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Cross-Sectional Study | Definitions, Uses & Examples

Published on 5 May 2022 by Lauren Thomas .

A cross-sectional study is a type of research design in which you collect data from many different individuals at a single point in time. In cross-sectional research, you observe variables without influencing them.

Researchers in economics, psychology, medicine, epidemiology, and the other social sciences all make use of cross-sectional studies in their work. For example, epidemiologists who are interested in the current prevalence of a disease in a certain subset of the population might use a cross-sectional design to gather and analyse the relevant data.

Table of contents

Cross-sectional vs longitudinal studies, when to use a cross-sectional design, how to perform a cross-sectional study, advantages and disadvantages of cross-sectional studies, frequently asked questions about cross-sectional studies.

The opposite of a cross-sectional study is a longitudinal study . While cross-sectional studies collect data from many subjects at a single point in time, longitudinal studies collect data repeatedly from the same subjects over time, often focusing on a smaller group of individuals connected by a common trait.

Both types are useful for answering different kinds of research questions . A cross-sectional study is a cheap and easy way to gather initial data and identify correlations that can then be investigated further in a longitudinal study.

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When you want to examine the prevalence of some outcome at a certain moment in time, a cross-sectional study is the best choice.

Sometimes a cross-sectional study is the best choice for practical reasons – for instance, if you only have the time or money to collect cross-sectional data, or if the only data you can find to answer your research question were gathered at a single point in time.

As cross-sectional studies are cheaper and less time-consuming than many other types of study, they allow you to easily collect data that can be used as a basis for further research.

Descriptive vs analytical studies

Cross-sectional studies can be used for both analytical and descriptive purposes:

• An analytical study tries to answer how or why a certain outcome might occur.
• A descriptive study only summarises said outcome using descriptive statistics.

To implement a cross-sectional study, you can rely on data assembled by another source or collect your own. Governments often make cross-sectional datasets freely available online.

Prominent examples include the censuses of several countries like the US or France , which survey a cross-sectional snapshot of the country’s residents on important measures. International organisations like the World Health Organization or the World Bank also provide access to cross-sectional datasets on their websites.

However, these datasets are often aggregated to a regional level, which may prevent the investigation of certain research questions. You will also be restricted to whichever variables the original researchers decided to study.

If you want to choose the variables in your study and analyse your data on an individual level, you can collect your own data using research methods such as surveys . It’s important to carefully design your questions and choose your sample .

Like any research design , cross-sectional studies have various benefits and drawbacks.

• Because you only collect data at a single point in time, cross-sectional studies are relatively cheap and less time-consuming than other types of research.
• Cross-sectional studies allow you to collect data from a large pool of subjects and compare differences between groups.
• Cross-sectional studies capture a specific moment in time. National censuses, for instance, provide a snapshot of conditions in that country at that time.

Disadvantages

• It is difficult to establish cause-and-effect relationships using cross-sectional studies, since they only represent a one-time measurement of both the alleged cause and effect.
• Since cross-sectional studies only study a single moment in time, they cannot be used to analyse behavior over a period of time or establish long-term trends.
• The timing of the cross-sectional snapshot may be unrepresentative of behaviour of the group as a whole. For instance, imagine you are looking at the impact of psychotherapy on an illness like depression. If the depressed individuals in your sample began therapy shortly before the data collection, then it might appear that therapy causes depression even if it is effective in the long term.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

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Cross-Sectional Study: Definition, Designs & Examples

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A cross-sectional study design is a type of observational study, or descriptive research, that involves analyzing information about a population at a specific point in time.

This design measures the prevalence of an outcome of interest in a defined population. It provides a snapshot of the characteristics of the population at a single point in time.

It can be used to assess the prevalence of outcomes and exposures, determine relationships among variables, and generate hypotheses about causal connections between factors to be explored in experimental designs.

Typically, these studies are used to measure the prevalence of health outcomes and describe the characteristics of a population.

In this study, researchers examine a group of participants and depict what already exists in the population without manipulating any variables or interfering with the environment.

Cross-sectional studies aim to describe a variable , not measure it. They can be beneficial for describing a population or “taking a snapshot” of a group of individuals at a single moment in time.

In epidemiology and public health research, cross-sectional studies are used to assess exposure (cause) and disease (effect) and compare the rates of diseases and symptoms of an exposed group with an unexposed group.

Cross-sectional studies are also unique because researchers are able to look at numerous characteristics at once.

For example, a cross-sectional study could be used to investigate whether exposure to certain factors, such as overeating, might correlate to particular outcomes, such as obesity.

While this study cannot prove that overeating causes obesity, it can draw attention to a relationship that might be worth investigating.

Cross-sectional studies can be categorized based on the nature of the data collection and the type of data being sought.

Analytical Studies

In analytical cross-sectional studies, researchers investigate an association between two parameters. They collect data for exposures and outcomes at one specific time to measure an association between an exposure and a condition within a defined population.

The purpose of this type of study is to compare health outcome differences between exposed and unexposed individuals.

Descriptive Studies

• Descriptive cross-sectional studies are purely used to characterize and assess the prevalence and distribution of one or many health outcomes in a defined population.
• They can assess how frequently, widely, or severely a specific variable occurs throughout a specific demographic.
• This is the most common type of cross-sectional study.
• Evaluating the COVID-19 positivity rates among vaccinated and unvaccinated adolescents
• Investigating the prevalence of dysfunctional breathing in patients treated for asthma in primary care (Wang & Cheng, 2020)
• Analyzing whether individuals in a community have any history of mental illness and whether they have used therapy to help with their mental health
• Comparing grades of elementary school students whose parents come from different income levels
• Determining the association between gender and HIV status (Setia, 2016)
• Investigating suicide rates among individuals who have at least one parent with chronic depression
• Assessing the prevalence of HIV and risk behaviors in male sex workers (Shinde et al., 2009)
• Examining sleep quality and its demographic and psychological correlates among university students in Ethiopia (Lemma et al., 2012)
• Calculating what proportion of people served by a health clinic in a particular year have high cholesterol
• Analyzing college students’ distress levels with regard to their year level (Leahy et al., 2010)

Simple and Inexpensive

These studies are quick, cheap, and easy to conduct as they do not require any follow-up with subjects and can be done through self-report surveys.

Minimal room for error

Because all of the variables are analyzed at once, and data does not need to be collected multiple times, there will likely be fewer mistakes as a higher level of control is obtained.

Multiple variables and outcomes can be researched and compared at once

Researchers are able to look at numerous characteristics (ie, age, gender, ethnicity, and education level) in one study.

The data can be a starting point for future research

The information obtained from cross-sectional studies enables researchers to conduct further data analyses to explore any causal relationships in more depth.

Limitations

Does not help determine cause and effect.

Cross-sectional studies can be influenced by an antecedent consequent bias which occurs when it cannot be determined whether exposure preceded disease. (Alexander et al.)

Report bias is probable

Cross-sectional studies rely on surveys and questionnaires, which might not result in accurate reporting as there is no way to verify the information presented.

The timing of the snapshot is not always representative

Cross-sectional studies do not provide information from before or after the report was recorded and only offer a single snapshot of a point in time.

It cannot be used to analyze behavior over a period of time

Cross-sectional studies are designed to look at a variable at a particular moment, while longitudinal studies are more beneficial for analyzing relationships over extended periods.

Cross-Sectional vs. Longitudinal

Both cross-sectional and longitudinal studies are observational and do not require any interference or manipulation of the study environment.

However, cross-sectional studies differ from longitudinal studies in that cross-sectional studies look at a characteristic of a population at a specific point in time, while longitudinal studies involve studying a population over an extended period.

Longitudinal studies require more time and resources and can be less valid as participants might quit the study before the data has been fully collected.

Unlike cross-sectional studies, researchers can use longitudinal data to detect changes in a population and, over time, establish patterns among subjects.

Cross-sectional studies can be done much quicker than longitudinal studies and are a good starting point to establish any associations between variables, while longitudinal studies are more timely but are necessary for studying cause and effect.

Alexander, L. K., Lopez, B., Ricchetti-Masterson, K., & Yeatts, K. B. (n.d.). Cross-sectional Studies. Eric Notebook. Retrieved from https://sph.unc.edu/wp-content/uploads/sites/112/2015/07/nciph_ERIC8.pdf

Cherry, K. (2019, October 10). How Does the Cross-Sectional Research Method Work? Verywell Mind. Retrieved from https://www.verywellmind.com/what-is-a-cross-sectional-study-2794978

Cross-sectional vs. longitudinal studies. Institute for Work & Health. (2015, August). Retrieved from https://www.iwh.on.ca/what-researchers-mean-by/cross-sectional-vs-longitudinal-studies

Leahy, C. M., Peterson, R. F., Wilson, I. G., Newbury, J. W., Tonkin, A. L., & Turnbull, D. (2010). Distress levels and self-reported treatment rates for medicine, law, psychology and mechanical engineering tertiary students: cross-sectional study. The Australian and New Zealand journal of psychiatry, 44(7), 608–615.

Lemma, S., Gelaye, B., Berhane, Y. et al. Sleep quality and its psychological correlates among university students in Ethiopia: a cross-sectional study. BMC Psychiatry 12, 237 (2012).

Wang, X., & Cheng, Z. (2020). Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations. Chest, 158(1S), S65–S71.

Setia M. S. (2016). Methodology Series Module 3: Cross-sectional Studies. Indian journal of dermatology, 61 (3), 261–264.

Shinde S, Setia MS, Row-Kavi A, Anand V, Jerajani H. Male sex workers: Are we ignoring a risk group in Mumbai, India? Indian J Dermatol Venereol Leprol. 2009;75:41–6.

Further Information

• Setia, M. S. (2016). Methodology series module 3: Cross-sectional studies. Indian journal of dermatology, 61(3), 261.
• Sedgwick, P. (2014). Cross sectional studies: advantages and disadvantages. Bmj, 348.

1. Are cross-sectional studies qualitative or quantitative?

Cross-sectional studies can be either qualitative or quantitative , depending on the type of data they collect and how they analyze it. Often, the two approaches are combined in mixed-methods research to get a more comprehensive understanding of the research problem.

2. What’s the difference between cross-sectional and cohort studies?

A cohort study is a type of longitudinal study that samples a group of people with a common characteristic. One key difference is that cross-sectional studies measure a specific moment in time, whereas  cohort studies  follow individuals over extended periods.

Another difference between these two types of studies is the subject pool. In cross-sectional studies, researchers select a sample population and gather data to determine the prevalence of a problem.

Cohort studies, on the other hand, begin by selecting a population of individuals who are already at risk for a specific disease.

3. What’s the difference between cross-sectional and case-control studies?

Case-control studies differ from cross-sectional studies in that case-control studies compare groups retrospectively and cannot be used to calculate relative risk.

In these studies, researchers study one group of people who have developed a particular condition and compare them to a sample without the disease.

Case-control studies are used to determine what factors might be associated with the condition and help researchers form hypotheses about a population.

4. Does a cross-sectional study have a control group?

A cross-sectional study does not need to have a control group , as the population studied is not selected based on exposure.

In a cross-sectional study, data are collected from a sample of the target population at a specific point in time, and everyone in the sample is assessed in the same way. There isn’t a manipulation of variables or a control group as there would be in an experimental study design.

5. Is a cross-sectional study prospective or retrospective?

A cross-sectional study is generally considered neither prospective nor retrospective because it provides a “snapshot” of a population at a single point in time.

Cross-sectional studies are not designed to follow individuals forward in time ( prospective ) or look back at historical data ( retrospective ), as they analyze data from a specific point in time.

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Cross-Sectional Study in Research

Introduction

What is a cross-sectional study in research, what is the difference between cross-sectional and longitudinal research, cross-sectional study examples, types of cross-sectional studies, benefits of cross-sectional studies, challenges of cross-sectional studies.

Cross-sectional studies are a fundamental research method used across various fields to analyze data at a specific point in time. By comparing different subjects without considering the time variable, these studies can provide valuable insights into the prevalence and characteristics of phenomena within a population.

This article explores the concept of cross-sectional research, outlining its key features, applications, and how it differs from longitudinal studies. We will also examine examples of cross-sectional data, discuss the various types of cross-sectional studies, and highlight both the advantages and challenges associated with this research method. Understanding when and how to employ research methods for a cross-sectional study design is crucial for researchers aiming to draw accurate and meaningful conclusions from their data .

A cross-sectional study is a type of observational research design that analyzes data from a population, or a representative subset, at one specific point in time. Unlike longitudinal studies that observe the same subjects over a period of time to detect changes, cross-sectional studies focus on finding relationships and prevalences within a predefined snapshot. This method is particularly useful for understanding the current status of a phenomenon or to identify associations between variables without inferring causal relationships.

In practice, cross-sectional studies collect data across a wide range of subjects at a single moment, aiming to capture a comprehensive picture of a particular research question. Researchers might analyze various factors, including demographic information, behaviors, conditions, or outcomes, to discern patterns or correlations within the population studied.

Though these studies cannot determine cause and effect, they are invaluable for generating hypotheses or propositions, informing policy decisions, and guiding future research. Their descriptive nature and relative ease of execution make cross-sectional studies a common starting point in many research endeavors, providing a foundational understanding of the context and variables of interest.

The primary distinction between cross-sectional and longitudinal research lies in how and when the data is collected. Cross-sectional studies differ in that they capture data at a single point in time, offering a snapshot that helps to identify the prevalence and relationships between variables within a specific moment that further research might be able to explore. In contrast, a longitudinal study involves collecting data from the same subjects repeatedly over an extended period of time, enabling the observation of changes and developments in the variables of interest.

While cross-sectional studies are efficient for gathering data at one point in time and are less costly and time-consuming than longitudinal studies, they fall short in tracking changes over time or establishing cause-and-effect relationships. On the other hand, longitudinal studies excel in observing how variables evolve, providing insights into dynamics and causal pathways. However, longitudinal data collection requires more resources, time, and a rigorous design to manage participant attrition and ensure consistent data collection over the study period.

Another key difference is in the potential for cohort effects. A cross-sectional analysis might conflate age-related changes with generational effects because different age groups are compared at one particular point in time. Longitudinal research, by observing the same individuals over time, can differentiate between aging effects and cohort effects, offering a clearer view of how specific and multiple variables change throughout an individual's life or over time.

Cross-sectional studies are employed across various disciplines to investigate multiple phenomena at a specific point in time. These studies offer insights into the prevalence, distribution, and potential associations between variables within a defined population.

Below are three examples from different fields illustrating how cross-sectional research is applied to glean valuable findings.

Healthcare: Prevalence of a medical condition

In medical research, cross-sectional studies are frequently used to determine the prevalence of diseases or health outcomes in a population. For instance, a study might collect cross-sectional data from a diverse sample of individuals to assess the current prevalence of diabetes. By analyzing factors such as age, lifestyle, and comorbidities, researchers can identify patterns and risk factors associated with the disease, aiding in public health planning and intervention strategies.

Education: Analyzing student performance

Educational researchers often use a cross-sectional design to evaluate student performance across different grades or age groups at a single point in time. Such a study could compare test scores to analyze trends and disparities in educational achievement. By examining variables like socio-economic status, teaching methods, and school resources, educators and policymakers can identify areas needing improvement or intervention.

Economics: Employment trends analysis

In economics, a cross-sectional survey can provide snapshots of employment trends within a specific region or sector. An example might involve analyzing the employment rates, job types, and economic sectors in a country at a given time. This data can reveal insights into the economic health, workforce distribution, and potential areas for economic development or policy focus, informing stakeholders and guiding decision-making processes.

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Cross-sectional studies can be categorized into different types based on their objectives and methodologies . These variations allow researchers to adapt the cross-sectional approach to suit specific research questions and contexts.

By understanding the different types of cross-sectional studies, researchers can select the most appropriate design to obtain reliable and relevant data. Below are four common types of cross-sectional studies, each with its unique focus and application.

Descriptive cross-sectional studies

Descriptive cross-sectional studies aim to provide a detailed snapshot of a population or phenomenon at a particular point in time. These studies focus on 'what exists' or 'what is prevalent' without delving into relationships between variables or concepts.

For example, a descriptive research study might catalog various health behaviors within a specific demographic group to inform public health initiatives. The primary goal is to describe characteristics, frequencies, or distributions as they exist in the study population.

Analytical cross-sectional studies

Unlike descriptive studies that focus on prevalence and distribution, analytical cross-sectional studies aim to uncover potential associations between variables. These studies often compare different groups within the population to identify factors that may correlate with certain outcomes.

For instance, an analytical cross-sectional study might investigate the relationship between lifestyle choices and blood pressure levels across various age groups. While these studies can suggest associations, they do not establish cause and effect.

Exploratory cross-sectional studies

Exploratory cross-sectional studies are conducted to explore potential relationships or hypotheses when little is known about a subject. These studies are particularly useful in emerging fields or for new phenomena. By examining available data, they can generate hypotheses for further research without committing extensive resources to long-term studies.

An example might be exploring the usage patterns of a new technology within a population to identify trends and areas for in-depth study.

Explanatory cross-sectional studies

Explanatory cross-sectional studies go beyond identifying associations; they aim to explain why certain patterns or relationships are observed. These studies often incorporate theoretical frameworks or models to analyze the data within a broader context, providing deeper insights into the underlying mechanisms or factors.

For example, an explanatory cross-sectional study could investigate why certain educational strategies are associated with better student outcomes, integrating theories of learning and cognition.

Cross-sectional studies are a crucial tool in the repertoire of research methodologies , offering unique advantages that make them particularly suitable for various research contexts. These studies are instrumental in providing a snapshot of a specific point in time, which can be invaluable for understanding the status quo and informing future research directions. Below, we explore three significant benefits of employing cross-sectional studies in research endeavors.

Cost-effectiveness

One of the primary benefits of cross-sectional studies is their cost-effectiveness compared to longitudinal studies . Since they are conducted at a single point in time and do not require follow-ups, the financial resources, time, and logistical efforts needed are considerably lower. This efficiency makes cross-sectional studies an appealing option for researchers with limited budgets or those seeking preliminary data before committing to more extensive research.

Cross-sectional studies are inherently timely, providing quick snapshots that are especially valuable in fast-paced research areas where timely data is crucial. They allow researchers to collect and analyze data relatively quickly, offering insights that are current and relevant. This timeliness is particularly beneficial for informing immediate policy decisions or for studies in fields where trends may change rapidly, such as technology or public health.

Versatility

The versatility of cross-sectional studies is evident in their wide applicability across various fields and purposes. They can be designed to explore numerous variables and their interrelations within different populations and settings. This flexibility enables researchers to tailor studies to specific research questions, making cross-sectional studies a versatile tool for exploratory research, hypothesis generation , or situational analysis across disciplines.

Despite their utility in various fields of research, cross-sectional studies face distinct challenges that can affect the validity and applicability of their findings. Understanding these limitations is crucial for researchers to design robust studies and for readers to interpret results appropriately. Here are three key challenges commonly associated with cross-sectional studies.

Causality determination

One of the inherent limitations of cross-sectional studies is their inability to establish causality. Since data is collected at a single point in time, it is challenging to ascertain whether a relationship between two variables is causal or merely correlational. This limitation necessitates cautious interpretation of results, as establishing temporal precedence is essential for causal inference, which cross-sectional designs cannot provide.

Selection bias

Selection bias can occur in cross-sectional studies if the sample is not representative of the population from which it was drawn. This can happen due to non-random sampling methods or non-response, leading to skewed results that do not accurately reflect the broader population. Such bias can compromise the generalizability of the study's findings, making it critical to employ rigorous sampling methods and consider potential biases during analysis.

Cross-sectional confounding

Cross-sectional studies can also be susceptible to confounding, where an external variable influences both the independent and dependent variables , creating a spurious association. Without longitudinal data , it is difficult to control for or identify these confounding factors, which can lead to erroneous conclusions. Researchers must carefully consider potential confounders and employ statistical methods to adjust for these variables where possible.

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What is a Cross-Sectional Study? Definition, Advantages, Disadvantages, and Examples

Table of Contents

What is a cross-sectional study ?

So, “what is a cross-sectional study?” Here is a simple cross-sectional study definition: A cross-sectional study is an observational study design that examines data on various variables gathered at a single time point within a sample population or predefined subgroup, offering a depiction of the population’s characteristics.

It is a time-saving, cost-effective, and straightforward approach for gathering preliminary data, wherein a researcher collects data at a single point in time (there is no prospective or retrospective follow-up) and observes variables without influencing them. The prevalence of an outcome at a given point in time can be determined in this manner.

What is the purpose of a cross-sectional study?

The purpose of a cross-sectional study is basically to take a “slice” or a “snapshot” of a population. In the fields of epidemiology and public health research, cross-sectional studies are used to evaluate associations, e.g., between exposure and disease, and to compare disease and symptom rates between an exposed group and an unexposed group.

Another purpose of a cross-sectional study is to simultaneously describe multiple characteristics. For instance, it can be employed to explore whether factors like excessive screen time, social media use, and resulting social pressures are linked to specific outcomes such as anxiety.

While such studies cannot establish a causal link and do not quantify a variable, they can highlight a relationship that might be worth further investigation. One of the advantages of a cross-sectional study is that it plays a key role in developing hypotheses and in laying the foundation for a more comprehensive research project.

Characteristics of a cross-sectional study

Now, let’s delve into the key characteristics of a cross-sectional study:

• Cross-sectional studies examine a fixed set of variables within a specific timeframe. Researchers use the same measuring tools and data points throughout their investigation.
• Although different cross-sectional studies may focus on the same variable of interest, they do so by observing distinct groups of subjects; each study captures a fresh set of participants.
• Cross-sectional analyses focus on a single point in time, marked by a clear starting and stopping point.
• In a cross-sectional study, researchers can zero in on a single independent variable, while also accommodating one or more dependent variables in their examination.

These studies can map the prevailing variables that coexist at a specific point in time. For instance, cross-sectional data can reveal the buying preferences of a population at a given time and how they correlate with economic trends.

Cross-sectional study examples

From the following cross-sectional study examples, we see that these studies gather data from participants sharing similarity across most variables, except for the variable(s) under scrutiny.

Some fictional cross-sectional study examples across various fields are as follows:

Agriculture : Examining pesticide use and knowledge of smallholder farmers in a specific region.

Nutrition : Fruit and nut consumption in a region according to gender and educational level.

Psychology : Psychological impact of the COVID-19 pandemic on healthcare workers in a region.

Economics : Economic burden of unemployment during armed conflict in a particular region.

Psychology : Psychological status of male prisoners at a particular facility.

Healthcare and medicine : (i) Population-based surveys, e.g., the prevalence of twin births in a village, or (ii) prevalence in clinical studies, e.g., antibiotic resistance in Clostridium difficile isolates in a tertiary care hospital.

Cross-sectional studies equip scholars and policymakers with actionable data that can be acquired quickly, facilitating informed decision-making and the development of products or services.

T ypes of cross-sectional studies

The main types of cross-sectional studies are d

escriptive, analytical, and repeated/serial.

Descriptive cross-sectional studies: These characterize the prevalence of one or more outcomes in a particular population, e.g., examining the prevalence of Alzheimer’s disease in a target population.

Analytical cross-sectional studies: Data are obtained for both exposure and outcome at a specific point in time to compare the outcome differences between exposed and unexposed subjects. Such studies answer how or why a certain outcome might occur, e.g., looking at vascular disease, traumatic brain injury, and family history to explain why some adults are much more likely to get Alzheimer’s disease than others.

Repeated (or serial) cross-sectional studies: Data are obtained from the same target population at different time points. At each time point, researchers select a different sample (different subjects) from the same target population. Repeated cross-sectional studies can therefore examine changes in a population over time. An example of serial cross-sectional study could be one that investigates the prevalence and risk factors of Alzheimer’s disease in adults aged 50-80 years in a specific decade.

Advantages and disadvantages of cross-sectional studies

Let’s look at the pros and cons of cross-sectional studies.

Advantages of a cross-sectional study

• Relatively quick and inexpensive to conduct
• No potential ethical issues
• Multiple outcomes and exposures can be studied
• Helpful for generating hypotheses
• Many findings can be used to create an in-depth research study
• Data are obtained from a large pool of subjects, and differences between groups can be compared.

Disadvantages of a cross-sectional study

• Cannot measure incidence
• Deriving causal inferences is challenging as it is a one-time measurement of the apparent cause and effect
• Associations identified might be difficult to interpret
• Cannot determine temporal relations between outcomes and risk factors
• Not suitable for studying rare diseases or sporadic events
• Susceptible to biases
• Cannot be used to analyze trends over a period of time.

Limitations of cross-sectional studies

It is important to know the limitations of cross-sectional studies. Here are some important limitations:

• A cross-sectional study is a one-time measurement of exposure and outcome. Therefore, it does not determine cause-and-effect relationships.
• Such studies are prone to certain biases: report bias (because surveys and questionnaires might not result in accurate reporting) and sampling bias (owing to the need to select a sample of subjects from a large and heterogeneous study population).
• Researchers need to be extremely careful about interpreting the associations and direction of associations from cross-sectional studies.
• Cross-sectional surveys may not be sufficient to understand disease trends. In clinical studies, the prevalence of an outcome depends on disease incidence and length of survival following the outcome.
• One of the disadvantages of a cross-sectional study is that it does not provide information from before or after the data were obtained.
• Cross-sectional studies cannot be used to analyze behavior or trends over time.

Cross-sectional vs. longitudinal studies

It is critical to understand the key features of cross-sectional vs. longitudinal studies before you choose the study design to answer your research question. While both cross-sectional and longitudinal studies are observational, not requiring manipulation of the study environment, they differ in a number of ways (Table).

Table: Cross-sectional vs. longitudinal studies

Frequently asked questions

A cross-sectional study is a type of observational research design that involves collecting data from a group of participants at a single point in time to assess various characteristics or variables of interest.

The primary goal of a cross-sectional study is to describe the prevalence of a specific condition or characteristic within a defined population at a particular moment in time.

What are some advantages of cross-sectional studies?

Cross-sectional studies are relatively quick and cost-effective. They are useful for generating hypotheses and identifying potential research directions.

What are the limitations of a cross-sectional study?

Cross-sectional studies do not allow researchers to track changes over time, making them unsuitable for studying temporal relationships. They cannot establish cause-and-effect relationships.

Can cross-sectional studies be used to study rare conditions or events?

Cross-sectional studies are not the best choice for studying rare events because of the need for a sufficiently large sample size to obtain meaningful results.

What are some suitable cross-sectional study examples?

Some potential cross-sectional study examples could be determining (i) the prevalence of obesity in teenagers from high-income families; (ii) the prevalence of accelerated skin aging, and the association between skin wrinkles and sunscreen application in women; or (iii) the prevalence and risk factors and geographic of reduced visual acuity in secondary students in a specific decade.

Setia, M. S. Methodology Series Module 3: Cross-sectional studies. Indian J Dermatol . (2016) 61(3): 261–264. doi: 10.4103/0019-5154.182410

Wang, X., & Cheng, Z. Cross-sectional studies: strengths, weaknesses, and recommendations. Chest (2020) 158(1) Suppl , S65–S71. https://doi.org/10.1016/j.chest.2020.03.012

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Cross Sectional Study: Overview, Examples & Benefits

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What is a Cross Sectional Study?

A cross-sectional study is an experimental design that analyzes data from a representative sample at a specific point in time. Researchers usually evaluate multiple attributes at once when using this design. Unlike longitudinal studies , these studies don’t track changes over time.

Typically, researchers use this design to simply observe the prevalence of a condition or outcome at a specific moment and do not manipulate any variables or treatment conditions. This non-interference makes cross sectional studies a type of observational study . Think of it as taking a ‘photograph’ of a population at a particular time. Researchers collect data once, offering insights into various factors at that point.

Scientists use two broad types of cross-sectional studies:

• Descriptive : Summarizes the prevalence of a condition with descriptive statistics .
• Analytical : Evaluates relationships between variables to understand how outcomes occur.

While analysts can explore correlations between the variables in cross-sectional studies, these studies are not good at identifying causal relationships. Instead, they are relatively inexpensive and quick projects that can lay the groundwork for more in-depth longitudinal studies.

Imagine a study assessing the dietary habits of 5,000 people from different cities at one point in time. This study could reveal prevalent nutritional patterns and health indicators across these populations.

For example, researchers frequently use cross-sectional studies in the following fields:

• Public Health : Assessing health status or disease prevalence in a community.
• Sociology : Understanding current social conditions.
• Market Research : Gauging consumer preferences.
• Education : Evaluating recent educational outcomes.

Learn more about Experimental Designs: Definition and Types .

Duration of Cross-Sectional Studies

Cross-sectional studies are typically shorter in duration compared to longitudinal studies. They are conducted all at once, although the planning and analysis phases can span several months or a year.

Implementing a Cross-Sectional Study: Your Choices

When conducting a cross-sectional study, you face a critical decision: collect new data or use pre-existing datasets.

Option 1: Utilizing Existing Data

Many organizations, including governments and research institutes, frequently release data from cross-sectional studies. A classic example is the U.S. National Health and Nutrition Examination Survey (NHANES), which provides a snapshot of the nation’s health and nutritional status.

Such data is typically robust and can offer immediate insights. However, it’s less customizable than data you collect yourself. The data might be generalized to ensure privacy, limiting detailed analysis. Additionally, the original study’s variables restrict you, and you can’t modify data collection to meet your study’s needs.

If you choose pre-existing data, carefully evaluate the dataset’s source and the specifics of the data provided.

Option 2: Collecting Data Yourself

Opting to collect your own data gives you control over the variables and the nature of the information gathered. Here are some standard data collection methods for a cross-sectional study.

• Surveys and Questionnaires: Ideal for gathering a wide range of information quickly.
• Observational Methods: Useful for capturing data in natural settings.
• Interviews: Provide in-depth insights but can be time-consuming.

For all these methods, selecting a representative sample of your target population is crucial because it ensures the findings apply to the broader population and enhances the study’s overall validity and reliability .

Self-collected data can be tailored to your specific research question, offering depth and relevance. However, this approach requires carefully planning your sampling method to ensure representativeness and avoid biases.

In summary, whether you choose to use existing datasets or collect your own data, each approach has its own set of advantages and challenges. The key is aligning your choice with your research objectives and available resources.

Advantages of a Cross-Sectional Study

A cross-sectional study can be efficient regarding time and resources, making it ideal for initial explorations that might not be possible with longitudinal studies.

These studies can also gather a vast amount of data on various variables at once from a large sample, allowing you to compare subgroups, all of which are more costly in longitudinal studies.

For example, imagine a cross-sectional study surveying 10,000 people nationwide about their dietary habits. This study can simultaneously collect data on numerous variables like age, income, education, and health status.

With such a comprehensive dataset, researchers can assess dietary patterns across different subgroups, such as comparing eating habits between urban and rural residents or analyzing how dietary choices vary with income levels. This ability to gather and compare many variables at once is a crucial strength of this research design, providing a rich, multifaceted snapshot of the population.

Disadvantages of a Cross-Sectional Study

A cross-sectional study can identify correlations but not causal relationships . They record one-time measurements and can’t determine a sequence of events. In essence, they simultaneously measure possible causes and effects, making them hard to distinguish.

For example, a this type of research might find a correlation between high stress levels and poor sleep quality, but it can’t confirm if stress causes poor sleep or vice versa.

These studies provide only a snapshot and cannot track behaviors or trends over time. Consequently, they can’t establish how variables evolve or interact longitudinally.

For example, a survey conducted during an economic downturn might reflect unusually high levels of financial stress in the population, which may not be indicative of general long-term trends.

In conclusion, cross-sectional studies offer valuable insights into the status of a population at a specific point. They are handy for exploratory research and identifying potential areas for more in-depth study. Understanding their strengths and limitations is crucial for researchers to utilize this method effectively.

Wang, X., & Cheng, Z. (2020). Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations . Chest, 158(1), S65-S71. DOI:10.1016/j.chest.2020.03.012.

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Quantitative study designs: Cross-Sectional Studies

Quantitative study designs.

• Introduction
• Cohort Studies
• Randomised Controlled Trial
• Case Control
• Cross-Sectional Studies
• Study Designs Home

Cross-Sectional Study

The Australian Census run by the Australian Bureau of Statistics, is an example of a whole of population cross-sectional study.

Data on a number of aspects of the Australian population is gathered through completion of a survey within every Australian household on the same night. This provides a snapshot of the Australian population at that instance.

Cross-sectional studies look at a population at a single point in time, like taking a slice or cross-section of a group, and variables are recorded for each participant.

This may be a single snapshot for one point in time or may look at a situation at one point in time and then follow it up with another or multiple snapshots at later points; this is then termed a repeated cross-sectional data analysis. 

The stages of a Cross-Sectional study

Repeated Cross-Sectional Data Analysis

Which clinical questions does a Cross-Sectional study best answer?

Please note the Introduction , where there is a table under "Which study type will answer my clinical question?" .  You may find that there are only one or two question types that your study answers – that’s ok. 

Cross-sectional study designs are useful when:

• Answering questions about the incidence or prevalence of a condition, belief or situation.
• Establishing what the norm is for a specific demographic at a specific time. For example: what is the most common or normal age for students completing secondary education in Victoria?
• Justifying further research on a topic. Cross-sectional studies can infer a relationship or correlation but are not always sufficient to determine a direct cause. As a result, these studies often pave the way for other investigations.  

What are the advantages and disadvantages to consider when using a Cross-Sectional study design?

What does a strong Cross-Sectional study look like?

• Appropriate recruitment of participants. The sample of participants must be an accurate representation of the population being measured.
• Sample size. As is the case for most study types a larger sample size gives greater power and is more ideal for a strong study design. Within a cross-sectional study a sample size of at least 60 participants is recommended, although this will depend on suitability to the research question and the variables being measured.
• A suitable number of variables. Cross-sectional studies ideally measure at least three variables in order to develop a well-rounded understanding of the potential relationships of the two key conditions being measured.

What are the pitfalls to look for?

Cross-sectional studies are at risk of participation bias, or low response rates from participants. If a large number of surveys are sent out and only a quarter are completed and returned then this becomes an issue as those who responded may not be a true representation of the overall population.

Critical appraisal tools 

To assist with critically appraising cross-sectional studies there are some tools / checklists you can use.

• Axis Appraisal Tool for Cross Sectional Studies
• Critical Appraisal Tool for Cross- Sectional Studies (CAT-CSS)
• Critical appraisal tool for cross-sectional studies using biomarker data (BIOCROSS)
• CEBM Critical Appraisal of a Cross-Sectional Study (Survey)
• JBI Critical Appraisal checklist for analytical cross-sectional studies
• Specialist Unit for Review Evidence (SURE) 2018. Questions to assist with the critical appraisal of cross sectional studies
• STROBE Checklist for cross-sectional studies

Real World Examples

The Australian National Survey of Mental Health and Wellbeing (NSMHWB)

https://www.abs.gov.au/statistics/health/mental-health/national-survey-mental-health-and-wellbeing-summary-results/2007

A widely known example of cross-sectional study design, the Australian National Survey of Mental Health and Wellbeing (NSMHWB). This study was a national epidemiological survey of mental disorders investigating the questions: How many people meet DSM-IV and ICD-10 diagnostic criteria for the major mental disorders? How disabled are they by their mental disorders? And, how many have seen a health professional for their mental disorder?

References and Further Reading

Australian Government Department of Health. (2003). The Australian National Survey of Mental Health and Wellbeing (NSMHWB). 2019, from https://www.abs.gov.au/statistics/health/mental-health/national-survey-mental-health-and-wellbeing-summary-results/2007

Bowers, D. a., Bewick, B., House, A., & Owens, D. (2013). Understanding clinical papers (Third edition. ed.): Wiley Blackwell.

Gravetter, F. J. a., & Forzano, L.-A. B. (2012). Research methods for the behavioral sciences (Fourth edition. ed.): Wadsworth Cengage Learning.

Greenhalgh, T. a. (2014). How to read a paper : the basics of evidence-based medicine (Fifth edition. ed.): John Wiley & Sons Inc.

Hoffmann, T. a., Bennett, S. P., & Mar, C. D. (2017). Evidence-Based Practice Across the Health Professions (Third edition. ed.): Elsevier.

Howitt, D., & Cramer, D. (2008). Introduction to research methods in psychology (Second edition. ed.): Prentice Hall.

Kelly, P. J., Kyngdon, F., Ingram, I., Deane, F. P., Baker, A. L., & Osborne, B. A. (2018). The Client Satisfaction Questionnaire‐8: Psychometric properties in a cross‐sectional survey of people attending residential substance abuse treatment. Drug and Alcohol Review, 37(1), 79-86. doi: 10.1111/dar.12522

Lawrence, D., Hancock, K. J., & Kisely, S. (2013). The gap in life expectancy from preventable physical illness in psychiatric patients in Western Australia: retrospective analysis of population based registers. BMJ: British Medical Journal, 346(7909), 13-13.

Nasir, B. F., Toombs, M. R., Kondalsamy-Chennakesavan, S., Kisely, S., Gill, N. S., Black, E., Ranmuthugala, G., Ostini, R., Nicholson, G. C., Hayman, N., & Beccaria, G.. (2018). Common mental disorders among Indigenous people living in regional, remote and metropolitan Australia: A cross-sectional study. BMJ Open , 8 (6). https://doi.org/10.1136/bmjopen-2017-020196

Robson, C., & McCartan, K. (2016). Real world research (Fourth Edition. ed.): Wiley.

Sedgwick, P. (2014). Cross sectional studies: advantages and disadvantages. BMJ : British Medical Journal, 348, g2276. doi: 10.1136/bmj.g2276

Setia, M. S. (2016). Methodology Series Module 3: Cross-sectional Studies. Indian journal of dermatology, 61(3), 261-264. doi: 10.4103/0019-5154.182410

Shafiei, T., Biggs, L. J., Small, R., McLachlan, H. L., & Forster, D. A. (2018). Characteristics of women calling the panda perinatal anxiety & depression australia national helpline: A cross-sectional study. Archives of Women's Mental Health. doi: 10.1007/s00737-018-0868-4

Van Heyningen, T., Honikman, S., Myer, L., Onah, M. N., Field, S., & Tomlinson, M. (2017). Prevalence and predictors of anxiety disorders amongst low-income pregnant women in urban South Africa: a cross-sectional study. Archives of Women's Mental Health(6), 765. doi: 10.1007/s00737-017-0768-z

Vogt, W. P. (2005). Dictionary of statistics & methodology : a nontechnical guide for the social sciences (Third edition. ed.): Sage Publications.

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Cross-Sectional Research Designs

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• Cite this reference work entry

• Manuel C. Voelkle 3 , 4 &
• Martin Hecht 3  

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Between-person designs ; Cross-sectional design ; Research designs

The term cross-sectional research design is typically used in opposition to longitudinal research designs. It may thus be defined as a design without repeated measurements of the same attribute of any individual unit i = 1, … , N (e.g., a person). Although no repeated measures are taken for the same unit of interest, cross-sectional designs are often used for longitudinal inferences, for example, in studies of human development across the lifespan. This encyclopedia entry provides a short overview of prominent cross-sectional research designs and points to potential pitfalls when drawing inferences about change from cross-sectional data.

Introduction

The majority of research designs in psychology are cross-sectional designs as defined above. Typically, however, they are only explicitly referred to as such when contrasted with longitudinal designs. This is particularly important when cross-sectional...

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Baltes, P. B. (1968). Longitudinal and cross-sectional sequences in the study of age and generation effects. Human Development, 11 , 145–171.

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Baltes, P. B., Reese, H. W., & Nesselroade, J. R. (1988). Life-span developmental psychology: Introduction to research methods (2nd ed.). Hillsdale: Erlbaum.

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OECD. (2012). PISA 2009 Technical Report . Paris: OECD Publishing. https://doi.org/10.1787/9789264167872-en

Schaie, K. W. (1994). Developmental designs revisited. In S. H. Cohen & H. W. Reese (Eds.), Life-span developmental psychology (pp. 45–64). Hillsdale: Erlbaum.

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Cross-Sectional Study: What it is + Free Examples

A cross-sectional study is used to collect data from a population simultaneously. It is a snapshot of the population at a particular moment rather than a study that tracks changes over time. This design is often used in fields such as public health, sociology, and psychology to gather information about the characteristics, attitudes, and behaviors of a group of individuals .

This blog will discuss what cross-sectional studies are. We’ll review examples and explain the types of cross-sectional studies you might perform. We’ll also take a closer look at the benefits of this valuable research for the work you do.

What is a Cross-Sectional Study?

A cross-sectional study is a type of observational research that analyzes data of variables collected at one given point in time across a sample population or a pre-defined subset.

This study type is also known as cross-sectional analysis, transverse study, or prevalence study. Although this research does not involve conducting experiments, researchers often use it to understand outcomes in the physical and social sciences and many business industries.

Characteristics of Cross-Sectional Studies

When researchers conduct cross-sectional studies, they look at a specific group of people at a single point in time. Here are some simple characteristics of cross-sectional studies that might help you understand them better:

• Researchers can conduct cross-sectional studies with the same set of variables over a set period.
• Similar research may look at the same variable of interest, but each study observes a new set of subjects.
• The cross-sectional analysis assesses topics during a single instance with a defined start and stopping point, unlike longitudinal studies, where variables can change during extensive research.
• Cross-sectional studies allow the researcher to look at one independent variable and one or more dependent variables as the focus of the cross-sectional study.

Want a fitting metaphor? Think of a snapshot of a group of people at one event, say a family reunion. The people in that extended family are used to determine what is happening in real-time at the moment.

All people have at least one variable in common – being related – and multiple variables they do not share. You could make all kinds of observations and analyses from that starting point. Hence, this research type “takes the pulse” of population data at any given time.

You can also use this type of research to map prevailing variables that exist at a particular given point—for example, cross-sectional data on past drinking habits and a current diagnosis of liver failure.

Cross-Sectional Study Examples

The data collected in cross-sectional studies involves subjects or participants who are similar in all variables – except the one that is under review. This variable remains constant throughout the study. This is unlike a longitudinal study , where variables can change throughout the research. Consider these examples for more clarity:

• Retail: In retail, this research can be conducted on men and women in a specific age range to reveal similarities and differences in spending trends related to gender.
• Education : Cross-sectional studies in school are beneficial in understanding how students who scored within a particular grade range in the same preliminary courses perform with a new curriculum .
• Healthcare: Scientists in healthcare may use cross-sectional studies to understand how children ages 2-12 across the United States are prone to calcium deficiency.
• Business: In business, researchers can study how people of different socio-economic statuses from one  geographic segment  respond to one change in an offering.
• Psychology: The cross-sectional study definition in psychology is research that involves different groups of people who do not share the same variable of interest (like the variable you’re focusing on) but who do share other relevant variables. These could include age range, gender identity, socio-economic status, and so on.

This research allows scholars and strategists to quickly collect cross-sectional data that helps in decision-making and offering products or services.

Types of Cross-Sectional Studies

When you conduct a cross-sectional research study, you will engage in one or both types of research: descriptive or analytical. Read their descriptions to see how they might apply to your work.

• Descriptive Research:  A cross-sectional study may be entirely descriptive research . A cross-sectional descriptive survey assesses how frequently, widely, or severely the variable of interest occurs throughout a specific demographic . Please think of the retail example we mentioned above. In that example, researchers make focused observations to identify spending trends. They might use those findings to develop products and services and market existing offerings. They aren’t necessarily looking at why these gendered trends occur in the first place.
• Analytical Research: A cross-sectional survey investigates the association between two related or unrelated parameters. This research isn’t entirely foolproof, though, because outside variables and outcomes are simultaneous, and their studies are, too. For example, to validate whether coal miners could develop bronchitis, look only at the variables in a mine. What it doesn’t account for is that a predisposition to bronchitis could be hereditary, or this health condition could be present in the coal workers before their employment in the mine. Other medical research has shown that coal mining is detrimental to the lungs, but you don’t want those assumptions to bias your current study.

Researchers usually use descriptive and analytical research methods in real-life cross-sectional studies.

Benefits of a Cross-Sectional Study

Are you curious whether this research is the right approach for your next study? A Cross-Sectional Survey is an efficient and revealing way to collect data. Check out some of the critical advantages of conducting online research using cross-sectional studies and see if it’s a good fit for your needs.

• Relatively quick to conduct.
• Researchers can collect all variables at a point in time.
• Multiple outcomes can be researched at once.
• Prevalence for all factors can be measured.
• Suitable for descriptive analysis .
• Researchers can use it as a springboard for further research.

If you are looking for an approach that studies subjects and variables over time, you might prefer a longitudinal study. Additionally, you could follow your research with a longitudinal study. It is easy to confuse the two research methods, so we’ve broken it down here:

We recently published a blog that talks about Causal Research ; why don’t you check it out for more ideas?

Cross-Sectional vs. Longitudinal Studies

Although they are both quantitative research methods, there are a few differences when comparing and contrasting cross-sectional and  longitudinal studies .

Researchers prefer cross-sectional studies to find common points between variables. Still, they use longitudinal studies, due to their nature, to dissect the research from the cross-sectional studies for further research.

Examples of Cross-Sectional Data

Now that you have a better understanding of what cross-sectional research is and how to perform your studies, let’s look at two examples in more detail:

Example 1: Gender and Phone Sales

Phone companies rely on advanced and innovative features to drive sales.  Research  by a phone manufacturer throughout the target demographic market validates the expected adoption rate and potential phone sales. In cross-sectional studies, researchers enroll men and women across regions and age ranges for research. 

If the results show that Asian women would not buy the phone because it is bulky, the mobile phone company can tweak the design to make it less bulky. They can also develop and market a smaller phone to appeal to a more inclusive group of women.

Example 2: Men and Cancer

Another example of a cross-sectional study would be a medical study examining the prevalence of cancer amongst a defined population. The researcher can evaluate people of different ages, ethnicities, geographical locations, and social backgrounds.

If a significant number of men from a particular age group are more prone to have the disease, the researcher can conduct further studies to understand the reasons. A longitudinal study is best used, in this case, to study the same participants over time.

Create and Analyse a Cross-Sectional Study Survey

It’s your turn! Whether you’re building a marketing strategy or performing a cutting-edge medical study, you can get started by creating an intuitive survey from QuestionPro. Please choose from one of our 350+ survey templates, or build your own and leverage our reporting tools to discover deep insights to apply to your best work.

You can use single-ease questions . A single-ease research question is a straightforward query that elicits a concise and uncomplicated response.

Also, you can find advanced data analysis tools such as trend analysis and dashboards to visualize your information and do your own cross-sectional studies simply and efficiently.

A cross-sectional study provides valuable insights into a population’s characteristics, attitudes, and behaviors at a single point in time. As with any research design , cross-sectional studies should be used with other research methods to provide a complete study. Overall, cross-sectional studies can be a valuable tool for researchers looking to understand a population quickly.

With QuestionPro, you can conduct cross-sectional studies with ease. QuestionPro provides various tools for analyzing your collected data, cross-tabulation, and more. Whether you’re a researcher, marketer, or business professional, QuestionPro can help you gather the data you need to make informed decisions.

FREE TRIAL         LEARN MORE

Frequently Asked Questions (FAQ)

A cross-sectional study is a type of research that collects data from a group of people at a single point in time to analyze characteristics and relationships.

They are valuable for understanding the current status of a condition or behavior within a population, making them great for initial assessments.

Cross-sectional studies capture data at a one-time point, while longitudinal studies track the same individuals over an extended period to observe changes.

It’s cost-effective, quick to conduct, and provides a broad view of a population’s characteristics or behaviors at a specific time.

The primary goal of a cross-sectional study is to examine and analyze the relationships or associations between different variables within a population at a specific point in time.

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Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations

Affiliations.

• 1 Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH. Electronic address: [email protected].
• 2 Department of Respiratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China.
• PMID: 32658654
• DOI: 10.1016/j.chest.2020.03.012

Cross-sectional studies are observational studies that analyze data from a population at a single point in time. They are often used to measure the prevalence of health outcomes, understand determinants of health, and describe features of a population. Unlike other types of observational studies, cross-sectional studies do not follow individuals up over time. They are usually inexpensive and easy to conduct. They are useful for establishing preliminary evidence in planning a future advanced study. This article reviews the essential characteristics, describes strengths and weaknesses, discusses methodological issues, and gives our recommendations on design and statistical analysis for cross-sectional studies in pulmonary and critical care medicine. A list of considerations for reviewers is also provided.

Keywords: bias; confounding; cross-sectional studies; prevalence; sampling.

Copyright © 2020 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

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• Evidence-based medicine, systematic reviews, and guidelines in interventional pain management: part 6. Systematic reviews and meta-analyses of observational studies. Manchikanti L, Datta S, Smith HS, Hirsch JA. Manchikanti L, et al. Pain Physician. 2009 Sep-Oct;12(5):819-50. Pain Physician. 2009. PMID: 19787009
• [The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies]. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; Iniciativa STROBE. von Elm E, et al. Rev Esp Salud Publica. 2008 May-Jun;82(3):251-9. doi: 10.1590/s1135-57272008000300002. Rev Esp Salud Publica. 2008. PMID: 18711640 Spanish.
• Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. Vandenbroucke JP, von Elm E, Altman DG, Gøtzsche PC, Mulrow CD, Pocock SJ, Poole C, Schlesselman JJ, Egger M; STROBE initiative. Vandenbroucke JP, et al. Ann Intern Med. 2007 Oct 16;147(8):W163-94. doi: 10.7326/0003-4819-147-8-200710160-00010-w1. Ann Intern Med. 2007. PMID: 17938389
• Professional beliefs of physicians and allied health professionals and their willingness to promote health in primary care: a cross-sectional survey. Brandt SK, Essig S, Balthasar A. Brandt SK, et al. BMC Prim Care. 2024 May 27;25(1):188. doi: 10.1186/s12875-024-02412-6. BMC Prim Care. 2024. PMID: 38802787 Free PMC article.
• Seroprevalence and Associated Risk Factors of Toxoplasma gondii in Patients Diagnosed with Schizophrenia: A Case-Control Cross Sectional Study. Grada S, Mihu AG, Oatis DA, Marc CC, Chicea LM, Petrescu C, Lupu AM, Olariu TR. Grada S, et al. Biomedicines. 2024 May 1;12(5):998. doi: 10.3390/biomedicines12050998. Biomedicines. 2024. PMID: 38790960 Free PMC article.
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Organizing Your Social Sciences Research Paper

• Types of Research Designs
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
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Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE: Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

• This is a collaborative and adaptive research design that lends itself to use in work or community situations.
• Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
• When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
• Action research studies often have direct and obvious relevance to improving practice and advocating for change.
• There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

• It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
• Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
• Personal over-involvement of the researcher may bias research results.
• The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
• Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA:  Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

• Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
• A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
• Design can extend experience or add strength to what is already known through previous research.
• Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
• The design can provide detailed descriptions of specific and rare cases.
• A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
• Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
• Design does not facilitate assessment of cause and effect relationships.
• Vital information may be missing, making the case hard to interpret.
• The case may not be representative or typical of the larger problem being investigated.
• If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
• Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

• Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
• The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
• Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
• Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
• Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

• A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
• Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
• The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
• Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field  [i.e., the act of triangulating the data].
• Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
• The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

• Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
• A well-reasoned and well-documented justification for identification and selection of the studies;
• Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
• Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
• Justification of the techniques used to evaluate the studies.
• Can be an effective strategy for determining gaps in the literature.
• Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
• Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
• Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
• Can be used to generate new hypotheses or highlight research problems for future studies.
• Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
• A large sample size can yield reliable, but not necessarily valid, results.
• A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
• Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

• Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
• Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
• Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
• Can provide a basis for applying ethical decision-making to practice.
• Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
• Brings clarity to general guiding practices and principles of an individual or group.
• Philosophy informs methodology.
• Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
• Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
• Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
• Limited application to specific research problems [answering the "So What?" question in social science research].
• Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
• While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
• There are limitations in the use of metaphor as a vehicle of philosophical analysis.
• There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

• The researcher has a limitless option when it comes to sample size and the sampling schedule.
• Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
• This is a useful design for exploratory studies.
• There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
• Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
• The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
• The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
• Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

• A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
• The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
• They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
• Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
• The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
• Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
• The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
• Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
• Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
• The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
• The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods .  David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research."  Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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• What is a cross-sectional study?

Last updated

6 February 2023

Reviewed by

Miroslav Damyanov

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Read on to learn about cross-sectional studies. We’ll explore examples, types, advantages, and limitations of cross-sectional studies, plus when you might use them.

Analyze cross-sectional studies

Dovetail streamlines cross-sectional studies to help you uncover and share actionable insights

A cross-sectional study is also known as a prevalence or transverse study. It’s a tool that allows researchers to collect data across a pre-defined subset or sample population at a single point in time. The information is typically about many individuals with multiple variables, such as gender and age. Although researchers get to analyze these variables, they do not manipulate them.

This study type is commonly used in clinical research, business-related studies, and population studies.

Once the researcher has selected the ideal study period and participant group, the study usually takes place as a survey or physical experiment.

• Characteristics of cross-sectional studies

Primary characteristics of cross-sectional studies include the following:

Consistent variables : Researchers carry out a cross-sectional study over a specific period with the same set of variables (income, gender, age, etc.).

Observational nature : Researchers record findings about a specific population but do not alter variables—they just observe.

Well-defined extremes : The analysis includes defined start and stop points which allow all variables to stay the same.

Singular instances : Only one topic or instance can be analyzed with a cross-sectional study. This allows for more accurate data collection .

• Examples of cross-sectional studies

Variables remain the same during a cross-sectional study. This makes it a useful research tool in various sectors and circumstances across multiple industries.

Here are some examples to give you better clarity:

Healthcare : Scientists might leverage cross-sectional research to assess how children aged 3–10 are prone to calcium deficiency.

Retail : Researchers use cross-sectional studies to identify similarities and differences in spending habits between men and women within a specific age group.

Education : These studies help reveal how students with a specific grade range perform when schools introduce a new curriculum.

Business: Researchers might leverage cross-sectional studies to understand how a geographic segment responds to offers and discounts.

• Types of cross-sectional studies

We can categorize cross-sectional studies into two distinct types: descriptive and analytical research. However, the researcher may use one or both types to gather and analyze data.

Here is a description of the two to help you understand how they may apply to your work.

Descriptive research

A descriptive cross-sectional survey or study assesses how commonly or frequently the primary variable occurs within a select demographic. This enables you to identify any problem areas within the group.

Descriptive research makes trend identification easy, facilitating the development of products and services that fit a particular population.

Analytical research

An analytical cross-sectional study investigates the relationship between two related or unrelated parameters. Outside variables may affect the study while the investigation is ongoing, however.

Note that the original results and data are studied together simultaneously in an analytical cross-sectional study.

• Cross-sectional versus longitudinal studies

Although longitudinal and cross-sectional studies are both observational, they are relatively different types of research design.

Below are the main differences between cross-sectional and  longitudinal studies :

Sample group

A cross-sectional study will include several variables and sample groups, meaning it will collect data for all the different sample groups at once. However, in longitudinal studies, the same groups with similar variables can be observed repeatedly.

Cross-sectional studies are usually cheaper to conduct than longitudinal studies, so they are ideal if you have a limited budget.

Participants in longitudinal studies have to commit for an extended period, which significantly increases costs. Cross-sectional studies, on the other hand, are shorter and require less effort.

Data is collected only once in cross-sectional research. In contrast, longitudinal research takes considerable time because data is collected across numerous periods (potentially decades).

Researchers don’t necessarily seek causation in longitudinal research. This means the data will lack context regarding previous participant behavior.

Longitudinal research, on the other hand, clearly shows how data evolves. This means you can infer cause-and-effect relationships.

• How to perform a cross-sectional study

You will need to follow these steps to conduct a cross-sectional study:

Formulate research questions and hypotheses . You will also need to identify your target population at this stage.

Design the research . You will need to leverage observation rather than experiments when collecting data. However, you can always use non-experimental techniques such as questionnaires or surveys. As a result, this type of research will let you collect both quantitative and qualitative data .

Conduct the research . You can collect your data or assemble it from another source. In most instances, governments make cross-sectional datasets available to the public (through censuses) that can help with your research. The World Bank and World Health Organization also provide cross-sectional datasets on their websites.

Analyze the data . Data analysis will depend on the type of data collection method you use.

• Advantages and disadvantages of cross-sectional studies

Are you considering whether a cross-sectional study is an ideal approach for your next research? It’s an efficient and effective way to gather data. Check out some of the key advantages and disadvantages of cross-sectional studies.

Advantages of cross-sectional research

Quick to conduct

Multiple outcomes are researched at once

Relatively inexpensive

Used as a basis for further research

Researchers gather all variables at a single point in time

It’s possible to measure the prevalence of all factors

Ideal for descriptive analysis

Disadvantages of cross-sectional research

Preventing other variables from influencing the study is challenging

Researchers cannot infer cause-and-effect relationships

Requires large, heterogeneous samples, which increases the chances of sampling bias

The select population and period may not be representative

• When to use a cross-sectional design

Cross-sectional studies are useful when:

You need answers to questions regarding the prevalence and incidence of a situation, belief, or condition.

Establishing the norm in a particular demographic at a specified time. For instance, what is the average age for completing studies in Dallas?

Justifying the need to conduct further research on a specific topic. With cross-sectional research, you can infer a correlation without determining a direct cause. This makes it easier to justify conducting other investigations.

• The bottom line

A cross-sectional study is essential when researching the prevailing characteristics in a given population at a single point in time. Cross-sectional studies are often used to analyze demography, financial reports, and election polls. You could also use them in medical research or when building a marketing strategy, for instance.

Are cross-sectional studies quantitative or qualitative?

Cross-sectional research can be both qualitative and quantitative.

Do cross-sectional studies have control groups?

Cross-sectional studies don’t need a control group as the selected population is not based on exposure.

What are the limitations of cross-sectional studies?

Limitations of cross-sectional studies include the inability to make causal inferences, study rare illnesses, and access incidence. Researchers select a subject sample from a large and heterogeneous population.

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• > The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
• > Cross-Sectional Studies

Book contents

• The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
• Cambridge Handbooks in Psychology
• Copyright page
• Contributors
• Part I From Idea to Reality: The Basics of Research
• Part II The Building Blocks of a Study
• Part III Data Collection
• 13 Cross-Sectional Studies
• 14 Quasi-Experimental Research
• 15 Non-equivalent Control Group Pretest–Posttest Design in Social and Behavioral Research
• 16 Experimental Methods
• 17 Longitudinal Research: A World to Explore
• 18 Online Research Methods
• 19 Archival Data
• 20 Qualitative Research Design
• Part IV Statistical Approaches
• Part V Tips for a Successful Research Career

13 - Cross-Sectional Studies

from Part III - Data Collection

Published online by Cambridge University Press:  25 May 2023

Cross-sectional studies are a type of observational studies in which the researcher commonly assesses the exposure, outcome, and other variables (such as confounding variables) at the same time. They are also referred to as “ prevalence studies. ” These studies are useful in a range of disciplines across the social and behavioral sciences. The common statistical estimates from these studies are correlation values, prevalence estimates, prevalence odds ratios, and prevalence ratios. These studies can be completed relatively quickly, are relatively inexpensive to conduct, and may be used to generate new hypotheses. However, the major limitation of these studies are biases due to sampling, length-time bias, same source bias, and the inability to have a clear temporal association between exposure and outcome in many scenarios. The researcher should be careful while interpreting the measure of association from these studies, as it may not be appropriate to make causal inferences from these associations.

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Further reading.

The following are sources that describe various aspects of cross-sectional studies.

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• Cross-Sectional Studies
• By Maninder Singh Setia
• Edited by Austin Lee Nichols , Central European University, Vienna , John Edlund , Rochester Institute of Technology, New York
• Book: The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
• Online publication: 25 May 2023
• Chapter DOI: https://doi.org/10.1017/9781009010054.014

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Microbe Notes

Cross-Sectional Study- Definition, Types, Applications, Advantages, Limitations

A cross-sectional study (also known as a cross-sectional analysis, transverse study, prevalence study) is a type of observational study that analyzes data from a population, or a representative subset, at a specific point in time.

• It examines the relationship between diseases (or other health-related states) and other variables of interest as they exist in a defined population at a single point in time or over a short period of time (e.g. calendar year).
• Cross-sectional studies can be thought of as providing a snapshot of the frequency of a disease or other health-related characteristics (e.g. exposure variables) in a population at a given point in time.
• Cross-sectional studies measure the prevalence of disease and thus are often called prevalence studies.
• They are used to assess the burden of disease or health needs of a population and are particularly useful in informing the planning and allocation of health resources.
• In a cross-sectional study, the measurements of exposure and effect are made at the same time.
• The most common type of cross-sectional studies is the environmental health survey, in which participants are enrolled from exposed and unexposed areas to collect the information on health status, exposure to environmental factors, and potential confounding factors.
• As the disease and exposure data are collected simultaneously, the causal temporality of disease and exposure needs further evaluation.

In a cross-sectional study, all factors (exposure, outcome, and confounders) are measured simultaneously. The main outcome measure obtained from a cross-sectional study is prevalence, that is:

In an analytical cross-sectional study, the odds ratio can be used to assess the strength of an association between a risk factor and health outcome of interest, provided that the current exposure accurately reflects the past exposure.

Types of Cross-sectional Study

Table of Contents

Interesting Science Videos

Descriptive

• A cross-sectional survey may be purely descriptive and used to assess the burden of a particular disease in a defined population.
• For example, a random sample of schools across London may be used to assess the prevalence of asthma among 12-14-year-olds.
• Analytical cross-sectional surveys may also be used to investigate the association between a putative risk factor and a health outcome.
• However, this type of study is limited in its ability to draw valid conclusions as to the association between a risk factor and health outcome. In a cross-sectional survey, the risk factors and the outcome are measured simultaneously, and therefore it may be difficult to determine whether the exposure proceeded or followed the disease.

In practice, cross-sectional studies will include an element of both types of design.

Applications of Cross-sectional studies

• Cross-sectional studies are relatively easy and inexpensive to conduct and are useful for investigating exposures that are fixed characteristics of individuals, such as ethnicity or blood group.
• In sudden outbreaks of disease, a cross-sectional study to measure several exposures can be the most convenient first step in investigating the cause.
• Data from cross-sectional studies are helpful in assessing the health care needs of populations.
• Data from repeated cross-sectional surveys using independent random samples with standardized definitions and survey methods provide useful indications of trends.
• Many countries conduct regular cross-sectional surveys on representative samples of their populations focusing on personal and demographic characteristics, illnesses and health-related habits.
• Frequency of disease and risk factors can then be examined in relation to age, sex, and ethnicity.
• Cross-sectional studies of risk factors for chronic diseases have been done in a wide range of countries.

Advantages of Cross-sectional studies

• Relatively quick and easy to conduct (no long periods of follow-up).
• Data on all variables is only collected once.
• Able to measure prevalence for all factors under investigation.
• Multiple outcomes and exposures can be studied.
• The prevalence of the disease or other health-related characteristics is important in public health for assessing the burden of disease in a specified population and in planning and allocating health resources.
• Good for descriptive analyses and for generating hypotheses.

Limitations of Cross-sectional studies

• Difficult to determine whether the outcome followed exposure in time or exposure resulted from the outcome.
• Not suitable for studying rare diseases or diseases with a short duration.
• As cross-sectional studies measure prevalent rather than incident cases, the data will always reflect determinants of survival as well as aetiology.
• Unable to measure incidence.
• Associations identified may be difficult to interpret.
• Susceptible to bias due to low response and misclassification due to recall bias.
• Non-response is a particular problem affecting cross-sectional studies and can result in bias of the measures of outcome. This is a particular problem when the characteristics of non-responders differ from responders.
• Park, K. (n.d.). Park’s textbook of preventive and social medicine.
• Gordis, L. (2014). Epidemiology (Fifth edition.). Philadelphia, PA: Elsevier Saunders.
• https://www.healthknowledge.org.uk/e-learning/epidemiology/practitioners/introduction-study-design-css
• https://www.bmj.com/about-bmj/resources-readers/publications/epidemiology-uninitiated/8-case-control-and-cross-sectional
• https://sph.unc.edu/files/2015/07/nciph_ERIC8.pdf
• https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/cross-sectional-study

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Critical Appraisal Resources for Evidence-Based Nursing Practice

• Levels of Evidence
• Systematic Reviews
• Randomized Controlled Trials
• Quasi-Experimental Studies
• Case-Control Studies
• Cohort Studies
• Analytical Cross-Sectional Studies

What is an Analytical Cross-Sectional Study?

Pro tips: analytical cross-sectional study checklist, articles on cross-sectional study design and methodology.

• Qualitative Research

E-Books for Terminology and Definitions

An analytical cross-sectional study is a type of quantitative, non-experimental research design. These studies seek to "gather data from a group of subjects at only one point in time" (Schmidt & Brown, 2019, p. 206).  The purpose is to measure the association between an exposure and a disease, condition or outcome within a defined population.  Cross-sectional studies often utilize surveys or questionnaires to gather data from participants (Schmidt & Brown, 2019, pp. 206-207).  

Schmidt N. A. & Brown J. M. (2019). Evidence-based practice for nurses: Appraisal and application of research  (4th ed.). Jones & Bartlett Learning. 

Each JBI Checklist provides tips and guidance on what to look for to answer each question.   These tips begin on page 4. 

Below are some additional  Frequently Asked Questions  about the Analytical Cross-Sectional Studies  Checklist  that have been asked students in previous semesters. 

For more help:  Each JBI Checklist provides detailed guidance on what to look for to answer each question on the checklist.  These explanatory notes begin on page four of each Checklist. Please review these carefully as you conduct critical appraisal using JBI tools. 

Kesmodel U. S. (2018). Cross-sectional studies - what are they good for?   Acta Obstetricia et Gynecologica Scandinavica ,  97 (4), 388–393. https://doi.org/10.1111/aogs.13331

Pandis N. (2014). Cross-sectional studies .  American Journal of Orthodontics and Dentofacial Orthopedics ,  146 (1), 127–129. https://doi.org/10.1016/j.ajodo.2014.05.005

Savitz, D. A., & Wellenius, G. A. (2023). Can cross-sectional studies contribute to causal inference? It depends .  American Journal of Epidemiology ,  192 (4), 514–516. https://doi.org/10.1093/aje/kwac037

Wang, X., & Cheng, Z. (2020). Cross-sectional studies: Strengths, weaknesses, and recommendations .  Chest ,  158 (1S), S65–S71. https://doi.org/10.1016/j.chest.2020.03.012

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• Next: Qualitative Research >>
• Last Updated: Feb 22, 2024 11:26 AM
• URL: https://libguides.utoledo.edu/nursingappraisal

Frequently asked questions

What is the difference between a longitudinal study and a cross-sectional study.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

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

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

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.

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

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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• Cross-Sectional Studies: Types, Pros, Cons & Uses

As a researcher, when you want to study the relationship between two variables to determine if there’s a cause and effect factor what do you do?

Although there are diverse ways to measure the prevailing characteristics in a sample group, a cross-sectional study is the most efficient. Read on to understand the concept of a cross-sectional study, and how you can apply it to your research study. 

What is a Cross-Sectional Study?

A cross-sectional study is a type of observational study where participants selected are chosen solely on the addition and subtraction yardstick initially designed for the study. In this study, the exposure of the participants and results are measured consecutively by the researcher.

Cross-sectional studies are used in population surveys and can be performed quickly with less cost. They can be conducted before a population study and also be used as a basis when studying a population with similar traits.

A researcher should note that a cross-sectional study is a one-time measurement of results and it is difficult to determine a cause-and-effect relationship from the outcome. The prevalence of a particular factor can, however, be analyzed in a cross-sectional study.

Read: Research Report: Definition, Types + [Writing Guide]

Characteristics of Cross-Sectional Studies

1. As a researcher, you can conduct cross-sectional research using the same set of values of variables at a time.

2. Closely related studies may consider the same variable as the desired interest, however, each study analyzes new data.

3. There is always a starting point and a stopping point in a cross-sectional study. This study analyzes subjects in a single stage.

4. A researcher can only focus on one independent variable and one dependent variable in a cross-sectional study.

Let’s briefly look into this example of a cross-sectional study

A family at a wedding took a snapshot.

We can determine what is happening in real-time using the family in that photo. This is because all the people in the picture in the photo share one common variable and other variables that are not common to the entire group.

The common variable is their relationship as a family while the uncommon variables are individual traits.

A researcher can derive some observations and make some analysis starting from the point of the snapshot which is why a cross-sectional study is said to have a starting point and a stopping point.

The researcher can also use a cross-sectional study to determine a common variable or prevailing variable at a specific point in time. This can be in the past or in the present. For example, If we look back at the family in the photo, the researcher can decide to use a cross-sectional study to analyze whether there is a similar trait or same sense of humor.

Types of Cross-Sectional Studies 

There are two types of cross-sectional studies and they are:

• Descriptive Research

Descriptive research analyzes how frequently or wide the variables of interest appear in a particular population. In descriptive research, the researcher tries to identify the trends and use the outcome to develop products or services that can be useful for the population. Descriptive research is not necessarily looking for why the trends or patterns in the study were present.

• Analytical Research

Analytical research on the other hand studies the relationship between two common variables and two uncommon variables.

It is noteworthy that original data and results are simultaneously studied together.

For example, to determine whether a Tobacco factory worker could develop lung problems, the study will focus on the variables in the tobacco factory. It will not look at the probability of other factors causing the lung problem or even the possibility that the lung problem started before the worker gained employment in the tobacco factory.

It should be noted here that the researchers use both descriptive research and analytical research methods when conducting a cross-sectional study.

Read: What is Applied Research? + [Types, Examples & Method]

Issues in the Design of Cross-Sectional Surveys

There are some issues in the design of cross-sectional surveys and we are going to examine them.

1. Selecting a sample group to represent the entire population: We have noted that a cross-sectional study serves as a representative of a population.

For a cross-sectional study to be valid, characteristics from the population have to be present in the sample group. For example, a researcher studies the prevalence of diabetes in women between the age of 45 to 60 in a city. To carry out this study, the researcher has to select women to represent the population of age group 45 to 60, using randomization.

2. Sample Size: the sample size selected by the researcher should be large enough to properly analyze the prevalence of the condition.

3. The possibility of research bias in a cross-sectional study : There can be an issue in a cross-sectional study if the characteristics of the participants are different from the characteristics of the nonparticipants. This can lead to bias in the outcome of the study.

Read: Experimental Research Designs: Types, Examples & Methods

Limitations of Cross-Sectional Study

It is difficult to assess cause and effect relationships in a cross-sectional study. This is because the cross-sectional study is a single-time analysis of the exposure and the result.

For example, if a researcher wishes to find the association between diet and obesity, they will conduct a cross-sectional study. Sample size will be selected to represent the population and the sample size selected can be 150 participants.

Their BMI (Body Mass Index), dietary and exercise habits at a specific time will also be examined in the study. If obese participants changed their diet, started to eat more vegetables, and began to exercise, it implies that in the cross-sectional study, there may be more results showing that obese participants are more likely to exercise and eat veggies.

So a researcher must be careful when interpreting the sample direction and relationships in a cross-sectional study.

Another limitation of a cross-sectional study is that the prevailing result is dependent on the incident and the time it took to recover after the result.

If a cross-sectional study is used alone to study diseases in the medical field, it might be difficult to fully understand the trends of the disease.

Read: Correlational Research Designs: Types, Examples & Methods

Advantages of a Cross-Sectional Study

• Cross-sectional studies are faster to conduct.
• It is also pocket-friendly. The researcher can save costs using a cross-sectional study.
• A cross-sectional study provides information that is important for developing cohort studies. This is because a cross-sectional study is conducted before forming a basis for a cohort study.
• The data on all the variables are collected only once.
• A cross-sectional study is useful in public health analysis to assess the prevalence of diseases in a particular population.

Read: 11 Retrospective vs Prospective Cohort Study Differences

Disadvantages of a Cross-Sectional Study

• Cross-sectional studies may not determine what comes first between an outcome or an exposure. Did the outcome come before the exposure or did the exposure before the outcome?
• A cross-sectional study is not the best for researching uncommon diseases and diseases that occur in a short time.
• A cross-sectional study analyzes the dominant variable and not the causal event. The results from the cross-sectional study will reflect the prevalent case.
• A cross-sectional study does not measure incidence.
• Identified relationships in a cross-sectional study may not be easy to interpret.
• A cross-sectional study is at risk of bias because the respondents are small.

How to Create a Cross-Sectional Study Survey with Formplus

You can create just about any form you can think of or imagine on Formplus. The templates available are over 1000 and they are super easy to use. 

Here’s a detailed guideline on how you can create a cross-sectional survey.

1. Sign in to your Formplus account. If you have not yet signed up on Formplus, it’s absolutely free.

2. On the form builder, develop the information that will be on your survey. Outline your research goals and then choose your population sample.

3. You can begin to develop your survey questions after which you will choose the method you want to use to apply your survey.

4. Customize your surveys using any of the available themes or using a custom CSS.

5. Formplus allows you to share your survey on social media platforms and through emails

6 Gather your survey responses and analyze them on Formplus. Set your survey to segregate answers based on age, socioeconomic status, and gender.

Application of Cross-Sectional Studies and When to Use

You can use a cross-sectional study when;

• You need to collect data as the basis for further research, 
• To examine the prevalence of some outcomes at a certain period
• Your resources can only collect cross-sectional data or its the only answer you can find

You want to know the number of students relocating to a new area so you can find out how much money you’d gain from renting your apartment in that area over the next five months. Since all you need to know is the current number of new students, all you need is a cross-sectional study.

Let’s say you want to know the demographic of Londoners who like Almond milk. A cross-sectional design will help you identify the patterns for this research.

FAQs About Cross-Sectional Studies

• What is the evidence level in a cross-sectional study?

Data about a particular subject is mostly gathered at a point in time in a cross-sectional study.

For example, you can send the questionnaire to an industry where forestry is prevailing. The questionnaire may ask questions such as, is there a presence of osteoarthritis in the industry?

Asking this question helps a researcher to determine if there are cases of this condition.

The questionnaire can also help researchers to gather information about the condition, find out if there has been any exposure, and analyze the relationship that may exist.

Although it can be a little bit difficult to trust the results of the relationship, the information gathered from cross-sectional research can be the beginning of a study that will lead to more substantial designs. Here, the researcher can merge the initial outcome and find the most accurate results.

• How long do cross-sectional studies last?

If you go back to the definition of a cross-sectional study, It says cross-sectional study research is a particular subject at a particular point in time.

This means that research of longer years, mostly later than five years, would blur the line of cross-sectional study although this depends on the data being studied. In real life, research can be conducted for five years. There is no reason why a five-year period shouldn’t be enough to conduct a cross-sectional study.

A rule of thumb is that if the data you want to collect cannot be gathered in a short amount of time, then perhaps other research methods cannot do it even for a longer time.

What this means is that you can collect survey data in five years. However, what you want to do with the collected data will determine whether the time is enough or not. 

Your research questions will determine if the time you are using for the study is sufficient to get the desired outcome.

• Is cross-sectional or longitudinal better?

You’re going to look at this from the angle of characteristics of a cross-sectional study and that of a longitudinal study.

A Cross-sectional study is done faster and easier than a longitudinal study. That is why most researchers begin their studies with a cross-sectional study so that they can establish if there’s a relationship between the variables of their research.

After the initial cross-sectional study the researcher would then conduct a longitudinal study. This is because a longitudinal study is also observational in nature,  just as a cross-sectional study.

Unlike cross-sectional studies where the researcher conducts a single finding on the subject, in longitudinal studies, the researcher conducts a series of observations on one subject over a long period of time. This study is to analyze if there is a cause and effect in the variables.

In a longitudinal study, the researcher can detect changes in the characters of the sample group and the participants. We may not be able to conclude on which of the studies is better. However, if you need to conduct a quick and less expensive study your best bet is a cross-sectional study.

If you want to conduct longer research and you want to measure developments in your population then make use of longitudinal study. We can then say that the right method of study to apply in research depends on what is to be researched, the objectives and goals of the research, along with the cost and time available for the research.

• Is a cross-sectional study qualitative?

Most cross-sectional studies are quantitative. They gather data through interviews, questionnaires, and focus groups over a certain period in time which may be in the past or the present, and then analyze the results.

However, there are some qualitative cross-sectional study and they can also be a mixture of both quantitative and qualitative. For example, most research in the medical industry is cross-sectional qualitative studies.

• What is the difference between a cross-sectional study and a cohort study?

Cross-sectional studies are cheap and quick to conduct.

Questionnaires are usually used to conduct cross-sectional studies so information is gathered quickly.

A cross-sectional study focuses on the predominant incidents in the population and determines the outcome so it doesn’t study the relationship between variables.

However, in cohort study both the treatment group and controlled group results are analyzed. This is because the researcher tries to find the relationship between the cause and effect in a cohort study. A cohort study requires a large population and it is very expensive. Inadequate data in a cohort study would lead to errors in the result.

• How do you collect data from a cross-sectional study?

A researcher can use self-developed questionnaires to gather data in a cross-sectional study. This is because the aim of a cross-sectional study is mostly to analyze the characteristics of a sample group. Therefore a researcher can put up survey questions for the respondents so as to measure the variables of interest.

If your interest as a researcher is to study the causal relationship that exists between two variables in a population, the most accurate method to achieve that is a cross-sectional study. Start by creating online surveys with Formplus.

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• v.11(11); 2021

Original research

Multimorbidity, psychoactive substance use and psychological distress among acute medically ill patients: a cross-sectional study, saranda kabashi.

1 Department of Forensic Sciences, Oslo University Hospital, Oslo, Norway

2 Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway

Danil Gamboa

Vigdis vindenes.

3 Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway

Thomas Berg

Thor arthur hilberg.

4 Fürst Medical Laboratory, Oslo, Norway

Benedicte Jørgenrud

Anners lerdal.

5 Department of Interdiciplinary Health Sciencies, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway

6 Department of Research, Lovisenberg Diaconal Hospital, Oslo, Norway

Stig Tore Bogstrand

Associated data.

bmjopen-2021-052428supp001.pdf

Data are available upon reasonable request. No additional data available due to institutional policies.

In order to target the complex health needs of patients with multimorbidity using psychoactive substances, knowledge regarding the association between substance use and multimorbidity in an acute setting is needed.

Examine psychoactive substance use patterns among acute medically ill patients, and determine the association between multimorbidity and substance use, and psychological distress.

Cross-sectional study.

Setting and participants

2874 acute medically ill patients admitted to a medical emergency department in Oslo, Norway.

Measurements

Primary outcome: multimorbidity recorded by the presence of ≥2 International Classification of Diseases 10th revision—physical and/or mental health conditions per patient, extracted from medical records. Predictor variables: self-reported data on age, sex, occupational status, psychological distress (Hopkins Symptom Check List-5), alcohol use (Alcohol Use Disorder Identification Test-4) and results from blood samples on psychoactive medicinal and illicit drugs.

Of all patients, 57.2% had multimorbidity. Of these, 62.6% reported psychological distress, 85.5% consumed either alcohol, medicinal and/or illicit drugs and 64.4% combined alcohol with psychoactive medicinal drugs. Patients with risky alcohol use were more likely to have multimorbidity compared with patients with low-risk alcohol use (OR 1.53; 95% CI 1.05 to 2.24). Patients using psychoactive medicinal drugs were more likely to have multimorbidity compared with non-users (OR 1.34; 95% CI 1.07 to 1.67).

Multimorbidity was associated with psychoactive medicinal drug and risky alcohol use, and psychological distress. Substance use was widespread, with alcohol and psychoactive medicinal drugs most frequently combined. Monitoring substance use among multimorbid patients is necessary to develop tailored treatments, and reduce burden on the healthcare system.

Strengths and limitations of this study

• This is one of few studies examining multimorbidity (≥2 physical/mental conditions) in relation to psychoactive substance use and psychological distress in acute secondary healthcare settings—a primary contact point for many non-treatment seeking patients.
• The study showed the need for monitoring of psychoactive substance use in the disease management of patients with multimorbidity, in order to improve the adherence to treatment and its outcomes, and reduce burden on the healthcare system.
• The study combines data from objective blood samples and self-reported questionnaires on psychoactive substances, and uses objective medical records on the health conditions.
• To reflect a more realistic daily clinical practice, acute diseases and other signs and symptoms were included in the definition of multimorbidity; this might have inflated the prevalence of multimorbidity.
• This study design was not able to distinguish between medical and non-medical use of psychoactive prescription drugs.

Introduction

Patients with multimorbidity—two or more disorders—are more likely to use multiple prescribed medications, live more years of life with disability and die prematurely. 1 2 As such, multimorbidity exacts a great toll on the healthcare system. 3 Psychoactive substance use leads to adverse health effects, influences existing disorders and complicates disease management in patients with multimorbidity. 4 Therefore, psychoactive substance use among patients with multimorbidity should be addressed in both epidemiological research and in healthcare settings, for example, when developing effective interventions and adequate care for people with multimorbidity. Depression, anxiety, alcohol use—and illicit drug use—disorders are causing the highest proportions of disability-adjusted life-years worldwide. 5 Substance use disorders (SUDs) often co-occur with other medical disorders and are associated with psychological conditions such as depression and anxiety. 6 Hence, it is important to examine the mental state of multimorbid patients using psychoactive substances. A combination of alcohol, psychoactive medicinal and illicit drugs might lead to detrimental health outcomes 7 ; including an elevated risk of overdose when opioids are used in combination with other central nervous system (CNS) depressants, such as benzodiazepines and alcohol, 8 and increased adverse effects of stimulants such as cocaine when combined with alcohol, particularly on the cardiovascular and cerebrovascular systems. 8–10

These substance groups are highly prevalent in acute care settings 11 12 and are, therefore, important to consider in the management of multimorbid patients presented to for example, emergency departments (EDs). The intersection between multimorbidity and substance use is growing with the growing ageing population, as shown in the increasing age of people in opioid treatment in many countries. 13 Subsequently, older adults should be screened for substance use, since multimorbidity is more prevalent in this group. 14

Research on multimorbidity is usually conducted in general populations and primary care settings, and pertains to older patient populations. 14–16 Alcohol, psychoactive medicinal and illicit substance use have been shown to be associated mostly with individual diseases and not with co-occurring diseases; there is a scarcity of reports examining the association between multimorbidity and all three substance groups in the same study population; the majority of the evidence on substance use and health outcomes including mortality pertains to alcohol and tobacco. 17–19 Individual studies have explored alcohol as a risk factor for multimorbidity with mixed results, 18–21 others demonstrate an association of multimorbidity and polypharmacy (use of multiple medications in a single individual). 22 However, data on the association of multimorbidity and illicit drug use are scant. 15 Moreover, there is a paucity of reports focusing solely on multimorbidity in relation to psychoactive substance use and psychological distress. 15 Studies using patient-reported diseases have shown to be biased in the disease measurement compared with those using administrative hospital data. 23

Our aims were to (1) examine the distribution of psychoactive substance use patterns among patients with multimorbidity of age ≥18 years presented to an ED and (2) determine the association between multimorbidity and psychological distress, and substance use including alcohol, illicit and psychoactive medicinal drugs.

Study design and participants

This study uses data from a cross-sectional observational study conducted from November 2016 to December 2017, and the study design of the data gathering has been previously described in detail. 24 In brief, the study site was Lovisenberg Diaconal Hospital in Oslo, where all patients having acute medical disorders from the defined hospital catchment area are presented to. The hospital has a catchment area comprising four inner city boroughs with low life-expectancy—and income rates compared with the rest of Norway. 25 Patients admitted to the medical wards (acute medically ill patients aged ≥18) of the ED were recruited to the study. 24

Patient and public involvement statement

No patients nor any other members of the public were involved in the design, conduct or reporting of this study. Informed written consent was obtained from all participants.

Data collection

In addition to self-reported data and data from the blood samples presented in our previous studies, 24 26 for this analysis the patients’ electronic medical records were systematically reviewed for registration of diagnoses codes according to the 10th revision of the International Classification of Diseases (ICD-10). A limited number of the patients were diagnosed with solely mental and behavioural diseases assigned by F00-F99 ICD-10 diagnostic codes. We chose, however, to include these patients in our study (which are taken into account in the sensitivity analysis), because they represent a complex patient group that poses a challenge to the healthcare system and deserves exploration.

Sociodemographic measures such as sex, age (18–35 years, 36–50 years, 51–65 years, 66–80 years, >80 years) and occupational status (economically active, non-economically active and retired) were self-reported.

When multiple outcomes are examined in relation to multimorbidity, the use of disease counts is suggested to be the most adequate manner to measure multimorbidity. 2 27 Therefore, we defined multimorbidity as the presence of two or more medical and/or mental health conditions in one patient. ICD-10 codes of chapter F00–F99 comprising mental and behavioural diseases were defined as mental health conditions, while all other ICD-10 chapters were defined as medical conditions. ICD-10 codes for physical and mental diseases, intoxications, signs and symptoms, abnormal findings and complaints were included. ICD-10 codes for social circumstances, injury and external causes of morbidity and mortality were excluded, that is, patients having only these diagnostic codes were excluded entirely from the dataset.

Psychological distress the past 14 days was assessed by patient-reported anxiety and depression by Hopkins Symptom Checklist 5 (SCL-5). 28 SCL-5 is used as a dichotomised variable with a cut-off score >2 being the positive outcome (a valid predictor of psychological distress) and <2 being the negative outcome (no psychological distress). 28

Blood samples provided by patients were analysed for the most commonly prescribed psychoactive medicinal drugs including analgesics and CNS depressants (15 different medicinal drugs including tramadol, opioids, benzodiazepines and z-hypnotics) and illicit drugs including stimulants (amphetamines, cocaine, 3,4-Methylenedioxymethamphetamine (MDMA)), heroin and cannabis ( Tetrahydrocannabinol (THC)). 29 Compared with alcohol screening questionnaires, which correlate well with blood samples, 30 drug screening questionnaires have shown to be less sensitive in detection of drug use compared with biological sample tests. 31 Hence, psychoactive medicinal and illicit drugs detected in blood were used as dichotomised variables, with the positive outcome being at least one drug found in the blood sample of each patient and the negative outcome being no drugs detected.

Self-reported alcohol use the past year was measured by Alcohol Use Disorder Identification Test 4 (AUDIT-4). 24 The total score ranges from 0 to 16. We categorised AUDIT-4 scores into five categories including abstainers: (1) abstinence (scores 0), (2) low-risk drinking (scores 1–3), (3) alcohol use in excess of low-risk guidelines (scores 4–6), (4) hazardous drinking (scores 7–8), (5) risky alcohol use and possible alcohol dependence (scores ≥9). 24 32 Ideally, the reference group should have been teetotalers, although several concerns are related to this group. 33 However, due to the under-representativeness of this group the group of low-risk drinkers is used as a reference in our study.

Statistical analyses

Differences in mean number of health conditions between men and women, illicit drug use and psychological distress were analysed using t-test. Differences in mean number of health conditions across age groups, occupational status groups, psychoactive medicinal drug use and alcohol use patterns were analysed with one-way analysis of variance (ANOVA). The chi-squared (χ²) test was used to measure differences in prevalence of multimorbidity between variables.

Subsequently, we assessed the prevalence of all three substance groups used individually and in combination with each other among single diseased and multimorbid patients by using χ² test.

Binary logistic regression was employed to examine the likelihood of being multimorbid based on predictor variables. Total number of observations was reported for each variable in the tables to indicate missing data, and cases with any missing data were excluded from the logistic regression analysis.

The level of significance was p<0.05 for all statistical tests.

All statistical analyses were performed using R software V.4.0.3.

Sensitivity analysis

In addition to the main analysis described above, we conducted a sensitivity analysis to test whether the association between multimorbidity and substance use and/or psychological distress was influenced by the inclusion of mental and behavioural diagnoses (F00–F99 codes).

The sensitivity analysis was conducted rerunning the logistic regression where all F00–F99 codes were excluded (they were set to not count in the outcome variable) and we were left analysing only medical multimorbidity (≥2 medical conditions only per patient). The group of patients having any of the F00–F99 codes in either the index diagnoses or secondary diagnoses comprised 250 patients in total. Of these,51 (1.9% of the total study sample of 2725 patients) patients diagnosed with solely F00–F99 codes were excluded from the model, while the remaining 199 patients (7.4% of the study sample) with physical diagnoses in addition to the F00–F99 codes were not excluded from the analysis, but ended up having one disorder (diagnostic code) less.

A total of 2874 patients were enrolled and after excluding patients with injury as main diagnoses and those with missing ICD-10 diagnoses codes, we ended up with 2725 patients. Of these 1558 (57.2%) were multimorbid. Following the inclusion of covariables (any case with missing data was excluded) the sample size for the complete analysis was 2136. The mean (SD) age was 56 years (20 years) and the mean (SD) number of disorders in the whole population was 2.24 (1.54), with men and women equally represented ( table 1 ).

Demography, psychological distress, substance use and multimorbidity

*Differences between means within each variable differed significantly p<0·005 (t-test for independent samples for sex, illicit drug use and psychological distress); ANOVA for age groups, occupational status, psychoactive medicinal drug use and alcohol use patterns).

†Differences between categories within each variable except for sex differed significantly p<0.005 (chi-squared (χ²) test for 2×n tables).

ANOVA, one-way analysis of variance; AUDIT-4, Alcohol Use Disorder Identification Test 4; SCL-5, Symptom Checklist 5.

The number of disorders and the proportion of patients with multimorbidity increased significantly with age and was higher for those being non-economically active and retired. Men had a significantly higher number of disorders compared with women ( table 1 ). Of the young patients (18–34 years) 35.0% were multimorbid ( table 1 ). Of these, 40.3% used alcohol above the recommended guidelines, 12.6% used it hazardously and 8.9% in a risky manner. Prevalence of risky alcohol use was higher among patients aged 35–49 years and 50–64 years (12.7% and 12.1%) and decreased slightly for those aged 65–79 years (8.7%). Of the young multimorbid patients (18–34 years) 14.1% used illicit drugs, 14.1% used psychoactive medicinal drugs and 31.4% reported to have psychological distress. Compared with the young patients, 18–34 among multimorbid patients aged ≥35 the prevalence of psychological distress decreased with increasing age (for those aged 35–49 years the prevalence was 30.2%; for those 50–64 years it was 26.3%; 65–79 years: 23.9% and for those >80 years it was 13.8%) while the prevalence of psychoactive medicinal drug use increased substantially with increasing age (for those aged 35–49 years the prevalence was 32.6%; for those 50–64 years it was 45.4%; 65–79 years: 43.8% and for those >80 years it was 41.2%). Illicit drugs were most prevalent among those aged 35–64 years (16.9%) and decreased substantially with increasing age (for those 50–64 years it was 10.7%; 65–79 years 1.3% and for those >80 years it was 0.8%).

The prevalence of multimorbidity in patients using psychoactive medicinal drugs was higher (68.7%, mean number of disorders: 2.56; SD: 1.61) than for those using illicit drugs (62.0%, mean number of disorders: 2.23; SD: 1.47) ( table 1 ). 65.5% of those using one medication and 73.3% using ≥2 medications were multimorbid, respectively ( table 1 ).

Overall, 85.5% of patients with multimorbidity used any of the three substance groups, either individually or in combination with each other. The prevalence of individual and combined substance use is shown in table 2 . Of all patients, 2.9% used all three substance groups concomitantly, and of these 61.8% were multimorbid compared with those without multimorbidity (38.2%). The most frequent combination of substances was alcohol and psychoactive medicinal drugs (18.3%). Of a total of 787 patients using psychoactive medicinal drugs ( table 1 ), only 28.2% used these alone and not in combination with illicit drugs or alcohol. Combinations of drugs in general were more prevalent in patients with multimorbidity compared with those without multimorbidity, except for the combination of illicit drugs and alcohol, which was more frequent among those without multimorbidity (53.5% vs 46.5%) (table 2).

Prevalence of individual substance use and combined substance use (alcohol use includes any level of drinking, from low-risk drinking to risky drinking) among patients with and without multimorbidity (single diseased)

*Differences between the prevalence of psychoactive substance use and combination of substances among single- diseased and those with multimorbidity differed significantly with a p<0.001 for all of the substances and combinations of substances (chi-squared (χ²) test for 2×n tables).

Table 3 depicts the ORs for the association between multimorbidity and the predictor variables including alcohol use patterns, illicit and psychoactive medicinal drug use, and psychological distress. The likelihood for being multimorbid increased substantially with increasing age. Patients with risky alcohol use were more likely to be multimorbid compared with those with low risk drinking habits (OR 1.53; 95% CI 1.05 to 2.24), the same applied to abstainers (OR 1.50; 95% CI 1.15 to 1.95). Multimorbidity was not significantly associated with hazardous drinking (OR 1.18; 95% CI 0.81 to 1.71). There was a significant positive association between multimorbidity and psychoactive medicinal drug use (OR 1.34; 95% CI 1.07 to 1.67), and multimorbidity and psychological distress (OR 1.28 95% CI 1.01 to 1.63). No significant association between multimorbidity and illicit drugs was detected.

ORs for multimorbidity by age, sex, occupational status, psychological distress and substance use (n=2136)

*All adjusted for the other listed variables in model.

SCL-5, Symptom Checklist 5.

The results from our sensitivity analyses were similar to those from the main regression model ( online supplemental table 1 ) except for self-reported psychological distress which was not significantly associated with multimorbidity. The ORs for all variables included in the model were slightly attenuated and remained significantly associated with multimorbidity.

Supplementary data

In this study of acute medically ill patients, we found an association between multimorbidity and psychological distress, psychoactive medicinal drug and risky alcohol use. No association between multimorbidity and illicit drug use was found. Substance use was widespread and the majority of multimorbid patients used alcohol and psychoactive medicinal drugs and a combination of both.

Our findings are commensurate with prior reports on the high prevalence of multimorbidity among those non-economically active and older populations and the association between ageing and multimorbidity. 1 34 The increase in global ageing and long-term conditions indicate that the number of people with multimorbidity in the future is set to rise. 1 Older adults are often not screened for SUDs. 9 This underpins the importance of the identification of substance use among the elderly; which if not integrated in the disease management might compromise their treatment effectiveness. This applies particularly to alcohol which was widely used by the older patients in our study, which are prescribed medications for multiple conditions. Therefore, the probability for adverse events, non-adherence and drug interactions might be elevated due to the diminished metabolic efficiency for both alcohol and other substances and requires careful management among the older patients drinking in a risky manner. 35 36 Nevertheless, in our study, the prevalence of multimorbidity among the young patients (18-34) was high (35%). The reason for this might be that our hospital comprises patients from boroughs with low income and low life expectation rates in Norway. 25 This finding is in concordance with other studies reporting an earlier manifestation of multimorbidity among those socioeconomically deprived. 34 The majority of the young patients in our study used substances, and one-third reported to have psychological distress. From a preventive perspective, the young patients should be timely targeted in view of their substance use, mental health and overall morbidity in order to avoid decrements in quality of life, health complications and possible frailty in later stages of life.

Patients with psychological distress were more likely to be multimorbid compared with single-diseased patients. However, our sensitivity analysis showed that this association did not remain after removal of mental- and behavioural disorders. This might indicate that self-reported psychological distress was mainly associated to mental and behavioural disorders, as previously reported. 6

The observed augmented risk for multimorbidity among abstainers in our main analysis may reflect the fact that some former drinkers became abstainers due to health problems. 37 These results indicate that risky alcohol use should be considered in a multifaceted management regimen for multimorbid patients.

The prevalence of psychoactive medicinal drug use was higher among multimorbid patients compared with single-diseased patients. Nonetheless, 73.3% of patients using two or more psychoactive medical drugs were multimorbid, which might reflect a plausible unhealthy drug use. Clinical guidelines rarely account for multimorbidity. 38 As a result, patients with multimorbidity might be prescribed several drugs, although each of these is recommended by a disorder-specific guideline, leading to a possibly higher number of drugs used. However, we examined only psychoactive medicinal drugs, and some of the patients might have used them non-medically. Regardless of the manner of use, when several psychoactive medications are used by multimorbid patients the risk of drug–drug interactions increases with the number of co-existing disorders and the number of drugs taken. 39

More than half of the patients combining all three substances were multimorbid. The adverse effects of multimorbidity and substance use on the functioning and quality of life might be greater than the individual effects expected from multimorbidity or substance use alone. A high proportion of multimorbid patients combined psychoactive medicinal drugs and alcohol. Patients used mainly benzodiazepines, opioids and z-hypnotics; which when combined with alcohol might generate an additive effect with increased CNS suppression and an increased risk of adverse events and fatal outcomes, even when the individual substances are used as prescribed. 40 Regarding this, assessing alcohol use among multimorbid patients using prescription psychoactive medications should be a priority, in order to target patients that need to reduce their alcohol use. Furthermore, interventions that target reductions in alcohol consumption do not necessarily incorporate other substances, except for tobacco use. 41 Given the high prevalence of substance use among hospitalised populations, including ours, other substances should be incorporated alongside alcohol interventions. 11

Only a minority of patients did use illicit drugs alone, these were mostly combined with alcohol and psychoactive medicinal drugs. Since patients are more prone to using psychoactive medicinal drugs non-medically, the combination of illicit and psychoactive medicinal drugs might indicate a non-medical use of these prescription drugs. 42 A combination of psychoactive medicinal and illicit drugs can have serious medical consequences, reflected in increased ED visits. 11 43 Furthermore, use of illicit drugs may impair adherence to prescribed controlled regimens in some patients and cause detrimental drug-drug interactions. 44 Given the under-reporting in both psychoactive medicinal and illicit drugs, 31 45 blood sample screening might be an appropriate tool to assess drug use and deliver adequate care to these patients.

More than half of our patient population was multimorbid. Patients with multimorbidity have more frequent and complex interactions with the healthcare services and account for substantial healthcare costs. 46 Integrating substance use in the disease management of patients with multimorbidity is important for the burden reduction on the healthcare system. Several brief instruments measuring substance use in addition to blood sample screening may be used and should be a priority among patients with multimorbidity. 47 48 Furthermore, monitoring multimorbidity in relation to substance use might mitigate this significant public health challenge. In view of its magnitude, an improvement will require a coherent and focused action across multiple sectors and among policy-makers.

Limitations

Due to the cross-sectional design of the study, the ability to make casual inference was limited. However, this was beyond the scope of this study.

The use of blood samples for assessment of psychoactive medicinal and illicit drugs does not compare directly to self-reported alcohol use which measures alcohol consumption during a year period. Nonetheless, an under-reporting of drug use is evident in studies comparing self-reported drug use with biological samples. 31 45 49 Therefore, a recent and objective blood sample might reflect to an extent the drug use among patients. Given the dose–response association between AUDIT-4 and the biological marker phosphatidylethanol, 24 the results from all three substance groups are to a great extent comparable to each other.

We were not able to distinguish between medical and non-medical use of psychoactive prescription drugs. Regardless of manner of use, examining their distribution, concomitant use and combination patterns with other substances in patients with multimorbidity is of importance.

Finally, the inclusion of acute diseases and other signs and symptoms might have inflated the prevalence of multimorbidity. 50 However, this might reflect a more realistic daily clinical practice, as previously suggested. 51

Conclusions

The observed association between multimorbidity and risky alcohol use, and psychoactive medicinal drug use among patients adds further value to the evidence on substances’ harms to health.

Our findings call for more research on multiple psychoactive substance use and multimorbidity. Research on the relationship between multimorbidity and substance use patterns and/or drug–drug interactions among medical patients at all ages is warranted. Consequently, this may have great implications for the clinical practice and public health.

Supplementary Material

Twitter: @AnnersLerdal

Contributors: SK drafted the manuscript and conducted the statistical data analyses. SK, VV, DG, BJ, AL and STB organised or contributed to the data acquisition. SK, TB and BJ organised and conducted the laboratory analyses. TAH and all authors were responsible for study design, interpretation of findings, critical revision of the article and final approval of the manuscript. STB is the guarantor of this study.

Funding: The study was partly sponsored by the Ministry of Health and Care Services, Oslo, Norway (Grant B-1408).

Disclaimer: The sponsor had no role in the study design, in the collection, analyses or interpretation of the data, in the writing of the report or the decision to submit for publication.

Competing interests: None declared.

Provenance and peer review: Not commissioned; externally peer reviewed.

Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Data availability statement

Ethics statements, patient consent for publication.

Not applicable.

Ethics approval

The study was approved by Regional Ethics Committee for South Eastern Norway (2015/2404).