes of Human-Robot Interaction, Characteristics of the Elderly, and Research Methods are not yet mature, but they hold potential for development. Themes of Digital Healthcare Technology, Psychological Factors, and Socio-Cultural Factors are closely related to other themes, displaying core immaturity but significant potential.

In summary, the research hotspots in the field of older adults’ technology acceptance are diverse and dynamic, demonstrating the academic community’s profound understanding of how older adults interact with technology across various life contexts and needs. Under the influence of AI and big data, research should continue to focus on the application of emerging technologies among older adults, exploring in depth how they adapt to and effectively use these technologies. This not only enhances the quality of life and healthcare experiences for older adults but also drives ongoing innovation and development in this field.

Research agenda

Based on the above research findings, to further understand and promote technology acceptance and usage among older adults, we recommend future studies focus on refining theoretical models, exploring long-term usage, and assessing user experience in the following detailed aspects:

Refinement and validation of specific technology acceptance models for older adults: Future research should focus on developing and validating technology acceptance models based on individual characteristics, particularly considering variations in technology acceptance among older adults across different educational levels and cultural backgrounds. This includes factors such as age, gender, educational background, and cultural differences. Additionally, research should examine how well specific technologies, such as wearable devices and mobile health applications, meet the needs of older adults. Building on existing theoretical models, this research should integrate insights from multiple disciplines such as psychology, sociology, design, and engineering through interdisciplinary collaboration to create more accurate and comprehensive models, which should then be validated in relevant contexts.

Deepening the exploration of the relationship between long-term technology use and quality of life among older adults: The acceptance and use of technology by users is a complex and dynamic process (Seuwou et al. 2016 ). Existing research predominantly focuses on older adults’ initial acceptance or short-term use of new technologies; however, the impact of long-term use on their quality of life and health is more significant. Future research should focus on the evolution of older adults’ experiences and needs during long-term technology usage, and the enduring effects of technology on their social interactions, mental health, and life satisfaction. Through longitudinal studies and qualitative analysis, this research reveals the specific needs and challenges of older adults in long-term technology use, providing a basis for developing technologies and strategies that better meet their requirements. This understanding aids in comprehensively assessing the impact of technology on older adults’ quality of life and guiding the optimization and improvement of technological products.

Evaluating the Importance of User Experience in Research on Older Adults’ Technology Acceptance: Understanding the mechanisms of information technology acceptance and use is central to human-computer interaction research. Although technology acceptance models and user experience models differ in objectives, they share many potential intersections. Technology acceptance research focuses on structured prediction and assessment, while user experience research concentrates on interpreting design impacts and new frameworks. Integrating user experience to assess older adults’ acceptance of technology products and systems is crucial (Codfrey et al. 2022 ; Wang et al. 2019 ), particularly for older users, where specific product designs should emphasize practicality and usability (Fisk et al. 2020 ). Researchers need to explore innovative age-appropriate design methods to enhance older adults’ usage experience. This includes studying older users’ actual usage preferences and behaviors, optimizing user interfaces, and interaction designs. Integrating feedback from older adults to tailor products to their needs can further promote their acceptance and continued use of technology products.

Conclusions

This study conducted a systematic review of the literature on older adults’ technology acceptance over the past decade through bibliometric analysis, focusing on the distribution power, research power, knowledge base and theme progress, research hotspots, evolutionary trends, and quality distribution. Using a combination of quantitative and qualitative methods, this study has reached the following conclusions:

Technology acceptance among older adults has become a hot topic in the international academic community, involving the integration of knowledge across multiple disciplines, including Medical Informatics, Health Care Sciences Services, and Ergonomics. In terms of journals, “PSYCHOLOGY, EDUCATION, HEALTH” represents a leading field, with key publications including Computers in Human Behavior , Journal of Medical Internet Research , and International Journal of Human-Computer Interaction . These journals possess significant academic authority and extensive influence in the field.

Research on technology acceptance among older adults is particularly active in developed countries, with China and USA publishing significantly more than other nations. The Netherlands leads in high average citation rates, indicating the depth and impact of its research. Meanwhile, the UK stands out in terms of international collaboration. At the institutional level, City University of Hong Kong and The University of Hong Kong in China are in leading positions. Tilburg University in the Netherlands demonstrates exceptional research quality through its high average citation count. At the author level, Chen from China has the highest number of publications, while Peek from the Netherlands has the highest average citation count.

Co-citation analysis of references indicates that the knowledge base in this field is divided into three main categories: theoretical model deepening, emerging technology applications, and research methods and evaluation. Seminal literature focuses on four areas: specific technology use by older adults, expansion of theoretical models of technology acceptance, information technology adoption behavior, and research perspectives. Research themes have evolved from initial theoretical deepening and analysis of influencing factors to empirical studies on individual factors and emerging technologies.

Keyword analysis indicates that TAM and UTAUT are the most frequently occurring terms, while “assistive technology” and “virtual reality” are focal points with high frequency and centrality. Keyword clustering analysis reveals that research hotspots are concentrated on the influencing factors of technology adoption, human-robot interaction experiences, mobile health management, and technology for aging in place. Time-zone evolution mapping and burst keyword analysis have revealed the research evolution from preliminary exploration of influencing factors, to enhancements in quality of life and health management, and onto advanced technology applications and deepening of theoretical models. Furthermore, analysis of research quality distribution indicates that Usage Experience and Assisted Living Technology have become core topics, while Smart Devices, Theoretical Models, and Mobile Health Applications point towards future research directions.

Through this study, we have systematically reviewed the dynamics, core issues, and evolutionary trends in the field of older adults’ technology acceptance, constructing a comprehensive Knowledge Mapping of the domain and presenting a clear framework of existing research. This not only lays the foundation for subsequent theoretical discussions and innovative applications in the field but also provides an important reference for relevant scholars.

Limitations

To our knowledge, this is the first bibliometric analysis concerning technology acceptance among older adults, and we adhered strictly to bibliometric standards throughout our research. However, this study relies on the Web of Science Core Collection, and while its authority and breadth are widely recognized, this choice may have missed relevant literature published in other significant databases such as PubMed, Scopus, and Google Scholar, potentially overlooking some critical academic contributions. Moreover, given that our analysis was confined to literature in English, it may not reflect studies published in other languages, somewhat limiting the global representativeness of our data sample.

It is noteworthy that with the rapid development of AI technology, its increasingly widespread application in elderly care services is significantly transforming traditional care models. AI is profoundly altering the lifestyles of the elderly, from health monitoring and smart diagnostics to intelligent home systems and personalized care, significantly enhancing their quality of life and health care standards. The potential for AI technology within the elderly population is immense, and research in this area is rapidly expanding. However, due to the restrictive nature of the search terms used in this study, it did not fully cover research in this critical area, particularly in addressing key issues such as trust, privacy, and ethics.

Consequently, future research should not only expand data sources, incorporating multilingual and multidatabase literature, but also particularly focus on exploring older adults’ acceptance of AI technology and its applications, in order to construct a more comprehensive academic landscape of older adults’ technology acceptance, thereby enriching and extending the knowledge system and academic trends in this field.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/6K0GJH .

Abdi S, de Witte L, Hawley M (2020) Emerging technologies with potential care and support applications for older people: review of gray literature. JMIR Aging 3(2):e17286. https://doi.org/10.2196/17286

Article   PubMed   PubMed Central   Google Scholar  

Achuthan K, Nair VK, Kowalski R, Ramanathan S, Raman R (2023) Cyberbullying research—Alignment to sustainable development and impact of COVID-19: Bibliometrics and science mapping analysis. Comput Human Behav 140:107566. https://doi.org/10.1016/j.chb.2022.107566

Article   Google Scholar  

Ahmad A, Mozelius P (2022) Human-Computer Interaction for Older Adults: a Literature Review on Technology Acceptance of eHealth Systems. J Eng Res Sci 1(4):119–126. https://doi.org/10.55708/js0104014

Ale Ebrahim N, Salehi H, Embi MA, Habibi F, Gholizadeh H, Motahar SM (2014) Visibility and citation impact. Int Educ Stud 7(4):120–125. https://doi.org/10.5539/ies.v7n4p120

Amin MS, Johnson VL, Prybutok V, Koh CE (2024) An investigation into factors affecting the willingness to disclose personal health information when using AI-enabled caregiver robots. Ind Manag Data Syst 124(4):1677–1699. https://doi.org/10.1108/IMDS-09-2023-0608

Baer NR, Vietzke J, Schenk L (2022) Middle-aged and older adults’ acceptance of mobile nutrition and fitness apps: a systematic mixed studies review. PLoS One 17(12):e0278879. https://doi.org/10.1371/journal.pone.0278879

Barnard Y, Bradley MD, Hodgson F, Lloyd AD (2013) Learning to use new technologies by older adults: Perceived difficulties, experimentation behaviour and usability. Comput Human Behav 29(4):1715–1724. https://doi.org/10.1016/j.chb.2013.02.006

Berkowsky RW, Sharit J, Czaja SJ (2017) Factors predicting decisions about technology adoption among older adults. Innov Aging 3(1):igy002. https://doi.org/10.1093/geroni/igy002

Braun MT (2013) Obstacles to social networking website use among older adults. Comput Human Behav 29(3):673–680. https://doi.org/10.1016/j.chb.2012.12.004

Article   MathSciNet   Google Scholar  

Campo-Prieto P, Rodríguez-Fuentes G, Cancela-Carral JM (2021) Immersive virtual reality exergame promotes the practice of physical activity in older people: An opportunity during COVID-19. Multimodal Technol Interact 5(9):52. https://doi.org/10.3390/mti5090052

Chen C (2006) CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol 57(3):359–377. https://doi.org/10.1002/asi.20317

Chen C, Dubin R, Kim MC (2014) Emerging trends and new developments in regenerative medicine: a scientometric update (2000–2014). Expert Opin Biol Ther 14(9):1295–1317. https://doi.org/10.1517/14712598.2014.920813

Article   PubMed   Google Scholar  

Chen C, Leydesdorff L (2014) Patterns of connections and movements in dual‐map overlays: A new method of publication portfolio analysis. J Assoc Inf Sci Technol 65(2):334–351. https://doi.org/10.1002/asi.22968

Chen J, Wang C, Tang Y (2022) Knowledge mapping of volunteer motivation: A bibliometric analysis and cross-cultural comparative study. Front Psychol 13:883150. https://doi.org/10.3389/fpsyg.2022.883150

Chen JY, Liu YD, Dai J, Wang CL (2023) Development and status of moral education research: Visual analysis based on knowledge graph. Front Psychol 13:1079955. https://doi.org/10.3389/fpsyg.2022.1079955

Chen K, Chan AH (2011) A review of technology acceptance by older adults. Gerontechnology 10(1):1–12. https://doi.org/10.4017/gt.2011.10.01.006.00

Chen K, Chan AH (2014) Gerontechnology acceptance by elderly Hong Kong Chinese: a senior technology acceptance model (STAM). Ergonomics 57(5):635–652. https://doi.org/10.1080/00140139.2014.895855

Chen K, Zhang Y, Fu X (2019) International research collaboration: An emerging domain of innovation studies? Res Policy 48(1):149–168. https://doi.org/10.1016/j.respol.2018.08.005

Chen X, Hu Z, Wang C (2024) Empowering education development through AIGC: A systematic literature review. Educ Inf Technol 1–53. https://doi.org/10.1007/s10639-024-12549-7

Chen Y, Chen CM, Liu ZY, Hu ZG, Wang XW (2015) The methodology function of CiteSpace mapping knowledge domains. Stud Sci Sci 33(2):242–253. https://doi.org/10.16192/j.cnki.1003-2053.2015.02.009

Codfrey GS, Baharum A, Zain NHM, Omar M, Deris FD (2022) User Experience in Product Design and Development: Perspectives and Strategies. Math Stat Eng Appl 71(2):257–262. https://doi.org/10.17762/msea.v71i2.83

Dai J, Zhang X, Wang CL (2024) A meta-analysis of learners’ continuance intention toward online education platforms. Educ Inf Technol 1–36. https://doi.org/10.1007/s10639-024-12654-7

Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340. https://doi.org/10.2307/249008

Delmastro F, Dolciotti C, Palumbo F, Magrini M, Di Martino F, La Rosa D, Barcaro U (2018) Long-term care: how to improve the quality of life with mobile and e-health services. In 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pp. 12–19. IEEE. https://doi.org/10.1109/WiMOB.2018.8589157

Dupuis K, Tsotsos LE (2018) Technology for remote health monitoring in an older population: a role for mobile devices. Multimodal Technol Interact 2(3):43. https://doi.org/10.3390/mti2030043

Ferguson C, Hickman LD, Turkmani S, Breen P, Gargiulo G, Inglis SC (2021) Wearables only work on patients that wear them”: Barriers and facilitators to the adoption of wearable cardiac monitoring technologies. Cardiovasc Digit Health J 2(2):137–147. https://doi.org/10.1016/j.cvdhj.2021.02.001

Fisk AD, Czaja SJ, Rogers WA, Charness N, Sharit J (2020) Designing for older adults: Principles and creative human factors approaches. CRC Press. https://doi.org/10.1201/9781420080681

Friesen S, Brémault-Phillips S, Rudrum L, Rogers LG (2016) Environmental design that supports healthy aging: Evaluating a new supportive living facility. J Hous Elderly 30(1):18–34. https://doi.org/10.1080/02763893.2015.1129380

Garcia Reyes EP, Kelly R, Buchanan G, Waycott J (2023) Understanding Older Adults’ Experiences With Technologies for Health Self-management: Interview Study. JMIR Aging 6:e43197. https://doi.org/10.2196/43197

Geng Z, Wang J, Liu J, Miao J (2024) Bibliometric analysis of the development, current status, and trends in adult degenerative scoliosis research: A systematic review from 1998 to 2023. J Pain Res 17:153–169. https://doi.org/10.2147/JPR.S437575

González A, Ramírez MP, Viadel V (2012) Attitudes of the elderly toward information and communications technologies. Educ Gerontol 38(9):585–594. https://doi.org/10.1080/03601277.2011.595314

Guner H, Acarturk C (2020) The use and acceptance of ICT by senior citizens: a comparison of technology acceptance model (TAM) for elderly and young adults. Univ Access Inf Soc 19(2):311–330. https://doi.org/10.1007/s10209-018-0642-4

Halim I, Saptari A, Perumal PA, Abdullah Z, Abdullah S, Muhammad MN (2022) A Review on Usability and User Experience of Assistive Social Robots for Older Persons. Int J Integr Eng 14(6):102–124. https://penerbit.uthm.edu.my/ojs/index.php/ijie/article/view/8566

He Y, He Q, Liu Q (2022) Technology acceptance in socially assistive robots: Scoping review of models, measurement, and influencing factors. J Healthc Eng 2022(1):6334732. https://doi.org/10.1155/2022/6334732

Heerink M, Kröse B, Evers V, Wielinga B (2010) Assessing acceptance of assistive social agent technology by older adults: the almere model. Int J Soc Robot 2:361–375. https://doi.org/10.1007/s12369-010-0068-5

Ho A (2020) Are we ready for artificial intelligence health monitoring in elder care? BMC Geriatr 20(1):358. https://doi.org/10.1186/s12877-020-01764-9

Hoque R, Sorwar G (2017) Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. Int J Med Inform 101:75–84. https://doi.org/10.1016/j.ijmedinf.2017.02.002

Hota PK, Subramanian B, Narayanamurthy G (2020) Mapping the intellectual structure of social entrepreneurship research: A citation/co-citation analysis. J Bus Ethics 166(1):89–114. https://doi.org/10.1007/s10551-019-04129-4

Huang R, Yan P, Yang X (2021) Knowledge map visualization of technology hotspots and development trends in China’s textile manufacturing industry. IET Collab Intell Manuf 3(3):243–251. https://doi.org/10.1049/cim2.12024

Article   ADS   Google Scholar  

Jing Y, Wang C, Chen Y, Wang H, Yu T, Shadiev R (2023) Bibliometric mapping techniques in educational technology research: A systematic literature review. Educ Inf Technol 1–29. https://doi.org/10.1007/s10639-023-12178-6

Jing YH, Wang CL, Chen ZY, Shen SS, Shadiev R (2024a) A Bibliometric Analysis of Studies on Technology-Supported Learning Environments: Hotopics and Frontier Evolution. J Comput Assist Learn 1–16. https://doi.org/10.1111/jcal.12934

Jing YH, Wang HM, Chen XJ, Wang CL (2024b) What factors will affect the effectiveness of using ChatGPT to solve programming problems? A quasi-experimental study. Humanit Soc Sci Commun 11:319. https://doi.org/10.1057/s41599-024-02751-w

Kamrani P, Dorsch I, Stock WG (2021) Do researchers know what the h-index is? And how do they estimate its importance? Scientometrics 126(7):5489–5508. https://doi.org/10.1007/s11192-021-03968-1

Kim HS, Lee KH, Kim H, Kim JH (2014) Using mobile phones in healthcare management for the elderly. Maturitas 79(4):381–388. https://doi.org/10.1016/j.maturitas.2014.08.013

Article   MathSciNet   PubMed   Google Scholar  

Kleinberg J (2002) Bursty and hierarchical structure in streams. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 91–101. https://doi.org/10.1145/775047.775061

Kruse C, Fohn J, Wilson N, Patlan EN, Zipp S, Mileski M (2020) Utilization barriers and medical outcomes commensurate with the use of telehealth among older adults: systematic review. JMIR Med Inform 8(8):e20359. https://doi.org/10.2196/20359

Kumar S, Lim WM, Pandey N, Christopher Westland J (2021) 20 years of electronic commerce research. Electron Commer Res 21:1–40. https://doi.org/10.1007/s10660-021-09464-1

Kwiek M (2021) What large-scale publication and citation data tell us about international research collaboration in Europe: Changing national patterns in global contexts. Stud High Educ 46(12):2629–2649. https://doi.org/10.1080/03075079.2020.1749254

Lee C, Coughlin JF (2015) PERSPECTIVE: Older adults’ adoption of technology: an integrated approach to identifying determinants and barriers. J Prod Innov Manag 32(5):747–759. https://doi.org/10.1111/jpim.12176

Lee CH, Wang C, Fan X, Li F, Chen CH (2023) Artificial intelligence-enabled digital transformation in elderly healthcare field: scoping review. Adv Eng Inform 55:101874. https://doi.org/10.1016/j.aei.2023.101874

Leydesdorff L, Rafols I (2012) Interactive overlays: A new method for generating global journal maps from Web-of-Science data. J Informetr 6(2):318–332. https://doi.org/10.1016/j.joi.2011.11.003

Li J, Ma Q, Chan AH, Man S (2019) Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Appl Ergon 75:162–169. https://doi.org/10.1016/j.apergo.2018.10.006

Article   ADS   PubMed   Google Scholar  

Li X, Zhou D (2020) Product design requirement information visualization approach for intelligent manufacturing services. China Mech Eng 31(07):871, http://www.cmemo.org.cn/EN/Y2020/V31/I07/871

Google Scholar  

Lin Y, Yu Z (2024a) An integrated bibliometric analysis and systematic review modelling students’ technostress in higher education. Behav Inf Technol 1–25. https://doi.org/10.1080/0144929X.2024.2332458

Lin Y, Yu Z (2024b) A bibliometric analysis of artificial intelligence chatbots in educational contexts. Interact Technol Smart Educ 21(2):189–213. https://doi.org/10.1108/ITSE-12-2022-0165

Liu L, Duffy VG (2023) Exploring the future development of Artificial Intelligence (AI) applications in chatbots: a bibliometric analysis. Int J Soc Robot 15(5):703–716. https://doi.org/10.1007/s12369-022-00956-0

Liu R, Li X, Chu J (2022) Evolution of applied variables in the research on technology acceptance of the elderly. In: International Conference on Human-Computer Interaction, Cham: Springer International Publishing, pp 500–520. https://doi.org/10.1007/978-3-031-05581-23_5

Luijkx K, Peek S, Wouters E (2015) “Grandma, you should do it—It’s cool” Older Adults and the Role of Family Members in Their Acceptance of Technology. Int J Environ Res Public Health 12(12):15470–15485. https://doi.org/10.3390/ijerph121214999

Lussier M, Lavoie M, Giroux S, Consel C, Guay M, Macoir J, Bier N (2018) Early detection of mild cognitive impairment with in-home monitoring sensor technologies using functional measures: a systematic review. IEEE J Biomed Health Inform 23(2):838–847. https://doi.org/10.1109/JBHI.2018.2834317

López-Robles JR, Otegi-Olaso JR, Porto Gomez I, Gamboa-Rosales NK, Gamboa-Rosales H, Robles-Berumen H (2018) Bibliometric network analysis to identify the intellectual structure and evolution of the big data research field. In: International Conference on Intelligent Data Engineering and Automated Learning, Cham: Springer International Publishing, pp 113–120. https://doi.org/10.1007/978-3-030-03496-2_13

Ma Q, Chan AH, Chen K (2016) Personal and other factors affecting acceptance of smartphone technology by older Chinese adults. Appl Ergon 54:62–71. https://doi.org/10.1016/j.apergo.2015.11.015

Ma Q, Chan AHS, Teh PL (2021) Insights into Older Adults’ Technology Acceptance through Meta-Analysis. Int J Hum-Comput Interact 37(11):1049–1062. https://doi.org/10.1080/10447318.2020.1865005

Macedo IM (2017) Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Comput Human Behav 75:935–948. https://doi.org/10.1016/j.chb.2017.06.013

Maidhof C, Offermann J, Ziefle M (2023) Eyes on privacy: acceptance of video-based AAL impacted by activities being filmed. Front Public Health 11:1186944. https://doi.org/10.3389/fpubh.2023.1186944

Majumder S, Aghayi E, Noferesti M, Memarzadeh-Tehran H, Mondal T, Pang Z, Deen MJ (2017) Smart homes for elderly healthcare—Recent advances and research challenges. Sensors 17(11):2496. https://doi.org/10.3390/s17112496

Article   ADS   PubMed   PubMed Central   Google Scholar  

Mhlanga D (2023) Artificial Intelligence in elderly care: Navigating ethical and responsible AI adoption for seniors. Available at SSRN 4675564. 4675564 min) Identifying citation patterns of scientific breakthroughs: A perspective of dynamic citation process. Inf Process Manag 58(1):102428. https://doi.org/10.1016/j.ipm.2020.102428

Mitzner TL, Boron JB, Fausset CB, Adams AE, Charness N, Czaja SJ, Sharit J (2010) Older adults talk technology: Technology usage and attitudes. Comput Human Behav 26(6):1710–1721. https://doi.org/10.1016/j.chb.2010.06.020

Mitzner TL, Savla J, Boot WR, Sharit J, Charness N, Czaja SJ, Rogers WA (2019) Technology adoption by older adults: Findings from the PRISM trial. Gerontologist 59(1):34–44. https://doi.org/10.1093/geront/gny113

Mongeon P, Paul-Hus A (2016) The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics 106:213–228. https://doi.org/10.1007/s11192-015-1765-5

Mostaghel R (2016) Innovation and technology for the elderly: Systematic literature review. J Bus Res 69(11):4896–4900. https://doi.org/10.1016/j.jbusres.2016.04.049

Mujirishvili T, Maidhof C, Florez-Revuelta F, Ziefle M, Richart-Martinez M, Cabrero-García J (2023) Acceptance and privacy perceptions toward video-based active and assisted living technologies: Scoping review. J Med Internet Res 25:e45297. https://doi.org/10.2196/45297

Naseri RNN, Azis SN, Abas N (2023) A Review of Technology Acceptance and Adoption Models in Consumer Study. FIRM J Manage Stud 8(2):188–199. https://doi.org/10.33021/firm.v8i2.4536

Nguyen UP, Hallinger P (2020) Assessing the distinctive contributions of Simulation & Gaming to the literature, 1970–2019: A bibliometric review. Simul Gaming 51(6):744–769. https://doi.org/10.1177/1046878120941569

Olmedo-Aguirre JO, Reyes-Campos J, Alor-Hernández G, Machorro-Cano I, Rodríguez-Mazahua L, Sánchez-Cervantes JL (2022) Remote healthcare for elderly people using wearables: A review. Biosensors 12(2):73. https://doi.org/10.3390/bios12020073

Pan S, Jordan-Marsh M (2010) Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective. Comput Human Behav 26(5):1111–1119. https://doi.org/10.1016/j.chb.2010.03.015

Pan X, Yan E, Cui M, Hua W (2018) Examining the usage, citation, and diffusion patterns of bibliometric map software: A comparative study of three tools. J Informetr 12(2):481–493. https://doi.org/10.1016/j.joi.2018.03.005

Park JS, Kim NR, Han EJ (2018) Analysis of trends in science and technology using keyword network analysis. J Korea Ind Inf Syst Res 23(2):63–73. https://doi.org/10.9723/jksiis.2018.23.2.063

Peek ST, Luijkx KG, Rijnaard MD, Nieboer ME, Van Der Voort CS, Aarts S, Wouters EJ (2016) Older adults’ reasons for using technology while aging in place. Gerontology 62(2):226–237. https://doi.org/10.1159/000430949

Peek ST, Luijkx KG, Vrijhoef HJ, Nieboer ME, Aarts S, van der Voort CS, Wouters EJ (2017) Origins and consequences of technology acquirement by independent-living seniors: Towards an integrative model. BMC Geriatr 17:1–18. https://doi.org/10.1186/s12877-017-0582-5

Peek ST, Wouters EJ, Van Hoof J, Luijkx KG, Boeije HR, Vrijhoef HJ (2014) Factors influencing acceptance of technology for aging in place: a systematic review. Int J Med Inform 83(4):235–248. https://doi.org/10.1016/j.ijmedinf.2014.01.004

Peek STM, Luijkx KG, Vrijhoef HJM, Nieboer ME, Aarts S, Van Der Voort CS, Wouters EJM (2019) Understanding changes and stability in the long-term use of technologies by seniors who are aging in place: a dynamical framework. BMC Geriatr 19:1–13. https://doi.org/10.1186/s12877-019-1241-9

Perez AJ, Siddiqui F, Zeadally S, Lane D (2023) A review of IoT systems to enable independence for the elderly and disabled individuals. Internet Things 21:100653. https://doi.org/10.1016/j.iot.2022.100653

Piau A, Wild K, Mattek N, Kaye J (2019) Current state of digital biomarker technologies for real-life, home-based monitoring of cognitive function for mild cognitive impairment to mild Alzheimer disease and implications for clinical care: systematic review. J Med Internet Res 21(8):e12785. https://doi.org/10.2196/12785

Pirzada P, Wilde A, Doherty GH, Harris-Birtill D (2022) Ethics and acceptance of smart homes for older adults. Inform Health Soc Care 47(1):10–37. https://doi.org/10.1080/17538157.2021.1923500

Pranckutė R (2021) Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications 9(1):12. https://doi.org/10.3390/publications9010012

Qian K, Zhang Z, Yamamoto Y, Schuller BW (2021) Artificial intelligence internet of things for the elderly: From assisted living to health-care monitoring. IEEE Signal Process Mag 38(4):78–88. https://doi.org/10.1109/MSP.2021.3057298

Redner S (1998) How popular is your paper? An empirical study of the citation distribution. Eur Phys J B-Condens Matter Complex Syst 4(2):131–134. https://doi.org/10.1007/s100510050359

Sayago S (ed.) (2019) Perspectives on human-computer interaction research with older people. Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-030-06076-3

Schomakers EM, Ziefle M (2023) Privacy vs. security: trade-offs in the acceptance of smart technologies for aging-in-place. Int J Hum Comput Interact 39(5):1043–1058. https://doi.org/10.1080/10447318.2022.2078463

Schroeder T, Dodds L, Georgiou A, Gewald H, Siette J (2023) Older adults and new technology: Mapping review of the factors associated with older adults’ intention to adopt digital technologies. JMIR Aging 6(1):e44564. https://doi.org/10.2196/44564

Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Fürstenau D, Biessmann F, Wolf-Ostermann K (2021) Application scenarios for artificial intelligence in nursing care: rapid review. J Med Internet Res 23(11):e26522. https://doi.org/10.2196/26522

Seuwou P, Banissi E, Ubakanma G (2016) User acceptance of information technology: A critical review of technology acceptance models and the decision to invest in Information Security. In: Global Security, Safety and Sustainability-The Security Challenges of the Connected World: 11th International Conference, ICGS3 2017, London, UK, January 18-20, 2017, Proceedings 11:230-251. Springer International Publishing. https://doi.org/10.1007/978-3-319-51064-4_19

Shiau WL, Wang X, Zheng F (2023) What are the trend and core knowledge of information security? A citation and co-citation analysis. Inf Manag 60(3):103774. https://doi.org/10.1016/j.im.2023.103774

Sinha S, Verma A, Tiwari P (2021) Technology: Saving and enriching life during COVID-19. Front Psychol 12:647681. https://doi.org/10.3389/fpsyg.2021.647681

Soar J (2010) The potential of information and communication technologies to support ageing and independent living. Ann Telecommun 65:479–483. https://doi.org/10.1007/s12243-010-0167-1

Strotmann A, Zhao D (2012) Author name disambiguation: What difference does it make in author‐based citation analysis? J Am Soc Inf Sci Technol 63(9):1820–1833. https://doi.org/10.1002/asi.22695

Talukder MS, Sorwar G, Bao Y, Ahmed JU, Palash MAS (2020) Predicting antecedents of wearable healthcare technology acceptance by elderly: A combined SEM-Neural Network approach. Technol Forecast Soc Change 150:119793. https://doi.org/10.1016/j.techfore.2019.119793

Taskin Z, Al U (2019) Natural language processing applications in library and information science. Online Inf Rev 43(4):676–690. https://doi.org/10.1108/oir-07-2018-0217

Touqeer H, Zaman S, Amin R, Hussain M, Al-Turjman F, Bilal M (2021) Smart home security: challenges, issues and solutions at different IoT layers. J Supercomput 77(12):14053–14089. https://doi.org/10.1007/s11227-021-03825-1

United Nations Department of Economic and Social Affairs (2023) World population ageing 2023: Highlights. https://www.un.org/zh/193220

Valk CAL, Lu Y, Randriambelonoro M, Jessen J (2018) Designing for technology acceptance of wearable and mobile technologies for senior citizen users. In: 21st DMI: Academic Design Management Conference (ADMC 2018), Design Management Institute, pp 1361–1373. https://www.dmi.org/page/ADMC2018

Van Eck N, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84(2):523–538. https://doi.org/10.1007/s11192-009-0146-3

Vancea M, Solé-Casals J (2016) Population aging in the European Information Societies: towards a comprehensive research agenda in eHealth innovations for elderly. Aging Dis 7(4):526. https://doi.org/10.14336/AD.2015.1214

Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: Toward a unified view. MIS Q 27(3):425–478. https://doi.org/10.2307/30036540

Wagner N, Hassanein K, Head M (2010) Computer use by older adults: A multi-disciplinary review. Comput Human Behav 26(5):870–882. https://doi.org/10.1016/j.chb.2010.03.029

Wahlroos N, Narsakka N, Stolt M, Suhonen R (2023) Physical environment maintaining independence and self-management of older people in long-term care settings—An integrative literature review. J Aging Environ 37(3):295–313. https://doi.org/10.1080/26892618.2022.2092927

Wang CL, Chen XJ, Yu T, Liu YD, Jing YH (2024a) Education reform and change driven by digital technology: a bibliometric study from a global perspective. Humanit Soc Sci Commun 11(1):1–17. https://doi.org/10.1057/s41599-024-02717-y

Wang CL, Dai J, Zhu KK, Yu T, Gu XQ (2023a) Understanding the Continuance Intention of College Students Toward New E-learning Spaces Based on an Integrated Model of the TAM and TTF. Int J Hum-comput Int 1–14. https://doi.org/10.1080/10447318.2023.2291609

Wang CL, Wang HM, Li YY, Dai J, Gu XQ, Yu T (2024b) Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy. Int J Hum-comput Int 1–23. https://doi.org/10.1080/10447318.2024.2383033

Wang J, Zhao W, Zhang Z, Liu X, Xie T, Wang L, Zhang Y (2024c) A journey of challenges and victories: a bibliometric worldview of nanomedicine since the 21st century. Adv Mater 36(15):2308915. https://doi.org/10.1002/adma.202308915

Wang J, Chen Y, Huo S, Mai L, Jia F (2023b) Research hotspots and trends of social robot interaction design: A bibliometric analysis. Sensors 23(23):9369. https://doi.org/10.3390/s23239369

Wang KH, Chen G, Chen HG (2017) A model of technology adoption by older adults. Soc Behav Personal 45(4):563–572. https://doi.org/10.2224/sbp.5778

Wang S, Bolling K, Mao W, Reichstadt J, Jeste D, Kim HC, Nebeker C (2019) Technology to Support Aging in Place: Older Adults’ Perspectives. Healthcare 7(2):60. https://doi.org/10.3390/healthcare7020060

Wang Z, Liu D, Sun Y, Pang X, Sun P, Lin F, Ren K (2022) A survey on IoT-enabled home automation systems: Attacks and defenses. IEEE Commun Surv Tutor 24(4):2292–2328. https://doi.org/10.1109/COMST.2022.3201557

Wilkowska W, Offermann J, Spinsante S, Poli A, Ziefle M (2022) Analyzing technology acceptance and perception of privacy in ambient assisted living for using sensor-based technologies. PloS One 17(7):e0269642. https://doi.org/10.1371/journal.pone.0269642

Wilson J, Heinsch M, Betts D, Booth D, Kay-Lambkin F (2021) Barriers and facilitators to the use of e-health by older adults: a scoping review. BMC Public Health 21:1–12. https://doi.org/10.1186/s12889-021-11623-w

Xia YQ, Deng YL, Tao XY, Zhang SN, Wang CL (2024) Digital art exhibitions and psychological well-being in Chinese Generation Z: An analysis based on the S-O-R framework. Humanit Soc Sci Commun 11:266. https://doi.org/10.1057/s41599-024-02718-x

Xie H, Zhang Y, Duan K (2020) Evolutionary overview of urban expansion based on bibliometric analysis in Web of Science from 1990 to 2019. Habitat Int 95:102100. https://doi.org/10.1016/j.habitatint.2019.10210

Xu Z, Ge Z, Wang X, Skare M (2021) Bibliometric analysis of technology adoption literature published from 1997 to 2020. Technol Forecast Soc Change 170:120896. https://doi.org/10.1016/j.techfore.2021.120896

Yap YY, Tan SH, Choon SW (2022) Elderly’s intention to use technologies: a systematic literature review. Heliyon 8(1). https://doi.org/10.1016/j.heliyon.2022.e08765

Yu T, Dai J, Wang CL (2023) Adoption of blended learning: Chinese university students’ perspectives. Humanit Soc Sci Commun 10:390. https://doi.org/10.1057/s41599-023-01904-7

Yusif S, Soar J, Hafeez-Baig A (2016) Older people, assistive technologies, and the barriers to adoption: A systematic review. Int J Med Inform 94:112–116. https://doi.org/10.1016/j.ijmedinf.2016.07.004

Zhang J, Zhu L (2022) Citation recommendation using semantic representation of cited papers’ relations and content. Expert Syst Appl 187:115826. https://doi.org/10.1016/j.eswa.2021.115826

Zhao Y, Li J (2024) Opportunities and challenges of integrating artificial intelligence in China’s elderly care services. Sci Rep 14(1):9254. https://doi.org/10.1038/s41598-024-60067-w

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This research was supported by the Social Science Foundation of Shaanxi Province in China (Grant No. 2023J014).

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Xianru Shang, Zijian Liu, Chen Gong, Zhigang Hu & Yuexuan Wu

Department of Education Information Technology, Faculty of Education, East China Normal University, Shanghai, China

Chengliang Wang

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Shang, X., Liu, Z., Gong, C. et al. Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023. Humanit Soc Sci Commun 11 , 1115 (2024). https://doi.org/10.1057/s41599-024-03658-2

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What’s driving five-year movement itch in regional Australia?

Key points:

• Regional cities with higher numbers of graduates in the workforce and increased lifestyle activity grew faster
• Proximity to the coast, major metropolitan areas, and the availability of air services, positively influenced growth
• Cheaper housing was a common attraction across all three local government areas (LGAs)
• Respondents highlighted 'lifestyle', 'personal reasons', and 'employment' as the top factors influencing their decision to relocate
• Respondents however were more likely to move again within five years due to gaps in health and education services, unaffordable housing or lack of rental availability, crime rates, and climate
• Survey respondents indicated a preference to move to other regional cities (46 percent) over capital cities (30 percent), countering the broader urbanisation trend in Australia.

The nations obsession with regional Australia continues, finds research led by the Sydney School of Architecture, Design and Planning . Undertaken for the  Australian Housing and Urban Research Institute  (AHURI), The paper ‘Place-based drivers and effective management of population growth and change in regional Australia’ reveals the factors and trends influencing population change across regional Australia.

The study highlights the critical importance of aligning policy responses with population changes to effectively manage regional growth.

Despite the strong appeal of these regional areas, the potential for population turnover remains high. Survey data revealed that 44 percent of respondents in Broken Hill, 35 percent in Ballarat, and 30 percent in Port Macquarie Hastings are likely to move within the next five years.

Interviews revealed that inadequate secondary and tertiary education, limited health and disability services, crime rates, climate concerns, and rising housing costs were significant reasons for leaving these areas.

To foster population growth and retention, policies aimed at enhancing liveability are critical. These include improving housing affordability and availability, bolstering local health and education services, upgrading local transport, water and road infrastructure, and increasing funding for regional airports, and tertiary campuses.

"Both federal, state and local governments have a role to play in improving liveability and working with the community and industry to provide improved services that encourage thriving populations in regional centres," said Dr Werner.

Potential policy solutions include limiting short-term rental accommodations (STRA), increasing government support for social and affordable housing, and attracting trade workers to boost housing construction.

A significant challenge in keeping pace with population change is the complexity of addressing areas that involve multiple public and private sector entities beyond local government control such as health, housing, transport, and education, underscoring the need for cooperation between all levels of government, as well as industry.

Over the past five years, the appeal of more affordable housing and an enhanced lifestyle has been the major driver for people moving from metropolitan to regional areas while higher regional housing prices discourage that movement.

"While some people move to regional cities for housing and rental affordability, it can also prompt existing residents to leave if prices in those regions rise as a result. Housing solutions for key workers are as important in regional centres as they are in metropolitan regions," said Dr Werner.

These findings underscore the importance of place-based attributes in influencing growth, decline, and population turnover in regional cities. Supporting the development of local businesses, especially in sectors like tourism, hospitality, tertiary education, and renewable energy, could attract new residents, retain local youth, and diversify regional economies.

“Employment remains a crucial driver for migration to regional Australia, making policies that foster economic growth and local job creation vital for sustaining and expanding regional populations,” said Dr Werner.

The study was authored by Dr Caitlin Buckle, Dr Greta Werner , Professor Nicole Gurran , Dr Glen Searle , Associate Professor Somwrita Sarkar , Associate Professor Nick Osbaldiston and Durba Kundu.

Read the full report

Sally quinn.

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Essential workers face ever greater challenges, the push and pull affecting population growth, affordable rentals out of reach for low-income workers.

Chapter 8 Sampling

Sampling is the statistical process of selecting a subset (called a “sample”) of a population of interest for purposes of making observations and statistical inferences about that population. Social science research is generally about inferring patterns of behaviors within specific populations. We cannot study entire populations because of feasibility and cost constraints, and hence, we must select a representative sample from the population of interest for observation and analysis. It is extremely important to choose a sample that is truly representative of the population so that the inferences derived from the sample can be generalized back to the population of interest. Improper and biased sampling is the primary reason for often divergent and erroneous inferences reported in opinion polls and exit polls conducted by different polling groups such as CNN/Gallup Poll, ABC, and CBS, prior to every U.S. Presidential elections.

The Sampling Process

Figure 8.1. The sampling process

The sampling process comprises of several stage. The first stage is defining the target population. A population can be defined as all people or items ( unit of analysis ) with the characteristics that one wishes to study. The unit of analysis may be a person, group, organization, country, object, or any other entity that you wish to draw scientific inferences about. Sometimes the population is obvious. For example, if a manufacturer wants to determine whether finished goods manufactured at a production line meets certain quality requirements or must be scrapped and reworked, then the population consists of the entire set of finished goods manufactured at that production facility. At other times, the target population may be a little harder to understand. If you wish to identify the primary drivers of academic learning among high school students, then what is your target population: high school students, their teachers, school principals, or parents? The right answer in this case is high school students, because you are interested in their performance, not the performance of their teachers, parents, or schools. Likewise, if you wish to analyze the behavior of roulette wheels to identify biased wheels, your population of interest is not different observations from a single roulette wheel, but different roulette wheels (i.e., their behavior over an infinite set of wheels).

The second step in the sampling process is to choose a sampling frame . This is an accessible section of the target population (usually a list with contact information) from where a sample can be drawn. If your target population is professional employees at work, because you cannot access all professional employees around the world, a more realistic sampling frame will be employee lists of one or two local companies that are willing to participate in your study. If your target population is organizations, then the Fortune 500 list of firms or the Standard & Poor’s (S&P) list of firms registered with the New York Stock exchange may be acceptable sampling frames.

Note that sampling frames may not entirely be representative of the population at large, and if so, inferences derived by such a sample may not be generalizable to the population. For instance, if your target population is organizational employees at large (e.g., you wish to study employee self-esteem in this population) and your sampling frame is employees at automotive companies in the American Midwest, findings from such groups may not even be generalizable to the American workforce at large, let alone the global workplace. This is because the American auto industry has been under severe competitive pressures for the last 50 years and has seen numerous episodes of reorganization and downsizing, possibly resulting in low employee morale and self-esteem. Furthermore, the majority of the American workforce is employed in service industries or in small businesses, and not in automotive industry. Hence, a sample of American auto industry employees is not particularly representative of the American workforce. Likewise, the Fortune 500 list includes the 500 largest American enterprises, which is not representative of all American firms in general, most of which are medium and small-sized firms rather than large firms, and is therefore, a biased sampling frame. In contrast, the S&P list will allow you to select large, medium, and/or small companies, depending on whether you use the S&P large-cap, mid-cap, or small-cap lists, but includes publicly traded firms (and not private firms) and hence still biased. Also note that the population from which a sample is drawn may not necessarily be the same as the population about which we actually want information. For example, if a researcher wants to the success rate of a new “quit smoking” program, then the target population is the universe of smokers who had access to this program, which may be an unknown population. Hence, the researcher may sample patients arriving at a local medical facility for smoking cessation treatment, some of whom may not have had exposure to this particular “quit smoking” program, in which case, the sampling frame does not correspond to the population of interest.

The last step in sampling is choosing a sample from the sampling frame using a well-defined sampling technique. Sampling techniques can be grouped into two broad categories: probability (random) sampling and non-probability sampling. Probability sampling is ideal if generalizability of results is important for your study, but there may be unique circumstances where non-probability sampling can also be justified. These techniques are discussed in the next two sections.

Probability Sampling

Probability sampling is a technique in which every unit in the population has a chance (non-zero probability) of being selected in the sample, and this chance can be accurately determined. Sample statistics thus produced, such as sample mean or standard deviation, are unbiased estimates of population parameters, as long as the sampled units are weighted according to their probability of selection. All probability sampling have two attributes in common: (1) every unit in the population has a known non-zero probability of being sampled, and (2) the sampling procedure involves random selection at some point. The different types of probability sampling techniques include:

Simple random sampling. In this technique, all possible subsets of a population (more accurately, of a sampling frame) are given an equal probability of being selected. The probability of selecting any set of n units out of a total of N units in a sampling frame is N C n . Hence, sample statistics are unbiased estimates of population parameters, without any weighting. Simple random sampling involves randomly selecting respondents from a sampling frame, but with large sampling frames, usually a table of random numbers or a computerized random number generator is used. For instance, if you wish to select 200 firms to survey from a list of 1000 firms, if this list is entered into a spreadsheet like Excel, you can use Excel’s RAND() function to generate random numbers for each of the 1000 clients on that list. Next, you sort the list in increasing order of their corresponding random number, and select the first 200 clients on that sorted list. This is the simplest of all probability sampling techniques; however, the simplicity is also the strength of this technique. Because the sampling frame is not subdivided or partitioned, the sample is unbiased and the inferences are most generalizable amongst all probability sampling techniques.

Systematic sampling. In this technique, the sampling frame is ordered according to some criteria and elements are selected at regular intervals through that ordered list. Systematic sampling involves a random start and then proceeds with the selection of every k th element from that point onwards, where k = N / n , where k is the ratio of sampling frame size N and the desired sample size n , and is formally called the sampling ratio . It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first k elements on the list. In our previous example of selecting 200 firms from a list of 1000 firms, you can sort the 1000 firms in increasing (or decreasing) order of their size (i.e., employee count or annual revenues), randomly select one of the first five firms on the sorted list, and then select every fifth firm on the list. This process will ensure that there is no overrepresentation of large or small firms in your sample, but rather that firms of all sizes are generally uniformly represented, as it is in your sampling frame. In other words, the sample is representative of the population, at least on the basis of the sorting criterion.

Stratified sampling. In stratified sampling, the sampling frame is divided into homogeneous and non-overlapping subgroups (called “strata”), and a simple random sample is drawn within each subgroup. In the previous example of selecting 200 firms from a list of 1000 firms, you can start by categorizing the firms based on their size as large (more than 500 employees), medium (between 50 and 500 employees), and small (less than 50 employees). You can then randomly select 67 firms from each subgroup to make up your sample of 200 firms. However, since there are many more small firms in a sampling frame than large firms, having an equal number of small, medium, and large firms will make the sample less representative of the population (i.e., biased in favor of large firms that are fewer in number in the target population). This is called non-proportional stratified sampling because the proportion of sample within each subgroup does not reflect the proportions in the sampling frame (or the population of interest), and the smaller subgroup (large-sized firms) is over-sampled . An alternative technique will be to select subgroup samples in proportion to their size in the population. For instance, if there are 100 large firms, 300 mid-sized firms, and 600 small firms, you can sample 20 firms from the “large” group, 60 from the “medium” group and 120 from the “small” group. In this case, the proportional distribution of firms in the population is retained in the sample, and hence this technique is called proportional stratified sampling. Note that the non-proportional approach is particularly effective in representing small subgroups, such as large-sized firms, and is not necessarily less representative of the population compared to the proportional approach, as long as the findings of the non-proportional approach is weighted in accordance to a subgroup’s proportion in the overall population.

Cluster sampling. If you have a population dispersed over a wide geographic region, it may not be feasible to conduct a simple random sampling of the entire population. In such case, it may be reasonable to divide the population into “clusters” (usually along geographic boundaries), randomly sample a few clusters, and measure all units within that cluster. For instance, if you wish to sample city governments in the state of New York, rather than travel all over the state to interview key city officials (as you may have to do with a simple random sample), you can cluster these governments based on their counties, randomly select a set of three counties, and then interview officials from every official in those counties. However, depending on between- cluster differences, the variability of sample estimates in a cluster sample will generally be higher than that of a simple random sample, and hence the results are less generalizable to the population than those obtained from simple random samples.

Matched-pairs sampling. Sometimes, researchers may want to compare two subgroups within one population based on a specific criterion. For instance, why are some firms consistently more profitable than other firms? To conduct such a study, you would have to categorize a sampling frame of firms into “high profitable” firms and “low profitable firms” based on gross margins, earnings per share, or some other measure of profitability. You would then select a simple random sample of firms in one subgroup, and match each firm in this group with a firm in the second subgroup, based on its size, industry segment, and/or other matching criteria. Now, you have two matched samples of high-profitability and low-profitability firms that you can study in greater detail. Such matched-pairs sampling technique is often an ideal way of understanding bipolar differences between different subgroups within a given population.

Multi-stage sampling. The probability sampling techniques described previously are all examples of single-stage sampling techniques. Depending on your sampling needs, you may combine these single-stage techniques to conduct multi-stage sampling. For instance, you can stratify a list of businesses based on firm size, and then conduct systematic sampling within each stratum. This is a two-stage combination of stratified and systematic sampling. Likewise, you can start with a cluster of school districts in the state of New York, and within each cluster, select a simple random sample of schools; within each school, select a simple random sample of grade levels; and within each grade level, select a simple random sample of students for study. In this case, you have a four-stage sampling process consisting of cluster and simple random sampling.

Non-Probability Sampling

Nonprobability sampling is a sampling technique in which some units of the population have zero chance of selection or where the probability of selection cannot be accurately determined. Typically, units are selected based on certain non-random criteria, such as quota or convenience. Because selection is non-random, nonprobability sampling does not allow the estimation of sampling errors, and may be subjected to a sampling bias. Therefore, information from a sample cannot be generalized back to the population. Types of non-probability sampling techniques include:

Convenience sampling. Also called accidental or opportunity sampling, this is a technique in which a sample is drawn from that part of the population that is close to hand, readily available, or convenient. For instance, if you stand outside a shopping center and hand out questionnaire surveys to people or interview them as they walk in, the sample of respondents you will obtain will be a convenience sample. This is a non-probability sample because you are systematically excluding all people who shop at other shopping centers. The opinions that you would get from your chosen sample may reflect the unique characteristics of this shopping center such as the nature of its stores (e.g., high end-stores will attract a more affluent demographic), the demographic profile of its patrons, or its location (e.g., a shopping center close to a university will attract primarily university students with unique purchase habits), and therefore may not be representative of the opinions of the shopper population at large. Hence, the scientific generalizability of such observations will be very limited. Other examples of convenience sampling are sampling students registered in a certain class or sampling patients arriving at a certain medical clinic. This type of sampling is most useful for pilot testing, where the goal is instrument testing or measurement validation rather than obtaining generalizable inferences.

Quota sampling. In this technique, the population is segmented into mutually-exclusive subgroups (just as in stratified sampling), and then a non-random set of observations is chosen from each subgroup to meet a predefined quota. In proportional quota sampling , the proportion of respondents in each subgroup should match that of the population. For instance, if the American population consists of 70% Caucasians, 15% Hispanic-Americans, and 13% African-Americans, and you wish to understand their voting preferences in an sample of 98 people, you can stand outside a shopping center and ask people their voting preferences. But you will have to stop asking Hispanic-looking people when you have 15 responses from that subgroup (or African-Americans when you have 13 responses) even as you continue sampling other ethnic groups, so that the ethnic composition of your sample matches that of the general American population. Non-proportional quota sampling is less restrictive in that you don’t have to achieve a proportional representation, but perhaps meet a minimum size in each subgroup. In this case, you may decide to have 50 respondents from each of the three ethnic subgroups (Caucasians, Hispanic-Americans, and African- Americans), and stop when your quota for each subgroup is reached. Neither type of quota sampling will be representative of the American population, since depending on whether your study was conducted in a shopping center in New York or Kansas, your results may be entirely different. The non-proportional technique is even less representative of the population but may be useful in that it allows capturing the opinions of small and underrepresented groups through oversampling.

Expert sampling. This is a technique where respondents are chosen in a non-random manner based on their expertise on the phenomenon being studied. For instance, in order to understand the impacts of a new governmental policy such as the Sarbanes-Oxley Act, you can sample an group of corporate accountants who are familiar with this act. The advantage of this approach is that since experts tend to be more familiar with the subject matter than non-experts, opinions from a sample of experts are more credible than a sample that includes both experts and non-experts, although the findings are still not generalizable to the overall population at large.

Snowball sampling. In snowball sampling, you start by identifying a few respondents that match the criteria for inclusion in your study, and then ask them to recommend others they know who also meet your selection criteria. For instance, if you wish to survey computer network administrators and you know of only one or two such people, you can start with them and ask them to recommend others who also do network administration. Although this method hardly leads to representative samples, it may sometimes be the only way to reach hard-to-reach populations or when no sampling frame is available.

Statistics of Sampling

In the preceding sections, we introduced terms such as population parameter, sample statistic, and sampling bias. In this section, we will try to understand what these terms mean and how they are related to each other.

When you measure a certain observation from a given unit, such as a person’s response to a Likert-scaled item, that observation is called a response (see Figure 8.2). In other words, a response is a measurement value provided by a sampled unit. Each respondent will give you different responses to different items in an instrument. Responses from different respondents to the same item or observation can be graphed into a frequency distribution based on their frequency of occurrences. For a large number of responses in a sample, this frequency distribution tends to resemble a bell-shaped curve called a normal distribution , which can be used to estimate overall characteristics of the entire sample, such as sample mean (average of all observations in a sample) or standard deviation (variability or spread of observations in a sample). These sample estimates are called sample statistics (a “statistic” is a value that is estimated from observed data). Populations also have means and standard deviations that could be obtained if we could sample the entire population. However, since the entire population can never be sampled, population characteristics are always unknown, and are called population parameters (and not “statistic” because they are not statistically estimated from data). Sample statistics may differ from population parameters if the sample is not perfectly representative of the population; the difference between the two is called sampling error . Theoretically, if we could gradually increase the sample size so that the sample approaches closer and closer to the population, then sampling error will decrease and a sample statistic will increasingly approximate the corresponding population parameter.

If a sample is truly representative of the population, then the estimated sample statistics should be identical to corresponding theoretical population parameters. How do we know if the sample statistics are at least reasonably close to the population parameters? Here, we need to understand the concept of a sampling distribution . Imagine that you took three different random samples from a given population, as shown in Figure 8.3, and for each sample, you derived sample statistics such as sample mean and standard deviation. If each random sample was truly representative of the population, then your three sample means from the three random samples will be identical (and equal to the population parameter), and the variability in sample means will be zero. But this is extremely unlikely, given that each random sample will likely constitute a different subset of the population, and hence, their means may be slightly different from each other. However, you can take these three sample means and plot a frequency histogram of sample means. If the number of such samples increases from three to 10 to 100, the frequency histogram becomes a sampling distribution. Hence, a sampling distribution is a frequency distribution of a sample statistic (like sample mean) from a set of samples , while the commonly referenced frequency distribution is the distribution of a response (observation) from a single sample . Just like a frequency distribution, the sampling distribution will also tend to have more sample statistics clustered around the mean (which presumably is an estimate of a population parameter), with fewer values scattered around the mean. With an infinitely large number of samples, this distribution will approach a normal distribution. The variability or spread of a sample statistic in a sampling distribution (i.e., the standard deviation of a sampling statistic) is called its standard error . In contrast, the term standard deviation is reserved for variability of an observed response from a single sample.

Figure 8.2. Sample Statistic.

The mean value of a sample statistic in a sampling distribution is presumed to be an estimate of the unknown population parameter. Based on the spread of this sampling distribution (i.e., based on standard error), it is also possible to estimate confidence intervals for that prediction population parameter. Confidence interval is the estimated probability that a population parameter lies within a specific interval of sample statistic values. All normal distributions tend to follow a 68-95-99 percent rule (see Figure 8.4), which says that over 68% of the cases in the distribution lie within one standard deviation of the mean value (µ + 1σ), over 95% of the cases in the distribution lie within two standard deviations of the mean (µ + 2σ), and over 99% of the cases in the distribution lie within three standard deviations of the mean value (µ + 3σ). Since a sampling distribution with an infinite number of samples will approach a normal distribution, the same 68-95-99 rule applies, and it can be said that:

• (Sample statistic + one standard error) represents a 68% confidence interval for the population parameter.
• (Sample statistic + two standard errors) represents a 95% confidence interval for the population parameter.
• (Sample statistic + three standard errors) represents a 99% confidence interval for the population parameter.

Figure 8.3. The sampling distribution.

A sample is “biased” (i.e., not representative of the population) if its sampling distribution cannot be estimated or if the sampling distribution violates the 68-95-99 percent rule. As an aside, note that in most regression analysis where we examine the significance of regression coefficients with p<0.05, we are attempting to see if the sampling statistic (regression coefficient) predicts the corresponding population parameter (true effect size) with a 95% confidence interval. Interestingly, the “six sigma” standard attempts to identify manufacturing defects outside the 99% confidence interval or six standard deviations (standard deviation is represented using the Greek letter sigma), representing significance testing at p<0.01.

Figure 8.4. The 68-95-99 percent rule for confidence interval.

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Yoga: Effectiveness and Safety

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Yoga is an ancient and complex practice, rooted in Indian philosophy. It began as a spiritual practice but has become popular as a way of promoting physical and mental well-being.

Although classical yoga also includes other elements, yoga as practiced in the United States typically emphasizes physical postures (asanas), breathing techniques (pranayama), and meditation (dyana). 

There are many different yoga styles, ranging from gentle practices to physically demanding ones. Differences in the types of yoga used in research studies may affect study results. This makes it challenging to evaluate research on the health effects of yoga.

Yoga and two practices of Chinese origin— tai chi and qigong —are sometimes called “meditative movement” practices. All three practices include both meditative elements and physical ones.

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Research suggests that yoga may:

• Help improve general wellness by relieving stress, supporting good health habits, and improving mental/emotional health, sleep, and balance.
• Relieve neck pain, migraine or tension-type headaches, and pain associated with knee osteoarthritis. It may also have a small benefit for low-back pain.
• Help people with overweight or obesity lose weight.
• Help people quit smoking.
• Help people manage anxiety symptoms or depression.
• Relieve menopause symptoms.
• Be a helpful addition to treatment programs for substance use disorders.
• Help people with chronic diseases manage their symptoms and improve their quality of life.

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Studies have suggested possible benefits of yoga for several aspects of wellness, including stress management, mental/emotional health, promoting healthy eating/activity habits, sleep, and balance. 

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• A 2020 review of 12 recent studies (672 total participants) of a variety of types of yoga for stress management in healthy adults found beneficial effects of yoga on measures of perceived stress in all the studies.
• Of 17 older studies (1,070 total participants) of yoga for stress management included in a 2014 review, 12 showed improvements in physical or psychological measures related to stress.
• Mental/emotional health. In a 2018 review of 14 studies (involving 1,084 total participants) that assessed the effects of yoga on positive aspects of mental health, most found evidence of benefits, such as improvements in resilience or general mental well-being.
• In a 2021 study in which 60 women with obesity were randomly assigned to 12 yoga sessions or a waiting list, the beneficial effect of yoga on body mass index (BMI, an estimate of body fat based on height and weight) was found to depend on changes in physical activity and daily fruit and vegetable intake. 
• A 2018 survey of young adults (involving 1,820 participants) showed that practicing yoga regularly was associated with better eating and physical activity habits. In interviews, survey respondents said they thought yoga encouraged greater mindfulness and motivated them to participate in other forms of activity and to eat healthier. In addition, they saw the yoga community as a social circle that encourages connection, where healthy eating is commonplace.
• In questionnaires and interviews, participants in a 2022 British study of a yoga intervention for people who were at risk for certain health conditions said that they had made changes in their lifestyles in response to the yoga program. They reported reducing consumption of unhealthy foods, increasing fruit and vegetable intake, and increasing their overall levels of physical activity.
• Sleep. Yoga has been shown to be helpful for sleep in multiple studies of cancer patients, women with sleep problems, and older adults. Individual studies of population groups including health care workers, people with arthritis, and women with menopause symptoms have also reported improved sleep from yoga. 
• Balance. In a 2014 review, 11 of 15 studies (688 total participants) that looked at the effect of yoga on balance in healthy people showed improvements in at least one outcome related to balance.   Several newer studies have provided additional evidence supporting a beneficial effect of yoga on balance in community-dwelling older adults.

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Research has been done on yoga for several conditions that involve pain, including low-back pain, neck pain, headaches, and knee osteoarthritis. For low-back pain, a large amount of research has been done, and the evidence suggests a slight benefit. For the other conditions, the evidence looks promising, but the amount of research is relatively small.

• A 2022 review of 21 studies (2,223 total participants) of yoga interventions for low-back pain found that yoga is slightly better than no exercise, but the small difference may not be important to patients. There was evidence that participating in yoga was associated with slight improvements in physical function (ability to be active) and mental quality of life (emotional problems) in people with low-back pain. It was unclear whether there was any difference between the effects of yoga and those of other types of exercise.
• A 2020 report by the Agency for Healthcare Research and Quality evaluated 10 studies of yoga for low-back pain (involving 1,520 total participants) and found that yoga improved pain and function in both the short term (1 to 6 months) and intermediate term (6 to 12 months). The effects of yoga were similar to those of exercise and massage.
• A clinical practice guideline issued by the American College of Physicians in 2017 recommends using nondrug methods for the initial treatment of chronic low-back pain. Yoga is one of several suggested nondrug approaches. 
• Neck pain. A 2019 review of 10 studies (686 total participants) found that practicing yoga reduced the intensity of neck pain, decreased disability related to neck pain, and improved range of motion in the neck.
• A 2020 review of 6 studies (240 participants) of yoga for chronic or episodic headaches (tension headaches or migraines) found evidence of reductions in headache frequency, headache duration, and pain intensity, with effects seen mostly in people with tension headaches. Because of the small numbers of studies and participants, as well as limitations in the quality of the studies, these results should be considered preliminary.
• A 2022 review of 6 studies (445 participants) of yoga for migraine suggested that yoga was associated with decreases in pain intensity, headache frequency, and headache duration, and reduced the impact of migraine on daily life. However, most of the studies included small numbers of people, and the types of yoga therapy varied among studies, so the results are not conclusive. Also, most of the studies were done in Asia, and their findings might not apply to other populations.
• A 2019 review of 9 studies (640 total participants) showed that yoga may be helpful for improving pain, function, and stiffness in people with osteoarthritis of the knee. However, the number of studies was small, and the research was not of high quality.
• A 2019 guideline from the American College of Rheumatology and the Arthritis Foundation conditionally recommends yoga for people with knee osteoarthritis based on similarities to tai chi, which has been better studied and is strongly recommended by the same guideline.

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There’s evidence that yoga may help people lose weight.

• A 2022 review of 22 studies (1,178 participants) of yoga interventions for people with overweight or obesity showed reductions in body weight, BMI, body fat, and waist size.
• Longer and more frequent yoga sessions (at least 75 to 90 minutes, at least 3 times per week).
• A longer duration of the overall program (3 months or more).
• A yoga-based dietary component.
• A residential component (such as a full weekend to start the program).
• A larger number of elements of yoga.
• Home practice.

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There’s evidence that yoga may help people stop smoking. 

• A 2019 NCCIH-funded study with 227 participants compared yoga classes with general wellness classes as additions to a conventional once-weekly counseling program. The people in the yoga group were 37 percent more likely to have quit smoking by the end of the 8-week program. However, 6 months after treatment, there was no difference between the groups in the proportion of people who were still not smoking.
• A study published in 2020 showed a reduction in cigarette cravings after a single yoga session, as compared with a wellness education session. The study participants were people who were trying to cut back or stop smoking.

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Yoga can be a helpful addition to treatment for depression. It may also be helpful for anxiety symptoms in a variety of populations, but there’s little evidence of a benefit for people with anxiety disorders. Yoga might have benefits for people with post-traumatic stress disorder (PTSD).

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• In a 2017 review of 23 studies (involving 1,272 participants) of people with depressive symptoms (although not necessarily diagnosed with depression), yoga was helpful in reducing symptoms in 14 of the studies.
• A 2020 review of 7 studies (260 participants) of yoga interventions for people who had been diagnosed with major depressive disorder concluded that yoga may have small additional benefits for depression symptoms when used along with other forms of treatment.

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• A 2019 review of 38 studies (2,295 participants) of yoga for anxiety symptoms found that yoga had a substantial beneficial effect, with the greatest effects seen in studies performed in India. The studies included a variety of different groups of people, including healthy people such as students and military personnel, patients with various physical or mental health conditions, and caregivers.
• A 2021 review looked at the evidence on yoga for people who have been diagnosed with anxiety disorders. The reviewers identified some promising results, but they were unable to reach conclusions about whether yoga is helpful because not enough rigorous studies have been done.
• A 2021 study of Kundalini yoga for generalized anxiety disorder (226 participants, 155 of whom completed the study), supported by NCCIH, found that Kundalini yoga improved symptoms but was less helpful than cognitive behavioral therapy, an established first-line treatment for this condition.

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• A 2018 evaluation of 7 studies (284 participants) of yoga for people with post-traumatic stress disorder (PTSD) found only low-quality evidence of a possible benefit. 

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Yoga seems to be at least as effective as other types of exercise in relieving menopause symptoms. A 2018 evaluation of 13 studies (more than 1,300 participants) of yoga for menopause symptoms found that yoga reduced physical symptoms such as hot flashes as well as psychological symptoms such as anxiety or depression.

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A small amount of research has looked at the possible benefits of incorporating yoga into treatment programs for various types of substance use disorders (opioid, alcohol, or tobacco use disorders or others). In a 2021 review of 8 studies (1,889 participants), 7 studies showed evidence of beneficial effects in terms of reduced use of the substance or reduction in symptoms such as pain, stress, or anxiety.

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There’s promising evidence that yoga may help people with some chronic diseases manage their symptoms and improve their quality of life. Thus, yoga could be a helpful addition to treatment programs. 

• In a 2018 evaluation of 138 studies on the use of yoga in patients with various types of cancer (10,660 total participants), most of the studies found that yoga improved patients’ physical and psychological symptoms and quality of life. 
• A 2021 review looked at 26 studies of yoga for depressive symptoms (1,486 participants) and 16 studies of yoga for anxiety symptoms (977 participants) in people with cancer. Small-to-moderate beneficial effects were seen for both types of symptoms. 
• Many yoga studies have focused on women who have or have had breast cancer. A 2022 review examined 23 studies that looked at the effects of yoga interventions on various symptoms in women with breast cancer during active cancer treatment. The majority of the studies showed significant benefits of yoga on quality of life, fatigue, nausea/vomiting, sleep quality, anxiety, depression, stress, or wound healing, suggesting that yoga may be helpful for symptom management.
• A review of 8 studies (92 participants) suggested that yoga may have benefits for sleep, anxiety, fatigue, and quality of life in children and adolescents with cancer.
• Chronic obstructive pulmonary disease (COPD). A 2019 review of 11 studies (586 participants) of breathing-focused yoga interventions for people with Parkinson’s disease found beneficial effects of these interventions on exercise capacity, lung function, and quality of life.
• HIV/AIDS. A 2019 review of 7 studies (396 participants) of yoga interventions for people with HIV/AIDS found that yoga was a promising intervention for stress management.
• A 2016 review of 15 studies of yoga for asthma (involving 1,048 total participants, most of whom were adults) concluded that yoga probably leads to small improvements in quality of life and symptoms.
• A 2020 review of 9 studies (1,230 participants) of yoga-based interventions for children or adolescents with asthma found that the use of yoga was associated with improvements in lung function, stress/anxiety, and quality of life. However, because of wide variation in both the populations who were studied and the yoga interventions that were tested, it was unclear which components of yoga and how much yoga are needed to provide benefits.
• Multiple sclerosis. Two recent reviews on yoga for people with multiple sclerosis showed little evidence of benefits. One review found a significant benefit only for fatigue (comparable to the effect of other types of exercise), and the other found no benefits for any aspect of quality of life. 
• Parkinson’s disease.   A 2022 review (14 studies, 444 participants) suggests that yoga may have benefits for mobility, balance, and quality of life for people with mild-to-moderate Parkinson’s disease. The studies that were reviewed also suggest that yoga interventions are safe and acceptable for people with this condition.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} What does research show about practicing yoga during pregnancy?

Physical activities such as yoga are safe and desirable for most pregnant women as long as appropriate precautions are taken. Yoga may have health benefits for pregnant women, such as decreasing stress, anxiety, and depression.

• If you are pregnant, you should be evaluated by your health care provider to make sure there’s no medical reason why you shouldn’t exercise.
• You may need to modify some activities, including yoga, during pregnancy. You should avoid “hot yoga” while you are pregnant because it can cause overheating. You also need to avoid activities, including yoga poses, that involve long periods of being still or lying on your back. Talk with your health care provider about how to adjust your physical activity during pregnancy.  
• A 2022 analysis of 29 studies of pregnancy yoga interventions (2,217 participants) found that these programs reduced anxiety, depression, perceived stress, and duration of labor and increased the likelihood of a normal vaginal birth. However, because the yoga programs varied widely and because some of the studies had weaknesses in their methods, additional rigorous research is needed to better understand the effects of yoga during pregnancy and to find out what types of yoga programs are best in terms of both effectiveness and safety.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} Does yoga have benefits for children?

Research suggests that yoga may have several potential benefits for children.

• A 2020 review of 27 studies (1,805 total participants) of yoga interventions in children or adolescents found reductions in anxiety or depression in 70 percent of the studies, with more promising results for anxiety. Some of the studies involved children who had or were at risk for various physical or mental health disorders and others involved groups of children in schools. The quality of the studies was relatively weak, and the results cannot be considered conclusive. 
• A 2021 review evaluated 9 studies (289 total participants) of yoga interventions for weight loss in children or adolescents with obesity or overweight. Some of the studies evaluated yoga alone; others evaluated yoga in combination with other interventions such as changes in diet. The majority of the yoga interventions had beneficial effects on weight loss and related behavior changes. The studies were small, and some did not use the most rigorous study designs.
• A 2022 review of 21 studies (2,227 participants) of school-based yoga interventions in students age 5 to 15 showed promising results suggesting that yoga may enhance mental health among children and adolescents.
• Yoga interventions in educational settings have also been studied in preschool-aged children (age 3 to 5). A 2021 review of studies of yoga and mindfulness practices in this age group suggested that these practices may have benefits for social-emotional functioning, although more research is needed before definite conclusions can be reached.
• A small amount of evidence suggests that school-based yoga programs may have academic and psychological benefits for neurodiverse children.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} What are the risks of yoga?

Yoga is generally considered a safe form of physical activity for healthy people when performed properly, under the guidance of a qualified instructor. However, as with other forms of physical activity, injuries can occur. The most common injuries are sprains and strains, and the parts of the body most commonly injured are the knee or lower leg. Serious injuries are rare. The risk of injury associated with yoga is lower than that for higher impact physical activities.

Older adults may need to be particularly cautious when practicing yoga. The rate of yoga-related injuries treated in emergency departments is higher in people age 65 and older than in younger adults.

To reduce your chances of getting hurt while doing yoga:

• Practice yoga under the guidance of a qualified instructor. Learning yoga on your own without supervision has been associated with increased risks.
• If you’re new to yoga, avoid extreme practices such as headstands, shoulder stands, the lotus position, and forceful breathing.
• Be aware that hot yoga has special risks related to overheating and dehydration.
• Pregnant women, older adults, and people with health conditions should talk with their health care providers and the yoga instructor about their individual needs. They may need to avoid or modify some yoga poses and practices. Some of the health conditions that may call for modifications in yoga include preexisting injuries, such as knee or hip injuries, lumbar spine disease, severe high blood pressure, balance issues, and glaucoma.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} How popular is yoga in the United States?

According to a national survey, the percentage of U.S. adults who practiced yoga increased from 5.0 percent in 2002 to 15.8 percent in 2022.

For children, there are data from 2017; in that year, 8.4 percent of U.S. children age 4 to 17 practiced yoga.

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National survey data from 2012 showed that 94 percent of adults who practiced yoga did it for wellness-related reasons, while 17.5 percent did it to treat a specific health condition. Some people reported doing both. 

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} Do different groups of people have different experiences with yoga?

Much of the research on yoga in the United States has been conducted in predominantly female, non-Hispanic White, well-educated people with relatively high incomes. Other people—particularly members of minority groups and those with lower incomes—have been underrepresented in yoga studies.

Different groups of people may have different yoga-related experiences, and the results of studies that did not examine a diverse population may not apply to everyone.

• Differences related to age. In one survey, people age 40 to 54 were more likely to be motivated to practice yoga to increase muscle strength or lose weight, while those age 55 or older were more likely to be motivated by age-related chronic health issues. People age 65 and older may be more likely to need treatment for yoga-related injuries.
• Differences related to gender. A study found evidence for differences between men and women in the effects of specific yoga poses on muscles. A study in veterans found preliminary evidence that women might benefit more than men from yoga interventions for chronic back pain.
• Differences related to Hispanic ethnicity. U.S. national survey data show lower participation in yoga among Hispanic adults, compared to non-Hispanic White adults (8.0 percent vs. 17.1 percent of adults in 2017). A small 2021 survey of U.S. Hispanic adults with low incomes showed that cost was the most common barrier to participation in yoga. Other perceived barriers included concern about the need for physical flexibility (especially among men and those with no prior experience with yoga), thinking that they would feel like outsiders in a yoga class (among those with no prior experience), and considering yoga boring (among young adults).

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NCCIH is sponsoring a variety of yoga studies, including:

• An evaluation of emotion regulation as a mechanism of action in yoga interventions for chronic low-back pain.
• A study of yoga for chronic pain in people who are being treated for opioid use disorder.
• A study of the effects of yoga postures and slow, deep breathing in people with hypertension (high blood pressure).

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• Don’t use yoga to postpone seeing a health care provider about a medical problem.
• Ask about the training and experience of the yoga instructor you’re considering.
• Take charge of your health—talk with your health care providers about any complementary health approaches you use. Together, you can make shared, well-informed  decisions.

For More Information

Nccih clearinghouse.

The NCCIH Clearinghouse provides information on NCCIH and complementary and integrative health approaches, including publications and searches of Federal databases of scientific and medical literature. The Clearinghouse does not provide medical advice, treatment recommendations, or referrals to practitioners.

Toll-free in the U.S.: 1-888-644-6226

Telecommunications relay service (TRS): 7-1-1

Website: https://www.nccih.nih.gov

Email: [email protected] (link sends email)

Know the Science

NCCIH and the National Institutes of Health (NIH) provide tools to help you understand the basics and terminology of scientific research so you can make well-informed decisions about your health. Know the Science features a variety of materials, including interactive modules, quizzes, and videos, as well as links to informative content from Federal resources designed to help consumers make sense of health information.

Explaining How Research Works (NIH)

Know the Science: How To Make Sense of a Scientific Journal Article

Understanding Clinical Studies (NIH)

A service of the National Library of Medicine, PubMed® contains publication information and (in most cases) brief summaries of articles from scientific and medical journals. For guidance from NCCIH on using PubMed, see How To Find Information About Complementary Health Approaches on PubMed .

Yoga for Health—Systematic Reviews/Reviews/Meta-analyses

Yoga for Health—Randomized Controlled Trials

Website: https://pubmed.ncbi.nlm.nih.gov/

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• American College of Obstetricians and Gynecologists. FAQs: Exercise During Pregnancy. Accessed at acog.org/womens-health/faqs/exercise-during-pregnancy on January 3, 2023.
• Anheyer D, Klose P, Lauche R, et al.  Yoga for treating headaches: a systematic review and meta-analysis. Journal of General Internal Medicine. 2020;35(3):846-854.
• Batrakoulis A. Psychophysiological adaptations to yoga practice in overweight and obese individuals: a topical review . Diseases. 2022;10(4):107.
• Black LI, Barnes PM, Clarke TC, Stussman BJ, Nahin RL.  Use of yoga, meditation, and chiropractors among U.S. children aged 4–17 years. NCHS Data Brief, no 324. Hyattsville, MD: National Center for Health Statistics. 2018.
• Bock BC, Dunsiger SI, Rosen RK, et al.  Yoga as a complementary therapy for smoking cessation: results from BreathEasy, a randomized clinical trial. Nicotine and Tobacco Research.  2019;21(11):1517-1523.
• Clarke TC, Barnes PM, Black LI, Stussman BJ, Nahin RL.  Use of yoga, meditation, and chiropractors among U.S. adults aged 18 and older. NCHS Data Brief, no 325. Hyattsville, MD: National Center for Health Statistics. 2018.
• Corrigan L, Moran P, McGrath N, et al. The characteristics and effectiveness of pregnancy yoga interventions: a systematic review and meta-analysis . BMC Pregnancy and Childbirth. 2022;22(1):250.
• Cramer H, Krucoff C, Dobos G.  Adverse events associated with yoga: a systematic review of published case reports and case series. PLoS One. 2013;8(10):e75515.
• Cramer H, Lauche R, Klose P, et al.  Yoga for improving health-related quality of life, mental health and cancer-related symptoms in women diagnosed with breast cancer. Cochrane Database of Systematic Reviews. 2017;(1):CD010802. Accessed at  cochranelibrary.com  on March 17, 2023.
• Cramer H, Ostermann T, Dobos G.  Injuries and other adverse events associated with yoga practice: a systematic review of epidemiological studies. Journal of Science and Medicine in Sport. 2018;21(2):147-154.
• Cramer H, Ward L, Saper R, et al.  The safety of yoga: a systematic review and meta-analysis of randomized controlled trials. American Journal of Epidemiology. 2015;182(4):281-293.
• Domingues RB.  Modern postural yoga as a mental health promoting tool: a systematic review. Complementary Therapies in Clinical Practice . 2018;31:248-255.
• Gonzalez M, Pascoe MC, Yang G, et al. Yoga for depression and anxiety symptoms in people with cancer: a systematic review and meta-analysis . Psychooncology. 2021;30(8):1196-1208.
• James-Palmer A, Anderson EZ, Zucker L, et al.  Yoga as an intervention for the reduction of symptoms of anxiety and depression in children and adolescents: a systematic review. Frontiers in Pediatrics. 2020;8:78.
• Khunti K, Boniface S, Norris E, et al. The effects of yoga on mental health in school-aged children: a systematic review and narrative synthesis of randomised control trials . Clinical Child Psychology and Psychiatry. 2023;28(3):1217-1238.
• Kolasinski SL, Neogi T, Hochberg MC, et al.  2019 American College of Rheumatology/Arthritis Foundation guideline for the management of osteoarthritis of the hand, hip, and knee. Arthritis and Rheumatology . 2020;72(2):220-233.
• Lauche R, Hunter DJ, Adams J, et al. Yoga for osteoarthritis: a systematic review and meta-analysis . Current Rheumatology Reports. 2019;21(9):47.
• Li Y, Li S, Jiang J, et al.  Effects of yoga on patients with chronic nonspecific neck pain: a PRISMA systematic review and meta-analysis. Medicine (Baltimore). 2019;98(8):e14649.
• Long C, Ye J, Chen M, et al. Effectiveness of yoga therapy for migraine treatment: a meta-analysis of randomized controlled studies . American Journal of Emergency Medicine. 2022;58:95-99.
• Qaseem A, Wilt TJ, McLean RM, et al.  Noninvasive treatments for acute, subacute, and chronic low back pain: a clinical practice guideline from the American College of Physicians. Annals of Internal Medicine. 2017;166(7):514-530.
• Rioux JG, Ritenbaugh C.  Narrative review of yoga intervention clinical trials including weight-related outcomes. Alternative Therapies in Health and Medicine. 2013;19(3):32-46.
• Selvan P, Hriso C, Mitchell J, et al. Systematic review of yoga for symptom management during conventional treatment of breast cancer patients . Complementary Therapies in Clinical Practice.  2022;48:101581.
• Seshadri A, Adaji A, Orth SS, et al. Exercise, yoga, and tai chi for treatment of major depressive disorder in outpatient settings: a systematic review and meta-analysis . Primary Care Companion for CNS Disorders. 2020;23(1):20r2722.
• Skelly AC, Chou R, Dettori JR, et al.  Noninvasive Nonpharmacological Treatment for Chronic Pain: A Systematic Review Update. Comparative Effectiveness Review no. 227. Rockville, MD: Agency for Healthcare Research and Quality; 2020. AHRQ publication no. 20-EHC009.
• Swain TA, McGwin G.  Yoga-related injuries in the United States from 2001 to 2014. Orthopaedic Journal of Sports Medicine. 2016;4(11):2325967116671703.
• Wang F, Szabo A.  Effects of yoga on stress among healthy adults: a systematic review. Alternative Therapies in Health and Medicine. 2020;26(4):AT6214.
• Wieland LS, Skoetz N, Pilkington K, et al. Yoga for chronic non-specific low back pain . Cochrane Database of Systematic Reviews. 2022;11(11):CD010671. Accessed at cochranelibrary.com on January 9, 2023.

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• Agarwal RP, Maroko-Afek A. Yoga into cancer care: a review of the evidence-based research.  International Journal of Yoga.  2018;11(1):3-29.
• Alphonsus KB, Su Y, D’Arcy C. The effect of exercise, yoga and physiotherapy on the quality of life in people with multiple sclerosis: systematic review and meta-analysis.  Complementary Therapies in Medicine.  2019;43:188-195.
• Anheyer D, Koch AK, Thoms MS, et al. Yoga in women with abdominal obesity—do lifestyle factors mediate the effect? Secondary analysis of a RCT.  Complementary Therapies in Medicine.  2021;60:102741.
• Bandealy SS, Sheth NC, Matuella SK, et al. Mind-body interventions for anxiety disorders: a review of the evidence base for mental health practitioners . Focus (American Psychiatric Publishing).  2021;19(2):173-183.
• Bolgla LA, Amodio L, Archer K, et al. Trunk and hip muscle activation during yoga poses: do sex-differences exist?  Complementary Therapies in Clinical Practice.  2018;31:256-261.
• Bridges L, Sharma M. The efficacy of yoga as a form of treatment for depression.  Journal of Evidence-Based Complementary and Alternative Medicine.  2017;22(4):1017-1028.
• Cheshire A, Richards R, Cartwright T. ‘Joining a group was inspiring’: a qualitative study of service users’ experiences of yoga on social prescription.  BMC Complementary Medicine and Therapies.  2022;22(1):67.
• Cocchiara RA, Peruzzo M, Mannocci A, et al. The use of yoga to manage stress and burnout in healthcare workers: a systematic review.  Journal of Clinical Medicine.  2019;8(3):284.
• Cramer H, Anheyer D, Saha FJ, et al. Yoga for posttraumatic stress disorder—a systematic review and meta-analysis.  BMC Psychiatry.  2018;18(1):72.
• Cramer H, Haller H, Klose P, et al. The risks and benefits of yoga for patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis.  Clinical Rehabilitation.  2019;33(12):1847-1862.
• Cramer H, Peng W, Lauche R. Yoga for menopausal symptoms—a systematic review and meta-analysis.  Maturitas.  2018;109:13-25.
• Cramer H, Quinker D, Schumann D, et al. Adverse effects of yoga: a national cross-sectional survey.  BMC Complementary and Alternative Medicine.  2019;19(1):190.
• Cramer H, Ward L, Steel A, et al. Prevalence, patterns, and predictors of yoga use: results of a U.S. nationally representative survey.  American Journal of Preventive Medicine.  2016;50(2):230-235.
• Dai C-L, Sharma M, Chen C-C, et al. Yoga as an alternative therapy for weight management in child and adolescent obesity: a systematic review and implications for research.  Alternative Therapies in Health and Medicine.  2021;27(1):48-55.
• Dunne EM, Balletto BL, Donahue ML, et al. The benefits of yoga for people living with HIV/AIDS: a systematic review and meta-analysis.  Complementary Therapies in Clinical Practice.  2019;34:157-164.
• Forseth B, Hunter SD. Range of yoga intensities from savasana to sweating: a systematic review.  Journal of Physical Activity and Health.  2020;17(2):242-249.
• Gothe NP, McAuley E. Yoga is as good as stretching-strengthening exercises in improving functional fitness outcomes: results from a randomized controlled trial.  Journals of Gerontology. Series A, Biological Sciences and Medical Sciences.  2016;71(3):406-411.
• Green E, Huynh A, Broussard L, et al. Systematic review of yoga and balance: effect on adults with neuromuscular impairment.  American Journal of Occupational Therapy.  2019;73(1):7301205150p1-7301205150p11.
• Groessl EJ, Weingart KR, Johnson N, et al. The benefits of yoga for women veterans with chronic low back pain.  Journal of Alternative and Complementary Medicine.  2012;18(9):832-838.
• Hart N, Fawkner S, Niven A, et al. Scoping review of yoga in schools: mental health and cognitive outcomes in both neurotypical and neurodiverse youth populations . Children (Basel).  2022;9(6):849.
• Jeffries ER, Zvolensky MJ, Buckner JD. The acute impact of hatha yoga on craving among smokers attempting to reduce or quit.  Nicotine & Tobacco Research.  2020;22(3):446-451.
• Jeter PE, Nkodo A-F, Moonaz SH, et al. A systematic review of yoga for balance in a healthy population.  Journal of Alternative and Complementary Medicine.  2014;20(4):221-232.
• Keosaian JE, Lemaster CM, Dresner D, et al. “We’re all in this together”: a qualitative study of predominantly low income minority participants in a yoga trial for chronic low back pain.  Complementary Therapies in Medicine.  2016;24:34-39.
• Klifto CS, Bookman JS, Kaplan DJ, et al. Musculoskeletal injuries in yoga.  Bulletin of the Hospital for Joint Diseases.  2018;76(3):192-197.
• Lack S, Brown R, Kinser PA. An integrative review of yoga and mindfulness-based approaches for children and adolescents with asthma.  Journal of Pediatric Nursing.  2020;52:76-81.
• Lin P-J, Peppone LJ, Janelsins MC, et al. Yoga for the management of cancer treatment-related toxicities.  Current Oncology Reports.  2018;20(1):5.
• Lipton L. Using yoga to treat disease: an evidence-based review.  Journal of the American Academy of Physician Assistants.  2008;21(2):34-36, 38, 41.
• Middleton KR, López MM, Moonaz SH, et al. A qualitative approach exploring the acceptability of yoga for minorities living with arthritis: “Where are the people who look like me?”  Complementary Therapies in Medicine.  2017;31:82-89.
• Mooventhan A, Nivethitha L. Evidence based effects of yoga practice on various health related problems of elderly people: a review.  Journal of Bodywork and Movement Therapies.  2017;21(4):1028-1032.
• Nahin RL, Rhee A, Stussman B. Use of complementary health approaches overall and for pain management by US adults. JAMA. 2024;331(7):613-615.
• Newton KM, Reed SD, Guthrie KA, et al. Efficacy of yoga for vasomotor symptoms: a randomized controlled trial.  Menopause.  2014;21(4):339-346.
• Physical activity and exercise during pregnancy and the postpartum period: ACOG committee opinion, Number 804.  Obstetrics and Gynecology.  2020;135(4):e178-e188.
• Sekendiz B. An epidemiological analysis of yoga-related injury presentations to emergency departments in Australia.  Physician and Sportsmedicine.  2020;48(3):349-353.
• Sharma M. Yoga as an alternative and complementary approach for stress management: a systematic review.  Journal of Evidence-Based Complementary and Alternative Medicine.  2014;19(1):59-67.
• Shohani M, Kazemi F, Rahmati S, et al. The effect of yoga on the quality of life and fatigue in patients with multiple sclerosis: a systematic review and meta-analysis of randomized clinical trials.  Complementary Therapies in Clinical Practice.  2020;39:101087.
• Sivaramakrishnan D, Fitzsimons C, Kelly P, et al. The effects of yoga compared to active and inactive controls on physical function and health related quality of life in older adults—systematic review and meta-analysis of randomised controlled trials.  International Journal of Behavioral Nutrition and Physical Activity . 2019;16(1):33. 
• Spadola CE, Rottapel R, Khandpur N, et al. Enhancing yoga participation: a qualitative investigation of barriers and facilitators to yoga among predominantly racial/ethnic minority, low-income adults.  Complementary Therapies in Clinical Practice.  2017;29:97-104.
• Stritter W, Everding J, Luchte J, et al. Yoga, meditation and mindfulness in pediatric oncology – a review of literature.  Complementary Therapies in Medicine.  2021;63:102791.
• Stussman BJ, Black LI, Barnes PM, Clarke TC, Nahin RL. Wellness-related use of common complementary health approaches among adults: United States, 2012. National health statistics reports; no 85. Hyattsville, MD: National Center for Health Statistics. 2015.
• Suárez-Iglesias D, Santos L, Sanchez-Lastra MA, et al. Systematic review and meta-analysis of randomised controlled trials on the effects of yoga in people with Parkinson’s disease.  Disability and Rehabilitation . 2022;44(21):6210-6229.
• Sun Y, Lamoreau R, O’Connell S, et al. Yoga and mindfulness interventions for preschool-aged children in educational settings: a systematic review.  International Journal of Environmental Research and Public Health.  2021;18(11):6091.
• Taibi DM, Vitiello MV. A pilot study of gentle yoga for sleep disturbance in women with osteoarthritis.  Sleep Medicine.  2011;12(5):512-517.
• Thind H, Garcia A, Velez M, et al. If we offer, will they come: perceptions of yoga among Hispanics.  Complementary Therapies in Medicine.  2021;56:102622.
• Walia N, Matas J, Turner A, et al. Yoga for substance use: a systematic review.  Journal of the American Board of Family Medicine.  2021;34(5):964-973.
• Wang W-L, Chen K-H, Pan Y-C, et al. The effect of yoga on sleep quality and insomnia in women with sleep problems: a systematic review and meta-analysis.  BMC Psychiatry.  2020;20(1):195.
• Watts AW, Rydell SA, Eisenberg ME, et al. Yoga’s potential for promoting healthy eating and physical activity behaviors among young adults: a mixed-methods study.  International Journal of Behavioral Nutrition and Physical Activity.  2018;15(1):42.
• Wertman A, Wister AV, Mitchell BA. On and off the mat: yoga experiences of middle-aged and older adults.  Canadian Journal on Aging.  2016;35(2):190-205.
• Yang Z-Y, Zhong H-B, Mao C, et al. Yoga for asthma.  Cochrane Database of Systematic Reviews . 2016;4(4):CD010346. Accessed at cochranelibrary.com on March 22, 2023.
• Zoogman S, Goldberg SB, Vousoura E, et al. Effect of yoga-based interventions for anxiety symptoms: a meta-analysis of randomized controlled trials.  Spirituality in Clinical Practice.  2019;6(4):256-278.

Acknowledgments

NCCIH thanks Inna Belfer, M.D., Ph.D., and David Shurtleff, Ph.D., NCCIH, for their review of the 2023 update of this publication.

This publication is not copyrighted and is in the public domain. Duplication is encouraged.

NCCIH has provided this material for your information. It is not intended to substitute for the medical expertise and advice of your health care provider(s). We encourage you to discuss any decisions about treatment or care with your health care provider. The mention of any product, service, or therapy is not an endorsement by NCCIH.

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