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Experimental Design in Statistics w/ 11 Examples!

// Last Updated: September 20, 2020 - Watch Video //

A proper experimental design is a critical skill in statistics.

Jenn, Founder Calcworkshop ® , 15+ Years Experience (Licensed & Certified Teacher)

Without proper controls and safeguards, unintended consequences can ruin our study and lead to wrong conclusions.

So let’s dive in to see what’s this is all about!

What’s the difference between an observational study and an experimental study?

An observational study is one in which investigators merely measure variables of interest without influencing the subjects.

And an experiment is a study in which investigators administer some form of treatment on one or more groups?

In other words, an observation is hands-off, whereas an experiment is hands-on.

So what’s the purpose of an experiment?

To establish causation (i.e., cause and effect).

All this means is that we wish to determine the effect an independent explanatory variable has on a dependent response variable.

The explanatory variable explains a response, similar to a child falling and skins their knee and starting to cry. The child is crying in response to falling and skinning their knee. So the explanatory variable is the fall, and the response variable is crying.

Explanatory Vs Response Variable In Everyday Life

Let’s look at another example. Suppose a medical journal describes two studies in which subjects who had a seizure were randomly assigned to two different treatments:

• No treatment.
• A high dose of vitamin C.

The subjects were observed for a year, and the number of seizures for each subject was recorded. Identify the explanatory variable (independent variable), response variable (dependent variable), and include the experimental units.

The explanatory variable is whether the subject received either no treatment or a high dose of vitamin C. The response variable is whether the subject had a seizure during the time of the study. The experimental units in this study are the subjects who recently had a seizure.

Okay, so using the example above, notice that one of the groups did not receive treatment. This group is called a control group and acts as a baseline to see how a new treatment differs from those who don’t receive treatment. Typically, the control group is given something called a placebo, a substance designed to resemble medicine but does not contain an active drug component. A placebo is a dummy treatment, and should not have a physical effect on a person.

Before we talk about the characteristics of a well-designed experiment, we need to discuss some things to look out for:

• Confounding
• Lurking variables

Confounding happens when two explanatory variables are both associated with a response variable and also associated with each other, causing the investigator not to be able to identify their effects and the response variable separately.

A lurking variable is usually unobserved at the time of the study, which influences the association between the two variables of interest. In essence, a lurking variable is a third variable that is not measured in the study but may change the response variable.

For example, a study reported a relationship between smoking and health. A study of 1430 women were asked whether they smoked. Ten years later, a follow-up survey observed whether each woman was still alive or deceased. The researchers studied the possible link between whether a woman smoked and whether she survived the 10-year study period. They reported that:

• 21% of the smokers died
• 32% of the nonsmokers died

So, is smoking beneficial to your health, or is there something that could explain how this happened?

Older women are less likely to be smokers, and older women are more likely to die. Because age is a variable that influences the explanatory and response variable, it is considered a confounding variable.

But does smoking cause death?

Notice that the lurking variable, age, can also be a contributing factor. While there is a correlation between smoking and mortality, and also a correlation between smoking and age, we aren’t 100% sure that they are the cause of the mortality rate in women.

Lurking – Confounding – Correlation – Causation Diagram

Now, something important to point out is that a lurking variable is one that is not measured in the study that could influence the results. Using the example above, some other possible lurking variables are:

• Stress Level.

These variables were not measured in the study but could influence smoking habits as well as mortality rates.

What is important to note about the difference between confounding and lurking variables is that a confounding variable is measured in a study, while a lurking variable is not.

Additionally, correlation does not imply causation!

Alright, so now it’s time to talk about blinding: single-blind, double-blind experiments, as well as the placebo effect.

A single-blind experiment is when the subjects are unaware of which treatment they are receiving, but the investigator measuring the responses knows what treatments are going to which subject. In other words, the researcher knows which individual gets the placebo and which ones receive the experimental treatment. One major pitfall for this type of design is that the researcher may consciously or unconsciously influence the subject since they know who is receiving treatment and who isn’t.

A double-blind experiment is when both the subjects and investigator do not know who receives the placebo and who receives the treatment. A double-blind model is considered the best model for clinical trials as it eliminates the possibility of bias on the part of the researcher and the possibility of producing a placebo effect from the subject.

The placebo effect is when a subject has an effect or response to a fake treatment because they “believe” that the result should occur as noted by Yale . For example, a person struggling with insomnia takes a placebo (sugar pill) but instantly falls asleep because they believe they are receiving a sleep aid like Ambien or Lunesta.

Placebo Effect – Real Life Example

So, what are the three primary requirements for a well-designed experiment?

• Randomization

In a controlled experiment , the researchers, or investigators, decide which subjects are assigned to a control group and which subjects are assigned to a treatment group. In doing so, we ensure that the control and treatment groups are as similar as possible, and limit possible confounding influences such as lurking variables. A replicated experiment that is repeated on many different subjects helps reduce the chance of variation on the results. And randomization means we randomly assign subjects into control and treatment groups.

When subjects are divided into control groups and treatment groups randomly, we can use probability to predict the differences we expect to observe. If the differences between the two groups are higher than what we would expect to see naturally (by chance), we say that the results are statistically significant.

For example, if it is surmised that a new medicine reduces the effects of illness from 72 hours to 71 hours, this would not be considered statistically significant. The difference from 72 hours to 71 hours is not substantial enough to support that the observed effect was due to something other than normal random variation.

Now there are two major types of designs:

• Completely-Randomized Design (CRD)
• Block Design

A completely randomized design is the process of assigning subjects to control and treatment groups using probability, as seen in the flow diagram below.

Completely Randomized Design Example

A block design is a research method that places subjects into groups of similar experimental units or conditions, like age or gender, and then assign subjects to control and treatment groups using probability, as shown below.

Randomized Block Design Example

Additionally, a useful and particular case of a blocking strategy is something called a matched-pair design . This is when two variables are paired to control for lurking variables.

For example, imagine we want to study if walking daily improved blood pressure. If the blood pressure for five subjects is measured at the beginning of the study and then again after participating in a walking program for one month, then the observations would be considered dependent samples because the same five subjects are used in the before and after observations; thus, a matched-pair design.

Please note that our video lesson will not focus on quasi-experiments. A quasi experimental design lacks random assignments; therefore, the independent variable can be manipulated prior to measuring the dependent variable, which may lead to confounding. For the sake of our lesson, and all future lessons, we will be using research methods where random sampling and experimental designs are used.

Together we will learn how to identify explanatory variables (independent variable) and response variables (dependent variables), understand and define confounding and lurking variables, see the effects of single-blind and double-blind experiments, and design randomized and block experiments.

Experimental Designs – Lesson & Examples (Video)

1 hr 06 min

• Introduction to Video: Experiments
• 00:00:29 – Observational Study vs Experimental Study and Response and Explanatory Variables (Examples #1-4)
• Exclusive Content for Members Only
• 00:09:15 – Identify the response and explanatory variables and the experimental units and treatment (Examples #5-6)
• 00:14:47 – Introduction of lurking variables and confounding with ice cream and homicide example
• 00:18:57 – Lurking variables, Confounding, Placebo Effect, Single Blind and Double Blind Experiments (Example #7)
• 00:27:20 – What was the placebo effect and was the experiment single or double blind? (Example #8)
• 00:30:36 – Characteristics of a well designed and constructed experiment that is statistically significant
• 00:35:08 – Overview of Complete Randomized Design, Block Design and Matched Pair Design
• 00:44:23 – Design and experiment using complete randomized design or a block design (Examples #9-10)
• 00:56:09 – Identify the response and explanatory variables, experimental units, lurking variables, and design an experiment to test a new drug (Example #11)
• Practice Problems with Step-by-Step Solutions
• Chapter Tests with Video Solutions

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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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Understanding Statistics and Experimental Design

How to Not Lie with Statistics

• Open Access
• © 2019

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• Michael H. Herzog 0 ,
• Gregory Francis 1 ,
• Aaron Clarke 2

Brain Mind Institute, École Polytechnique Fédérale de Lausanne Brain Mind Institute, Lausanne, Switzerland

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Dept. Psychological Sciences, Purdue University Dept. Psychological Sciences, West Lafayette, USA

Psychology department, bilkent university, ankara, turkey.

• Short and mathematical as simple as possible
• Provides a full account to the mostly used statistical tests
• Makes the key statistical concepts and reasoning readily accessible
• Teaches the reader the meta-statistical principles
• Offers a completely new way of judging the quality of scientific studies in science and daily life

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Selected Statistical Methods in Experimental Studies

Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations

The bayesian new statistics: hypothesis testing, estimation, meta-analysis, and power analysis from a bayesian perspective.

• Experimental design
• statistics in life sciences
• concepts of statistics
• correlations
• meta-statistics
• reproduction
• hypothesis testing
• simple probabilities
• questionable research practices

Table of contents (12 chapters)

Front matter, the essentials of statistics, basic probability theory.

• Michael H. Herzog, Gregory Francis, Aaron Clarke

Experimental Design and the Basics of Statistics: Signal Detection Theory (SDT)

The core concept of statistics, variations on the t -test, the multiple testing problem, experimental design: model fits, power, and complex designs, correlation, meta-analysis and the science crisis, meta-analysis, understanding replication, magnitude of excess success, suggested improvements and challenges.

“Readers with little or no background in statistics will appreciate how these fundamental concepts are so well illustrated in this book to establish the solid foundation of probability and statistics.” (David Han, Mathematical Reviews, April, 2020)

Authors and Affiliations

Michael H. Herzog

Gregory Francis

Aaron Clarke

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Book Title : Understanding Statistics and Experimental Design

Book Subtitle : How to Not Lie with Statistics

Authors : Michael H. Herzog, Gregory Francis, Aaron Clarke

Series Title : Learning Materials in Biosciences

DOI : https://doi.org/10.1007/978-3-030-03499-3

Publisher : Springer Cham

eBook Packages : Biomedical and Life Sciences , Biomedical and Life Sciences (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s) 2019

Softcover ISBN : 978-3-030-03498-6 Published: 22 August 2019

eBook ISBN : 978-3-030-03499-3 Published: 13 August 2019

Series ISSN : 2509-6125

Series E-ISSN : 2509-6133

Edition Number : 1

Number of Pages : XI, 142

Number of Illustrations : 6 b/w illustrations, 29 illustrations in colour

Topics : Molecular Medicine , Biostatistics , Science Education , Statistics for Life Sciences, Medicine, Health Sciences , Psychology Research , Behavioral Sciences

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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.

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Appropriate design of research and statistical analyses: observational versus experimental studies

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Corresponding author: Hyun Kang, M.D., Ph.D., Department of Anesthesiology and Pain Medicine, Chung-Ang Universtiy College of Medicine, 224-1, Heuksuk-dong, Dongjak-gu, Seoul 156-756, Korea. Tel: 82-2-6299-2571, Fax: 82-2-6299-2585, [email protected]

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With the recent increases in the number of published articles, following a trend of placing greater emphasis on the importance of research, there is an increasing need for the correct application of statistical methods and for the appropriate choice of valid experimental designs. Scientific research studies can be crudely divided into two general types: experimental studies and observational studies. Both study types have advantages and disadvantages, and the choice of study type depends on factors such as the purpose of the research and the nature of the phenomenon to be evaluated.

Experimental studies have higher internal validity; specifically, when the experiment is repeated under the same experimental conditions, the results will be the same. On the other hand, observational studies may have greater external validity; for example, the results of the study may be applicable to typical clinical practice. Because participants are assigned to control and treatment groups, and the conditions under which the study is conducted and data are collected are controlled by the researcher in experimental studies, factors that are of no interest can be eliminated or controlled. Thus, experimental studies can establish evidence of causation between variables, whereas observational studies can show only associations between variables [ 1 ].

Randomization is used for assigning participants to different groups in an experimental study. This process eliminates selection bias and confounding bias, and ensures that the groups are comparable despite the presence of factors other than the one being investigated. As Dr. Oh [ 2 ] has pointed out, the randomization process will, on average, evenly balance factors that were measured, were not measured, or could not be measured, and this justifies the statistical analysis [ 3 ]. However, randomization does not guarantee that there are no statistically significant differences in terms of baseline characteristics between groups. It only ensures that the differences between control and treatment groups in terms of baseline characteristics are due solely to chance. Accordingly, it must be remembered that even when randomization is executed correctly, baseline characteristics between control and treatment groups may still differ. For example, when simple randomization based on 20 baseline characteristics is used to assign participants to groups, the actual likelihood that at least one characteristic will, by chance alone, show a significant imbalance between the two groups is 64% at a two-sided value of P < 0.05 [ 4 ]. After a study has been conducted, clinically relevant imbalances should be dealt with by an adjusted analysis of the data. If imbalances considered to be important to the final results are expected, an analysis plan, including an adjusted analysis, can be included when the study is designed.

An observational study examines an existing association between variables based on observations of what is happening or has happened as a result of something else. Nothing is done to influence the results, and the participants are grouped based on their characteristics with respect to the variables and not by randomization. The researcher has no control over the study process or the allocation of participants in an observational study. This can result in bias masking of causality or in false suggestions of correlations.

Despite these limitations, observational studies are commonly used in situations in which experimental studies are inappropriate or impossible. Experimental studies are precluded when they 1) are unethical; 2) involve rare diseases and patients; 3) include variables that are practically impossible to manipulate, such as inherent traits; or 4) are too costly and time-consuming to be conducted on a large scale. For example, an experimental study comparing the risk for developing lung cancer between smokers and non-smokers would raise ethical concerns, as making subjects smoke in order to assess the impact of smoking on lung cancer would deny participants of the right to make their own decision. Intubation difficulty scores are essentially an inherent trait and cannot be controlled; thus, the study by Seo et al. [ 5 ] is an example of the inability to practically manipulate a variable.

Although a poor source of data regarding causality of a treatment or intervention, observational studies can contribute important information, provided the data are analyzed and interpreted appropriately, with consideration of the biases and confounders [ 6 ]. To control for confounding arising from a lack of comparability between groups, methods such as matching, stratification, multivariate regression (multiple linear regression, multiple logistic regression), propensity scores, and instrumental variable analysis can be used. Using these methods, the level of one or more factors can be made a constant in order to evaluate the variation in outcome variables derived from a change in the risk factor of interest. These manipulations are referred to as 'statistical adjustments' or 'controlling' for confounding issues. However, these methods can only adjust for or control for known sources of bias under a specific set of assumptions.

Among the statistical adjustment methods, logistic regression analysis is popular and widely used. It is similar to linear regression, but is used for predicting the outcome of a categorical independent variable based on a calculated odds ratio (OR), which is a measure of the association between an exposure and an outcome. The OR reflects the odds of an outcome occurring given a particular exposure compared with the odds of the same outcome occurring in the absence of that exposure. In logistic regression analysis, the regression coefficient (β1) of the equation is estimated, and the exponential function of the regression coefficient ( e β1 ) is the OR associated with a one-unit increase in the exposure [ 7 ]. For categorical variables, ORs can be directly interpreted between groups. However, for continuous variables, ORs can be interpreted differently depending on the unit of the independent variable of interest.

As in other statistical analyses, there are two hypotheses of interest in logistic regression. The null hypothesis (H 0 ) is that all of the regression coefficients in the equation are zero. The alternate hypothesis (H 1 ) is that at least one of the regression coefficients in the equation is not zero, which would mean that the model derived from the logistic regression and currently being considered is accurate. In Seo's study, [ 5 ] the null hypothesis of the logistic regression analysis was that all of the regression coefficients in the equation predicting difficult intubation have a value of zero. The alternate hypothesis was that at least one of the regression coefficients in the equation predicting difficult intubation differs significantly from zero, indicating that the model currently proposed to predict difficult intubation is accurate.

When interpreting the results of logistic regression, the absence of multicollinearity among independent variables should be evaluated. Multicollinearity means that two or more independent variables in a multiple regression or multiple logistic regression analysis are in fact highly correlated with each other. In the presence of multicollinearity, it is difficult to determine reliable estimates of individual coefficients, resulting in incorrect conclusions about the relationship between the dependent and independent variables. Thus, when performing multiple logistic regression using independent variables with similar characteristics, researchers should report whether multicollinerity was present, and if so, how it was treated in the statistical analysis. For example, in the study by Seo et al. [ 5 ], a discussion regarding multicollinearity and relevant statistical methods would alleviate possible doubts that the total airway score, upper lip bite test, head and neck movement, interincisor gap, body mass index, and Mallampati classification have similar characteristics in predicting a difficult airway. Additionally, reporting the overall model evaluation, goodnessof-fit statistics, and validation of predicted probabilities would also help to clarify and support the results.

In summary, experimental studies are considered to be more reliable than observational studies because the process can be controlled and randomization can eliminate bias and ensure comparable study groups in experimental studies. Furthermore, causality can be established in experimental studies. Nevertheless, when experimental studies are inappropriate or impossible, observational studies can provide important information, if the data are analyzed and interpreted using suitable statistical methods. The type of study to be performed should be determined based on the purpose of the study, the nature of the phenomenon, and the characteristics of the variables. The statistical methods should be appropriate for the design and hypothesis of the study, and should be applicable to the types and characteristics of the variables assessed in the study.

Improvements in research methodologies and increased understanding by readers of research articles will increase the debate regarding the correct application of statistics and the selection of appropriate study designs. This phenomenon may have positive ramifications by providing an opportunity to re-think research articles and by raising the quality of papers published in the Korean Journal of Anesthesiology . Finally, I thank Dr. Oh for the keen observations and encourage all readers to participate in this necessary debate.

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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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Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Design

Experimental design is a process of planning and conducting scientific experiments to investigate a hypothesis or research question. It involves carefully designing an experiment that can test the hypothesis, and controlling for other variables that may influence the results.

Experimental design typically includes identifying the variables that will be manipulated or measured, defining the sample or population to be studied, selecting an appropriate method of sampling, choosing a method for data collection and analysis, and determining the appropriate statistical tests to use.

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

In this design, participants are randomly assigned to one of two or more groups, and each group is exposed to a different treatment or condition.

Randomized Block Design

This design involves dividing participants into blocks based on a specific characteristic, such as age or gender, and then randomly assigning participants within each block to one of two or more treatment groups.

Factorial Design

In a factorial design, participants are randomly assigned to one of several groups, each of which receives a different combination of two or more independent variables.

Repeated Measures Design

In this design, each participant is exposed to all of the different treatments or conditions, either in a random order or in a predetermined order.

Crossover Design

This design involves randomly assigning participants to one of two or more treatment groups, with each group receiving one treatment during the first phase of the study and then switching to a different treatment during the second phase.

Split-plot Design

In this design, the researcher manipulates one or more variables at different levels and uses a randomized block design to control for other variables.

Nested Design

This design involves grouping participants within larger units, such as schools or households, and then randomly assigning these units to different treatment groups.

Laboratory Experiment

Laboratory experiments are conducted under controlled conditions, which allows for greater precision and accuracy. However, because laboratory conditions are not always representative of real-world conditions, the results of these experiments may not be generalizable to the population at large.

Field Experiment

Field experiments are conducted in naturalistic settings and allow for more realistic observations. However, because field experiments are not as controlled as laboratory experiments, they may be subject to more sources of error.

Experimental Design Methods

Experimental design methods refer to the techniques and procedures used to design and conduct experiments in scientific research. Here are some common experimental design methods:

Randomization

This involves randomly assigning participants to different groups or treatments to ensure that any observed differences between groups are due to the treatment and not to other factors.

Control Group

The use of a control group is an important experimental design method that involves having a group of participants that do not receive the treatment or intervention being studied. The control group is used as a baseline to compare the effects of the treatment group.

Blinding involves keeping participants, researchers, or both unaware of which treatment group participants are in, in order to reduce the risk of bias in the results.

Counterbalancing

This involves systematically varying the order in which participants receive treatments or interventions in order to control for order effects.

Replication

Replication involves conducting the same experiment with different samples or under different conditions to increase the reliability and validity of the results.

This experimental design method involves manipulating multiple independent variables simultaneously to investigate their combined effects on the dependent variable.

This involves dividing participants into subgroups or blocks based on specific characteristics, such as age or gender, in order to reduce the risk of confounding variables.

Data Collection Method

Experimental design data collection methods are techniques and procedures used to collect data in experimental research. Here are some common experimental design data collection methods:

Direct Observation

This method involves observing and recording the behavior or phenomenon of interest in real time. It may involve the use of structured or unstructured observation, and may be conducted in a laboratory or naturalistic setting.

Self-report Measures

Self-report measures involve asking participants to report their thoughts, feelings, or behaviors using questionnaires, surveys, or interviews. These measures may be administered in person or online.

Behavioral Measures

Behavioral measures involve measuring participants’ behavior directly, such as through reaction time tasks or performance tests. These measures may be administered using specialized equipment or software.

Physiological Measures

Physiological measures involve measuring participants’ physiological responses, such as heart rate, blood pressure, or brain activity, using specialized equipment. These measures may be invasive or non-invasive, and may be administered in a laboratory or clinical setting.

Archival Data

Archival data involves using existing records or data, such as medical records, administrative records, or historical documents, as a source of information. These data may be collected from public or private sources.

Computerized Measures

Computerized measures involve using software or computer programs to collect data on participants’ behavior or responses. These measures may include reaction time tasks, cognitive tests, or other types of computer-based assessments.

Video Recording

Video recording involves recording participants’ behavior or interactions using cameras or other recording equipment. This method can be used to capture detailed information about participants’ behavior or to analyze social interactions.

Data Analysis Method

Experimental design data analysis methods refer to the statistical techniques and procedures used to analyze data collected in experimental research. Here are some common experimental design data analysis methods:

Descriptive Statistics

Descriptive statistics are used to summarize and describe the data collected in the study. This includes measures such as mean, median, mode, range, and standard deviation.

Inferential Statistics

Inferential statistics are used to make inferences or generalizations about a larger population based on the data collected in the study. This includes hypothesis testing and estimation.

Analysis of Variance (ANOVA)

ANOVA is a statistical technique used to compare means across two or more groups in order to determine whether there are significant differences between the groups. There are several types of ANOVA, including one-way ANOVA, two-way ANOVA, and repeated measures ANOVA.

Regression Analysis

Regression analysis is used to model the relationship between two or more variables in order to determine the strength and direction of the relationship. There are several types of regression analysis, including linear regression, logistic regression, and multiple regression.

Factor Analysis

Factor analysis is used to identify underlying factors or dimensions in a set of variables. This can be used to reduce the complexity of the data and identify patterns in the data.

Structural Equation Modeling (SEM)

SEM is a statistical technique used to model complex relationships between variables. It can be used to test complex theories and models of causality.

Cluster Analysis

Cluster analysis is used to group similar cases or observations together based on similarities or differences in their characteristics.

Time Series Analysis

Time series analysis is used to analyze data collected over time in order to identify trends, patterns, or changes in the data.

Multilevel Modeling

Multilevel modeling is used to analyze data that is nested within multiple levels, such as students nested within schools or employees nested within companies.

Applications of Experimental Design 

Experimental design is a versatile research methodology that can be applied in many fields. Here are some applications of experimental design:

• Medical Research: Experimental design is commonly used to test new treatments or medications for various medical conditions. This includes clinical trials to evaluate the safety and effectiveness of new drugs or medical devices.
• Agriculture : Experimental design is used to test new crop varieties, fertilizers, and other agricultural practices. This includes randomized field trials to evaluate the effects of different treatments on crop yield, quality, and pest resistance.
• Environmental science: Experimental design is used to study the effects of environmental factors, such as pollution or climate change, on ecosystems and wildlife. This includes controlled experiments to study the effects of pollutants on plant growth or animal behavior.
• Psychology : Experimental design is used to study human behavior and cognitive processes. This includes experiments to test the effects of different interventions, such as therapy or medication, on mental health outcomes.
• Engineering : Experimental design is used to test new materials, designs, and manufacturing processes in engineering applications. This includes laboratory experiments to test the strength and durability of new materials, or field experiments to test the performance of new technologies.
• Education : Experimental design is used to evaluate the effectiveness of teaching methods, educational interventions, and programs. This includes randomized controlled trials to compare different teaching methods or evaluate the impact of educational programs on student outcomes.
• Marketing : Experimental design is used to test the effectiveness of marketing campaigns, pricing strategies, and product designs. This includes experiments to test the impact of different marketing messages or pricing schemes on consumer behavior.

Examples of Experimental Design 

Here are some examples of experimental design in different fields:

• Example in Medical research : A study that investigates the effectiveness of a new drug treatment for a particular condition. Patients are randomly assigned to either a treatment group or a control group, with the treatment group receiving the new drug and the control group receiving a placebo. The outcomes, such as improvement in symptoms or side effects, are measured and compared between the two groups.
• Example in Education research: A study that examines the impact of a new teaching method on student learning outcomes. Students are randomly assigned to either a group that receives the new teaching method or a group that receives the traditional teaching method. Student achievement is measured before and after the intervention, and the results are compared between the two groups.
• Example in Environmental science: A study that tests the effectiveness of a new method for reducing pollution in a river. Two sections of the river are selected, with one section treated with the new method and the other section left untreated. The water quality is measured before and after the intervention, and the results are compared between the two sections.
• Example in Marketing research: A study that investigates the impact of a new advertising campaign on consumer behavior. Participants are randomly assigned to either a group that is exposed to the new campaign or a group that is not. Their behavior, such as purchasing or product awareness, is measured and compared between the two groups.
• Example in Social psychology: A study that examines the effect of a new social intervention on reducing prejudice towards a marginalized group. Participants are randomly assigned to either a group that receives the intervention or a control group that does not. Their attitudes and behavior towards the marginalized group are measured before and after the intervention, and the results are compared between the two groups.

When to use Experimental Research Design 

Experimental research design should be used when a researcher wants to establish a cause-and-effect relationship between variables. It is particularly useful when studying the impact of an intervention or treatment on a particular outcome.

Here are some situations where experimental research design may be appropriate:

• When studying the effects of a new drug or medical treatment: Experimental research design is commonly used in medical research to test the effectiveness and safety of new drugs or medical treatments. By randomly assigning patients to treatment and control groups, researchers can determine whether the treatment is effective in improving health outcomes.
• When evaluating the effectiveness of an educational intervention: An experimental research design can be used to evaluate the impact of a new teaching method or educational program on student learning outcomes. By randomly assigning students to treatment and control groups, researchers can determine whether the intervention is effective in improving academic performance.
• When testing the effectiveness of a marketing campaign: An experimental research design can be used to test the effectiveness of different marketing messages or strategies. By randomly assigning participants to treatment and control groups, researchers can determine whether the marketing campaign is effective in changing consumer behavior.
• When studying the effects of an environmental intervention: Experimental research design can be used to study the impact of environmental interventions, such as pollution reduction programs or conservation efforts. By randomly assigning locations or areas to treatment and control groups, researchers can determine whether the intervention is effective in improving environmental outcomes.
• When testing the effects of a new technology: An experimental research design can be used to test the effectiveness and safety of new technologies or engineering designs. By randomly assigning participants or locations to treatment and control groups, researchers can determine whether the new technology is effective in achieving its intended purpose.

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

• Identify a Research Question : Start by identifying a research question that you want to answer through the experiment. The question should be clear, specific, and testable.
• Develop a Hypothesis: Based on your research question, develop a hypothesis that predicts the relationship between the independent and dependent variables. The hypothesis should be clear and testable.
• Design the Experiment : Determine the type of experimental design you will use, such as a between-subjects design or a within-subjects design. Also, decide on the experimental conditions, such as the number of independent variables, the levels of the independent variable, and the dependent variable to be measured.
• Select Participants: Select the participants who will take part in the experiment. They should be representative of the population you are interested in studying.
• Randomly Assign Participants to Groups: If you are using a between-subjects design, randomly assign participants to groups to control for individual differences.
• Conduct the Experiment : Conduct the experiment by manipulating the independent variable(s) and measuring the dependent variable(s) across the different conditions.
• Analyze the Data: Analyze the data using appropriate statistical methods to determine if there is a significant effect of the independent variable(s) on the dependent variable(s).
• Draw Conclusions: Based on the data analysis, draw conclusions about the relationship between the independent and dependent variables. If the results support the hypothesis, then it is accepted. If the results do not support the hypothesis, then it is rejected.
• Communicate the Results: Finally, communicate the results of the experiment through a research report or presentation. Include the purpose of the study, the methods used, the results obtained, and the conclusions drawn.

Purpose of Experimental Design 

The purpose of experimental design is to control and manipulate one or more independent variables to determine their effect on a dependent variable. Experimental design allows researchers to systematically investigate causal relationships between variables, and to establish cause-and-effect relationships between the independent and dependent variables. Through experimental design, researchers can test hypotheses and make inferences about the population from which the sample was drawn.

Experimental design provides a structured approach to designing and conducting experiments, ensuring that the results are reliable and valid. By carefully controlling for extraneous variables that may affect the outcome of the study, experimental design allows researchers to isolate the effect of the independent variable(s) on the dependent variable(s), and to minimize the influence of other factors that may confound the results.

Experimental design also allows researchers to generalize their findings to the larger population from which the sample was drawn. By randomly selecting participants and using statistical techniques to analyze the data, researchers can make inferences about the larger population with a high degree of confidence.

Overall, the purpose of experimental design is to provide a rigorous, systematic, and scientific method for testing hypotheses and establishing cause-and-effect relationships between variables. Experimental design is a powerful tool for advancing scientific knowledge and informing evidence-based practice in various fields, including psychology, biology, medicine, engineering, and social sciences.

Advantages of Experimental Design 

Experimental design offers several advantages in research. Here are some of the main advantages:

• Control over extraneous variables: Experimental design allows researchers to control for extraneous variables that may affect the outcome of the study. By manipulating the independent variable and holding all other variables constant, researchers can isolate the effect of the independent variable on the dependent variable.
• Establishing causality: Experimental design allows researchers to establish causality by manipulating the independent variable and observing its effect on the dependent variable. This allows researchers to determine whether changes in the independent variable cause changes in the dependent variable.
• Replication : Experimental design allows researchers to replicate their experiments to ensure that the findings are consistent and reliable. Replication is important for establishing the validity and generalizability of the findings.
• Random assignment: Experimental design often involves randomly assigning participants to conditions. This helps to ensure that individual differences between participants are evenly distributed across conditions, which increases the internal validity of the study.
• Precision : Experimental design allows researchers to measure variables with precision, which can increase the accuracy and reliability of the data.
• Generalizability : If the study is well-designed, experimental design can increase the generalizability of the findings. By controlling for extraneous variables and using random assignment, researchers can increase the likelihood that the findings will apply to other populations and contexts.

Limitations of Experimental Design

Experimental design has some limitations that researchers should be aware of. Here are some of the main limitations:

• Artificiality : Experimental design often involves creating artificial situations that may not reflect real-world situations. This can limit the external validity of the findings, or the extent to which the findings can be generalized to real-world settings.
• Ethical concerns: Some experimental designs may raise ethical concerns, particularly if they involve manipulating variables that could cause harm to participants or if they involve deception.
• Participant bias : Participants in experimental studies may modify their behavior in response to the experiment, which can lead to participant bias.
• Limited generalizability: The conditions of the experiment may not reflect the complexities of real-world situations. As a result, the findings may not be applicable to all populations and contexts.
• Cost and time : Experimental design can be expensive and time-consuming, particularly if the experiment requires specialized equipment or if the sample size is large.
• Researcher bias : Researchers may unintentionally bias the results of the experiment if they have expectations or preferences for certain outcomes.
• Lack of feasibility : Experimental design may not be feasible in some cases, particularly if the research question involves variables that cannot be manipulated or controlled.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Experimental vs Observational Studies: Differences & Examples

Understanding the differences between experimental vs observational studies is crucial for interpreting findings and drawing valid conclusions. Both methodologies are used extensively in various fields, including medicine, social sciences, and environmental studies. 

Researchers often use observational and experimental studies to gather comprehensive data and draw robust conclusions about their investigating phenomena. 

This blog post will explore what makes these two types of studies unique, their fundamental differences, and examples to illustrate their applications.

What is an Experimental Study?

An experimental study is a research design in which the investigator actively manipulates one or more variables to observe their effect on another variable. This type of study often takes place in a controlled environment, which allows researchers to establish cause-and-effect relationships.

Key Characteristics of Experimental Studies:

• Manipulation: Researchers manipulate the independent variable(s).
• Control: Other variables are kept constant to isolate the effect of the independent variable.
• Randomization: Subjects are randomly assigned to different groups to minimize bias.
• Replication: The study can be replicated to verify results.

Types of Experimental Study

• Laboratory Experiments: Conducted in a controlled environment where variables can be precisely controlled.
• Field Research : These are conducted in a natural setting but still involve manipulation and control of variables.
• Clinical Trials: Used in medical research and the healthcare industry to test the efficacy of new treatments or drugs.

Example of an Experimental Study:

Imagine a study to test the effectiveness of a new drug for reducing blood pressure. Researchers would:

• Randomly assign participants to two groups: receiving the drug and receiving a placebo.
• Ensure that participants do not know their group (double-blind procedure).
• Measure blood pressure before and after the intervention.
• Compare the changes in blood pressure between the two groups to determine the drug’s effectiveness.

What is an Observational Study?

An observational study is a research design in which the investigator observes subjects and measures variables without intervening or manipulating the study environment. This type of study is often used when manipulating impractical or unethical variables.

Key Characteristics of Observational Studies:

• No Manipulation: Researchers do not manipulate the independent variable.
• Natural Setting: Observations are made in a natural environment.
• Causation Limitations: It is difficult to establish cause-and-effect relationships due to the need for more control over variables.
• Descriptive: Often used to describe characteristics or outcomes.

Types of Observational Studies: 

• Cohort Studies : Follow a control group of people over time to observe the development of outcomes.
• Case-Control Studies: Compare individuals with a specific outcome (cases) to those without (controls) to identify factors that might contribute to the outcome.
• Cross-Sectional Studies : Collect data from a population at a single point to analyze the prevalence of an outcome or characteristic.

Example of an Observational Study:

Consider a study examining the relationship between smoking and lung cancer. Researchers would:

• Identify a cohort of smokers and non-smokers.
• Follow both groups over time to record incidences of lung cancer.
• Analyze the data to observe any differences in cancer rates between smokers and non-smokers.

Difference Between Experimental vs Observational Studies

Choosing between experimental and observational studies.

The researchers relied on statistical analysis to interpret the results of randomized controlled trials, building upon the foundations established by prior research.

Use Experimental Studies When:

• Causality is Important: If determining a cause-and-effect relationship is crucial, experimental studies are the way to go.
• Variables Can Be Controlled: When you can manipulate and control the variables in a lab or controlled setting, experimental studies are suitable.
• Randomization is Possible: When random assignment of subjects is feasible and ethical, experimental designs are appropriate.

Use Observational Studies When:

• Ethical Concerns Exist: If manipulating variables is unethical, such as exposing individuals to harmful substances, observational studies are necessary.
• Practical Constraints Apply: When experimental studies are impractical due to cost or logistics, observational studies can be a viable alternative.
• Natural Settings Are Required: If studying phenomena in their natural environment is essential, observational studies are the right choice.

Strengths and Limitations

Experimental studies.

• Establish Causality: Experimental studies can establish causal relationships between variables by controlling and using randomization.
• Control Over Confounding Variables: The controlled environment allows researchers to minimize the influence of external variables that might skew results.
• Repeatability: Experiments can often be repeated to verify results and ensure consistency.

Limitations:

• Ethical Concerns: Manipulating variables may be unethical in certain situations, such as exposing individuals to harmful conditions.
• Artificial Environment: The controlled setting may not reflect real-world conditions, potentially affecting the generalizability of results.
• Cost and Complexity: Experimental studies can be costly and logistically complex, especially with large sample sizes.

Observational Studies

• Real-World Insights: Observational studies provide valuable insights into how variables interact in natural settings.
• Ethical and Practical: These studies avoid ethical concerns associated with manipulation and can be more practical regarding cost and time.
• Diverse Applications: Observational studies can be used in various fields and situations where experiments are not feasible.
• Lack of Causality: It’s easier to establish causation with manipulation, and results are limited to identifying correlations.
• Potential for Confounding: Uncontrolled external variables may influence the results, leading to biased conclusions.
• Observer Bias: Researchers may unintentionally influence outcomes through their expectations or interpretations of data.

Examples in Various Fields

• Experimental Study: Clinical trials testing the effectiveness of a new drug against a placebo to determine its impact on patient recovery.
• Observational Study: Studying the dietary habits of different populations to identify potential links between nutrition and disease prevalence.
• Experimental Study: Conducting a lab experiment to test the effect of sleep deprivation on cognitive performance by controlling sleep hours and measuring test scores.
• Observational Study: Observing social interactions in a public setting to explore natural communication patterns without intervention.

Environmental Science

• Experimental Study: Testing the impact of a specific pollutant on plant growth in a controlled greenhouse setting.
• Observational Study: Monitoring wildlife populations in a natural habitat to assess the effects of climate change on species distribution.

How QuestionPro Research Can Help in Experimental vs Observational Studies

Choosing between experimental and observational studies is a critical decision that can significantly impact the outcomes and interpretations of a study. QuestionPro Research offers powerful tools and features that can enhance both types of studies, giving researchers the flexibility and capability to gather, analyze, and interpret data effectively.

Enhancing Experimental Studies with QuestionPro

Experimental studies require a high degree of control over variables, randomization, and, often, repeated trials to establish causal relationships. QuestionPro excels in facilitating these requirements through several key features:

• Survey Design and Distribution: With QuestionPro, researchers can design intricate surveys tailored to their experimental needs. The platform supports random assignment of participants to different groups, ensuring unbiased distribution and enhancing the study’s validity.
• Data Collection and Management: Real-time data collection and management tools allow researchers to monitor responses as they come in. This is crucial for experimental studies where data collection timing and sequence can impact the results.
• Advanced Analytics: QuestionPro offers robust analytical tools that can handle complex data sets, enabling researchers to conduct in-depth statistical analyses to determine the effects of the experimental interventions.

Supporting Observational Studies with QuestionPro

Observational studies involve gathering data without manipulating variables, focusing on natural settings and real-world scenarios. QuestionPro’s capabilities are well-suited for these studies as well:

• Customizable Surveys: Researchers can create detailed surveys to capture a wide range of observational data. QuestionPro’s customizable templates and question types allow for flexibility in capturing nuanced information.
• Mobile Data Collection: For field research, QuestionPro’s mobile app enables data collection on the go, making it easier to conduct studies in diverse settings without internet connectivity.
• Longitudinal Data Tracking: Observational studies often require data collection over extended periods. QuestionPro’s platform supports longitudinal studies, allowing researchers to track changes and trends.

Experimental and observational studies are essential tools in the researcher’s toolkit. Each serves a unique purpose and offers distinct advantages and limitations. By understanding their differences, researchers can choose the most appropriate study design for their specific objectives, ensuring their findings are valid and applicable to real-world situations.

Whether establishing causality through experimental studies or exploring correlations with observational research designs, the insights gained from these methodologies continue to shape our understanding of the world around us. 

Whether conducting experimental or observational studies, QuestionPro Research provides a comprehensive suite of tools that enhance research efficiency, accuracy, and depth. By leveraging its advanced features, researchers can ensure that their studies are well-designed, their data is robustly analyzed, and their conclusions are reliable and impactful.

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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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What factors would influence the future of open access (OA) publishing?

• Chapter 9. Experimental studies

Randomised controlled trials

Crossover studies, experimental study of populations.

• Chapter 1. What is epidemiology?
• Chapter 2. Quantifying disease in populations
• Chapter 3. Comparing disease rates
• Chapter 4. Measurement error and bias
• Chapter 5. Planning and conducting a survey
• Chapter 6. Ecological studies
• Chapter 7. Longitudinal studies
• Chapter 8. Case-control and cross sectional studies
• Chapter 10. Screening
• Chapter 11. Outbreaks of disease
• Chapter 12. Reading epidemiological reports
• Chapter 13. Further reading

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