observational vs experiment study

Observational vs. Experimental Study: A Comprehensive Guide

Explore the fundamental disparities between experimental and observational studies in this comprehensive guide by Santos Research Center, Corp. Uncover concepts such as control group, random sample, cohort studies, response variable, and explanatory variable that shape the foundation of these methodologies. Discover the significance of randomized controlled trials and case control studies, examining causal relationships and the role of dependent variables and independent variables in research designs.

This enlightening exploration also delves into the meticulous scientific study process, involving survey members, systematic reviews, and statistical analyses. Investigate the careful balance of control group and treatment group dynamics, highlighting how researchers meticulously assign variables and analyze statistical patterns to discern meaningful insights. From dissecting issues like lung cancer to understanding sleep patterns, this guide emphasizes the precision of controlled experiments and controlled trials, where variables are isolated and scrutinized, paving the way for a deeper comprehension of the world through empirical research.

Introduction to Observational and Experimental Studies

These two studies are the cornerstones of scientific inquiry, each offering a distinct approach to unraveling the mysteries of the natural world.

Observational studies allow us to observe, document, and gather data without direct intervention. They provide a means to explore real-world scenarios and trends, making them valuable when manipulating variables is not feasible or ethical. From surveys to meticulous observations, these studies shed light on existing conditions and relationships.

Experimental studies , in contrast, put researchers in the driver's seat. They involve the deliberate manipulation of variables to understand their impact on specific outcomes. By controlling the conditions, experimental studies establish causal relationships, answering questions of causality with precision. This approach is pivotal for hypothesis testing and informed decision-making.

At Santos Research Center, Corp., we recognize the importance of both observational and experimental studies. We employ these methodologies in our diverse research projects to ensure the highest quality of scientific investigation and to answer a wide range of research questions.

Observational Studies: A Closer Look

In our exploration of research methodologies, let's zoom in on observational research studies—an essential facet of scientific inquiry that we at Santos Research Center, Corp., expertly employ in our diverse research projects.

What is an Observational Study?

Observational research studies involve the passive observation of subjects without any intervention or manipulation by researchers. These studies are designed to scrutinize the relationships between variables and test subjects, uncover patterns, and draw conclusions grounded in real-world data.

Researchers refrain from interfering with the natural course of events in controlled experiment. Instead, they meticulously gather data by keenly observing and documenting information about the test subjects and their surroundings. This approach permits the examination of variables that cannot be ethically or feasibly manipulated, making it particularly valuable in certain research scenarios.

Types of Observational Studies

Now, let's delve into the various forms that observational studies can take, each with its distinct characteristics and applications.

Cohort Studies: A cohort study is a type of observational study that entails tracking one group of individuals over an extended period. Its primary goal is to identify potential causes or risk factors for specific outcomes or treatment group. Cohort studies provide valuable insights into the development of conditions or diseases and the factors that influence them.

Case-Control Studies: Case-control studies, on the other hand, involve the comparison of individuals with a particular condition or outcome to those without it (the control group). These studies aim to discern potential causal factors or associations that may have contributed to the development of the condition under investigation.

Cross-Sectional Studies: Cross-sectional studies take a snapshot of a diverse group of individuals at a single point in time. By collecting data from this snapshot, researchers gain insights into the prevalence of a specific condition or the relationships between variables at that precise moment. Cross-sectional studies are often used to assess the health status of the different groups within a population or explore the interplay between various factors.

Advantages and Limitations of Observational Studies

Observational studies, as we've explored, are a vital pillar of scientific research, offering unique insights into real-world phenomena. In this section, we will dissect the advantages and limitations that characterize these studies, shedding light on the intricacies that researchers grapple with when employing this methodology.

Advantages: One of the paramount advantages of observational studies lies in their utilization of real-world data. Unlike controlled experiments that operate in artificial settings, observational studies embrace the complexities of the natural world. This approach enables researchers to capture genuine behaviors, patterns, and occurrences as they unfold. As a result, the data collected reflects the intricacies of real-life scenarios, making it highly relevant and applicable to diverse settings and populations.

Moreover, in a randomized controlled trial, researchers looked to randomly assign participants to a group. Observational studies excel in their capacity to examine long-term trends. By observing one group of subjects over extended periods, research scientists gain the ability to track developments, trends, and shifts in behavior or outcomes. This longitudinal perspective is invaluable when studying phenomena that evolve gradually, such as chronic diseases, societal changes, or environmental shifts. It allows for the detection of subtle nuances that may be missed in shorter-term investigations.

Limitations: However, like any research methodology, observational studies are not without their limitations. One significant challenge of statistical study lies in the potential for biases. Since researchers do not intervene in the subjects' experiences, various biases can creep into the data collection process. These biases may arise from participant self-reporting, observer bias, or selection bias in random sample, among others. Careful design and rigorous data analysis are crucial for mitigating these biases.

Another limitation is the presence of confounding variables. In observational studies, it can be challenging to isolate the effect of a specific variable from the myriad of other factors at play. These confounding variables can obscure the true relationship between the variables of interest, making it difficult to establish causation definitively. Research scientists must employ statistical techniques to control for or adjust these confounding variables.

Additionally, observational studies face constraints in their ability to establish causation. While they can identify associations and correlations between variables, they cannot prove causality or causal relationship. Establishing causation typically requires controlled experiments where researchers can manipulate independent variables systematically. In observational studies, researchers can only infer potential causation based on the observed associations.

Experimental Studies: Delving Deeper

In the intricate landscape of scientific research, we now turn our gaze toward experimental studies—a dynamic and powerful method that Santos Research Center, Corp. skillfully employs in our pursuit of knowledge.

What is an Experimental Study?

While some studies observe and gather data passively, experimental studies take a more proactive approach. Here, researchers actively introduce an intervention or treatment to an experiment group study its effects on one or more variables. This methodology empowers researchers to manipulate independent variables deliberately and examine their direct impact on dependent variables.

Experimental research are distinguished by their exceptional ability to establish cause-and-effect relationships. This invaluable characteristic allows researchers to unlock the mysteries of how one variable influences another, offering profound insights into the scientific questions at hand. Within the controlled environment of an experimental study, researchers can systematically test hypotheses, shedding light on complex phenomena.

Key Features of Experimental Studies

Central to statistical analysis, the rigor and reliability of experimental studies are several key features that ensure the validity of their findings.

Randomized Controlled Trials: Randomization is a critical element in experimental studies, as it ensures that subjects are assigned to groups in a random assignment. This randomly assigned allocation minimizes the risk of unintentional biases and confounding variables, strengthening the credibility of the study's outcomes.

Control Groups: Control groups play a pivotal role in experimental studies by serving as a baseline for comparison. They enable researchers to assess the true impact of the intervention being studied. By comparing the outcomes of the intervention group to those of survey members of the control group, researchers can discern whether the intervention caused the observed changes.

Blinding: Both single-blind and double-blind techniques are employed in experimental studies to prevent biases from influencing the study or controlled trial's outcomes. Single-blind studies keep either the subjects or the researchers unaware of certain aspects of the study, while double-blind studies extend this blindness to both parties, enhancing the objectivity of the study.

These key features work in concert to uphold the integrity and trustworthiness of the results generated through experimental studies.

Advantages and Limitations of Experimental Studies

As with any research methodology, this one comes with its unique set of advantages and limitations.

Advantages: These studies offer the distinct advantage of establishing causal relationships between two or more variables together. The controlled environment allows researchers to exert authority over variables, ensuring that changes in the dependent variable can be attributed to the independent variable. This meticulous control results in high-quality, reliable data that can significantly contribute to scientific knowledge.

Limitations: However, experimental ones are not without their challenges. They may raise ethical concerns, particularly when the interventions involve potential risks to subjects. Additionally, their controlled nature can limit their real-world applicability, as the conditions in experiments may not accurately mirror those in the natural world. Moreover, executing an experimental study in randomized controlled, often demands substantial resources, with other variables including time, funding, and personnel.

Observational vs Experimental: A Side-by-Side Comparison

Having previously examined observational and experimental studies individually, we now embark on a side-by-side comparison to illuminate the key distinctions and commonalities between these foundational research approaches.

Key Differences and Notable Similarities

Methodologies

Observational Studies : Characterized by passive observation, where researchers collect data without direct intervention, allowing the natural course of events to unfold.
Experimental Studies : Involve active intervention, where researchers deliberately manipulate variables to discern their impact on specific outcomes, ensuring control over the experimental conditions.
Observational Studies : Designed to identify patterns, correlations, and associations within existing data, shedding light on relationships within real-world settings.
Experimental Studies : Geared toward establishing causality by determining the cause-and-effect relationships between variables, often in controlled laboratory environments.
Observational Studies : Yield real-world data, reflecting the complexities and nuances of natural phenomena.
Experimental Studies : Generate controlled data, allowing for precise analysis and the establishment of clear causal connections.

Observational studies excel at exploring associations and uncovering patterns within the intricacies of real-world settings, while experimental studies shine as the gold standard for discerning cause-and-effect relationships through meticulous control and manipulation in controlled environments. Understanding these differences and similarities empowers researchers to choose the most appropriate method for their specific research objectives.

When to Use Which: Practical Applications

The decision to employ either observational or experimental studies hinges on the research objectives at hand and the available resources. Observational studies prove invaluable when variable manipulation is impractical or ethically challenging, making them ideal for delving into long-term trends and uncovering intricate associations between certain variables (response variable or explanatory variable). On the other hand, experimental studies emerge as indispensable tools when the aim is to definitively establish causation and methodically control variables.

At Santos Research Center, Corp., our approach to both scientific study and methodology is characterized by meticulous consideration of the specific research goals. We recognize that the quality of outcomes hinges on selecting the most appropriate method of research study. Our unwavering commitment to employing both observational and experimental research studies further underscores our dedication to advancing scientific knowledge across diverse domains.

Conclusion: The Synergy of Experimental and Observational Studies in Research

In conclusion, both observational and experimental studies are integral to scientific research, offering complementary approaches with unique strengths and limitations. At Santos Research Center, Corp., we leverage these methodologies to contribute meaningfully to the scientific community.

Explore our projects and initiatives at Santos Research Center, Corp. by visiting our website or contacting us at (813) 249-9100, where our unwavering commitment to rigorous research practices and advancing scientific knowledge awaits.

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Experiment vs Observational Study: Similarities & Differences

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experiment vs observational study, explained below

An experiment involves the deliberate manipulation of variables to observe their effect, while an observational study involves collecting data without interfering with the subjects or variables under study.

This article will explore both, but let’s start with some quick explanations:

Experimental Study : An experiment is a research design wherein an investigator manipulates one or more variables to establish a cause-effect relationship (Tan, 2022). For example, a pharmaceutical company may conduct an experiment to find out if a new medicine for diabetes is effective by administering it to a selected group (experimental group), while not administering it to another group (control group).
Observational Study : An observational study is a type of research wherein the researcher observes characteristics and measures variables of interest in a subset of a population, but does not manipulate or intervene (Atkinson et al., 2021). An example may be a sociologist who conducts a cross-sectional survey of the population to determine health disparities across different income groups.

Experiment vs Observational Study

1. experiment.

An experiment is a research method characterized by a high degree of experimental control exerted by the researcher. In the context of academia, it allows for the testing of causal hypotheses (Privitera, 2022).

When conducting an experiment, the researcher first formulates a hypothesis , which is a predictive statement about the potential relationship between at least two variables.

For instance, a psychologist may want to test the hypothesis that participation in physical exercise ( independent variable ) improves the cognitive abilities (dependent variable) of the elderly.

In an experiment, the researcher manipulates the independent variable(s) and then observes the effects on the dependent variable(s). This method of research involves two or more comparison groups—an experimental group that is subjected to the variable being tested and a control group that is not (Sampselle, 2012).

For instance, in the physical exercise study noted above, the psychologist would administer a physical exercise regime to an experimental group of elderly people, while a control group would continue with their usual lifestyle activities .

One of the unique features of an experiment is random assignment . Participants are randomly allocated to either the experimental or control groups to ensure that every participant has an equal chance of being in either group. This reduces the risk of confounding variables and increases the likelihood that the results are attributable to the independent variable rather than another factor (Eich, 2014).

For instance, in the physical exercise example, the psychologist would randomly assign participants to the experimental or control group to reduce the potential impact of external variables such as diet or sleep patterns.

1. Impacts of Films on Happiness: A psychologist might create an experimental study where she shows participants either a happy, sad, or neutral film (independent variable) then measures their mood afterward (dependent variable). Participants would be randomly assigned to one of the three film conditions.

2. Impacts of Exercise on Weight Loss: In a fitness study, a trainer could investigate the impact of a high-intensity interval training (HIIT) program on weight loss. Half of the participants in the study are randomly selected to follow the HIIT program (experimental group), while the others follow a standard exercise routine (control group).

3. Impacts of Veganism on Cholesterol Levels: A nutritional experimenter could study the effects of a particular diet, such as veganism, on cholesterol levels. The chosen population gets assigned either to adopt a vegan diet (experimental group) or stick to their usual diet (control group) for a specific period, after which cholesterol levels are measured.

Read More: Examples of Random Assignment

Strengths and Weaknesses


1. Able to establish cause-and-effect relationships due to direct manipulation of variables.	1. Potential lack of ecological validity: results may not apply to real-world scenarios due to the artificial, controlled environment.
2. High level of control reduces the influence of confounding variables.	2. Ethical constraints may limit the types of manipulations possible.
3. Replicable if well-documented, enabling others to validate or challenge results.	3. Can be costly and time-consuming to implement and control all variables.

Read More: Experimental Research Examples

2. Observational Study

Observational research is a non-experimental research method in which the researcher merely observes the subjects and notes behaviors or responses that occur (Ary et al., 2018).

This approach is unintrusive in that there is no manipulation or control exerted by the researcher. For instance, a researcher could study the relationships between traffic congestion and road rage by just observing and recording behaviors at a set of busy traffic lights, without applying any control or altering any variables.

In observational studies, the researcher distinguishes variables and measures their values as they naturally occur. The goal is to capture naturally occurring behaviors , conditions, or events (Ary et al., 2018).

For example, a sociologist might sit in a cafe to observe and record interactions between staff and customers in order to examine social and occupational roles .

There is a significant advantage of observational research in that it provides a high level of ecological validity – the extent to which the data collected reflects real-world situations – as the behaviors and responses are observed in a natural setting without experimenter interference (Holleman et al., 2020)

However, the inability to control various factors that might influence the observations may expose these studies to potential confounding bias , a consideration researchers must take into account (Schober & Vetter, 2020).

1. Behavior of Animals in the Wild: Zoologists often use observational studies to understand the behaviors and interactions of animals in their natural habitats. For instance, a researcher could document the social structure and mating behaviors of a wolf pack over a period of time.

2. Impact of Office Layout on Productivity: A researcher in organizational psychology might observe how different office layouts affect staff productivity and collaboration. This involves the observation and recording of staff interactions and work output without altering the office setting.

3. Foot Traffic and Retail Sales: A market researcher might conduct an observational study on how foot traffic (the number of people passing by a store) impacts retail sales. This could involve observing and documenting the number of walk-ins, time spent in-store, and purchase behaviors.

Read More: Observational Research Examples


1. Captures data in natural, real-world environments, increasing ecological validity.	1. Cannot establish cause-and-effect relationships due to lack of variable manipulation.
2. Can study phenomena that would be unethical or impractical to manipulate in an experiment.	2. Potential for confounding variables that influence the observed outcomes.
3. Generally less costly and time-consuming than experimental research.	3. Issues of observer bias or subjective interpretation can affect results.

Experimental and Observational Study Similarities and Differences

Experimental and observational research both have their place – one is right for one situation, another for the next.

Experimental research is best employed when the aim of the study is to establish cause-and-effect relationships between variables – that is, when there is a need to determine the impact of specific changes on the outcome (Walker & Myrick, 2016).

One of the standout features of experimental research is the control it gives to the researcher, who dictates how variables should be changed and assigns participants to different conditions (Privitera, 2022). This makes it an excellent choice for medical or pharmaceutical studies, behavioral interventions, and any research where hypotheses concerning influence and change need to be tested.

For example, a company might use experimental research to understand the effects of staff training on job satisfaction and productivity.

Observational research , on the other hand, serves best when it’s vital to capture the phenomena in their natural state, without intervention, or when ethical or practical considerations prevent the researcher from manipulating the variables of interest (Creswell & Poth, 2018).

It is the method of choice when the interest of the research lies in describing what is, rather than altering a situation to see what could be (Atkinson et al., 2021).

This approach might be utilized in studies that aim to describe patterns of social interaction, daily routines, user experiences, and so on. A real-world example of observational research could be a study examining the interactions and learning behaviors of students in a classroom setting.

I’ve demonstrated their similarities and differences a little more in the table below:


	To determine cause-and-effect relationships by manipulating variables.	To explore associations and correlations between variables without any manipulation.
Control	High level of control. The researcher determines and adjusts the conditions and variables.	Low level of control. The researcher observes but does not intervene with the variables or conditions.
Causality	Able to establish causality due to direct manipulation of variables.	Cannot establish causality, only correlations due to lack of variable manipulation.
Generalizability	Sometimes limited due to the controlled and often artificial conditions (lack of ecological validity).	Higher, as observations are typically made in more naturalistic settings.
Ethical Considerations	Some ethical limitations due to the direct manipulation of variables, especially if they could harm the subjects.	Fewer ethical concerns as there’s no manipulation, but privacy and informed consent are important when observing and recording data.
Data Collection	Often uses controlled tests, measurements, and tasks under specified conditions.	Often uses , surveys, interviews, or existing data sets.
Time and Cost	Can be time-consuming and costly due to the need for strict controls and sometimes large sample sizes.	Generally less time-consuming and costly as data are often collected from real-world settings without strict control.
Suitability	Best for testing hypotheses, particularly those involving .	Best for exploring phenomena in real-world contexts, particularly when manipulation is not possible or ethical.
Replicability	High, as conditions are controlled and can be replicated by other researchers.	Low to medium, as conditions are natural and cannot be precisely recreated.
Bias	or experimenter bias affecting the results.	Risk of observer bias, , and confounding variables affecting the results.

Experimental and observational research each have their place, depending upon the study. Importantly, when selecting your approach, you need to reflect upon your research goals and objectives, and select from the vast range of research methodologies , which you can read up on in my next article, the 15 types of research designs .

Ary, D., Jacobs, L. C., Irvine, C. K. S., & Walker, D. (2018). Introduction to research in education . London: Cengage Learning.

Atkinson, P., Delamont, S., Cernat, A., Sakshaug, J. W., & Williams, R. A. (2021). SAGE research methods foundations . New York: SAGE Publications Ltd.

Creswell, J.W., and Poth, C.N. (2018). Qualitative Inquiry and Research Design: Choosing among Five Approaches . New York: Sage Publications.

Eich, E. (2014). Business Research Methods: A Radically Open Approach . Frontiers Media SA.

Holleman, G. A., Hooge, I. T., Kemner, C., & Hessels, R. S. (2020). The ‘real-world approach’and its problems: A critique of the term ecological validity. Frontiers in Psychology , 11 , 721. doi: https://doi.org/10.3389/fpsyg.2020.00721

Privitera, G. J. (2022). Research methods for the behavioral sciences . Sage Publications.

Sampselle, C. M. (2012). The Science and Art of Nursing Research . South University Online Press.

Schober, P., & Vetter, T. R. (2020). Confounding in observational research. Anesthesia & Analgesia , 130 (3), 635.

Tan, W. C. K. (2022). Research methods: A practical guide for students and researchers . World Scientific.

Walker, D., and Myrick, F. (2016). Grounded Theory: An Exploration of Process and Procedure . New York: Qualitative Health Research.

Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 25 Number Games for Kids (Free and Easy)
Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 25 Word Games for Kids (Free and Easy)
Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 25 Outdoor Games for Kids
Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 50 Incentives to Give to Students

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What Is an Observational Study? | Guide & Examples

What Is an Observational Study? | Guide & Examples

Published on March 31, 2022 by Tegan George . Revised on June 22, 2023.

An observational study is used to answer a research question based purely on what the researcher observes. There is no interference or manipulation of the research subjects, and no control and treatment groups .

These studies are often qualitative in nature and can be used for both exploratory and explanatory research purposes. While quantitative observational studies exist, they are less common.

Observational studies are generally used in hard science, medical, and social science fields. This is often due to ethical or practical concerns that prevent the researcher from conducting a traditional experiment . However, the lack of control and treatment groups means that forming inferences is difficult, and there is a risk of confounding variables and observer bias impacting your analysis.

Types of observation, types of observational studies, observational study example, advantages and disadvantages of observational studies, observational study vs. experiment, other interesting articles, frequently asked questions.

There are many types of observation, and it can be challenging to tell the difference between them. Here are some of the most common types to help you choose the best one for your observational study.


	The researcher observes how the participants respond to their environment in “real-life” settings but does not influence their behavior in any way	Observing monkeys in a zoo enclosure
	Also occurs in “real-life” settings, but here, the researcher immerses themselves in the participant group over a period of time	Spending a few months in a hospital with patients suffering from a particular illness
	Utilizing coding and a strict observational schedule, researchers observe participants in order to count how often a particular phenomenon occurs	Counting the number of times children laugh in a classroom
	Hinges on the fact that the participants do not know they are being observed	Observing interactions in public spaces, like bus rides or parks
	Involves counting or numerical data	Observations related to age, weight, or height
	Involves “five senses”: sight, sound, smell, taste, or hearing	Observations related to colors, sounds, or music
	Investigates a person or group of people over time, with the idea that close investigation can later be to other people or groups	Observing a child or group of children over the course of their time in elementary school
	Utilizes primary sources from libraries, archives, or other repositories to investigate a	Analyzing US Census data or telephone records

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There are three main types of observational studies: cohort studies, case–control studies, and cross-sectional studies .

Cohort studies

Cohort studies are more longitudinal in nature, as they follow a group of participants over a period of time. Members of the cohort are selected because of a shared characteristic, such as smoking, and they are often observed over a period of years.

Case–control studies

Case–control studies bring together two groups, a case study group and a control group . The case study group has a particular attribute while the control group does not. The two groups are then compared, to see if the case group exhibits a particular characteristic more than the control group.

For example, if you compared smokers (the case study group) with non-smokers (the control group), you could observe whether the smokers had more instances of lung disease than the non-smokers.

Cross-sectional studies

Cross-sectional studies analyze a population of study at a specific point in time.

This often involves narrowing previously collected data to one point in time to test the prevalence of a theory—for example, analyzing how many people were diagnosed with lung disease in March of a given year. It can also be a one-time observation, such as spending one day in the lung disease wing of a hospital.

Observational studies are usually quite straightforward to design and conduct. Sometimes all you need is a notebook and pen! As you design your study, you can follow these steps.

Step 1: Identify your research topic and objectives

The first step is to determine what you’re interested in observing and why. Observational studies are a great fit if you are unable to do an experiment for practical or ethical reasons , or if your research topic hinges on natural behaviors.

Step 2: Choose your observation type and technique

In terms of technique, there are a few things to consider:

Are you determining what you want to observe beforehand, or going in open-minded?
Is there another research method that would make sense in tandem with an observational study?
If yes, make sure you conduct a covert observation.
If not, think about whether observing from afar or actively participating in your observation is a better fit.
How can you preempt confounding variables that could impact your analysis?
You could observe the children playing at the playground in a naturalistic observation.
You could spend a month at a day care in your town conducting participant observation, immersing yourself in the day-to-day life of the children.
You could conduct covert observation behind a wall or glass, where the children can’t see you.

Overall, it is crucial to stay organized. Devise a shorthand for your notes, or perhaps design templates that you can fill in. Since these observations occur in real time, you won’t get a second chance with the same data.

Step 3: Set up your observational study

Before conducting your observations, there are a few things to attend to:

Plan ahead: If you’re interested in day cares, you’ll need to call a few in your area to plan a visit. They may not all allow observation, or consent from parents may be needed, so give yourself enough time to set everything up.
Determine your note-taking method: Observational studies often rely on note-taking because other methods, like video or audio recording, run the risk of changing participant behavior.
Get informed consent from your participants (or their parents) if you want to record: Ultimately, even though it may make your analysis easier, the challenges posed by recording participants often make pen-and-paper a better choice.

Step 4: Conduct your observation

After you’ve chosen a type of observation, decided on your technique, and chosen a time and place, it’s time to conduct your observation.

Here, you can split them into case and control groups. The children with siblings have a characteristic you are interested in (siblings), while the children in the control group do not.

When conducting observational studies, be very careful of confounding or “lurking” variables. In the example above, you observed children as they were dropped off, gauging whether or not they were upset. However, there are a variety of other factors that could be at play here (e.g., illness).

Step 5: Analyze your data

After you finish your observation, immediately record your initial thoughts and impressions, as well as follow-up questions or any issues you perceived during the observation. If you audio- or video-recorded your observations, you can transcribe them.

Your analysis can take an inductive or deductive approach :

If you conducted your observations in a more open-ended way, an inductive approach allows your data to determine your themes.
If you had specific hypotheses prior to conducting your observations, a deductive approach analyzes whether your data confirm those themes or ideas you had previously.

Next, you can conduct your thematic or content analysis . Due to the open-ended nature of observational studies, the best fit is likely thematic analysis .

Step 6: Discuss avenues for future research

Observational studies are generally exploratory in nature, and they often aren’t strong enough to yield standalone conclusions due to their very high susceptibility to observer bias and confounding variables. For this reason, observational studies can only show association, not causation .

If you are excited about the preliminary conclusions you’ve drawn and wish to proceed with your topic, you may need to change to a different research method , such as an experiment.

Observational studies can provide information about difficult-to-analyze topics in a low-cost, efficient manner.
They allow you to study subjects that cannot be randomized safely, efficiently, or ethically .
They are often quite straightforward to conduct, since you just observe participant behavior as it happens or utilize preexisting data.
They’re often invaluable in informing later, larger-scale clinical trials or experimental designs.

Disadvantages

Observational studies struggle to stand on their own as a reliable research method. There is a high risk of observer bias and undetected confounding variables or omitted variables .
They lack conclusive results, typically are not externally valid or generalizable, and can usually only form a basis for further research.
They cannot make statements about the safety or efficacy of the intervention or treatment they study, only observe reactions to it. Therefore, they offer less satisfying results than other methods.

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The key difference between observational studies and experiments is that a properly conducted observational study will never attempt to influence responses, while experimental designs by definition have some sort of treatment condition applied to a portion of participants.

However, there may be times when it’s impossible, dangerous, or impractical to influence the behavior of your participants. This can be the case in medical studies, where it is unethical or cruel to withhold potentially life-saving intervention, or in longitudinal analyses where you don’t have the ability to follow your group over the course of their lifetime.

An observational study may be the right fit for your research if random assignment of participants to control and treatment groups is impossible or highly difficult. However, the issues observational studies raise in terms of validity , confounding variables, and conclusiveness can mean that an experiment is more reliable.

If you’re able to randomize your participants safely and your research question is definitely causal in nature, consider using an experiment.

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

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

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 .

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 .

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.

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.

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.

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What is an Observational Study: Definition & Examples

By Jim Frost 10 Comments

What is an Observational Study?

An observational study uses sample data to find correlations in situations where the researchers do not control the treatment, or independent variable, that relates to the primary research question. The definition of an observational study hinges on the notion that the researchers only observe subjects and do not assign them to the control and treatment groups. That’s the key difference between an observational study vs experiment. These studies are also known as quasi-experiments and correlational studies .

True experiments assign subject to the experimental groups where the researchers can manipulate the conditions. Unfortunately, random assignment is not always possible. For these cases, you can conduct an observational study.

In this post, learn about the types of observational studies, why they are susceptible to confounding variables, and how they compare to experiments. I’ll close this post by reviewing a published observational study about vitamin supplement usage.

Observational Study Definition

In an observational study, the researchers only observe the subjects and do not interfere or try to influence the outcomes. In other words, the researchers do not control the treatments or assign subjects to experimental groups. Instead, they observe and measure variables of interest and look for relationships between them. Usually, researchers conduct observational studies when it is difficult, impossible, or unethical to assign study participants to the experimental groups randomly. If you can’t randomly assign subjects to the treatment and control groups, then you observe the subjects in their self-selected states.

Observational Study vs Experiment

Randomized experiments provide better results than observational studies. Consequently, you should always use a randomized experiment whenever possible. However, if randomization is not possible, science should not come to a halt. After all, we still want to learn things, discover relationships, and make discoveries. For these cases, observational studies are a good alternative to a true experiment. Let’s compare the differences between an observational study vs. an experiment.

Random assignment in an experiment reduces systematic differences between experimental groups at the beginning of the study, which increases your confidence that the treatments caused any differences between groups you observe at the end of the study. In contrast, an observational study uses self-formed groups that can have pre-existing differences, which introduces the problem of confounding variables. More on that later!

In a randomized experiment, randomization tends to equalize confounders between groups and, thereby, prevents problems. In my post about random assignment , I describe that process as an elegant solution for confounding variables. You don’t need to measure or even know which variables are confounders, and randomization will still mitigate their effects. Additionally, you can use control variables in an experiment to keep the conditions as consistent as possible. For more detail about the differences, read Observational Study vs. Experiment .



Does not assign subjects to groups	Randomly assigns subjects to control and treatment groups
Does not control variables that can affect outcome	Administers treatments and controls influence of other variables
Correlational findings. Differences might be due to confounders rather than the treatment	More confident that treatments cause the differences in outcomes

If you’re looking for a middle ground choice between observational studies vs experiments, consider using a quasi-experimental design. These methods don’t require you to randomly assign participants to the experimental groups and still allow you to draw better causal conclusions about an intervention than an observational study. Learn more about Quasi-Experimental Design Overview & Examples .

Related posts : Experimental Design: Definition and Examples , Randomized Controlled Trials (RCTs) , and Control Groups in Experiments

Observational Study Examples

Photograph of a person observing to illustrate an observational study.

Consider using an observational study when random assignment for an experiment is problematic. This approach allows us to proceed and draw conclusions about effects even though we can’t control the independent variables. The following observational study examples will help you understand when and why to use them.

For example, if you’re studying how depression affects performance of an activity, it’s impossible to assign subjects to the depression and control group randomly. However, you can have subjects with and without depression perform the activity and compare the results in an observational study.

Or imagine trying to assign subjects to cigarette smoking and non-smoking groups randomly?! However, you can observe people in both groups and assess the differences in health outcomes in an observational study.

Suppose you’re studying a treatment for a disease. Ideally, you recruit a group of patients who all have the disease, and then randomly assign them to the treatment and control group. However, it’s unethical to withhold the treatment, which rules out a control group. Instead, you can compare patients who voluntarily do not use the medicine to those who do use it.

In all these observational study examples, the researchers do not assign subjects to the experimental groups. Instead, they observe people who are already in these groups and compare the outcomes. Hence, the scientists must use an observational study vs. an experiment.

Types of Observational Studies

The observational study definition states that researchers only observe the outcomes and do not manipulate or control factors . Despite this limitation, there various types of observational studies.

The following experimental designs are three standard types of observational studies.

Cohort Study : A longitudinal observational study that follows a group who share a defining characteristic. These studies frequently determine whether exposure to risk factor affects an outcome over time.
Case-Control Study : A retrospective observational study that compares two existing groups—the case group with the condition and the control group without it. Researchers compare the groups looking for potential risk factors for the condition.
Cross-Sectional Study : Takes a snapshot of a moment in time so researchers can understand the prevalence of outcomes and correlations between variables at that instant.

Qualitative research studies are usually observational in nature, but they collect non-numeric data and do not perform statistical analyses.

Retrospective studies must be observational.

Later in this post, we’ll closely examine a quantitative observational study example that assesses vitamin supplement consumption and how that affects the risk of death. It’s possible to use random assignment to place each subject in either the vitamin treatment group or the control group. However, the study assesses vitamin consumption in 40,000 participants over the course of two decades. It’s unrealistic to enforce the treatment and control protocols over such a long time for so many people!

Drawbacks of Observational Studies

While observational studies get around the inability to assign subjects randomly, this approach opens the door to the problem of confounding variables. A confounding variable, or confounder, correlates with both the experimental groups and the outcome variable. Because there is no random process that equalizes the experimental groups in an observational study, confounding variables can systematically differ between groups when the study begins. Consequently, confounders can be the actual cause for differences in outcome at the end of the study rather than the primary variable of interest. If an experiment does not account for confounding variables, confounders can bias the results and create spurious correlations .

Performing an observational study can decrease the internal validity of your study but increase the external validity. Learn more about internal and external validity .

Let’s see how this works. Imagine an observational study that compares people who take vitamin supplements to those who do not. People who use vitamin supplements voluntarily will tend to have other healthy habits that exist at the beginning of the study. These healthy habits are confounding variables. If there are differences in health outcomes at the end of the study, it’s possible that these healthy habits actually caused them rather than the vitamin consumption itself. In short, confounders confuse the results because they provide alternative explanations for the differences.

Despite the limitations, an observational study can be a valid approach. However, you must ensure that your research accounts for confounding variables. Fortunately, there are several methods for doing just that!

Learn more about Correlation vs. Causation: Understanding the Differences .

Accounting for Confounding Variables in an Observational Study

Because observational studies don’t use random assignment, confounders can be distributed disproportionately between conditions. Consequently, experimenters need to know which variables are confounders, measure them, and then use a method to account for them. It involves more work, and the additional measurements can increase the costs. And there’s always a chance that researchers will fail to identify a confounder, not account for it, and produce biased results. However, if randomization isn’t an option, then you probably need to consider an observational study.

Trait matching and statistically controlling confounders using multivariate procedures are two standard approaches for incorporating confounding variables.

Related post : Causation versus Correlation in Statistics

Matching in Observational Studies

Matching is a technique that involves selecting study participants with similar characteristics outside the variable of interest or treatment. Rather than using random assignment to equalize the experimental groups, the experimenters do it by matching observable characteristics. For every participant in the treatment group, the researchers find a participant with comparable traits to include in the control group. Matching subjects facilitates valid comparisons between those groups. The researchers use subject-area knowledge to identify characteristics that are critical to match.

For example, a vitamin supplement study using matching will select subjects who have similar health-related habits and attributes. The goal is that vitamin consumption will be the primary difference between the groups, which helps you attribute differences in health outcomes to vitamin consumption. However, the researchers are still observing participants who decide whether they consume supplements.

Matching has some drawbacks. The experimenters might not be aware of all the relevant characteristics they need to match. In other words, the groups might be different in an essential aspect that the researchers don’t recognize. For example, in the hypothetical vitamin study, there might be a healthy habit or attribute that affects the outcome that the researchers don’t measure and match. These unmatched characteristics might cause the observed differences in outcomes rather than vitamin consumption.

Learn more about Matched Pairs Design: Uses & Examples .

Using Multiple Regression in Observational Studies

Random assignment and matching use different methods to equalize the experimental groups in an observational study. However, statistical techniques, such as multiple regression analysis , don’t try to equalize the groups but instead use a model that accounts for confounding variables. These studies statistically control for confounding variables.

In multiple regression analysis, including a variable in the model holds it constant while you vary the variable/treatment of interest. For information about this property, read my post When Should I Use Regression Analysis?

As with matching, the challenge is to identify, measure, and include all confounders in the regression model. Failure to include a confounding variable in a regression model can cause omitted variable bias to distort your results.

Next, we’ll look at a published observational study that uses multiple regression to account for confounding variables.

Related post : Independent and Dependent Variables in a Regression Model

Vitamin Supplement Observational Study Example

Vitamins for the example of an observational study.

Murso et al. (2011)* use a longitudinal observational study that ran 22 years to assess differences in death rates for subjects who used vitamin supplements regularly compared to those who did not use them. This study used surveys to record the characteristics of approximately 40,000 participants. The surveys asked questions about potential confounding variables such as demographic information, food intake, health details, physical activity, and, of course, supplement intake.

Because this is an observational study, the subjects decided for themselves whether they were taking vitamin supplements. Consequently, it’s safe to assume that supplement users and non-users might be different in other ways. From their article, the researchers found the following pre-existing differences between the two groups:

Supplement users had a lower prevalence of diabetes mellitus, high blood pressure, and smoking status; a lower BMI and waist to hip ratio, and were less likely to live on a farm. Supplement users had a higher educational level, were more physically active and were more likely to use estrogen replacement therapy. Also, supplement users were more likely to have a lower intake of energy, total fat, and monounsaturated fatty acids, saturated fatty acids and to have a higher intake of protein, carbohydrates, polyunsaturated fatty acids, alcohol, whole grain products, fruits, and vegetables.

Whew! That’s a long list of differences! Supplement users were different from non-users in a multitude of ways that are likely to affect their risk of dying. The researchers must account for these confounding variables when they compare supplement users to non-users. If they do not, their results can be biased.

This example illustrates a key difference between an observational study vs experiment. In a randomized experiment, the randomization would have equalized the characteristics of those the researchers assigned to the treatment and control groups. Instead, the study works with self-sorted groups that have numerous pre-existing differences!

Using Multiple Regression to Statistically Control for Confounders

To account for these initial differences in the vitamin supplement observational study, the researchers use regression analysis and include the confounding variables in the model.

The researchers present three regression models. The simplest model accounts only for age and caloric intake. Next, are two models that include additional confounding variables beyond age and calories. The first model adds various demographic information and seven health measures. The second model includes everything in the previous model and adds several more specific dietary intake measures. Using statistical significance as a guide for specifying the correct regression model , the researchers present the model with the most variables as the basis for their final results.

It’s instructive to compare the raw results and the final regression results.

Raw results

The raw differences in death risks for consumers of folic acid, vitamin B6, magnesium, zinc, copper, and multivitamins are NOT statistically significant. However, the raw results show a significant reduction in the death risk for users of B complex, C, calcium, D, and E.

However, those are the raw results for the observational study, and they do not control for the long list of differences between the groups that exist at the beginning of the study. After using the regression model to control for the confounding variables statistically, the results change dramatically.

Adjusted results

Of the 15 supplements that the study tracked in the observational study, researchers found consuming seven of these supplements were linked to a statistically significant INCREASE in death risk ( p-value < 0.05): multivitamins (increase in death risk 2.4%), vitamin B6 (4.1%), iron (3.9%), folic acid (5.9%), zinc (3.0%), magnesium (3.6%), and copper (18.0%). Only calcium was associated with a statistically significant reduction in death risk of 3.8%.

In short, the raw results suggest that those who consume supplements either have the same or lower death risks than non-consumers. However, these results do not account for the multitude of healthier habits and attributes in the group that uses supplements.

In fact, these confounders seem to produce most of the apparent benefits in the raw results because, after you statistically control the effects of these confounding variables, the results worsen for those who consume vitamin supplements. The adjusted results indicate that most vitamin supplements actually increase your death risk!

This research illustrates the differences between an observational study vs experiment. Namely how the pre-existing differences between the groups allow confounders to bias the raw results, making the vitamin consumption outcomes look better than they really are.

In conclusion, if you can’t randomly assign subjects to the experimental groups, an observational study might be right for you. However, be aware that you’ll need to identify, measure, and account for confounding variables in your experimental design.

Jaakko Mursu, PhD; Kim Robien, PhD; Lisa J. Harnack, DrPH, MPH; Kyong Park, PhD; David R. Jacobs Jr, PhD; Dietary Supplements and Mortality Rate in Older Women: The Iowa Women’s Health Study ; Arch Intern Med . 2011;171(18):1625-1633.

Reader Interactions

December 30, 2023 at 5:05 am

I see, but our professor required us to indicate what year it was put into the article. May you tell me what year was this published originally? <3

December 30, 2023 at 3:40 pm

December 29, 2023 at 10:46 am

Hi, may I use your article as a citation for my thesis paper? If so, may I know the exact date you published this article? Thank you!

December 29, 2023 at 2:13 pm

Definitely feel free to cite this article! 🙂

When citing online resources, you typically use an “Accessed” date rather than a publication date because online content can change over time. For more information, read Purdue University’s Citing Electronic Resources .

November 18, 2021 at 10:09 pm

Love your content and has been very helpful!

Can you please advise the question below using an observational data set:

I have three years of observational GPS data collected on athletes (2019/2020/2021). Approximately 14-15 athletes per game and 8 games per year. The GPS software outputs 50+ variables for each athlete in each game, which we have narrowed down to 16 variables of interest from previous research.

2 factors 1) Period (first half, second half, and whole game), 2) Position (two groups with three subgroups in each – forwards (group 1, group 2, group 3) and backs (group 1, group 2, group 3))

16 variables of interest – all numerical and scale variables. Some of these are correlated, but not all.

My understanding is that I can use a oneway ANOVA for each year on it’s own, using one factor at a time (period or position) with post hoc analysis. This is fine, if data meets assumptions and is normally distributed. This tells me any significant interactions between variables of interest with chosen factor. For example, with position factor, do forwards in group 1 cover more total running distance than forwards in group 2 or backs in group 3.

However, I want to go deeper with my analysis. If I want to see if forwards in group 1 cover more total running distance in period 1 than backs in group 3 in the same period, I need an additional factor and the oneway ANOVA does not suit. Therefore I can use a twoway ANOVA instead of 2 oneway ANOVA’s and that solves the issue, correct?

This is complicated further by looking to compare 2019 to 2020 or 2019 to 2021 to identify changes over time, which would introduce a third independent variable.

I believe this would require a threeway ANOVA for this observational data set. 3 factors – Position, Period, and Year?

Are there any issues or concerns you see at first glance?

I appreciate your time and consideration.

April 12, 2021 at 2:02 pm

Could an observational study use a correlational design.

e.g. measuring effects of two variables on happiness, if you’re not intervening.

April 13, 2021 at 12:14 am

Typically, with observational studies, you’d want to include potential confounders, etc. Consequently, I’ve seen regression analysis used more frequently for observational studies to be able to control for other things because you’re not using randomization. You could use correlation to observe the relationship. However, you wouldn’t be controlling for potential confounding variables. Just something to consider.

April 11, 2021 at 1:28 pm

Hi, If I am to administer moderate doses of coffee for a hypothetical experiment, does it raise ethical concerns? Can I use random assignment for it?

April 11, 2021 at 4:06 pm

I don’t see any inherent ethical problems here as long as you describe the participant’s experience in the experiment including the coffee consumption. They key with human subjects is “informed consent.” They’re agreeing to participate based on a full and accurate understanding of what participation involves. Additionally, you as a researcher, understand the process well enough to be able to ensure their safety.

In your study, as long as subject know they’ll be drinking coffee and agree to that, I don’t see a problem. It’s a proven safe substance for the vast majority of people. If potential subjects are aware of the need to consume coffee, they can determine whether they are ok with that before agreeing to participate.

June 17, 2019 at 4:51 am

Really great article which explains observational and experimental study very well. It presents broad picture with the case study which helped a lot in understanding the core concepts. Thanks

Comments and Questions Cancel reply

Experimental Studies and Observational Studies

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Definitions

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In observational studies, investigators observe or assess individuals without manipulation or intervention. Observational studies are used for assessing the mean levels, the natural variation, and the structure of variables, as well as...

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What Is an Observational Study? | Guide & Examples

What Is an Observational Study? | Guide & Examples

Published on 5 April 2022 by Tegan George . Revised on 20 March 2023.

These studies are often qualitative in nature and can be used for both exploratory and explanatory research purposes. While quantitative observational studies exist, they are less common.

Types of observation, types of observational studies, observational study example, advantages and disadvantages of observational studies, observational study vs experiment, frequently asked questions.


	The researcher observes how the participants respond to their environment in ‘real-life’ settings but does not influence their behavior in any way	Observing monkeys in a zoo enclosure
	Also occurs in ‘real-life’ settings, but here, the researcher immerses themselves in the participant group over a period of time	Spending a few months in a hospital with patients suffering from a particular illness
	Utilising coding and a strict observational schedule, researchers observe participants in order to count how often a particular phenomenon occurs	Counting the number of times children laugh in a classroom
	Hinges on the fact that the participants do not know they are being observed	Observing interactions in public spaces, like bus rides or parks
	Involves counting or numerical data	Observations related to age, weight, or height
	Involves ‘five senses’: sight, sound, smell, taste, or hearing	Observations related to colors, sounds, or music
	Investigates a person or group of people over time, with the idea that close investigation can later be to other people or groups	Observing a child or group of children over the course of their time in elementary school
	Utilises primary sources from libraries, archives, or other repositories to investigate a research question	Analysing US Census data or telephone records

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There are three main types of observational studies: cohort studies, case–control studies, and cross-sectional studies.

Cohort studies

Case–control studies

For example, if you compared smokers (the case study group) with non-smokers (the control group), you could observe whether the smokers had more instances of lung disease than the non-smokers.

Cross-sectional studies

Cross-sectional studies analyse a population of study at a specific point in time.

This often involves narrowing previously collected data to one point in time to test the prevalence of a theory—for example, analysing how many people were diagnosed with lung disease in March of a given year. It can also be a one-time observation, such as spending one day in the lung disease wing of a hospital.

Observational studies are usually quite straightforward to design and conduct. Sometimes all you need is a notebook and pen! As you design your study, you can follow these steps.

Step 1: Identify your research topic and objectives

The first step is to determine what you’re interested in observing and why. Observational studies are a great fit if you are unable to do an experiment for ethical or practical reasons, or if your research topic hinges on natural behaviors.

Step 2: Choose your observation type and technique

In terms of technique, there are a few things to consider:

Are you determining what you want to observe beforehand, or going in open-minded?
Is there another research method that would make sense in tandem with an observational study?
If yes, make sure you conduct a covert observation.
If not, think about whether observing from afar or actively participating in your observation is a better fit.
How can you preempt confounding variables that could impact your analysis?
You could observe the children playing at the playground in a naturalistic observation.
You could spend a month at a day care in your town conducting participant observation, immersing yourself in the day-to-day life of the children.
You could conduct covert observation behind a wall or glass, where the children can’t see you.

Overall, it is crucial to stay organised. Devise a shorthand for your notes, or perhaps design templates that you can fill in. Since these observations occur in real time, you won’t get a second chance with the same data.

Step 3: Set up your observational study

Before conducting your observations, there are a few things to attend to:

Plan ahead: If you’re interested in day cares, you’ll need to call a few in your area to plan a visit. They may not all allow observation, or consent from parents may be needed, so give yourself enough time to set everything up.
Determine your note-taking method: Observational studies often rely on note-taking because other methods, like video or audio recording, run the risk of changing participant behavior.
Get informed consent from your participants (or their parents) if you want to record: Ultimately, even though it may make your analysis easier, the challenges posed by recording participants often make pen-and-paper a better choice.

Step 4: Conduct your observation

After you’ve chosen a type of observation, decided on your technique, and chosen a time and place, it’s time to conduct your observation.

Here, you can split them into case and control groups. The children with siblings have a characteristic you are interested in (siblings), while the children in the control group do not.

When conducting observational studies, be very careful of confounding or ‘lurking’ variables. In the example above, you observed children as they were dropped off, gauging whether or not they were upset. However, there are a variety of other factors that could be at play here (e.g., illness).

Step 5: Analyse your data

Your analysis can take an inductive or deductive approach :

If you conducted your observations in a more open-ended way, an inductive approach allows your data to determine your themes.
If you had specific hypotheses prior to conducting your observations, a deductive approach analyses whether your data confirm those themes or ideas you had previously.

Next, you can conduct your thematic or content analysis . Due to the open-ended nature of observational studies, the best fit is likely thematic analysis.

Step 6: Discuss avenues for future research

If you are excited about the preliminary conclusions you’ve drawn and wish to proceed with your topic, you may need to change to a different research method , such as an experiment.

Observational studies can provide information about difficult-to-analyse topics in a low-cost, efficient manner.
They allow you to study subjects that cannot be randomised safely, efficiently, or ethically .
They are often quite straightforward to conduct, since you just observe participant behavior as it happens or utilise preexisting data.
They’re often invaluable in informing later, larger-scale clinical trials or experiments.

Disadvantages

Observational studies struggle to stand on their own as a reliable research method. There is a high risk of observer bias and undetected confounding variables.
They lack conclusive results, typically are not externally valid or generalisable, and can usually only form a basis for further research.
They cannot make statements about the safety or efficacy of the intervention or treatment they study, only observe reactions to it. Therefore, they offer less satisfying results than other methods.

If you’re able to randomise your participants safely and your research question is definitely causal in nature, consider using an experiment.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched 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 analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are 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.

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Introduction to Data Science I & II

Observational versus experimental studies, observational versus experimental studies #.

In most research questions or investigations, we are interested in finding an association that is causal (the first scenario in the previous section ). For example, “Is the COVID-19 vaccine effective?” is a causal question. The researcher is looking for an association between receiving the COVID-19 vaccine and contracting (symptomatic) COVID-19, but more specifically wants to show that the vaccine causes a reduction in COVID-19 infections (Baden et al., 2020) 1 .

Experimental Studies #

There are 3 necessary conditions for showing that a variable X (for example, vaccine) causes an outcome Y (such as not catching COVID-19):

Temporal Precedence : We must show that X (the cause) happened before Y (the effect).

Non-spuriousness : We must show that the effect Y was not seen by chance.

No alternate cause : We must show that no other variable accounts for the relationship between X and Y .

If any of the three is not present, the association cannot be causal. If the proposed cause did not happen before the effect, it cannot have caused the effect. In addition, if the effect was seen by chance and cannot be replicated, the association is spurious and therefore not causal. Lastly, if there is another phenomenon that accounts for the association seen, then it cannot be a causal association. These conditions are therefore, necessary to show causality.

The best way to show all three necessary conditions is by conducting an experiment . Experiments involve controllable factors which are measured and determined by the experimenter, uncontrollable factors which are measured but not determined by the experimentor, and experimental variability or noise which is unmeasured and uncontrolled. Controllable factors that the experimenter manipulates in his or her experiment are known as independent variables . In our vaccination example, the independent variable is receipt of vaccine. Uncontrollable factors that are hypothesized to depend on the independent variable are known as dependent variables. The dependent variable in the vaccination example is contraction of COVID-19. The experimentor cannot control whether participants catch the disease, but can measure it, and it is hypothesized that catching the disease is dependent on vaccination status.

Control Groups #

When conducting an experiment, it is important to have a comparison or control group . The control group is used to better understand the effect of the independent variable. For example, if all patients are given the vaccine, it would be impossible to measure whether the vaccine is effective as we would not know the outcome if patients had not received the vaccine. In order to measure the effect of the vaccine, the researcher must compare patients who did not receive the vaccine to patients that did receive the vaccine. This comparison group of patients who did not receive the vaccine is the control group for the experiment. The control group allows the researcher to view an effect or association. When scientists say that the COVID-19 vaccine is 94% effective, this does not mean that only 6% of people who got the vaccine in their study caught COVID-19 (the number is actually much lower!). That would not take into account the rate of catching COVID-19 for those without a vaccine. Rather, 94% effective refers to having 94% lower incidence of infection compared to the control group.

Let’s illustrate this using data from the efficacy trial by Baden and colleagues in 2020. In their primary analysis, 14,073 participants were in the placebo group and 14,134 in the vaccine group. Of these participants, a total of 196 were diagnosed with COVID-19 during the 78 day follow-up period: 11 in the vaccine group and 186 in the placebo group. This means, 0.08% of those in the vaccine group and 1.32% of those in the placebo group were diagnosed with COVID-19. Dividing 0.08 by 1.32, we see that the proportion of cases in the vaccine group was only 6% of the proportion of cases in the placebo group. Therefore, the vaccine is 94% effective.

Chicago has a population of almost 3,000,000. Extrapolating using the numbers from above, without the vaccine, 39,600 people would be expected to catch COVID-19 in the period between 14 and 92 days after their second vaccine. If everyone were vaccinated, the expected number would drop to 2,400. This is a large reduction! However, it is important that the researcher shows this effect is non-spurious and therefore important and significant. One way to do this is through replication : applying a treatment independently across two or more experimental subjects. In our example, researchers conducted many similar experiments for multiple groups of patients to show that the effect can be seen reliably.

Randomization #

A researcher must also be able to show there is no alternate cause for the association in order to prove causality. This can be done through randomization : random assignment of treatment to experimental subjects. Consider a group of patients where all male patients are given the treatment and all female patients are in the control group. If an association is found, it would be unclear whether this association is due to the treatment or the fact that the groups were of differing sex. By randomizing experimental subjects to groups, researchers ensure there is no systematic difference between groups other than the treatment and therefore no alternate cause for the relationship between treatment and outcome.

Another way of ensuring there is no alternate cause is by blocking : grouping similar experimental units together and assigning different treatments within such groups. Blocking is a way of dealing with sources of variability that are not of primary interest to the experimenter. For example, a researcher may block on sex by grouping males together and females together and assigning treatments and controls within the different groups. Best practices are to block the largest and most salient sources of variability and randomize what is difficult or impossible to block. In our example blocking would account for variability introduced by sex whereas randomization would account for factors of variability such as age or medical history which are more difficult to block.

Observational Studies #

Randomized experiments are considered the “Gold Standard” for showing a causal relationship. However, it is not always ethical or feasible to conduct a randomized experiment. Consider the following research question: Does living in Northern Chicago increase life expectancy? It would be infeasible to conduct an experiment which randomly allocates people to live in different parts of the city. Therefore, we must turn to observational data to test this question. Where experiments involve one or more variables controlled by the experimentor (dose of a drug for example), in observational studies there is no effort or intention to manipulate or control the object of study. Rather, researchers collect data without interfering with the subjects. For example, researchers may conduct a survey gathering both health and neighborhood data, or they may have access to administrative data from a local hospital. In these cases, the researchers are merely observing variables and outcomes.

There are two types of observational studies: retrospective studies and prospective studies. In a retrospective study , data is collected after events have taken place. This may be through surveys, historical data, or administrative records. An example of a retrospective study would be using administrative data from a hospital to study incidence of disease. In contrast, a prospective study identifies subjects beforehand and collects data as events unfold. For example, one might use a prospective study to evaluate how personality traits develop in children, by following a predetermined set of children through elementary school and giving them personality assessments each year.

Baden LR, El Sahly HM, Essink B, Kotloff K, Frey S, Novak R, Diemert D, Spector SA, Rouphael N, Creech CB, McGettigan J. Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine. New England journal of medicine. 2020 Dec 30.

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Experiment vs Observational Study: A Deeper Look

When we read about research studies and reports, many are times that we fail to pay attention to the design of the study. For you to know the quality of the research findings, it is paramount to start by understanding some basics of research/study design.

The primary goal of doing a study is to evaluate the relationship between several variables. For example, does eating fast food result in teenagers being overweight? Or does going to college increase the chances of getting a job? Most studies fall into two main categories, observational and experimental studies, but what is the difference? Other widely accepted research types are cohort studies, randomized controls, and case-control studies, but these three are part of either experimental or observational study. Keep reading to understand the difference between observational study and experiment.

What Is An Observational Study?

To understand observational study vs experiment, let us start by looking at each of them.

So, what is an observational study ? This is a form of research where the measurement is done on the selected sample without running a control experiment. Therefore, the researcher observes the impact of a specific risk factor, such as treatment or intervention, without focusing on who is not exposed. It is simply a matter of observing what is happening.

When an observational report is released, it indicates that there might be a relationship between several variables, but this cannot be relied on. It is simply too weak or biased. We will demonstrate this with an example.

A study asking people how they liked a new film that was released a few months ago is a good example of an observational study. The researcher in the study does not have any control over the participants. Therefore, even if the study point to some relationship between the main variables, it is considered too weak. For example, the study did not factor in the possibility of viewers watching other films.

The main difference between an observational study and an experiment is that the latter is randomized . Again, unlike the observational study statistics, which are considered biased and weak, evidence from experimental research is stronger.

Advantages of Observational Studies

If you are thinking of carrying a research and have been wondering whether to go for randomized experiment vs observational study, here are some key advantages of the latter.

Because the observational study does not require the use of control, it is inexpensive to undertake. Suppose you take the example of a study looking at the impact of introducing a new learning method into a school. In that case, all you need is to ask any interested students to participate in a survey with questions, such as “yes” and “no.”
Doing observational research can also be pretty simple because you do not have to keep looking into multiple variables, and trying to control some groups.
Sometimes the observational method is the only way to study some things, such as exposure to specific threats. For example, it might not be ethical to expose people to harmful variables, such as radiation. However, it is possible to study the exposed population living in affected areas using observational studies.

While the advantages of observational research might appear attractive, you need to weigh them against the cons. To run conclusive observational research, you might require a lot of time. Sometimes, this might run for years or decades.

The results from observational studies are also open to a lot of criticism because of confounding biases. For example, a cohort study might conclude that most people who love to meditate regularly suffer less from heart issues. However, this alone might not be the only cause of low cases of heart problems. The people who medicate might also be following healthy diets and doing a lot of exercises to stay healthy.

Types of Observational Studies

Observational studies further branches into several categories, including cohort study, cross-sectional, and case-control. Here is a breakdown of these different types of studies:

Cohort Study

For study purposes, a “cohort” is a team or group of people who are somehow linked. Example, people born within a specific period might be referred to as a “birth cohort.”

The concept of cohort study edges close to that of experimental research. Here, the researcher records whether every participant in the cohort is affected by the selected variables. In a medical setting, the researcher might want to know whether the cohort population in the study got exposed to a certain variable and if they developed the medical condition of interest. This is the most preferred method of study when urgent response, especially to a public health concern, such as a disease outbreak is reported.

It is important to appreciate that this is different from experimental research because the investigator simply observes but does not determine the exposure status of the participants.

Case Control Study

In this type of study, the researcher enrolls people with a health issue and another without the problem. Then, the two groups are compared based on exposure. The control group is used to generate an estimate of the expected exposure in the population.

Cross-Sectional Research

This is the third type of observational type of study, and it involves taking a sample from a population that is exposed to health risk and measuring them to establish the extent of the outcome. This study is very common in health settings when researchers want to know the prevalence of a health condition at any specific moment. For example, in a cross-sectional study, some of the selected persons might have lived with high blood pressure for years, while others might have started seeing the signs recently.

Experimental Studies

Now that you know the observational study definition, we will now compare it with experiment research. So, what is experimental research?

In experimental design, the researcher randomly assigns a selected part of the population some treatment to make a cause and effect conclusion. The random selection of samples is largely what makes the experiment different from the observational study design.

The researcher controls the environment, such as exposure levels, and then checks the response produced by the population. In science, the evidence generated by experimental studies is stronger and less contested compared to that produced by observational studies.

Sometimes, you might find experimental study design being referred to as a scientific study. Always remember that when using experimental studies, you need two groups, the main experiment group (part of the population exposed to a variable) and the control (another group that does not get exposed/ treatment by the researcher).

Benefits of Using Experimental Study Design

Here are the main advantages to expect for using experimental study vs observational experiment.

Most experimental studies are shorter and smaller compared to observational studies.
The study, especially the selected sample and control group, is monitored closely to ensure the results are accurate.
Experimental study is the most preferred method of study when targeting uncontested results.

When using experimental studies, it is important to appreciate that it can be pretty expensive because you are essentially following two groups, the experiment sample and control. The cost also arises from the factor that you might need to control the exposure levels and closely follow the progress before drawing a conclusion.

Observational Study vs Experiment: Examples

Now that we have looked at how each design, experimental and observational, work, we will now turn to examples and identify their application.

To improve the quality of life, many people are trying to quit smoking by following different strategies, but it is true that quitting is not easy. So the methods that are used by smokers include:

Using drugs to reduce addiction to nicotine.
Using therapy to train smokers how to stop smoking.
Combining therapy and drugs.
Cold turkey (neither of the above).

The variable in the study is (I, II, III, IV), and the outcome or response is success or failure to quit the problem of smoking. If you select to use an observational method, the values of the variables (I, ii, iii, iv) would happen naturally, meaning that you would not control them. In an experimental study, values would be assigned by the researcher, implying that you would tell the participants the methods to use. Here is a demonstration:

Observational Study: Here, you would imagine a population of people trying to quit smoking. Then, use a survey, such as online or telephone interviews, to reach the smokers trying to stop the habit. After a year later, you reach the same persons again, to enquire whether they were successful. Note that you do not run any control over the population.
Experimental study: In this case, a representative sample of smokers trying to stop the habit is selected through a survey. Say you reach about 1000. Now, the number is divided into four groups of 250 persons, and each group is allocated one of the four methods above (i, ii, iii, or iv).

The results from the experimental study might be as shown below:

	Quit smoking successfully	Failed to quit smoking	Total number of participants	Percentage of those who quit smoking
Drug and therapy	83	167	250	33%
Drugs only	60	190	250	24%
Therapy only	59	191	250	24%
Cold turkey	12	238	250	5%

From the results of the experimental study, we can say that combining therapy and drugs method helped most smokers to quit the habit successfully. Therefore, a policy can be developed to adopt the most successful method for helping smokers quit the problem.

It is important to note that both studies commence with a random sample. The difference between an observational study and an experiment is that the sample is divided in the latter while it is not in the former. In the case of the experimental study, the researcher is controlling the main variables and then checking the relationship.

A researcher picked a random sample of learners in a class and asked them about their study habits at home. The data showed that students who used at least 30 minutes to study after school scored better grades than those who never studied at all.

This type of study can be classified as observational because the researcher simply asked the respondents about their study habits after school. Because there was no group given a particular treatment, the study cannot qualify as experimental.

In another study, the researcher randomly picked two groups of students in school to determine the effectiveness of a new study method. Group one was asked to follow the new method for a period of three months, while the other was asked to simply study the way they were used. Then, the researcher checked the scores between the two groups to determine if the new method is better.

So, is this an experimental or observational study? This type of study can be categorized as experimental because the researcher randomly picked two groups of respondents. Then, one group was given some treatment, and the other one was not.

In one of the studies, the researcher took a random sample of people and looked at their eating habits. Then, every member was classified as either healthy or at risk of developing obesity. The researcher also drew recommendations to help people at risk of developing overweight issues to avoid the problem.

This type of study is observational because the researcher took a random sample but did no accord any group a special treatment. The study simply observed the people’s eating habits and classified them.

In one of the studies done in Japan, the researcher wanted to know the levels of radioactive materials in people’s tissues after the bombing of Hiroshima and Nagasaki in 1945. Therefore, he took a random sample of 1000 people in the region and asked them to get checked to determine the levels of radiation in their tissues.

After the study, the researcher concluded that the level of radiation in people’s tissues is still very high and might be associated with different types of diseases being reported in the region. Can you determine what type of study design this is?

The research is an example observational study because it did not have any control. The researcher only observed the levels but did not have any type of control group. Again, there was no special treatment to one of the study populations.

Get Professional Help Whenever You Need It

If you are a researcher, it is very important to be able to define observational study and experiment research before commencing your work. This can help you to determine the different parameters and how to go about the study. As we have demonstrated, observational studies mainly involve gathering the findings from the field without trying to control the variables. Although this study’s results can be contested, it is the most recommended method when using other studies such as experimental design, is unfeasible or unethical.

Experimental studies giving the researcher greater control over the study population by controlling the variables. Although more expensive, it takes a relatively shorter time, and results are less biased.

Now, go ahead and design your study. Always remember that you can seek help from either your lecturer or an expert when designing the study. Once you understand the concept of observational study vs experiment well, research can become so enjoyable and fun.

Observational Study Designs: Synopsis for Selecting an Appropriate Study Design

Assad a rezigalla.

1 Department of Basic Medical Sciences, College of Medicine, University of Bisha, Bisha, SAU

The selection of a study design is the most critical step in the research methodology. Crucial factors should be considered during the selection of the study design, which is the formulated research question, as well as the method of participant selection. Different study designs can be applied to the same research question(s). Research designs are classified as qualitative, quantitative, and mixed design. Observational design occupies the middle and lower parts of the hierarchy of evidence-based pyramid. The observational design is subdivided into descriptive, including cross-sectional, case report or case series, and correlational, and analytic which includes cross-section, case-control, and cohort studies. Each research design has its uses and points of strength and limitations. The aim of this article to provide a simplified approach for the selection of descriptive study design.

Introduction and background

A research design is defined as the “set up to decide on, among other issues, how to collect further data, analyze and interpret them, and finally, to provide an answer to the question” [ 1 ]. The primary objective of a research design is to guarantee that the collected evidence allows the answering of the initial question(s) as clearly as possible [ 2 ]. Various study designs have been described in the literature [ 1 - 3 ]. Each of them deals with the specific type of research or research questions and has points of strength and weakness. Broadly, research designs are classified into qualitative and quantitative research and mixed methods [ 3 ]. The quantitative study design is subdivided into descriptive versus analytical study designs or as observational versus interventional (Figure (Figure1). 1 ). Descriptive designs occupy the middle and lower parts of the hierarchy of evidence-based medicine pyramid. Study designs are organized in a hierarchy beginning from the basic "case report" to the highly valued "randomised clinical trial" [ 4 - 5 ].

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Object name is cureus-0012-00000006692-i01.jpg

Case report

The case report describes an individual case or cases in their natural settings. Also, it describes unrecognized syndromes or variants, abnormal findings or outcomes, or association between risk factors and disease. It is the lowest level and the first line of evidence and usually deals with the newly emerging issues and ideas (Table (Table1) 1 ) [ 4 , 6 - 10 ].

Case Report Design
Strengths [ , - ]	Limitations [ , ]
Identification of new, abnormal, or variant presentation of diseases.	Lack of generalizability and implications.
Have significant educational value.	Uncontrolled.
Help in generating a hypothesis.	Selection bias.
Researching rare or uncommon disorders.	No epidemiological indices (parameters).
In-depth narrative case studies.	Over-interpretation.
Flexible structure.	Confidentiality.
	Causes may have other explanations.

Case series

A case series is a report on data from a subject group (multiple patients) without control [ 6 , 11 - 12 ]. Commonly, this design is used for the illustration of novel, unusual, or atypical features identified in medical practice [ 6 ]. The investigator is governed by the availability and accuracy of the records, which can cause biases [ 13 - 14 ]. Bias in a case series can be decreased through consecutive patient enrollment and predefined inclusion and exclusion criteria, explicit specification of study duration, and enrollment of participants (Table 2 ) [ 11 - 12 ].

Case Series
Strengths [ , - ]	Limitations [ , - ]
Educational.	Selection bias.
It described the outcomes of novel treatments.	Lack of control.
The gained information can be used to generate hypotheses.	Difficult to compare different cases.
Provide strong evidence with multiple cases.	The result may not be generalized.
Helpful in refining new techniques or treatment protocols.	Immediate follow-up.
Identify the rare manifestations of a disease or drug.	Have a lower position on the hierarchy of evidence.
Feasible study designs.

Correlational study design

Correlational studies (ecologic studies) explore the statistical relationships between the outcome of interest in population and estimate the exposures. It deals with the community rather than in individual cases. The correlational study design can compare two or more relevant variables and reports the association between them without controlling the variables. The aim of correlational study design or research is to uncover any types of systematic relationships between the studied variables. Ecological studies are often used to measure the prevalence and incidence of disease, mainly when the disease is rare. The populations compared can be defined in several ways, such as geographical, time trends, migrants, longitudinal, occupation, and social class. It should be considered that in ecological studies, the results are presented at the population (group) level rather than individuals. Ecological studies do not provide information about the degree or extent of exposure or outcome of interest for particular individuals within the study group (Table 3 ) [ 7 , 15 - 16 ]. For example, we do not know whether those individuals who died in the study group under observation had higher exposure than those remained alive.

Correlational study design
Strengths [ - ]	Limitations [ - ]
Quick and easy.	Correlations do not equal causation.
Describes the strength of relationships.	Correlations can be misused.
It is used to assess behavior.	Cannot be used to identify causal relationships
Predictor variables cannot be manipulated.	It cannot provide certain information.
Uses of data records.

Cross-sectional study design

The cross-sectional study examines the association between exposures and outcomes on a snap of time. The assessed associations are guided by sound hypotheses and seen as hypothesis-generating [ 17 ]. This design can be descriptive (when dealing with prevalence or survey) or analytic (when comparing groups) [ 17 - 18 ]. The selection of participants in a cross-sectional study design depends on the predefined inclusion and exclusion criteria [ 18 - 19 ]. This method of selection limits randomization (Table 4 ).

Cross-sectional Study Design
Strengths of [ , - ]	Limitations [ , - ]
Fast and inexpensive.	Difficult to derive causal relationships.
Useful for planning monitoring and evaluation of public health.	Prone to certain types of biases.
Efficient in studying rare diseases.	The response rate is critical.
There are seldom ethical difficulties.	The temporality of the design.
It can assess multiple outcomes.	No clear demarcation between exposure and effect.
Population-based surveys.
Estimation of prevalence.
Calculation of odds ratio.
The baseline for a cohort study.

Case-control study

A case-control study is an observational analytic retrospective study design [ 12 ]. It starts with the outcome of interest (referred to as cases) and looks back in time for exposures that likely caused the outcome of interest [ 13 , 20 ]. This design compares two groups of participants - those with the outcome of interest and the matched control [ 12 ]. The controls should match the group of interest in most of the aspects, except for the outcome of interest [ 18 ]. The controls should be selected from the same localization or setting of the cases [ 13 , 21 - 22 ]. Case-control studies can determine the relative importance of a predictor variable about the presence or absence of the disease (Table (Table5 5 ).

Case-control Study Design
Strengths [ , - ]	Limitations [ , - ]
Relatively fast in conduction in comparison with prospective cohort studies.	Not useful for rare exposures.
Comparatively, needs few participants and fewer resources.	Cannot estimate the incidence.
Useful for testing hypotheses.	Affect by observation and recall bias.
Useful in studying multiple exposures in the same outcome.
Can study the association of risk factors and outcomes in outbreak investigations.
It can generate much information from relatively few participants with unusual cases.
Feasible in diseases with a long latent period.

Cohort study design

The cohort study design is classified as an observational analytic study design. This design compares two groups, with exposure of interest and control one [ 12 , 18 , 22 - 24 ].

Cohort design starts with exposure of interest comparing them to non-exposed participants at the time of study initiation [ 18 , 22 , 24 ]. The non-exposed serve as external control. A cohort design can be either prospective [ 18 ] or retrospective [ 12 , 20 , 24 - 25 ]. In prospective cohort studies, the investigator measures a variety of variables that might be a risk factor or relevant to the development of the outcome of interest. Over time, the participants are observed to detect whether they develop the outcome of interest or not. In this case, the participants who do not develop the outcome of interest can act as internal controls. Retrospective cohort studies use data records that were documented for other purposes. The study duration may vary according to the commencement of data recording. Completion of the study is limited to the analysis of the data [ 18 , 22 , 24 ]. In 2016, Setia reported that, in some instances, cohort design could not be well-defined as prospective or retrospective; this happened when retrospective and prospective data were collected from the same participants (Table (Table6) 6 ) [ 24 ].

Cohort Study Design
Strengths [ , , ]	Limitations [ , , ]
The temporality between exposure and outcome is well-defined.	Inability to control all the confounding variables.
Study multiple outcomes in the same exposure.	A prospective cohort design is time-consuming and costly.
Efficient in rare outcomes if the rare outcome is common in some exposures.	Variables in the retrospective cohort study may not be very accurate since the collected data was not intended for research purposes.
Accurate measure of variables in prospective cohort design.	May not be very useful in case of rare outcomes.
The retrospective cohort is relatively fast in conduction and inexpensive.	In the prospective cohort design, the loss of follow-up is a critical problem.
Lack of bias in the retrospective cohort because the collected data was not initially for research.	Retrospective cohorts may be affected by recall bias.
It can measure potential causes and relative risk.	Ethical problems.

The selection of the study design is the most critical step in research methodology [ 4 , 26 ]. An appropriate study design guarantees the achievement of the research objectives. The crucial factors that should be considered in the selection of the study design are the formulated research question, as well as the method of sampling [ 4 , 27 ]. The study design determines the way of sampling and data analysis [ 4 ]. The selection of a research study design depends on many factors. Two crucial points that should be noted during the process selection include different study designs that may be applicable for the same research question(s) and researches may have grey areas in which they have different views about the type of study design [ 4 ].

Conclusions

The selection of appropriate study designs for research is critical. Many research designs can apply to the same research. Appropriate selection guarantees that the author will achieve the research objectives and address the research questions.

Acknowledgments

The author would like to acknowledge Dr. M. Abass, Dr. I. Eljack, Dr. K. Salih, Dr. I. Jack, and my colleagues. Special thanks and appreciation to the college dean and administration of the College of Medicine, University of Bisha (Bisha, Saudi Arabia) for help and allowing the use of facilities.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The authors have declared that no competing interests exist.

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Observational vs quasi-experimental design?

I am having some difficulty understanding the difference between and identifying an observational vs quasi-experimental design. From my understanding, an observational study is one in which the researcher does not influence the system and only records what they observe (duh). In an experimental study, the researcher manipulates the experimental units such they have different treatments and measures some resultant metric(s), usually to compare them. My understanding of quasi experimental studies is that the researcher uses groups that are already different from one another rather than apply the treatment to the EUs themselves.

For example, let's say a researcher is making observations on bird diversity in different land use types (eg forest vs agriculture). Odds are he or she isn't going to make a forest and farm and put birds in them to see which survive. They are going to go to several farms and several forests and observe the birds that live in each.

Now, it seems that this is a quasi-experimental design based on my above definitions, but does that mean that every comparison study is going to be either experimental or quasi experimental? I can't think of particularly many studies that would fall under the observational category, in that case (correlational, descriptive).

experiment-design
observational-study

5 Answers 5

First, as far as you have described the research design, the study is not a quasi-experiment.

I prefer the term natural experiment to quasi-experiment , because I think it more clearly communicates the fact that treatment needs to have been randomly assigned (or as-if randomly assigned). I use the term natural experiments below, but I consider the two equivalent in meaning.

You are correct that experiments are confined to those situations where a researcher actually manipulates treatment assignment.

Observational studies comprise anything that was not an experiment. Natural experiments are a subset of observational studies, but in a natural experiment units were assigned to treatment in a random process (or as-if random, or almost random).

You might look for a natural experiment (or quasi-experiment) if you were seeking to identify the causal effect of a treatment on a set of outcomes. Then you would look for a situation where assignment to that treatment was assigned randomly (or as-if randomly) by nature or a government program, for example. For example, if you wanted to study the impact of forest fires on bird diversity, you might find a place where the government has defined that it will fight fires when they come with X miles of residential areas. After forest fires, you could compare (i) bird diversity in areas affected by the forest fire just a little further than X miles away from residential areas (treatment group) to (ii) bird diversity in areas just a little less than X miles away from residential areas (control group). Because birds would not choose where to live prior to fire based on the government's designation of the distance X, we can expect that before the fire on either side of the X-mile cutoff, birds would be identical on average. There assignment to treatment (being "treated" by the forest fire) is as-if random on either side of the X-mile cutoff. This design is called a regression-discontinuity design [1] or a geographic regression discontinuity design [2].

Also, see more discussion of the difference here: Panel study is a quasi-experimental study? Quasi-experimental is the same as correlational?

https://en.wikipedia.org/wiki/Regression_discontinuity_design
"Geographic boundaries as regression discontinuities." LJ Keele, R Titiunik. Political Analysis , 2014

I can try to give an example from my own field, econometrics:

Economists are interested in "returns to schooling", i.e., how much more do you earn per additional year of schooling obtained.

An experiment is not an option, as, for good and obvious reasons, you cannot force people to continue or stop their educational career just because of the empirical analysis.

Observational data is generally tricky to interpret if you are interested in the causal effect of another year of schooling, because there are "confounders" that imply that the (generally positive) correlation between schooling and earnings is not (fully) causal. For example, more able, motivated and careful individuals can be thought to choose to obtain more schooling, and such individuals would have at least partly done well in the labor market without additional schooling.

Now, sometimes nature or law is kind enough to hand you a "quasi-experiment". In the above example, researchers have for example exploited changes in compulsory schooling laws. If, effective say 1955, all students in a country are obliged to attend secondary school for, say, 10 rather than 8 years, there will be at least some students who obtain more schooling only because of the new law, and not because they choose so.

Instrumental variable approaches may then be a credible way to exploit this so-called exogenous variation to say something about the causal effect of schooling.

The quasi experimental design is the one that uses an "experimental research procedure" but not all extraneous variables are controlled. Quasi experimental designs lack random assignment of participants to groups. Only in strong experimental designs is this achieved.

Causal inferences can only be done from quasi-experimental designs if (1) cause and effect covary, (2) cause must precede effect and (3) rival hypothesis must be implausible (so the relationship between variables must not be due a confounding extraneous variable).

Now, the third condition is hard to achieve since there is no randomization.

So we can see Quasi-experimental designs to be a better option than weak experimental designs and not as good as strong experimental designs.

For your bird-watching scenario, using a quasi-experimental design will not have a conclusive result and the relationship between the type of bird and the land use parameters might be affected by other variables like weather, migration seasons, temperature, humidity, wind orientation, etc. However this might be good enough for your study if you are not able to apply a strong experimental design.

On the other hand, the observational study draws inferences from a sample to a population where the independent variable is not under the control of the researcher. The observational study is then more into the data collection process, where you as a researcher must collect what you can, to draw inferences from there. The inference in this case (statistically speaking) could be managed by the amount and quality of attributes recorded. Naturalistic observation is conducted in real world observations and subject to noise and error. Since the observational study might be conducted in a single farm or a couple selected from the near-by surroundings, this will not be using a randomized sample which might also be prone to statistical error for causal relationships. The only way the observational study will be good enough to demonstrate cause and effect, will be when it is ran under laboratory conditions, say your birds are in a controlled environment, where several domes are created that represents each land/farm type and then you basically observe behavior or whatever you are measuring. The laboratory observation is closely similar to a quasi-experimental design since you are having control of a variable (the setting).

hope this helps.

I would like to answer your question from the Epidemiology point of view.

Basically,there are three kind of studies in Epidemiology, observational study, Experimental and theoretical study.

For observational studies, as a researcher you will not give any interventions to any groups you will study. You just collect data cross-sectionally, retrospectively or prospectively.

For experimental design, as a researcher you will allocate your intervention to some groups and other groups will not receive your intervention.

There are randomized experiments (such as clinical trials) and non-randomized experiment.

For randomized experiments patient belongs to which group is determined by randomization procedures).

For non-randomized experiment, which is also called quasi-experiment , there are no randomization procedures to allocate patients to different groups, it might just be done by convenience.

The point of experiments is to determine causality, which typically requires establishing that: 1) one thing happened before the other, 2) that the putative cause had some explanation mechanism for affecting the outcome, and 3) that there are no competing explanations or alternate causes. Also helps if the relationship is reliable--that the lights go on every time you hit the switch. Experiments are designed to establish these relationships, by controlling conditions to establish chronological sequence and control for possible alternate causes. Effective experimental design also includes a control: A population that is not given the experimental treatment.

In many cases, it's not possible/safe/legal to establish a control group before the experiment. In which case, it's a quasi-experiment. If the assumption that the affects of the treatment were random is true (ie, people weren't somehow selected for the treatment, by age/socio-economic status/race etc), then it's a control. The assumption is random assignment, but sometimes that assumption has to be relaxed (or controlled for); if not controlled for, it weakens the strength of your causal inferences.

Control groups also rely on the assumption that the two groups prior to the treatment were identical. This measurement is typically called the a 'pre-test' measurement. Then after the experimental treatment is applied, a 'post-test measure is made. The pre-test measure should be the same for both the treatment (experimental) and control groups. And then, if the experimental treatment did anything, the post-test value for the treatment and control groups should be different. In summary, both experiments (natural and otherwise) should have a pre-test, post-test and control group.

Actual observational studies belong to a completely different style of science: inductive rather than deductive, and can generally be identified with qualitative traditions. The essential difference is that numerical data sufficient for a statistical analysis isn't available. In a comparative case study, the sample might be as small as two. While their are a variety of qualitative techniques I'm not remotely qualified to talk about, I'll make a very broad generalization and say: they compare fewer, less well defined things, because part of the role of qualitative research is to define what things are: to create constructs an definitions and theorize (based on observations) what the possible causal relationships between things might be.

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7th - 9th , dependent vs. independent variables, 8th - 12th , qualitative and quantitative variables, 11th - 12th , intro to statistical studies, types of statistical studies, multiplying binomials, 8th - 9th .

Observation vs Experiment

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1. Multiple Choice Edit 1.5 minutes 1 pt Compare voter satisfaction levels between people assigned to use either paper ballots or touchscreen machines. Experiment Observational Study
2. Multiple Choice Edit 1.5 minutes 1 pt Determine which brands of orange juice people prefer. The people are randomly chosen at the supermarket and are asked to taste both brands without knowing which brand they are drinking. Experiment Observational Study
3. Multiple Choice Edit 1.5 minutes 1 pt Determine if people who take vitamin C every day are less likely to get colds. Experiment Observational Study
4. Multiple Choice Edit 1.5 minutes 1 pt Compare the grades on a final math test of 25 students who use calculators and 25 students who do not use calculators. The students decide which group they are in. Experiment Observational Study
5. Multiple Choice Edit 1.5 minutes 1 pt A teacher asks her students to write down all they eat in a day and then calculate the total number of calories consumed. Experiment Observational Study
6. Multiple Choice Edit 1.5 minutes 1 pt The cafeteria manager of a high school wants to find out if high prices are keeping students from using the cafeteria. Fifty students are chosen at random to receive half-price lunch passes every day for a month. The manager then records the number of passes used. Experiment Observational Study
7. Multiple Choice Edit 1.5 minutes 1 pt A Stat 113 instructor announces a study session to be held the night before a test. The instructor lists the students who attended the session and compares their scores to the remaining Stat 113 students’ scores. OBSERVATIONAL or EXPERIMENTAL?? Observational Experimental
8. Multiple Choice Edit 30 seconds 1 pt When a news article starts with the opening "A recent study suggests......", where does the information originate? Data collected in surveys Data collected by a researcher Data collected by the census All sources mentioned
9. Multiple Choice Edit 30 seconds 1 pt All statistical data and analysis tools are reliable and true. False True
10. Multiple Choice Edit 30 seconds 1 pt If I survey people about their height, the data will be, numerical - the data will be in numbers only categorical - descriptions and words.
13. Multiple Choice Edit 1.5 minutes 1 pt What do we call a study (like a questionnaire or opinion poll) that asks questions of a sample in hopes of learning something about the entire population? Observational Study Survey Experiment Sample
14. Multiple Choice Edit 1.5 minutes 1 pt What do we call a study that observes behaviors but does not interact with individuals? Observational Study Survey Experiment Sample
15. Multiple Choice Edit 1.5 minutes 1 pt Which of the following terms refers to the group that receives no treatment with the independent variable? Constant group Control group Dependent group Experimental group

A Parks Department employee wants to know if latex paint is more durable than non-latex paint. She has 50 park benches painted with latex paint and has 50 park benches painted with non-latex paint.

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A researcher asks college students how many hours of sleep they got on an average night and examines whether the number of hours of sleep affects the students’ grades

A grocery store conducts an online study in which customers are randomly selected and asked to provide feedback on their shopping experience

To test the redesign of it’s website, an online bookseller assembled 96 users of the site and randomly divided them into two groups. One group used the new website to make a purchase and one group used the old website to make the same purchase. Users of the new site were able to complete the purchase 22% faster

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DOI: 10.1016/j.radi.2024.07.009
Corpus ID: 271604786

An observational cross-sectional study of pharmaceutical waste disposal practices in Australian medical imaging departments: A comparison of community versus hospital practice.

K. MacDonald , M. Taylor , +2 authors J. Singleton
Published in Radiography 31 July 2024
Environmental Science, Medicine

29 References

The carbon footprint of hospital diagnostic imaging in australia, the global public health issue of pharmaceutical waste : what role for pharmacists, pharmaceutical waste disposal practices: a case study of an australian public hospital pharmacy department, do legislated carbon reduction targets influence pro-environmental behaviours in public hospital pharmacy departments using mixed methods to compare australia and the uk, considerations for environmental sustainability in clinical radiology and radiotherapy practice: a systematic literature review and recommendations for a greener practice., reducing contrast agent residuals in hospital wastewater: the greenwater study protocol, modeling the environmental and financial impact of multi-dose vs. single-dose iodinated contrast media packaging and delivery systems., eco-directed sustainable prescribing: feasibility for reducing water contamination by drugs., pharmacy students' perceptions on environmental sustainability in pharmacy education and practice, pharmaceuticals and iodinated contrast media in a hospital wastewater: a case study to analyse their presence and characterise their environmental risk and hazard., related papers.

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http://orcid.org/0000-0002-7648-1336 Jose Antonio Alonso-Cadenas 1 , 2 ,
http://orcid.org/0000-0003-0073-2650 Roberto Velasco 3 ,
Nuria Clerigué Arrieta 4 ,
Jone Amasorrain Urrutia 5 ,
Maria Suarez-Bustamante Huélamo 1 ,
Santiago Mintegi 6 , 7 , 8 ,
http://orcid.org/0000-0001-6542-4494 Borja Gomez 6 , 7
1 Emergency Department , Hospital Infantil Universitario Niño Jesús , Madrid , Spain
2 Instituto de Investigacion del Hospital de La Princesa , Madrid , Spain
3 Pediatric Emergency Department , Hospital Universitari Parc Tauli , Sabadell , Spain
4 Navarre Hospital Complex , Pamplona , Spain
5 Hospital of Mendaro , Mendaro , Spain
6 Pediatric Emergency Department , Hospital Universitario Cruces , Barakaldo , Spain
7 Biocruces Bizkaia Health Research Institute , Barakaldo , Spain
8 University of the Basque Country , Bilbao , Spain
Correspondence to Dr Jose Antonio Alonso-Cadenas; jalonsocadenas{at}gmail.com

Objectives To analyse the performance of blood enterovirus and parechovirus PCR testing (ev-PCR) for invasive bacterial infection (IBI) (isolation of a single bacterial pathogen in a blood or cerebrospinal fluid culture) when evaluating well-appearing infants ≤90 days of age with fever without a source (FWS).

Methods We describe the well-appearing infants ≤90 days of age with FWS and normal urine dipstick. We performed a prospective, observational multicentre study at five paediatric emergency departments between October 2020 and September 2023.

Results A total of 656 infants were included, 22 (3.4%) of whom were diagnosed with an IBI (bacteraemia in all of them and associated with meningitis in four). The blood ev-PCR test was positive in 145 (22.1%) infants. One patient with positive blood ev-PCR was diagnosed with an IBI, accounting for 0.7% (95% CI 0.02 to 3.8) compared with 4.1% (95% CI 2.6 to 6.2) in those with a negative test (p=0.04). All four patients with bacterial meningitis had a negative blood ev-PCR result. Infants with a positive blood ev-PCR had a shorter hospital stay (median 3 days, IQR 2–4) compared with 4 days (IQR 2–6) for those with negative blood ev-PCR (p=0.02), as well as shorter duration of antibiotic treatment (median 2 days, IQR 0–4 vs 2.5 days, IQR 0–7, p=0.01).

Conclusions Young febrile infants with a positive blood ev-PCR are at a low risk of having an IBI. Incorporating the blood ev-PCR test into clinical decision-making may help to reduce the duration of antibiotic treatments and length of hospital stay.

paediatric emergency medicine
emergency care

Data availability statement

Data are available on reasonable request.

https://doi.org/10.1136/archdischild-2024-327367

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Contributors JAAC contributed to the study conception and design, material preparation and analysis, wrote the first draft of the manuscript and act as the guarantor. BG and RV conceptualised and designed the study, coordinated and supervised the data collection, and critically reviewed the manuscript. NCA, JAU, MS-BH and SM revised the data collection form, collected data and critically reviewed the manuscript. All authors approved the final manuscript as submitted and agreed to be accountable for all aspects contained.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

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Conservation decision making and agricultural land leasing - an experimental investigation of the role of gender, cornhusker economics aug 7, 2024 conservation decision making and agricultural land leasing - an experimental investigation of the role of gender.

Introduction:

Adoption of conservation practices on agricultural land can generate significant private and public good benefits for society. However, most agricultural production occurs on rented land. The United States Department of Agriculture (USDA)’s 2014 Tenure, Ownership and Transition of Agricultural Land (TOTAL) survey documents that more than half of US farmland is leased, making conservation adoption dependent on both landowner and tenant preferences. Additionally, there is a demographic shift in agricultural landownership in the US, represented by more women becoming landowners. In states, like Iowa, 47% of all acres and 55% of all leased acres are owned by women. Findings from the 2017 Census of Agriculture indicate that 60% of farming operations have a female landowner (Census of Agriculture, 2017). This demographic shift must be considered in the context of the evolving role of women in agriculture, and their environmental attitudes and preferences. Specifically, how these factors influence the implementation of conservation land use practices on land rented to predominantly male tenants, who may not favor mandates from female landowners to implement these practices.

In this context, this research investigated the impact of landowner gender and of the different types of contract leases offered by them to a male tenant, on the conservation (or non-conservation) choice made by the tenant. Specifically, we investigate if a) female and male landowners offer different rental contracts and b) whether tenants choose different actions based on the landowner’s gender. For this purpose, we implemented a controlled, gender-context-loaded economic experiment with university students, in which we tested contract and land use choice under different treatments for three types of rental contracts – fixed rent, fixed rent with penalty, and fixed rent with discount. The study has two treatment arms: one varies the salience of gender and the other examines the impact of communication between the landowner and tenant.

The experimental methodology is critical for this research for multiple reasons. First, the discounted rental contract is a mechanism with a limited real-world counterpart, meaning that we cannot turn to observational studies to understand what type of behaviors this type of contract promotes relative to other contracts. Second, examining landowner-tenant bargaining and its evolution over time in a non-experimental setting is challenging because bargaining may take different forms and occur at moments difficult to track. This would prevent the generation of data about the bargaining process and non-confounded isolation of treatment effects. Also, we conduct lab experiments because recruitment of agricultural producers for research studies is quite challenging (Rosch et al., 2020; Weigel et al., 2021), given multiple demands on their time.

Experimental Design:

We implemented two primary treatments: priming participants’ gender identity and enabling communication between landowners and tenants. This resulted in a two-by-two factorial design, yielding four treatment conditions, with priming treatments denoted by P and communication treatments denoted by C. Within each treatment, we varied the gender of the landowner (female or male) while the tenant was always male, as outlined in Table 1. Including a control condition in which gender was not revealed and no communication was allowed, there were nine treatments. In treatments with communication, participants could communicate for 1 minute 30 seconds before making choices.

Table 1: Experiment Design

The priming intervention aimed to make gender identity salient. Female and Male participants read a text highlighting traditional feminine and masculine attributes respectively. After they read the text, we asked participants to state the degree to which they related to the passage on a four-point scale with 1 = Relate strongly, 2 = Relate somewhat, 3 = Relate very little, and 4 = Do not relate . Participants read the priming passage before being instructed about the decision task and the role that they would be assuming. In the control and non-priming treatments, participants moved to the decision task immediately. In all treatments except the control, participants’ gender was revealed using gender icons on the computer screens. Tables 2 and 3 represent the payoff tables displayed to each landowner and tenant representing the financial payoff to each participant depending upon contract offered and land use action selected.

The between subject experiment design was implemented online using oTree (Chen et al., 2016) with instructions delivered through Zoom. Participants were recruited randomly from the University of Nebraska-Lincoln between Spring 2021-Summer 2022. They reported their gender when signing and were randomly assigned to treatments based on gender. In the Zoom meeting room, participants’ names were anonymized as Participant 1, 2, etc. They were asked to keep their device microphones on mute and never to turn on their videos during the session. The instructions were shared via screen sharing in a presentation on Zoom. Participants also had access to detailed instructions through a link in the oTree interface that opened a Google document. The option to download or print this document was disabled.

Table 2: Payoff Table - Fixed Rent without and with Penalty

Table 3: Landowner's view of the payoff tables - Fixed Rent With Discount

The experimental design had multiple stages depending on the treatment. The decision task was common to all treatments. Participants answered a comprehension quiz and completed a practice session consisting of two rounds. Landowners were referred to as “Owners” and tenants as “Renters” to maintain a neutral context while preserving the hierarchical relationship. The conservation practice choice was Action C and no conservation choice was Action DC.

In the experiment, the contract choice was implemented in two steps. First, the landowner decided between fixed rent and fixed rent with discount. While making this choice, the payoff tables associated with these two contract choices were displayed on the computer screen. If the landowners chose fixed rent with discount, they selected the discount value. When making this choice, they saw the payoff table associated with the discount contract as presented in Table 3. If they chose a fixed rent contract, the next screen showed payoffs from both fixed rent and penalty contracts, and they had to make a contract choice.

Once the landowner made their choice, the tenant was informed about the contract choice and chose between C and DC. Participants’ payoffs were determined, and the next round began. The decision task was repeated for 15 rounds with fixed matching to see if interaction with the same person built reputation and impacted subsequent behaviors.

After completing the decision task, participants answered a demographic survey, including questions on whether they saw their counterpart as a collaborator or adversary. Payoffs from all rounds were converted into US dollars (US$) at a rate of 211 points per dollar and added to a $5 participation fee. Participants could also track their earnings and choices across rounds through a round history table. Non-communication treatments lasted for about an hour; communication treatments lasted for 1 hour 30 minutes. Average earnings per participant were $15.35 including participation fee. We collected 20 observations per landowner-tenant pair for 15 rounds in each treatment, resulting in 600 observations per treatment. With 360 students across the nine treatments, we obtained a data set of 5400 observations for each agent type.

Focusing on landowner behavior, Table 4 shows that in the control treatment (no gender reveal, no communication), most landowners chose the discount contract (63%), with the penalty contracts selected 27% of the time. Across all treatments revealing gender information, discount contracts were most frequently chosen. Penalty contracts followed, except in the FPC treatment, where fixed rent contracts were more common than penalties. Overall, landowners preferred discount or penalty contracts.

Next, focusing on gender-based behaviors, Table 4 shows that discount contracts are the modal contract for both females and males in all treatments. Chi-square tests indicate significant gender differences in contract offers, except in F and M treatments. Thus, without communication and/or explicit gender priming, no gender-based behavior differences are produced by only revealing landowner gender. These two manipulations allow landowners to consider their gender relative to their male tenants, influencing behavior during the landowner-tenant interaction.

Additionally, there are some key features to note in terms of the different types of contracts offered by each gender with and without priming. When participants can communicate but gender is not primed (FC vs. MC), female landowners offer more fixed rent contracts (8.0% vs 5.67%), fewer penalty contracts (15.7% vs 21.3%) and more discount contracts (76.3% vs 73.0%) than male landowners, showing a significant difference in contracts offered (chi square test p-value = 0.018). Essentially, female landowners are likely to either fall back on the status quo and offer a fixed rent contract (where they are essentially mandating no conservation thus acting as a “placeholder”) or adopt a softer approach via a discount contract to incentivize conservation, compared to their male counterparts. They are unlikely to use upfront punishment via a penalty contract to get to their desired outcome despite their higher payoffs when the tenant chooses C.

Interestingly, with priming, females choose more penalty contracts and fewer discount contracts than males, especially without communication (36.0% penalty in FP vs. 24.7% in MP; 51.33% discount in FP vs. 68.67% in MP; p-value = 0.000). This outcome can be explained by the fact that women relate more strongly to the priming text and view tenants as more adversarial than male landowners do. Hence it is possible that identity priming otherizes the male tenants for their female landowners. Combined with the fact that they view their male tenants as adversaries, female landowners appear to penalize their male tenants to discourage a DC choice rather than offering a discount contract, which would reduce their own rental income. While this type of penalizing behavior is atypical in rural communities, it is not without precedent as it is consistent with female landowners who adopt a changemaker position and mandate conservation (thus going against the social norm of not requiring that the tenant behave in a particular way). Additionally, while there is still a significant difference in contract offers ( p-value = 0.000 per chi-square test) with communication (FPC vs. MPC), differences in penalty and discount contracts offered by females and males are reduced (11.3% penalty in FPC vs.10.3% in MPC; 71.3% discount in FPC and 83.7% vs. in MPC). Thus, communication likely reduces female landowners’ otherization of male tenants, ameliorating gender-driven adversarial perceptions and narrowing the gap in contract offers of female and male landowners. Table 4 shows that 49.67% of tenants conserved in control treatments. Similarly, pooled across all gender revealed treatments, conserve was chosen on average 65.91% of the time, a value significantly greater than that obtained in the control setting ( p-value = 0.000). Thus, tenant’s behavior overall differs based on our treatments. Additionally, we also find gender-based differences in tenant’s behavior when gender is primed, both in the presence and absence of communication ( p-value = 0.000).

Conclusion:

The role of gender identity and norms is critical in determining conservation outcomes on leased land. Our work examines behavior and associated gender-based differences through an induced value laboratory experiment with students. Our study provides evidence that even after removing information barriers between landowners and tenants (because our experiment has common knowledge), an interesting dynamic unfolds where behavior of male landowners and tenants are different from those of female landowners and their respective male tenant. This behavior echoes what has been observed in the field, and our study shows that it is a gender issue, not just a matter of bridging any potential knowledge gap that exists between landowners and tenants regarding benefits of conservation. However, communication between parties can help especially to mitigate some of the behavioral friction that emerges between male tenants and female landowners. Additionally, priming in our experiment serves to mimic traditional gendered expectations of each negotiating party- on the field, these norms although not stated as such dictate relationships because lease agreements are verbal, informal, and bound by mutual social expectations.

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References:

Rosch, S., Raszap Skorbiansky, S., Weigel, C., Messer, K. D., & Hellerstein, D. (2020). Barriers to Using Economic Experiments in Evidence-Based Agricultural Policymaking. Applied Economic Perspectives and Policy, 43(2), 531–555. https://doi.org/10.1002/aepp.13091

USDA NASS (National Agricultural Statistics Service). 2015. Farmland Ownership and Tenure. Results from the 2014 Tenure, Ownership and Transition of Agricultural Land Survey. Washington, DC: USDA NASS https://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/TOTAL/

USDA NASS National Agricultural Statistics Service).2017. Census of Agriculture. https://www.nass.usda.gov/Publications/AgCensus/2017/index.php

United States Department of Agriculture, Economic Research Service : https://www.ers.usda.gov/data-products/chart-gallery/gallery/chart-detail/?chartId=104206 ( accessed on August 3, 2023)

Weigel C., Harden, S.C., Masuda, Y.J, Ranjan, P., Wardropper, C.B., Ferraro, P.J., Prokopy, L.S., Reddy, S.M.W. (2021). Using a randomized controlled trial to develop conservation strategies on rented farmlands. Conservation Letters, 14 (4) e12803 ; https://doi.org/10.1111/conl.12803

Diya Ganguly Post Doctoral Researcher Center for Experimental & Applied Economics University of Delaware diyaganguly.com [email protected]

Simanti Banerjee Professor Department of Agricultural Economics University of Nebraska-Lincoln [email protected] Chris Gustafson Professor Agricultural Economics Department University of Nebraska-Lincoln 314A Filley Hall Lincoln NE 68583 [email protected]

Introduction
Conclusions
Article Information

For primary and secondary outcomes, percent change and P values are presented. A Wald test was used to evaluate a significant difference in diet at 8 weeks from baseline (interaction term). Error bars indicate IQRs. HDL-C indicates high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; and TMAO, trimethylamine N -oxide.

Trial Protocol

eMethods. Supplementary Methods

eResults. Supplementary Results

eAppendix. Plant-Based Dietary Intervention Reporting Checklist

eFigure 1. TwiNS Study Design

eTable 1. Twin Lifestyle Behaviors, n = 42 (21 Pairs of Twins)

eFigure 2. Macronutrient Distribution by Phase and Diet

eTable 2. Macronutrient Distribution by Phase and Diet

eFigure 3. Fat Distribution by Phase and Diet

eTable 3. Fat Distribution by Phase and Diet

eFigure 4. Grain Distribution by Phase and Diet

eTable 4. Grain Distribution by Phase and Diet

eFigure 5. Protein Distribution by Phase and Diet

eTable 5. Protein Distribution by Phase and Diet

eFigure 6. Carbohydrate Distribution by Phase and Diet

eTable 6. Carbohydrate Distribution by Phase and Diet

eFigure 7. Dietary Cholesterol Distribution by Phase and Diet

eTable 7. Dietary Cholesterol Distribution by Phase and Diet

eFigure 8. Dietary Vitamin B12 Distribution by Phase and Diet

eTable 8. Dietary Vitamin B12 Distribution by Phase and Diet

eFigure 9. Dietary Iron Distribution by Phase and Diet

eTable 9. Dietary Iron Distribution by Phase and Diet

eFigure 10. Vegetable Servings Distribution by Phase and Diet

eTable 10. Vegetable Servings Distribution by Phase and Diet

eFigure 11. Animal-Based Protein Distribution by Phase and Diet

eTable 11. Animal-Based Protein Distribution by Phase and Diet

eFigure 12. Meat Alternatives Distribution by Phase and Diet

eTable 12. Meat Alternatives Distribution by Phase and Diet

eTable 13. Diet Satisfaction (D-Sat28) of Participants by Diet Assignment and Phase

eTable 14. Self-Efficacy to Plan, Shop, Cook, and Choose Meals by Diet Assignment and Phase

eTable 15. Diet Preferences of Participants by Diet Assignment

eTable 16. Perceptions of Delivered, Pre-Prepared Meals by Diet Assignment

eTable 17. Barriers to Adherence to Study Eating Patterns by Diet Assignment

eTable 18. Factors that Improve Dietary Adherence and Sustainability by Diet Assignment

eTable 19. Average Self-Rated Dietary Adherence by Diet Assignment and Phase

eTable 20. Cardiovascular Health Outcomes at the End of 4 Weeks and Main Effect Model Estimates, Standard Errors, and 95% Confidence Interval for Primary and Secondary Outcome Analysis

eFigure 13. Sensitivity Analysis of the Change in Trimethylamine N-Oxide (TMAO) With Three Outliers Removed, (Mean ± SE)

eTable 21. Paired T-Tests of Cardiovascular Health Outcomes at the End of 8 Weeks

eTable 22. Unpaired T-Tests of Cardiovascular Health Outcomes at the End of 8 Weeks

eTable 23. Average Macronutrient and Micronutrient Composition of Trifecta Food Delivery Meals by Meal Type and Diet Assignment

eReferences

Data Sharing Statement

Error in Results Section JAMA Network Open Correction December 26, 2023

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Landry MJ , Ward CP , Cunanan KM, et al. Cardiometabolic Effects of Omnivorous vs Vegan Diets in Identical Twins : A Randomized Clinical Trial . JAMA Netw Open. 2023;6(11):e2344457. doi:10.1001/jamanetworkopen.2023.44457

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Cardiometabolic Effects of Omnivorous vs Vegan Diets in Identical Twins : A Randomized Clinical Trial

1 Stanford Prevention Research Center, Department of Medicine, School of Medicine, Stanford University, Palo Alto, California
2 Department of Population Health and Disease Prevention, Program in Public Health, University of California, Irvine
3 Quantitative Sciences Unit, Department of Medicine, Stanford University, Palo Alto, California
4 Department of Microbiology and Immunology, School of Medicine, Stanford University, Stanford University, Palo Alto, California
5 Chan Zuckerberg Biohub, San Francisco, California
6 Center for Human Microbiome Studies, Stanford University School of Medicine, Stanford, California
Correction Error in Results Section JAMA Network Open

Question What are the cardiometabolic effects of a healthy plant-based (vegan) vs a healthy omnivorous diet among identical twins during an 8-week intervention?

Findings In this randomized clinical trial of 22 healthy, adult, identical twin pairs, those consuming a healthy vegan diet showed significantly improved low-density lipoprotein cholesterol concentration, fasting insulin level, and weight loss compared with twins consuming a healthy omnivorous diet.

Meaning The findings from this trial suggest that a healthy plant-based diet offers a significant protective cardiometabolic advantage compared with a healthy omnivorous diet.

Importance Increasing evidence suggests that, compared with an omnivorous diet, a vegan diet confers potential cardiovascular benefits from improved diet quality (ie, higher consumption of vegetables, legumes, fruits, whole grains, nuts, and seeds).

Objective To compare the effects of a healthy vegan vs healthy omnivorous diet on cardiometabolic measures during an 8-week intervention.

Design, Setting, and Participants This single-center, population-based randomized clinical trial of 22 pairs of twins (N = 44) randomized participants to a vegan or omnivorous diet (1 twin per diet). Participant enrollment began March 28, 2022, and continued through May 5, 2022. The date of final follow-up data collection was July 20, 2022. This 8-week, open-label, parallel, dietary randomized clinical trial compared the health impact of a vegan diet vs an omnivorous diet in identical twins. Primary analysis included all available data.

Intervention Twin pairs were randomized to follow a healthy vegan diet or a healthy omnivorous diet for 8 weeks. Diet-specific meals were provided via a meal delivery service from baseline through week 4, and from weeks 5 to 8 participants prepared their own diet-appropriate meals and snacks.

Main Outcomes and Measures The primary outcome was difference in low-density lipoprotein cholesterol concentration from baseline to end point (week 8). Secondary outcome measures were changes in cardiometabolic factors (plasma lipids, glucose, and insulin levels and serum trimethylamine N -oxide level), plasma vitamin B 12 level, and body weight. Exploratory measures were adherence to study diets, ease or difficulty in following the diets, participant energy levels, and sense of well-being.

Results A total of 22 pairs (N = 44) of twins (34 [77.3%] female; mean [SD] age, 39.6 [12.7] years; mean [SD] body mass index, 25.9 [4.7]) were enrolled in the study. After 8 weeks, compared with twins randomized to an omnivorous diet, the twins randomized to the vegan diet experienced significant mean (SD) decreases in low-density lipoprotein cholesterol concentration (−13.9 [5.8] mg/dL; 95% CI, −25.3 to −2.4 mg/dL), fasting insulin level (−2.9 [1.3] μIU/mL; 95% CI, −5.3 to −0.4 μIU/mL), and body weight (−1.9 [0.7] kg; 95% CI, −3.3 to −0.6 kg).

Conclusions and Relevance In this randomized clinical trial of the cardiometabolic effects of omnivorous vs vegan diets in identical twins, the healthy vegan diet led to improved cardiometabolic outcomes compared with a healthy omnivorous diet. Clinicians can consider this dietary approach as a healthy alternative for their patients.

Trial Registration ClinicalTrials.gov Identifier: NCT05297825

Plant-based diets have gained recent popularity not only for their lower environmental impact compared with an omnivorous dietary pattern but also for their health benefits. 1 , 2 The most significant global health crises affecting our generation are noncommunicable diseases and climate change, which are both inextricably linked to diet, 3 and dietary patterns high in plants and low in animal foods can maximize health and environmental benefits. 4 , 5 Plant-based diets contain a diverse family of dietary patterns, which encourage a reduced consumption of animal foods. 6 Abundant evidence from observational and intervention studies 7 - 13 indicates that vegan diets are associated with improved cardiovascular health and decreased risk of cardiovascular disease, likely because of the higher daily consumption of vegetables and fruits, legumes, whole grains and nuts, and seeds compared with other different types of dietary patterns. 14

A vegan dietary pattern is typically lower in energy density but higher in fiber, vitamins, minerals, and phytonutrients compared with other dietary patterns. 15 However, sometimes a vegan dietary pattern can limit specific nutrients, such as vitamin B 12 , iron, and calcium. 15 , 16 Most studies 17 , 18 examining vegan diets have been epidemiologic examinations, with a few reported clinical studies. 19 , 20 A confounding factor to consider in epidemiologic studies is the bias of self-decided vegans who may differ from nonvegans in factors that may influence diet and health. 21 In addition, a poorly formulated vegan diet can include low-quality plant foods, such as refined carbohydrates and added sugars. 22 To address these concerns, we designed a trial to compare the cardiometabolic effects of a healthy vegan diet with a healthy omnivorous diet, exposing both groups to vegetables, legumes, fruits, whole grains, nuts, and seeds. To control for genetic differences that might alter the cardiometabolic effects of diet, 23 we randomly assigned identical twins to follow the 2 diets for 8 weeks.

This study followed the ethical standards of the Declaration of Helsinki 24 and was approved by the Stanford University Human Subjects Committee on March 9, 2022. All study participants provided written informed consent. The trial protocol is given in Supplement 1 . Additional methods are available in the eMethods in Supplement 2 . This report follows the 25-item Consolidated Standards of Reporting Trials ( CONSORT ) reporting guideline of design, participants, interventions, outcomes, sample size, randomization, participant flow, baseline data, outcomes, ancillary analyses, limitations, and interpretation. Race and ethnicity data were collected via self-report and included to characterize the population for generalizability of findings.

This single-site, parallel-group, dietary intervention randomized clinical trial randomized healthy, adult identical twins to a healthy vegan or omnivorous diet for 8 weeks. Participant enrollment began March 28, 2022, and continued through May 5, 2022. The date of final follow-up data collection was July 20, 2022.

The primary outcome was the difference from baseline to 8 weeks in low-density lipoprotein cholesterol (LDL-C) levels between the diet groups. Secondary outcomes included differences from baseline to 8 weeks in body weight and levels of fasting triglycerides, high-density lipoprotein cholesterol, glucose, insulin, trimethylamine N -oxide (TMAO), and vitamin B 12 . Exploratory assessments included diet quality, adherence, and qualitative factors to help interpret the study’s findings (eFigure 1 in Supplement 2 ).

We aimed to recruit 22 pairs of identical twins 18 years or older, a sample size determined by resource availability rather than a formal power calculation. Identical twins were recruited primarily from the Stanford Twin Registry and randomized using computerized random-number generation by a statistician (K.M.C.) blinded to the intervention, delivery, or data collection. Adult twins 18 years or older willing to consume a plant-based (vegan) or omnivore diet for 8 weeks were included. We excluded participants who weighed 45.36 kg (100 lb) or less, had a body mass index (calculated as weight in kilograms divided by height in meters squared) of 40 or higher, had an LDL-C level of 190 mg/dL or higher (to convert to millimoles per liter, multiply by 0.0259), had a systolic blood pressure of 160 mm Hg or higher or diastolic blood pressure of 90 mm Hg or higher, or were pregnant. Individuals self-reported race and ethnicity for the purpose of demographic reporting. Inclusion and exclusion criteria have been previously published. 25

The study consisted of two 4-week phases: delivered meals and self-provided meals. Participants were provided all no-cost meals for the first 4 study weeks by a nationwide meal delivery company (Trifecta Nutrition). It was expected that after 4 weeks of food delivery and health educator counseling that participants would understand the amounts and types of foods they should purchase and prepare to achieve maximum adherence to the diets when self-providing meals.

Research staff worked with Trifecta Nutrition to develop menu offerings to match a healthy vegan and omnivorous diet, which emphasized vegetables, fruits, and whole grains while limiting added sugars and refined grains. During the initial 4 weeks, meals were delivered once each week, with 7 days of breakfast, lunch, and dinner meals. Participants also purchased and consumed snacks to meet their energy requirements following guidance from health educators.

Guiding principles were reinforced: (1) choose minimally processed foods; (2) build a balanced plate with vegetables, starch, protein, and healthy fats; (3) choose variety within each food group; and (4) individualize these guidelines to meet preferences and needs (eAppendix in Supplement 2 ). Although weight loss was not discouraged, our diet design did not include a prescribed energy restriction and was not intended to be a weight loss study. Participants were told to eat until they were satiated throughout the study.

Two types of dietary data were collected. For the primary reporting data, 3 unannounced 24-hour dietary recalls—a structured interview intended to capture detailed information about food and drink intakes—were administered within a 1-week window (2 weekdays and 1 weekend day) of each time point (baseline, week 4, and week 8). Data were collected via telephone by a registered dietitian (L.R.D.) using Nutrition Data System for Research (Nutrition Coordinating Center). For the secondary reporting data, participants were encouraged to log their food intake using the Cronometer app (Cronometer Pro, Nutrition Tracking Software for Professionals; Cronometer); these data were used by health educators for real-time guidance of participants.

At 3 time points, participants visited the Stanford Clinical and Translational Research Unit after an overnight fast of 10 to 12 hours: baseline, 4 weeks (phase 1), and 8 weeks (phase 2). Blood draw and clinical measures were assessed using standard methods (eMethods in Supplement 2 ). Stool samples were collected for future analysis to examine changes to the gut microbiome (eg, microbial diversity), metabolites, inflammatory markers, and additional health factors.

Descriptive statistics, mean (SD) or number (percentage), were used for continuous and categorical variables, respectively. Table 1 presents baseline summary statistics by study group. For the primary analysis, we investigated differences between groups in the change from baseline to week 8 for LDL-C between vegan and omnivorous diets among identical twins. Primary analysis included all available data. A linear mixed model was used and included fixed effects for diet and time (baseline as reference) and an interaction effect for diet × time and a random effect for twin pair to account for the correlation between identical twins (ie, random intercept allowed intercept to vary for each twin pair). A Wald test was used to evaluate a significant difference in diet at 8 weeks from baseline (interaction term). Finally, we present model estimates (95% CIs) for diet at 8 weeks. For each secondary outcome, we evaluated a statistical model similar to the primary model as described herein.

Analyses were completed using R Studio, version 2022.12.0 (Posit Software). A 2-sided P ≤ .05 was considered to be statistically significant. No correction was applied for multiple comparisons, and secondary and exploratory analyses should be interpreted accordingly.

A total of 22 pairs of randomized twins (N = 44) were enrolled in the study. The CONSORT flow diagram of participants ( Figure 1 ) shows 22 twin pairs randomized to receive either a vegan or omnivorous diet (1 twin per diet); 21 pairs in both groups contributed to the final analyses. Baseline characteristics ( Table 1 ) included the following: mean (SD) age, 39.6 (12.7) years; 34 (77.3%) female and 10 (22.7%) male; 5 (11.4%) Asian, 2 (4.5%) Black/African American, 1 (2.3%) Native Hawaiian/Pacific Islander, 32 (72.7%) White, 4 (9.1%) multiracial, and mean (SD) body mass index, 26.9 (4.9). Most twins (33 of 42 [78.6%]) currently lived with their twin, and most reported being similar to their twin (29 of 42 [69.0%]) ( Table 2 ; eTable 1 in Supplement 2 ).

Reported energy intake during each of the two 4-week phases (food delivery and self-provided) were lower compared with baseline for both groups (eFigures 1 to 5 and eTables 2 to 6 and 23 in Supplement 2 ). Intake of vegetables, animal-based protein sources, and plant-based protein sources by diet group and per intervention phase are provided in eFigures 6 to 12 and eTables 10 to 12 in Supplement 2 . Additional results are available in the eResults, eTables 7 to 9, and eFigures 7 to 9 in Supplement 2 .

Participants receiving the vegan diet showed a mean (SD) decrease of 13.9 (5.8) mg/dL (95% CI, −25.3 to −2.4 mg/dL) in the unadjusted mean LDL-C level at 8 weeks from baseline compared with participants receiving the omnivorous diet ( Table 2 ). As early as 4 weeks, we observed a significant decrease in mean LDL-C level among vegans compared with omnivores (eTable 20 in Supplement 2 ). The percentage of change from baseline to 8 weeks in primary and secondary outcomes between vegan and omnivorous diet groups ( Figure 2 ) showed a significant decrease in LDL-C level among the vegan compared with the omnivore group ( Table 2 ). Participants’ mean (SD) baseline LDL-C level was 114 (33.5) mg/dL, 26 leaving minimal room for participants to improve through diet alone.

Compared with participants receiving the omnivorous diet, participants receiving the vegan diet saw a significant mean (SD) decrease of 2.9 (1.3) μIU/mL in fasting insulin (95% CI, −5.3 to −0.4 μIU/mL) from baseline to 8 weeks ( P = .03) (to convert to picomoles per liter, multiply by 6.945) ( Table 2 ). Vegan participants had a significant mean (SD) decrease of −1.9 (0.7) kg in body weight (95% CI, −3.3 to −0.6 kg) from baseline to 8 weeks compared with participants on the omnivorous diet ( P = .01) ( Figure 2 ), although weight loss was observed for both diet groups. Vegans also experienced a larger but nonsignificant absolute median decrease in fasting high-density lipoprotein cholesterol, vitamin B 12 , and TMAO levels; a lesser but nonsignificant decrease in glucose levels; and a nonsignificant increase in fasting triglycerides at 8 weeks from baseline compared with omnivores ( Figure 2 ).

Three outlier TMAO levels greater than 15 μM were noted: 2 at baseline and 1 at 8 weeks. After the outliers were eliminated, the TMAO level was significantly different between diet groups at 8 weeks: in this analysis, participants on the vegan diet showed a mean (SD) decrease of −2.1 (0.7) μM (95% CI, −3.5 to −0.7 μM) in the difference of TMAO from baseline to 8 weeks compared with participants on the omnivorous diet (eFigure 13 in Supplement 2 ).

Paired and unpaired 2-tailed t tests indicate minimal differences between statistical analysis approach (eTables 21 and 22 in Supplement 2 ). Participants receiving the omnivorous diet had nominally higher diet satisfaction at weeks 4 and 8 compared with vegan participants (eTable 13 in Supplement 2 ). Additional results are available in eResults and eTables 14 to 20 in Supplement 2 .

In this randomized clinical trial of healthy, adult identical twins, the 8-week change in LDL-C level—the primary outcome—was significantly lower for twins receiving the vegan diet compared with twins receiving the omnivorous diet. Insulin levels and weight were also significantly lower among the twins on the vegan diet from baseline to 8 weeks. Vegan-diet participants had total lower protein intake as a percentage of calories, lower dietary satisfaction, lower intake of dietary cholesterol, but higher intake of vegetable servings and intake of dietary iron. Vegans had lower intake of vitamin B 12 , yet serum vitamin B 12 levels were not statistically different than omnivores at 8 weeks, likely because of preserved stores. 27 Long-term vegans are typically encouraged to take a cyanocobalamin (vitamin B 12 ) supplement.

Two factors may have limited our opportunity to observe additional differences between the study groups. First, participants in both diet groups were assigned to eat a healthy diet, usually healthier compared with their prestudy dietary pattern demonstrated by increased vegetable intake and decreased refined grains intake. Even the omnivorous participants improved their diet quality during the 8-week intervention (eg, increased vegetables and whole grain intake and decreased added sugars and refined grains). Second, within both groups, potential differences in clinical end point changes may have been blunted because participants were healthy at baseline. For example, participants’ mean baseline LDL-C level was 114 mg/dL, 26 leaving minimal room for participants to improve through diet alone. Nonetheless, we observed significant improvements in 3 clinical outcomes (LDL-C, insulin, and weight) among the vegan participants.

Our results corroborate a previous finding showing that eating a vegan diet can improve cardiovascular health. 28 A larger body of evidence from randomized clinical trials suggests that vegetarian and other plant-based dietary patterns lower weight 29 - 31 and improve lipid management, 30 , 32 , 33 glucose metabolism, 33 , 34 blood pressure, 35 - 37 and cardiometabolic health. 38 Our results also mirror a recently completed 2-year dietary intervention trial among African Americans randomized to a vegan or low-fat omnivorous diet, finding improvements in body weight and cardiovascular disease risk factors. 39

Novel to this study was our population of identical twins, a valuable resource in scientific research that provided a unique opportunity to investigate the effects of a dietary intervention while controlling for genetic and environmental factors, 40 influences that can significantly impact health outcomes, including body weight, cardiovascular health, and metabolic function. 40 , 41 Because identical twins have nearly identical DNA and many shared experiences (eg, upbringing, geographic region growing up, and similar exposure to other variables), observed differences in health outcomes after adoption of different dietary patterns can largely be attributed to the diet itself.

We were surprised that TMAO concentrations did not significantly differ between diets at 8 weeks because of the higher meat content in the omnivorous diet and of the meat TMAO precursors choline and carnitine. 42 , 43 Although some studies 44 , 45 report a positive association between the concentration of serum TMAO and development of cardiovascular disease, whether TMAO is a bystander or mediator of disease remains unknown. In a sensitivity analysis that removed 3 TMAO outlier participants, lower TMAO levels were found in the vegan participants. Prior research 42 , 43 has suggested that vegans have lower TMAO levels than meat or fish eaters because of the TMAO precursors choline and carnitine in animal products. In a recent crossover dietary trial (Study With Appetizing Plantfood-Meat Eating Alternative Trial [SWAP-MEAT]), 46 participants consuming plant-based alternative meat vs animal meat had significantly lower TMAO concentrations. In addition to our 3 TMAO outliers, we observed variability among participants in TMAO concentration changes. Further investigation is needed on TMAO as a risk factor for cardiovascular disease and the association of dietary choline and carnitine vs fish with serum TMAO concentrations.

A recent meta-epidemiologic study 47 examining dietary recommendations from current clinical practice guidelines recommends diets rich in unrefined plant foods and low in refined and animal-based foods. Clinical practice guidelines from the American Heart Association recommend that practitioners encourage patients to choose healthy sources of protein, mostly from plants, to promote cardiovascular health. 11 , 48 Additionally, Dietary Guidelines for Americans, 2020-2025 49 includes a healthy, vegetarian-style dietary pattern that can be adopted for improved health and chronic disease prevention. Although our findings suggest that vegan diets offer a protective cardiometabolic advantage compared with a healthy, omnivorous diet, excluding all meats and/or dairy products may not be necessary because research 22 , 50 suggests that cardiovascular benefits can be achieved with modest reductions in animal foods and increases in healthy plant-based foods compared with typical diets. We believe lower dietary satisfaction in the vegan group may have been attributable to the strictness of the vegan diet, creating more barriers for people to follow the vegan diet guidelines. Some people may find a less restrictive diet preferable for LDL-C–lowering effects. Future studies assessing health benefits of less strict plant-based diets will be necessary to assess these benefits, especially in a study model limiting additional biases (eg, in twins). Within a clinical setting, patients should be supported in choosing a dietary pattern that fits their needs and preferences. 41 , 51 Clinicians should allow patients to make informed choices that support them to choose which dietary approach is most suitable for them. At a population level, wider adoption of a culturally appropriate dietary pattern that is higher in plant foods and lower in animal foods can promote health and environmental benefits. 3 , 4 , 10 , 52

Several aspects of our design and implementation were strengths. First, enrolling identical twins was beneficial because we were able to eliminate the confounding influences of age, sex, and genetic factors that may affect clinical outcomes. Identical twins often share a similar environment and lifestyle, reducing environmental factors on the study results. Second, the initial 4-week period of food delivery facilitated participants’ high adherence to the diet, whereas the latter 4 weeks of self-provided foods increased generalizability. Third, we used LDL-C, a well-established cardiometabolic clinical value, as the primary outcome. 26 Fourth, we assessed an extensive set of well-studied secondary clinical outcomes to evaluate overall cardiometabolic health. Fifth, diet data collection using the state-of-the-art Nutrition Data System for Research allowed us to assess and report on adherence—an important metric in free-living trials 53 —and compare macronutrient and micronutrient intakes. Sixth, previous trials 11 , 13 , 31 , 50 , 54 , 55 have reported similar metabolic and weight loss benefits of vegan diets yet tended to focus on very low–fat vegan diets, study populations with diabetes or overweight, and comparison diets with limited attention to equipoise. Novelties of the current trial were the use of a more moderate- and higher-fat vegan diet (unsaturated fat), 11 the generally healthy population without diabetes or overweight, and a healthy omnivorous comparison diet (eg, higher in vegetables and fiber than the baseline diet). Seventh, to provide fair and objective comparisons and avoid “straw man” comparators, we emphasized high-quality, exemplary dietary choices to participants on both diets.

The study also has some limitations. First, the adult twin population was generally healthy and may not be generalizable to other populations. Second, we studied a small sample size (N = 44); however, the use of monozygotic twins may reduce issues of reproducibility because the twins acted as their own controls. Third, study duration was short (8 weeks); however, in this study as well as several previous trials, 46 , 56 clinically relevant changes in cardiovascular risk factors (eg, LDL-C and weight) were observed as early as 4 weeks into the intervention. Fourth, there was no follow-up period, which limited insights of poststudy stability and sustainability of diet behaviors. Fifth, our study was not designed to be isocaloric; thus, changes to LDL-C cannot be separated from weight loss observed in the study. We designed this study as a “free-living” study; thus, the behavior of following a vegan diet may induce the physiological changes we observed. However, the biological mechanisms cannot be determined to be causally from solely the vegan diet alone because of confounding variables (weight loss, decrease in caloric intake, and increase in vegetable intake). Sixth, diversity in education and socioeconomic status was lacking.

In this randomized clinical trial, we observed cardiometabolic advantages for the healthy vegan vs the healthy omnivorous diet among healthy, adult identical twins. Clinicians may consider recommending plant-based diets to reduce cardiometabolic risk factors, as well as aligning with environmental benefits.

Accepted for Publication: October 12, 2023.

Published: November 30, 2023. doi:10.1001/jamanetworkopen.2023.44457

Correction: This article was corrected on December 26, 2023, to fix the last sentence in the Secondary Outcomes subsection of the Results section.

Corresponding Author: Christopher D. Gardner, PhD, Stanford Prevention Research Center, Department of Medicine, School of Medicine, Stanford University, 3180 Porter Dr, Palo Alto, CA 94304 ( [email protected] ).

Author Contributions: Dr Gardner had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Landry and Ward are co–first authors.

Concept and design: Landry, Cunanan, Perelman, Robinson, J. L. Sonnenburg, Gardner.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Landry, Ward, Cunanan, Durand, Perelman, Dant.

Critical review of the manuscript for important intellectual content: Landry, Ward, Cunanan, Robinson, Hennings, Koh, Dant, Zeitlin, Ebel, E. D. Sonnenburg, J. L. Sonnenburg, Gardner.

Statistical analysis: Landry, Ward, Durand, Zeitlin.

Obtained funding: J. L. Sonnenburg, Gardner.

Administrative, technical, or material support: Perelman, Robinson, Hennings, Dant, E. D. Sonnenburg, J. L. Sonnenburg.

Supervision: Cunanan, Dant, E. D. Sonnenburg, J. L. Sonnenburg, Gardner.

Conflict of Interest Disclosures: Dr Ebel reported receiving grants from the National Science Foundation Postdoctoral Research Fellowship in Biology during the conduct of the study. Dr Gardner reported receiving funding from Beyond Meat outside the submitted work. Dr J. L. Sonnenburg is a Chan Zuckerberg Biohub investigator. No other disclosures were reported.

Funding/Support: This study was funded by the Vogt Foundation (Drs Robinson, J. L. Sonnenburg, and Gardner and Ms Hennings), grants UL1TR001085 and TL1R001085 from the Stanford Clinical and Translational Science Award unit (Dr O’Hara), and grant T32HL161270 from the National Heart, Lung, and Blood Institute (Dr Ward).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 3 .

Additional Contributions: We acknowledge the study participants without whom this investigation would not have been possible.

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Common low-calorie sweetener may be riskier for the heart than sugar, study suggests

Another study is raising concern about the safety of the widely used sugar alcohol sweetener erythritol , a low-calorie sugar substitute found in “keto-friendly” foods, baked goods and candies. Researchers from the Cleveland Clinic compared erythritol to typical sugar and found only erythritol caused worrisome cardiovascular effects.

Although the study was small, it’s the first head-to-head look at people’s blood levels after they consume products with erythritol or sugar (glucose).

“We compared the results, and glucose caused none of the problems,” said Dr. Stanley Hazen, a cardiologist at the Cleveland Clinic and the lead author of the study, published Thursday morning in the journal Arteriosclerosis, Thrombosis, and Vascular Biology.

Erythritol is one ingredient on a growing list of nonsugar sweeteners found in low-calorie and sugar-free foods. Erythritol and xylitol are sugar alcohols that are sweet like sugar but with far fewer calories. Erythritol is often mixed with another sweetener, stevia, and xylitol is often found in gum, mouthwash and toothpaste.

Earlier studies from Hazen’s lab — one published last year and the other in June — found potential links between the sugar alcohols and an increased risk of heart attacks and strokes. The research suggested both sugar alcohols might make blood platelets stickier and therefore more susceptible to clotting and blocking veins or arteries, in turn contributing to heart attacks and strokes.

For the new research, Hazen’s team analyzed the heart effects of erythritol and regular sugar — in this case, simple glucose — by enrolling two groups of healthy middle-aged male and female volunteers: 10 who consumed the erythritol and 10 who consumed sugar.

Both groups fasted overnight. In the morning, their blood was drawn to measure platelet activity. Then, half the volunteers drank glasses of water with 30 grams of glucose mixed in, and half drank glasses of water with 30 grams of erythritol. Hazen said 30 grams of erythritol is an amount typical of erythritol-sweetened foods.

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Around 30 minutes after each group consumed the sweetened drinks, their blood was drawn and retested. Researchers found the people who consumed erythritol had increased platelet aggregation — meaning the blood was more likely to clot. Adults who drank the normal sugar drink had no changes in platelet aggregation.

The researchers measured a 1,000-fold increase in blood erythritol levels in the group given the erythritol drink. Those who drank glucose water didn’t have any changes in blood erythritol levels, and their blood glucose levels were only slightly increased. The finding stood out to Hazen, because it far exceeded the trace levels of erythritol that occur naturally in the blood.

“The amount in sugar substitutes is thousands of folds higher than what is made in our bodies, so to call it ‘natural,’ it’s not,” he said. “Your best recommendation is to avoid the sugar substitutes, and sugar alcohols in particular, because there’s an acute increase in the likelihood of clotting events once you ingest them.”

The Food and Drug Administration considers artificial sweeteners, including erythritol and xylitol, as GRAS, or generally recognized as safe . Hazen hopes mounting evidence about the sugar alcohols might trigger the FDA to look more closely at the data.

Outside the U.S., the concerns have drawn interest among food regulators. Last year, for instance, the European Food Safety Authority recommended that the European Commission request data about how much erythritol is in food, which could help clarify the risks.

Do the findings indicate that erythritol is worse overall than high-calorie sugar? Valisa Hedrick, a registered dietitian at Virginia Tech, said a diet high in sugary foods can lead to elevated blood glucose levels that are also linked to stroke and clotting risks. Hedrick wasn’t involved in the Cleveland Clinic study.

The study has several important limitations. Beyond the small number of participants, it measured the effects of erythritol and glucose at only one point in time, as opposed to over months or years of consistent consumption, Hedrick noted.

And the amount of glucose in the sugar water — about 30 grams — is the equivalent of about 120 calories of sugar. Sugary beverages, especially juices and sodas, often contain more sugar.

For example, a 12-ounce can of Coca-Cola contains 39 grams of sugar, and 12 ounces of Mountain Dew contains 46 grams.

Michael Goran, a professor of pediatrics at the University of Southern California’s Keck School of Medicine, said it might also be worth comparing erythritol to both fructose and glucose. The combination of fructose and glucose is more typical of sugary juices and sodas than glucose alone, he said. Goran wasn’t part of the new study.

Hazen’s study looked at glucose alone.

Although the Cleveland Clinic study didn't find negative effects from consuming sugar, the researchers agreed the data doesn’t mean sugar is in the clear. Higher amounts of sugar may cause similar platelet effects, especially in people with diabetes, who can’t effectively regulate high blood glucose.

Hazen’s study focused specifically on healthy people, not people with diabetes.

It could also be important to analyze whether heart effects differ when people consume food with erythritol compared with water with erythritol, said Dr. Michelle Pearlman, a gastroenterologist who is CEO and a co-founder of the Prime Institute in Miami.

“Factors such as protein, fat, fiber and other nutrients might influence this response,” she said.

Ultimately, said Hedrick of Virginia Tech, the new study underscores the need for more research comparing the health effects of sweeteners versus sugar.

Hazen and his colleagues concluded the research by urging further studies focusing on erythritol’s heart risks, particularly in people already at higher risk of strokes and clotting.

NBC News contributor Caroline Hopkins is a health and science journalist who covers cancer treatment for Precision Oncology News. She is a graduate of the Columbia University Graduate School of Journalism.

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Cohort studies

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What is an Observational Study: Definition & Examples

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What Is an Observational Study? | Guide & Examples

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Cohort studies

Case–control studies

Cross-sectional studies

Step 1: Identify your research topic and objectives

Step 2: Choose your observation type and technique

Step 3: Set up your observational study

Step 4: Conduct your observation