observational vs experimental

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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Experimental Studies and Observational Studies

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• First Online: 01 January 2022
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Experimental studies: Experiments, Randomized controlled trials (RCTs) ; Observational studies: Non-experimental studies, Non-manipulation studies, Naturalistic studies

Definitions

The experimental study is a powerful methodology for testing causal relations between one or more explanatory variables (i.e., independent variables) and one or more outcome variables (i.e., dependent variable). In order to accomplish this goal, experiments have to meet three basic criteria: (a) experimental manipulation (variation) of the independent variable(s), (b) randomization – the participants are randomly assigned to one of the experimental conditions, and (c) experimental control for the effect of third variables by eliminating them or keeping them constant.

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 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.

Table of contents

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.

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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.

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 .

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

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

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.

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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

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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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3.4 - experimental and observational studies.

Now that Jaylen can weigh the different sampling strategies, he might want to consider the type of study he is conduction. As a note, for students interested in research designs, please consult STAT 503 for a much more in-depth discussion. However, for this example, we will simply distinguish between experimental and observational studies.

Now that we know how to collect data, the next step is to determine the type of study. The type of study will determine what type of relationship we can conclude.

There are predominantly two different types of studies: 

Let's say that there is an option to take quizzes throughout this class. In an  observational study , we may find that better students tend to take the quizzes and do better on exams. Consequently, we might conclude that there may be a relationship between quizzes and exam scores.

In an experimental study , we would randomly assign quizzes to specific students to look for improvements. In other words, we would look to see whether taking quizzes causes higher exam scores.

Causation Section  

It is very important to distinguish between observational and experimental studies since one has to be very skeptical about drawing cause and effect conclusions using observational studies. The use of random assignment of treatments (i.e. what distinguishes an experimental study from an observational study) allows one to employ cause and effect conclusions.

Ethics is an important aspect of experimental design to keep in mind. For example, the original relationship between smoking and lung cancer was based on an observational study and not an assignment of smoking behavior.

Frequently asked questions

How does an observational study differ from an experiment.

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.

Frequently asked questions: Methodology

Quantitative observations involve measuring or counting something and expressing the result in numerical form, while qualitative observations involve describing something in non-numerical terms, such as its appearance, texture, or color.

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

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

Inclusion and exclusion criteria are predominantly used in non-probability sampling . In purposive sampling and snowball sampling , restrictions apply as to who can be included in the sample .

Inclusion and exclusion criteria are typically presented and discussed in the methodology section of your thesis or dissertation .

The purpose of theory-testing mode is to find evidence in order to disprove, refine, or support a theory. As such, generalisability is not the aim of theory-testing mode.

Due to this, the priority of researchers in theory-testing mode is to eliminate alternative causes for relationships between variables . In other words, they prioritise internal validity over external validity , including ecological validity .

Convergent validity shows how much a measure of one construct aligns with other measures of the same or related constructs .

On the other hand, concurrent validity is about how a measure matches up to some known criterion or gold standard, which can be another measure.

Although both types of validity are established by calculating the association or correlation between a test score and another variable , they represent distinct validation methods.

Validity tells you how accurately a method measures what it was designed to measure. There are 4 main types of validity :

• Construct validity : Does the test measure the construct it was designed to measure?
• Face validity : Does the test appear to be suitable for its objectives ?
• Content validity : Does the test cover all relevant parts of the construct it aims to measure.
• Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?

Criterion validity evaluates how well a test measures the outcome it was designed to measure. An outcome can be, for example, the onset of a disease.

Criterion validity consists of two subtypes depending on the time at which the two measures (the criterion and your test) are obtained:

• Concurrent validity is a validation strategy where the the scores of a test and the criterion are obtained at the same time
• Predictive validity is a validation strategy where the criterion variables are measured after the scores of the test

Attrition refers to participants leaving a study. It always happens to some extent – for example, in randomised control trials for medical research.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Construct validity refers to how well a test measures the concept (or construct) it was designed to measure. Assessing construct validity is especially important when you’re researching concepts that can’t be quantified and/or are intangible, like introversion. To ensure construct validity your test should be based on known indicators of introversion ( operationalisation ).

On the other hand, content validity assesses how well the test represents all aspects of the construct. If some aspects are missing or irrelevant parts are included, the test has low content validity.

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

Construct validity has convergent and discriminant subtypes. They assist determine if a test measures the intended notion.

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

Reproducibility and replicability are related terms.

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

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

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

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

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

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

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

Snowball sampling is best used in the following cases:

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

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

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

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

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

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

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

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

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

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

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

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method .

This allows you to gather information from a smaller part of the population, i.e. the sample, and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling .

The two main types of social desirability bias are:

• Self-deceptive enhancement (self-deception): The tendency to see oneself in a favorable light without realizing it.
• Impression managemen t (other-deception): The tendency to inflate one’s abilities or achievement in order to make a good impression on other people.

Response bias refers to conditions or factors that take place during the process of responding to surveys, affecting the responses. One type of response bias is social desirability bias .

Demand characteristics are aspects of experiments that may give away the research objective to participants. Social desirability bias occurs when participants automatically try to respond in ways that make them seem likeable in a study, even if it means misrepresenting how they truly feel.

Participants may use demand characteristics to infer social norms or experimenter expectancies and act in socially desirable ways, so you should try to control for demand characteristics wherever possible.

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

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

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

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

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

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

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

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

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

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

Peer review is a process of evaluating submissions to an academic journal. Utilising rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication.

For this reason, academic journals are often considered among the most credible sources you can use in a research project – provided that the journal itself is trustworthy and well regarded.

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

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

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field.

It acts as a first defence, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias .

The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.

Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics .

You can use several tactics to minimise observer bias .

• Use masking (blinding) to hide the purpose of your study from all observers.
• Triangulate your data with different data collection methods or sources.
• Use multiple observers and ensure inter-rater reliability.
• Train your observers to make sure data is consistently recorded between them.
• Standardise your observation procedures to make sure they are structured and clear.

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

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

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

You can think of naturalistic observation as ‘people watching’ with a purpose.

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

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

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

Questionnaires can be self-administered or researcher-administered.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Overall, your focus group questions should be:

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

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

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

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

A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of four types of interviews .

The four most common types of interviews are:

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

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

Unstructured interviews are best used when:

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

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

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

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

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

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

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

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

When conducting research, collecting original data has significant advantages:

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

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

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

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

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

If something is a mediating variable :

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

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

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

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

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

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

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

Discrete and continuous variables are two types of quantitative variables :

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

In statistics, ordinal and nominal variables are both considered categorical variables .

Even though ordinal data can sometimes be numerical, not all mathematical operations can be performed on them.

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

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

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

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

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

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

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

There are 4 main types of extraneous variables :

• Demand characteristics : Environmental cues that encourage participants to conform to researchers’ expectations
• Experimenter effects : Unintentional actions by researchers that influence study outcomes
• Situational variables : Eenvironmental variables that alter participants’ behaviours
• Participant variables : Any characteristic or aspect of a participant’s background that could affect study results

The difference between explanatory and response variables is simple:

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

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

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

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

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

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

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

Independent variables are also called:

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

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

In statistics, dependent variables are also called:

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

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

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

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

Deductive reasoning is also called deductive logic.

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

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

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

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

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

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

Here are a few common types:

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

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

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

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

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

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

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

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

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

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

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

There are two subtypes of construct validity.

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

Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented.

With a biased final sample, you may not be able to generalise your findings to the original population that you sampled from, so your external validity is compromised.

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

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

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

Attrition bias is a threat to internal validity . In experiments, differential rates of attrition between treatment and control groups can skew results.

This bias can affect the relationship between your independent and dependent variables . It can make variables appear to be correlated when they are not, or vice versa.

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

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

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

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

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

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

There are three key steps in systematic sampling :

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

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

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

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

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

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

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

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

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

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

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

For a probability sample, you have to probability sampling at every stage. You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

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

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

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

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

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

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

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

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

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

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

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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

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

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

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

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

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

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

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

Advantages:

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.

Disadvantages:

• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

In a factorial design, multiple independent variables are tested.

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

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

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

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes
• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

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

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

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

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

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

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

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

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

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

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

Triangulation can help:

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

But triangulation can also pose problems:

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

There are four main types of triangulation :

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

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

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.

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.

These are four of the most common mixed methods designs :

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

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

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

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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Experiment vs Observational Study: Similarities & Differences

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

Learn about our Editorial Process

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

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

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:

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-2/ 10 Reasons you’re Perpetually Single
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 20 Montessori Toddler Bedrooms (Design Inspiration)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 21 Montessori Homeschool Setups
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 101 Hidden Talents Examples

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Observational Versus Experimental Studies: What’s the Evidence for a Hierarchy?

John concato.

Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut 06510, and the Clinical Epidemiology Research Center, West Haven Veterans Affairs Medical Center, West Haven, Connecticut 06516

Summary: The tenets of evidence-based medicine include an emphasis on hierarchies of research design (i.e., study architecture). Often, a single randomized, controlled trial is considered to provide “truth,” whereas results from any observational study are viewed with suspicion. This paper describes information that contradicts and discourages such a rigid approach to evaluating the quality of research design. Unless a more balanced strategy evolves, new claims of methodological authority may be just as problematic as the traditional claims of medical authority that have been criticized by proponents of evidence-based medicine.

INTRODUCTION

Evidence-based medicine classifies studies into grades of evidence based on research architecture. 1 , 2 This hierarchical approach to study design has been promoted widely in individual reports, meta-analyses, consensus statements, and educational materials for clinicians. For example, a prominent publication 3 reserved the highest grade for “at least one properly randomized, controlled trial,” and the lowest grade for descriptive studies (e.g., case series) and expert opinion. Observational studies, including cohort and case-control, fall into intermediate levels (Table ​ (Table1). 1 ). Although the quality of studies is sometimes evaluated within each grade, each category is considered methodologically superior to level(s) below it.

“Grades of Evidence” Rating the Purported Quality of Study Design 3

The ascendancy of randomized, controlled trials (experimental studies) to become the “gold standard” strategy for assessing the effectiveness of therapeutic agents 4 – 6 was based in part on a landmark paper 7 comparing published articles that used randomized and historical control trial designs. The corresponding results found that the agent being tested was considered effective in 44 of 56 (79%) historical controlled trials, but only 10 of 50 (20%) randomized, controlled trials. The authors concluded “biases in patient selection may irretrievably weight the outcome of historical controlled trials in favor of new therapies.” 7

Although the cited article 7 compared randomized, controlled trials to historical controlled trials only, contemporary criticisms of observational studies also include cohort studies with concurrent (nonhistorical) selection of control subjects as well as case-control designs. 8 A possibility exists, however, that data based on “weaker” forms of observational studies can be used mistakenly to criticize all observational research. The premise of this paper is that evidence-based medicine has contributed to the development of a rigid hierarchy of research design that underestimates the limitations of randomized, controlled trials, and overstates the limitations of observational studies.

WHY USE A HIERARCHY OF RESEARCH DESIGN?

A hierarchy of types of research design would be desirable for providing a “checklist” to evaluate clinical studies, but the complexity of medical research suggests that such approaches are overly simplistic. Although randomization protects against certain types of bias that can threaten the validity of a study (i.e., obtaining the correct answer to the question posed, among the study participants involved), a corresponding randomized, controlled trials protocol may restrict the sample of patients selected, the intervention delivered, or the outcome(s) measured, impairing the so-called generalizability of a study (i.e., the extent to which it applies to patients in the “real world”). For example, a randomized, controlled trial may exclude older patients, it may administer therapy in a manner that is difficult to replicate in actual practice, or it may use short-term or surrogate endpoints. In addition, numerous problems can occur when randomized, controlled trials are conducted improperly. Conversely, if properly-conducted observational studies can overcome threats to validity (using strategies discussed later in this paper), and if such studies incorporate more relevant clinical features, then corresponding results would likely be very generalizable to practicing clinicians. Yet, the conventional wisdom suggests that observational studies consistently provide biased results compared with randomized, controlled trials, regardless of the type of observational study or how well it was conducted. The remainder of this paper will focus on these issues.

EVIDENCE AGAINST A RIGID HIERARCHY

A recent study recognized that systematic reviews and meta-analyses offered an opportunity to test the implicit assumptions of grades (or levels) of evidence and similar hierarchies of research design. 9 We identified particular exposure-outcome associations that were studied with both randomized, controlled trials as well as cohort or case-control studies. The major distinctions of our approach (compared with prior research), however, were that we evaluated observational studies that used concurrent (not historical) control subjects, and we focused on summary results rather than individual study findings. The variation in point estimates of exposure-outcome associations provided data to confirm or refute the assumptions regarding observational studies, as well as the strengths and limitations of a “design hierarchy.”

Our methods involved identifying meta-analyses published in five major journals ( Annals of Internal Medicine , British Medical Journal , Journal of the American Medical Association , Lancet , and New England Journal of Medicine ) from 1991 to 1995, using searches of MEDLINE, with the terms “meta-analysis, ” “meta-analyses,” “pooling,” “combining,” “overview,” and “aggregation.” Additional references were found in Current Contents , supplemented by manual searches of the relevant journals. The meta-analyses identified via this process were then classified by consensus as including clinical trials only, observational studies only, or both. Clinical trials were defined as studies that used randomized interventions; observational studies included cohort or case-control designs. Meta-analyses were excluded if they were based on cohort studies with historical control subjects, or clinical trials with nonrandom assignment of interventions, or if they did not report results in the format of a point estimate (e.g., relative risk, odds ratio) and confidence intervals. The remaining meta-analyses were then reviewed, and the original studies cited in the bibliographies were retrieved.

The search strategy yielded 102 citations for meta-analyses, mainly involving (as expected) randomized, controlled trials only. Data for five clinical topics 10 – 15 met our eligibility criteria and provided sufficient data for analysis, involving 99 original articles and 1,871,681 total study subjects. The summary (pooled) point estimates are presented in Table ​ Table2, 2 , and the ranges of the point estimates are displayed in Figure 1 . For example, the relationship between treatment of hypertension and the first occurrence of stroke (i.e., primary prevention) was examined in meta-analyses of 14 randomized, controlled trials 15 and seven cohort studies. 10 The pooled results from randomized, controlled trials ( N = 36,894) found a point estimate of 0.58 (95% confidence interval 0.50–0.67); the pooled results from observational studies ( N = 405,511) found an adjusted point estimate of 0.62 (95% confidence interval 0.60–0.65). Results for other associations (Table ​ (Table2) 2 ) were also similar, based on data from randomized, controlled trials and observational studies. In another example, the effectiveness of bacillus Calmette-Guerin (BCG) vaccine against tuberculosis was examined in a meta-analysis 11 that included 13 randomized trials ( N = 359,922 subjects) with a pooled relative risk of 0.49 (95% confidence interval 0.34–0.70), and 10 case-control studies ( N = 6511 subjects) with a pooled odds ratio of 0.50 (95% confidence interval 0.39–0.65).

Range of relative risks or odds ratios, based on the following types of research design: bacillus Calmette-Guerin vaccine and tuberculosis (13 randomized, controlled trials and 10 case-control studies), screening mammography and breast cancer mortality (eight randomized, controlled trials and four case-control studies), treatment of hyperlipidemia and traumatic death among men (four randomized, controlled trials and 14 cohort studies), treatment of hypertension and stroke among men (11 randomized, controlled trials and seven cohort studies), treatment of hypertension and coronary heart disease among men (13 randomized, controlled trials and nine cohort studies). Filled circles, randomized, controlled trials; open circles, observational studies. (Reproduced with permission.)

Total Number of Subjects and Summary Estimates for the Impact of Five Interventions (“Clinical Topics”) Based on Type of Research Design

CHD = coronary heart disease; CI = confidence interval; RCT = randomized, controlled trial.

The results of our investigation contradict the idea of a “fixed” hierarchy of study design in clinical research. Importantly, another publication 16 addressing the same general question found “little evidence that estimates of treatment effects in observational studies reported after 1984 are either consistently larger than or qualitatively different from those obtained in randomized, controlled trials.” In addition, an evaluation 17 of the literature on screening mammography found similar results to ours on that particular topic. Thus, contrary to prevailing beliefs, average results from well-designed observational (cohort and case-control) studies did not systematically overestimate the magnitude of exposure-outcome associations reported in randomized, controlled trials. Rather, the summary results from randomized, controlled trials and observational studies were remarkably similar for each clinical question addressed.

Another finding, also contrary to current perceptions, was that observational studies individually demonstrated less variability (heterogeneity) in point estimates compared to the variability in point estimates observed in randomized, controlled trials on the same topic ( FIG. 1 ). Indeed, only among randomized, controlled trials did individual studies report results that were opposite to the direction of the pooled point estimate, representing a “paradoxical” finding (e.g., treatment of hypertension was associated with higher rates of coronary heart disease in several clinical trials).

One possible explanation for the finding that observational studies were less prone to heterogeneity in results (compared with randomized, controlled trials) is that each observational study is more likely to include a broad representation of the at-risk population. In addition, less opportunity exists for differences in the management of subjects “across” observational studies. For example, although general agreement exists that physicians do not use therapeutic agents in a uniform way, an observational study would generally include patients with a wider spectrum of severity (regarding the disease of interest), more comorbid ailments, and treatments that were tailored for each individual patient. In contrast, randomized, controlled trials may have distinct groups of patients based on specific inclusion and exclusion criteria, and the experimental protocol for therapy may not be representative of clinical practice. Therefore, randomized, controlled trials often have limited generalizability.

ADDITIONAL EVIDENCE AGAINST A RIGID HIERARCHY

At the time of our previous study, 9 investigations had already shown that observational cohort studies often produce results similar to those of randomized, controlled trials, when using similar criteria to assemble study participants and suitable methodological precautions. For example, an analysis of 18 randomized and nonrandomized studies in health services research found that treatment effects may differ based on research design but that “one method does not give a consistently greater effect than the other.” 18 In that assessment, results were found to be most similar when exclusion criteria across studies were comparable, and when prognostic factors were accounted for in observational studies. In addition, a specific strategy used to strengthen observational studies (called a “restrictive cohort” design 19 ) adapts principles of randomized, controlled trials to 1) identify a zero-time for determining patient eligibility and baseline prognostic risk, 2) use inclusion and exclusion criteria similar to clinical trials, 3) adjust for differences in baseline susceptibility for the outcome, and 4) use similar statistical strategies (e.g., intention-to-treat) as in randomized, controlled trials. When these procedures were used in a cohort study 19 evaluating the benefit of beta blockers after recovery from myocardial infarction, the restricted cohort produced results consistent with corresponding findings from the Beta-Blocker Heart Attack Trial. 20

A second line of evidence supporting our contention that research design should not be considered a rigid hierarchy is also available in the literature of other scientific disciplines that carry out subject-based intervention trials. Examples include a comprehensive review of psychological, educational, and behavioral treatment research 21 ; the findings from this review did not support a contention that observational studies overestimate effects relative to randomized, controlled trials.

Further evidence against a rigid hierarchy is based on results from the trials themselves. For example, a review of more than 200 randomized, controlled trials found numerous individual trials that were supportive, equivocal, or nonsupportive for each of 36 clinical topics. 22 Several publications have discussed various aspects of randomized, controlled trials in neurology. 23 – 28 Recent publications indicate that randomized, controlled trials continue to generate conflicting results, e.g., addressing the question of whether therapy with monoclonal antibodies improve outcomes among patients with septic shock. 29 , 30 In addition, results of “large, simple” randomized, controlled trials contribute to the evidence of contradictory results from randomized, controlled trials; one report found that results of meta-analyses based on randomized, controlled trials were often discordant with findings from large, simple trials on the same clinical topic. 31 Regardless of the reasons that individual randomized, controlled trials produce heterogeneous results, the available evidence indicates that a single randomized trial (or only one observational study) cannot be expected to provide a gold standard result for all clinical situations.

EXAMPLES FROM THE LITERATURE AND IMPLICATIONS FOR CLINICAL CARE

Vitamin e and coronary heart disease.

The Heart Outcomes Prevention Evaluation (HOPE) study, 32 a randomized, controlled trial, was cited as helping to “restrain earlier observational claims that vitamin E lowers the risk of cardiovascular disease.” 33 A review of this topic illustrates the methodological issues involved. Several observational studies 34 – 36 found a “positive” association; in contrast, the HOPE study suggested that vitamin E has no effect on cardiovascular outcomes. Yet, a thorough examination of randomized, controlled trials on this topic provides a more complete assessment. Although two randomized, controlled trials 37 , 38 also found no effect on mortality, two other randomized, controlled trials 39 , 40 found decreased mortality associated with vitamin E. Thus, data from clinical trials are themselves contradictory, and selecting one randomized, controlled trial as a gold standard to criticize observational studies is overly simplistic.

This clinical topic was used to support the statement that “…society expects us to evaluate new healthcare interventions by the most scientifically sound and rigorous methods available. Although observational studies often are cheaper, quicker, and less difficult to carry out, we should not lose sight of one simple fact: ignorance calls for careful experimentation. This means high-quality randomized, controlled trials, not observations that reflect personal choices and beliefs.” 33 An alternative, more rigorous, and less dogmatic approach would be to compare published studies based on components of their research design, whether randomized or observational (Table ​ (Table3), 3 ), and not make a priori judgments regarding a single randomized, controlled trial constituting a gold standard.

Foci for Comparison of Observational and Experimental Study Designs: Example of Vitamin E and Coronary Disease

Hormone replacement therapy and coronary heart disease

Another example of this controversy involves hormone replacement therapy disease for postmenopausal women. In summary, observational studies (such as the Nurses Health Study 41 ) suggested a protective benefit of hormones; whereas randomized, controlled trials (including the Women’s Health Initiative 42 and the Heart and Estrogen/Progestin Replacement Study 43 ) pointed to no benefit, or even harm. Rather than assume the randomized, controlled trials inherently reveal “truth,” potential explorations for the discordant findings could be explored. First, it should be noted that results of randomized, controlled trials and observational studies are remarkably consistent for most outcomes in studies of hormone replacement therapy, including stroke, breast cancer, colorectal cancer, hip fracture, and pulmonary embolism. The outcome of coronary artery disease has received most attention, and has been described as an anomaly. 44

An assessment of this topic described plausible methodological and biological explanations for the differences in findings. 44 For example, available data indicate that women with higher socioeconomic status are more likely to be hormone replacement therapy users and less likely to have coronary artery disease, suggesting that the observational studies were vulnerable to “healthy user bias” (or “confounding”) in this context. (Confounding, as a general term, occurs when a third variable, socioeconomic status in this situation, is related to both the exposure [hormone therapy] and outcome [coronary artery disease] variables for the association of interest. The exposure variable [hormone therapy] would then be described as a “marker” for the confounding variable, rather than actually causing the outcome.) In addition, the randomized, controlled trials themselves have been criticized for having bias. 45

Another issue involves incomplete capture of early clinical events. 44 Observational studies typically enroll participants who have been taking hormone replacement therapy for some time, whereas randomized clinical trials initiate therapy in nonusers. Accordingly, clinical events that occur soon after initiating the medication would be captured by randomized, controlled trials, but typical observational studies assess what is likely to happen when patients remain on therapy for an extended period of time (patients initiating therapy recently would account for a very small proportion of the overall population). Other explanations for discordant results involve differences in protocols among observational studies and randomized, controlled trials. For example, daily combinations of estrogen and progestin were administered in Women’s Health Initiative 42 and Heart and Estrogen/Progestin Replacement Study, 43 compared with estrogen alone or combined regimens for 10–14 days per month in observational studies such as the Nurses Health Study. 41

These differences are not “fatal flaws” of observational studies, unless a rigid opinion is adopted that designates randomized, controlled trials as infallible. Most of the issues raised involve either methodological differences without a definite “winner” (e.g., examining early vs late clinical events), or true biological differences (e.g., in patients or protocols). Regarding the issue of confounding (e.g., healthy user bias, as described previously), methods are available 19 to measure and adjust for such variables.

A MORE BALANCED VIEW OF OBSERVATIONAL AND EXPERIMENTAL EVIDENCE

Given that randomized, controlled trials have not and often cannot be done for many clinical interventions, much of the clinical care provided in neurology (and all other specialties in medicine) would necessarily be considered unsubstantiated, if observational studies are discounted from consideration. The available evidence suggests, however, that observational studies can be conducted with sufficient rigor to replicate the results of randomized, controlled trials. The key issue is designing appropriate observational studies, usually with suitable (observational) cohort or case-control architecture; a methodological task for investigators to complete and reviewers to evaluate.

Despite the consistency of our results 9 (involving five clinical topics and 99 separate studies), as well as confirmatory evidence available in the literature, 16 – 18 we believe that the role of observational studies may vary in different situations. For example, different exposures (e.g., surgical operations and other invasive therapies) may be more prone to selection bias in observational investigations than the drugs and noninvasive tests examined in our report, 9 and “softer” outcomes (e.g., functional status) may be assessed more readily in randomized, controlled trials. In addition, we emphasized the potential risk associated with poorly done observational studies; for example, to promote ineffective “alternative” therapies. 46

Finally, a point of emphasis involves the general belief that randomization is necessary to balance known and (especially) unknown potential factors that can cause biased estimates of treatment effects through confounding. Given that unknown factors, by definition, would not be recognized by clinicians, a bias in assigning treatment would not occur according to those factors. Although such factors could be associated with outcome, they would not be associated with exposure, and therefore would not be confounding variables and would not affect the validity of results.

Randomized, controlled trials will (and should) remain a prominent tool in clinical research, but the results of a single randomized, controlled trial, or only one observational study, should be interpreted cautiously. If a randomized, controlled trial is later determined to be “wrong” in its conclusions, evidence from both other trials and well designed cohort or case-control studies can and should be used to establish the “right” answers.

The issues raised in this paper are not intended to diminish the important role that randomized, controlled trials play in clinical medicine (e.g., for evaluating interventions or for satisfying regulatory criteria). Yet, the popular belief that randomized, controlled trials inherently produce gold standard results, and that all observational studies are inferior, does a disservice to patient care, clinical investigation, and education of health care professionals. We should recognize the potential problem we face, that “the justification for why studies are included or excluded from the evidence base can rest on competing claims of methodologic authority that look little different from the traditional claims of medical authority that proponents of evidence-based medicine have criticized…interpretive decisions by old pre-evidence-based medicine experts may be replaced by interpretive decisions from a new group of experts with evidence-based medicine credentials…” 47 A more balanced and scientifically justified approach is to evaluate the strengths and limitations of well done experimental and observational studies, recognizing the attributes of each type of design.

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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:

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.

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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.

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Observational Study Vs. Experimental Study: What’s The Difference?

• Parameter vs. Statistic
• Reoccurring vs. Recurring
• Linear vs. Nonlinear
• Observational Study vs. Experiment
• Histogram vs. Bar Graph
• Discrete vs. Continuous
• Validity vs. Reliability
• Type 1 vs. Type 2 Error
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• Sample vs. Population
• Interpolation vs. Extrapolation
• Exogenous vs. Endogenous

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While the majority of us are familiar with the idea of an experiment or an observation in the vernacular, you may wonder if it’s any different in scientific jargon. The short answer is no; however, there is more specificity to it in terms of scientific studies. Merriam-Webster has several different definitions for the word observation, but the one that’s the closest to the scientific sense is “an act of recognizing and noting a fact or occurrence often involving measurement with instruments.” Experiment, on the other hand, is defined as “an operation or procedure carried out under controlled conditions in order to discover an unknown effect or law, to test or establish a hypothesis, or to illustrate a known law.” So, in short, an observational study involves simply watching and recording what happens. An experiential study, on the other hand, involves carrying out a procedure and controlling the conditions under which it happens. Key Takeaways: Observational Study Experimental Study Observational studies require watching the subjects and recording their behavior. Experimental studies require some sort of intervention or change to compare against the control group. While easier and cheaper to put together, they’re generally not considered conclusive. They are usually considered much more definitive; however, they’re expensive, difficult, and time-consuming to put together and run. An observational study doesn’t require a controlled environment. Experimental studies have to have a controlled environment in order to control for outside influences. Studies of this type may have a control group, but it isn’t a necessity. Studies of this variety require a control group. Often a placebo is given in place of intervention, but it depends on what’s being studied. What Is an Observational Study?

Observational studies are useful in many different circumstances. They’re used in all manners of fields, from biology, ecology, sociology, and psychology .

One of the most prevalent forms of an observational study would be a survey. When issuing a survey, those running them do their level best to remove themselves from the answers given. Surveys are most often used in sociology and psychology, though they have many uses in medicine as well.

An observational study could also involve watching wildlife. This is a common way that researchers learn more about the natural world, as they observe how the ecosystem interacts without interfering with it.

Observational studies also have the bonus of being much less expensive to put together and run than experimental studies. It requires less time, staff, and planning to execute.

There are several different types of observational studies that are used under different conditions.

Cohort studies. Cohort studies are, by design, longitudinal, meaning that they’re long-term. They’re created by selecting a “cohort,” which is a group that shares a common characteristic. This can be a group that’s born at the same time, has the same health condition, or engages in a particular behavior, such as smoking.

Case-control studies. Case-control studies involve having both a “case” group and a “control” group. Most people are familiar with the idea of a control group – a selection of people that don’t do whatever’s in the experiment.

For example, if you’re comparing the effect pets have on mental health , the people in your control group wouldn’t have a pet. Alternatively, the case group would have a pet.

Cross-sectional studies. These types of studies narrow your observations to a period in time. This time period can be a month, say if you’re looking at how many people were killed in car crashes. Or it could be a physical observation, such as counting the number of car crash victims that come into the hospital emergency room on a particular night.

There are also different types of observation that are used in observational studies. These determine how the observer interacts – or doesn’t – with what’s being observed.

Naturalistic observation. This involves observing participants react in a “real-life” situation. Researchers don’t influence the subjects’ behavior in hopes it will be as natural as possible.

Covert observation. As the name implies, this type of observation requires that the participants don’t know that they’re being observed. Often done in public places in order to avoid ethical concerns.

Systematic observation. This type of observation is based on counting how many times a particular behavior or phenomenon happens. The behavior isn’t influenced, and researchers need to follow a strict observation schedule.

Quantitative observation. This type of observation relies on numerical data, such as the height or age of the subjects.

Case study. A study of this type requires long-term observation of an individual or small group. The idea is that such an observation can then be generalized to a larger group.

Participant observation. This is similar to the naturalistic observation in that it also observes real-life situations. However, the difference is that the researcher also participates in the activity – hence the name. Such as studying the culture of hospital staff while working as a nurse .

Qualitative observation. An observational type that is focused on the five senses.

Archival research. As the name suggests, this type of research is more removed. It involves investigating records rather than dealing with subjects or participants directly.

What Is an Experimental Study?

An experimental study involves altering conditions and measuring the results of that alteration. This can vary widely, with the best-known example being drug trials. The experimenter gives one group of people the new pharmaceutical while another group is given a placebo. The efficacy of the drug is then weighted against the severity of the side effects it produces.

Experimental studies are often the preferred method due to the fact that the conditions are more carefully controlled. This allows for greater scientific validity in the sense that they’re able to compensate and control for outside influences and factors, unlike observational studies.

Due to the fact that they’re so heavily controlled, they are, however, much more expensive. There are also circumstances where doing an experimental study would be unethical, such as studying the effects of corporal punishment on children.

No reasonable researcher could assign a cohort of children to be hit regularly while another group isn’t. Thus, that was studied via an observational study instead.

There are a few different varieties of experimental studies.

Randomized controlled trials. The linchpin of this style of study is randomization. In this type, the control group and experimental group are randomly assigned to their positions. This is considered the most scientifically rigorous way to do the assignments because it prevents biases from having an effect.

Community intervention trials. Rather than assigning individuals to be in the control group, a community intervention trial will select two different groups or communities.

One community will receive the intervention (whatever they’re testing, be it a drug, different type of construction, or footpaths), and the other won’t. That’s how you get your control group and study group.

Pragmatic clinical trials. This type of trial focuses on efficacy. It’s most often used in clinical trials, such as for a new medicine or treatment.

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Di has been a writer for more than half her life. Most of her writing so far has been fiction, and she’s gotten short stories published in online magazines Kzine and Silver Blade, as well as a flash fiction piece in the Bookends review. Di graduated from Mary Baldwin College (now University) with a degree in Psychology and Sociology.

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• Career Advice >
• Science Terms >
• Observational Study Vs Experimental Study

Guide to observational vs. experimental studies

• Observational studies
• Experimental studies
• Systematic reviews & meta-analyses
• Convincing evidence
• When to trust observational studies
• Pros and cons

Take home points

Although findings from the latest nutrition studies often make news headlines and are shared widely on social media, many aren’t based on strong scientific evidence.

You’ve no doubt noticed that there are conflicting reports about whether a food is good or bad for you. One day headlines will say drinking coffee is overwhelmingly beneficial, but the following day new headlines shout that coffee increases risk of heart attacks.

To say that this can be confusing and frustrating is an understatement. Many of us do our best to make food choices that will improve our health and quality of life. How can we know if the latest research being reported is reliable?

Generally speaking, the media fail to evaluate the evidence; instead, studies with “exciting” conclusions are turned into click-worthy headlines, no matter how weak the evidence is.

In this guide, we discuss the differences between observational and experimental studies, the advantages and disadvantages of each, and why in nearly all cases observational research shouldn’t be used when making decisions about your diet. After reading this guide, you may be able to identify media reports about nutritional science that you can safely ignore, i.e. most of them.

In our evidence-based guides at Diet Doctor, we make it simple by using a color code to show how strong evidence a study provides: strong , moderate , weak or very weak evidence. 3 After reading this guide, you’ll understand much more about what that means.

What is an observational study?

In an observational study (also known as an epidemiological study), researchers observe a group of people to see what happens to them over time. Although study participants may answer questions and fill out questionnaires, researchers don’t conduct any experiments and have no control over the participants.

An observational study is basically an exercise in statistics. Researchers try to find correlations between certain behaviours and certain outcomes. For example, do people who eat more vegetables have a larger or smaller risk of developing a certain disease?

Although the statistics from observational studies can show associations between certain behaviors and the development of a disease or condition, these associations may or may not be cause-effect relationships. 4 In most cases, an observational study is not enough to be able to tell. An observational study can often just provide very weak evidence . 5 A different kind of study, usually an experimental one, is needed to prove that something causes something else, for example that drinking coffee can make people lose weight.

There are good reasons for the famous quote stating that “there are three kinds of lies: lies, damned lies, and statistics.”

What is an experimental study?

In a nutrition-related experimental study (also known as a clinical trial or interventional study), researchers provide participants with a diet, nutrition education, or other kind of intervention and evaluate its effects.

Experimental evidence is considered stronger than observational evidence. Randomized, controlled trials (RCTs) are often referred to as the “gold standard” for evidence. They are designed to test an intervention against a different intervention (i.e. low carb vs. low fat), or against a control group that does not change its behaviors (i.e low carb vs standard American diet), under tightly monitored conditions.

Assigning participants randomly to either the experimental or the control group helps to ensure that both groups are similar in ways that are not being tested (such as income, education, level of exercise, etc.). This makes these studies (in best case) a fair comparison, and makes the evidence they provide far stronger: often moderately strong evidence .

The best RCTs use the actual development of the disease being studied or death of the participant as the outcome being measured. Because medical conditions may take many years to develop, decades-long RCTs are very expensive, making them impractical in most cases. Therefore, many RCTs are much shorter, and instead of measuring health outcomes, they measure changes in health markers that reflect disease risk, such as changes in blood sugar, insulin, or inflammation levels.

Unfortunately, this assumes the changes in a surrogate marker reflect a positive or negative impact on one’s health. As we have seen in many studies, this may not always be the case.

Systematic reviews and meta-analyses

A single study on its own is often not enough to provide clear answers about the relationship between food and health. Systematic reviews and meta-analyses are both ways of putting together multiple studies in an attempt to clarify what the evidence says.

A systematic review is a detailed, standardized process of gathering, assessing and synthesizing a collection of relevant studies on a particular topic.

A meta-analysis is a statistical procedure for combining data from the studies used in a systematic review.

Systematic reviews and meta-analyses may consist of observational research, experimental research, or a combination of both. They have historically been considered the strongest type of evidence; however, this is not always the case.

Systematic reviews and meta-analyses are sometimes seen as ways to “strengthen” the weak findings of observational studies. The thinking is that if a number of observational studies show the same effect, this must indicate a cause-effect relationship even if the effect is very small in all cases. But systematic reviews and meta-analyses made up of observational studies cannot override the fundamental principle that association is not causation. If you took a placebo pill that had no effect on a condition you wanted to treat, it wouldn’t work better if you took more of them! In the same way, weak observational studies do not develop rigor by combining many of them.

Only RCTs (experimental studies) can come close to establishing that a certain food or way of eating causes a particular outcome. Systematic reviews and meta-analyses based on experimental studies have a much greater chance of providing good evidence on which to base decisions about your own health. We grade these as strong evidence .

Why many observational studies don’t provide convincing evidence

Observational studies can only give us information about how certain behaviors and diseases are associated or correlated. An association must be very strong in order to indicate a potential cause-effect relationship, and even strong associations do not necessarily show this. For example, skirt-wearing is strongly associated with the likelihood of developing breast cancer (since they are mostly worn by women!), but it would be silly to suggest that wearing a skirt causes breast cancer.

Typically, the strength of associations in observational studies about nutrition and chronic diseases is small, as reflected by the low relative risks that are found. A relative risk of 1.0 means there is no association. In most observational studies about nutrition, the relative risk is close to 1.0, with a range of 0.8 to 1.5, indicating a weak association. 8 Weak associations are likely to be due to other factors such as random chance or confounding variables, and not likely to be a cause-effect relationship.

The reasons for such weak associations are often built into the design of observational studies. Because scientists are only observing a selected population, they cannot take into account all the possible factors that might affect how diet appears to be related to a disease.

For example, people who are concerned about their health are likely to choose foods they think will help prevent disease. But they are also more likely to do many other things they think will promote and protect their health, such as exercising regularly, avoiding smoking, and taking a multivitamin. It is hard to know which of these factors are responsible for outcomes found in an observational study.

Professor John Ioannidis is a highly-regarded expert in meta-research, the study of research practices and how to improve them. In September of 2018, he wrote an opinion piece for the Journal of the American Medical Association stating that nutrition observational studies are hopelessly flawed and in need of “radical reform.” 9 In the article, he points out that hidden factors that may bias the outcomes of an observational study are not accounted for (for instance, people who eat a lot of meat may also drink a lot of beer and get little exercise) and that findings are routinely influenced by researcher bias.

He also points out the absurdity of claiming that certain foods will increase lifespan for a specific length of time. As an example, various studies show that consuming hazelnuts, coffee, oranges, and other foods and beverages on a daily basis may each help extend life by several years.

“If you were to gain all the benefit speculated by each one of these studies, we would be able to live for 5,000 years,” says Ioannidis.

When can the results of observational research be trusted?

In other words, findings from observational studies can usually not be trusted on their own.

Pros and cons of observational studies

• Are much less expensive than clinical trials
• Can last for several years or even decades
• Can include tens of thousands of study participants
• Can look at development of a disease or death as an outcome
• Rely on self-reported data that often can’t be confirmed
• Have no control group for comparison
• Can’t take into account all of the factors that can influence the results
• Are not cause-effect relationships

Pros and cons of experimental studies

• Are tightly controlled and monitored
• Compare outcomes between those who receive an intervention and those who don’t
• Can use randomization to deal with unknown factors that might influence outcomes
• Are expensive and time consuming
• Use health markers rather than development of disease or death as endpoints
• Are typically smaller than most observational studies
• Are usually shorter than most observational studies

Observational research usually produces unreliable results, and these results are often given more attention in the media than they deserve.

Before changing your diet based on the most recent news story, find out a few things about the study being discussed. Is the study observational or experimental? Are the findings consistent with previous research, especially with higher-quality studies like experimental ones? If the study is observational, how strong were the associations between the outcome and the behavior, food, or diet being studied?

Most importantly, remember that observational studies usually can’t show that a specific food, diet or lifestyle caused a particular outcome. This normally requires an experimental study.

The bottom line is that most observational studies, and all the media headlines generated by them, can safely be ignored.

— Franziska Spritzler, RD

All articles and guides by Franziska Spritzler

Franziska Spritzler is a registered dietitian, author and certified diabetes educator who takes a low-carb, real-food approach to diabetes, weight management and overall health.

Adele Hite, RD

Adele came to rhetoric and communication from a Ph.D. program in nutritional epidemiology and a background in nutrition, dietetics, and public health. She’s animated by questions and concerns, many of which boil down to this: Why is nutrition [science, policy, discourse] the way it is?

Understanding absolute and relative risk

Guide Although it seems as if numbers should be objective and trustworthy, there are many ways that they can be used to distort the truth. Entire books have been written about this subject. Let’s take a look at the differences between absolute risk and relative risk.

The Diet Doctor policy for grading scientific evidence

American Journal of Clinical Nutrition 2013: Is everything we eat associated with cancer? A systematic cookbook review ↩

Advances in Nutrition 2018: Limiting dependence on nonrandomized studies and improving randomized trials in human nutrition research: why and how

JAMA 2018: The challenge of reforming nutritional epidemiologic research

PLoS Medicine 2005: Why most published research findings are false ↩

For the full details about our evidence-grading policy, see this page:

The Diet Doctor policy for grading scientific evidence ↩

A confounding variable is one that is not taken into consideration in the study. Confounding variables can introduce bias and indicate a relationship between a food or diet and a health outcome when there isn’t one. ↩

Though there are exceptions:

Advances in Nutrition 2018: Limiting dependence on nonrandomized studies and improving randomized trials in human nutrition research: why and how ↩

The Milbank Quarterly 2016: The mass production of redundant, misleading, and conflicted systematic reviews and meta-analyses ↩

There is some discussion about what is considered a “weak” versus a “strong” association and how strong an association must be to potentially indicate a cause-effect relationship.

A helpful comparision is that relative risks found in assocations between smoking and lung cancer were around 10.0 for moderate smokers and 20.0 for heavy smokers. This level of relative risk was strong enough for experts to argue for a cause-effect relationship.

American Journal of Clinical Nutrition 1999: Causal criteria in nutritional epidemiology ↩

Journal of the American Medical Association 2018: The challenge of reforming nutritional epidemiologic research ↩

In the past few decades, there have many instances where the results of observational nutrition studies have been contradicted in RCTs.

Significance 2011: Deming, data and observational studies: A process out of control and needing fixing

Seminars in Oncology 2010: Epidemiological and clinical studies of nutrition ↩

For us to use this evidence grade, HR needs to be consistently > 5 in several high-quality observational studies, with biological plausibility, no other obvious explanation and generally following the classic Bradford Hill criteria .

Proceedings of the Royal Society of Medicine 1965: The environment and disease: association or causation? By Sir Austin Bradford Hill ↩