Statistical Thinking: A Simulation Approach to Modeling Uncertainty (UM STAT 216 edition)
3.6 causation and random assignment.
Medical researchers may be interested in showing that a drug helps improve people’s health (the cause of improvement is the drug), while educational researchers may be interested in showing a curricular innovation improves students’ learning (the curricular innovation causes improved learning).
To attribute a causal relationship, there are three criteria a researcher needs to establish:
- Association of the Cause and Effect: There needs to be a association between the cause and effect.
- Timing: The cause needs to happen BEFORE the effect.
- No Plausible Alternative Explanations: ALL other possible explanations for the effect need to be ruled out.
Please read more about each of these criteria at the Web Center for Social Research Methods .
The third criterion can be quite difficult to meet. To rule out ALL other possible explanations for the effect, we want to compare the world with the cause applied to the world without the cause. In practice, we do this by comparing two different groups: a “treatment” group that gets the cause applied to them, and a “control” group that does not. To rule out alternative explanations, the groups need to be “identical” with respect to every possible characteristic (aside from the treatment) that could explain differences. This way the only characteristic that will be different is that the treatment group gets the treatment and the control group doesn’t. If there are differences in the outcome, then it must be attributable to the treatment, because the other possible explanations are ruled out.
So, the key is to make the control and treatment groups “identical” when you are forming them. One thing that makes this task (slightly) easier is that they don’t have to be exactly identical, only probabilistically equivalent . This means, for example, that if you were matching groups on age that you don’t need the two groups to have identical age distributions; they would only need to have roughly the same AVERAGE age. Here roughly means “the average ages should be the same within what we expect because of sampling error.”
Now we just need to create the groups so that they have, on average, the same characteristics … for EVERY POSSIBLE CHARCTERISTIC that could explain differences in the outcome.
It turns out that creating probabilistically equivalent groups is a really difficult problem. One method that works pretty well for doing this is to randomly assign participants to the groups. This works best when you have large sample sizes, but even with small sample sizes random assignment has the advantage of at least removing the systematic bias between the two groups (any differences are due to chance and will probably even out between the groups). As Wikipedia’s page on random assignment points out,
Random assignment of participants helps to ensure that any differences between and within the groups are not systematic at the outset of the experiment. Thus, any differences between groups recorded at the end of the experiment can be more confidently attributed to the experimental procedures or treatment. … Random assignment does not guarantee that the groups are matched or equivalent. The groups may still differ on some preexisting attribute due to chance. The use of random assignment cannot eliminate this possibility, but it greatly reduces it.
We use the term internal validity to describe the degree to which cause-and-effect inferences are accurate and meaningful. Causal attribution is the goal for many researchers. Thus, by using random assignment we have a pretty high degree of evidence for internal validity; we have a much higher belief in causal inferences. Much like evidence used in a court of law, it is useful to think about validity evidence on a continuum. For example, a visualization of the internal validity evidence for a study that employed random assignment in the design might be:
![random assignment establish cause and effect random assignment establish cause and effect](https://bookdown.org/img/internal-validity-evidence-barometer.png)
The degree of internal validity evidence is high (in the upper-third). How high depends on other factors such as sample size.
To learn more about random assignment, you can read the following:
- The research report, Random Assignment Evaluation Studies: A Guide for Out-of-School Time Program Practitioners
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3.6.1 Example: Does sleep deprivation cause an decrease in performance?
Let’s consider the criteria with respect to the sleep deprivation study we explored in class.
3.6.1.1 Association of cause and effect
First, we ask, Is there an association between the cause and the effect? In the sleep deprivation study, we would ask, “Is sleep deprivation associated with an decrease in performance?”
This is what a hypothesis test helps us answer! If the result is statistically significant , then we have an association between the cause and the effect. If the result is not statistically significant, then there is not sufficient evidence for an association between cause and effect.
In the case of the sleep deprivation experiment, the result was statistically significant, so we can say that sleep deprivation is associated with a decrease in performance.
3.6.1.2 Timing
Second, we ask, Did the cause come before the effect? In the sleep deprivation study, the answer is yes. The participants were sleep deprived before their performance was tested. It may seem like this is a silly question to ask, but as the link above describes, it is not always so clear to establish the timing. Thus, it is important to consider this question any time we are interested in establishing causality.
3.6.1.3 No plausible alternative explanations
Finally, we ask Are there any plausible alternative explanations for the observed effect? In the sleep deprivation study, we would ask, “Are there plausible alternative explanations for the observed difference between the groups, other than sleep deprivation?” Because this is a question about plausibility, human judgment comes into play. Researchers must make an argument about why there are no plausible alternatives. As described above, a strong study design can help to strengthen the argument.
At first, it may seem like there are a lot of plausible alternative explanations for the difference in performance. There are a lot of things that might affect someone’s performance on a visual task! Sleep deprivation is just one of them! For example, artists may be more adept at visual discrimination than other people. This is an example of a potential confounding variable. A confounding variable is a variable that might affect the results, other than the causal variable that we are interested in.
Here’s the thing though. We are not interested in figuring out why any particular person got the score that they did. Instead, we are interested in determining why one group was different from another group. In the sleep deprivation study, the participants were randomly assigned. This means that the there is no systematic difference between the groups, with respect to any confounding variables. Yes—artistic experience is a possible confounding variable, and it may be the reason why two people score differently. BUT: There is no systematic difference between the groups with respect to artistic experience, and so artistic experience is not a plausible explanation as to why the groups would be different. The same can be said for any possible confounding variable. Because the groups were randomly assigned, it is not plausible to say that the groups are different with respect to any confounding variable. Random assignment helps us rule out plausible alternatives.
3.6.1.4 Making a causal claim
Now, let’s see about make a causal claim for the sleep deprivation study:
- Association: There is a statistically significant result, so the cause is associated with the effect
- Timing: The participants were sleep deprived before their performance was measured, so the cause came before the effect
- Plausible alternative explanations: The participants were randomly assigned, so the groups are not systematically different on any confounding variable. The only systematic difference between the groups was sleep deprivation. Thus, there are no plausible alternative explanations for the difference between the groups, other than sleep deprivation
Thus, the internal validity evidence for this study is high, and we can make a causal claim. For the participants in this study, we can say that sleep deprivation caused a decrease in performance.
Key points: Causation and internal validity
To make a cause-and-effect inference, you need to consider three criteria:
- Association of the Cause and Effect: There needs to be a association between the cause and effect. This can be established by a hypothesis test.
Random assignment removes any systematic differences between the groups (other than the treatment), and thus helps to rule out plausible alternative explanations.
Internal validity describes the degree to which cause-and-effect inferences are accurate and meaningful.
Confounding variables are variables that might affect the results, other than the causal variable that we are interested in.
Probabilistic equivalence means that there is not a systematic difference between groups. The groups are the same on average.
How can we make "equivalent" experimental groups?
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Statistics By Jim
Making statistics intuitive
Random Assignment in Experiments
By Jim Frost 4 Comments
Random assignment uses chance to assign subjects to the control and treatment groups in an experiment. This process helps ensure that the groups are equivalent at the beginning of the study, which makes it safer to assume the treatments caused any differences between groups that the experimenters observe at the end of the study.
![random assignment establish cause and effect photogram of tumbling dice to illustrate a process for random assignment.](https://i0.wp.com/statisticsbyjim.com/wp-content/uploads/2019/05/random_dice.png?resize=300%2C236&ssl=1)
Huh? That might be a big surprise! At this point, you might be wondering about all of those studies that use statistics to assess the effects of different treatments. There’s a critical separation between significance and causality:
- Statistical procedures determine whether an effect is significant.
- Experimental designs determine how confidently you can assume that a treatment causes the effect.
In this post, learn how using random assignment in experiments can help you identify causal relationships.
Correlation, Causation, and Confounding Variables
Random assignment helps you separate causation from correlation and rule out confounding variables. As a critical component of the scientific method , experiments typically set up contrasts between a control group and one or more treatment groups. The idea is to determine whether the effect, which is the difference between a treatment group and the control group, is statistically significant. If the effect is significant, group assignment correlates with different outcomes.
However, as you have no doubt heard, correlation does not necessarily imply causation. In other words, the experimental groups can have different mean outcomes, but the treatment might not be causing those differences even though the differences are statistically significant.
The difficulty in definitively stating that a treatment caused the difference is due to potential confounding variables or confounders. Confounders are alternative explanations for differences between the experimental groups. Confounding variables correlate with both the experimental groups and the outcome variable. In this situation, confounding variables can be the actual cause for the outcome differences rather than the treatments themselves. As you’ll see, if an experiment does not account for confounding variables, they can bias the results and make them untrustworthy.
Related posts : Understanding Correlation in Statistics , Causation versus Correlation , and Hill’s Criteria for Causation .
Example of Confounding in an Experiment
![random assignment establish cause and effect A photograph of vitamin capsules to represent our experiment.](https://i0.wp.com/statisticsbyjim.com/wp-content/uploads/2019/05/vitamins.jpg?resize=300%2C200&ssl=1)
- Control group: Does not consume vitamin supplements
- Treatment group: Regularly consumes vitamin supplements.
Imagine we measure a specific health outcome. After the experiment is complete, we perform a 2-sample t-test to determine whether the mean outcomes for these two groups are different. Assume the test results indicate that the mean health outcome in the treatment group is significantly better than the control group.
Why can’t we assume that the vitamins improved the health outcomes? After all, only the treatment group took the vitamins.
Related post : Confounding Variables in Regression Analysis
Alternative Explanations for Differences in Outcomes
The answer to that question depends on how we assigned the subjects to the experimental groups. If we let the subjects decide which group to join based on their existing vitamin habits, it opens the door to confounding variables. It’s reasonable to assume that people who take vitamins regularly also tend to have other healthy habits. These habits are confounders because they correlate with both vitamin consumption (experimental group) and the health outcome measure.
Random assignment prevents this self sorting of participants and reduces the likelihood that the groups start with systematic differences.
In fact, studies have found that supplement users are more physically active, have healthier diets, have lower blood pressure, and so on compared to those who don’t take supplements. If subjects who already take vitamins regularly join the treatment group voluntarily, they bring these healthy habits disproportionately to the treatment group. Consequently, these habits will be much more prevalent in the treatment group than the control group.
The healthy habits are the confounding variables—the potential alternative explanations for the difference in our study’s health outcome. It’s entirely possible that these systematic differences between groups at the start of the study might cause the difference in the health outcome at the end of the study—and not the vitamin consumption itself!
If our experiment doesn’t account for these confounding variables, we can’t trust the results. While we obtained statistically significant results with the 2-sample t-test for health outcomes, we don’t know for sure whether the vitamins, the systematic difference in habits, or some combination of the two caused the improvements.
Learn why many randomized clinical experiments use a placebo to control for the Placebo Effect .
Experiments Must Account for Confounding Variables
Your experimental design must account for confounding variables to avoid their problems. Scientific studies commonly use the following methods to handle confounders:
- Use control variables to keep them constant throughout an experiment.
- Statistically control for them in an observational study.
- Use random assignment to reduce the likelihood that systematic differences exist between experimental groups when the study begins.
Let’s take a look at how random assignment works in an experimental design.
Random Assignment Can Reduce the Impact of Confounding Variables
Note that random assignment is different than random sampling. Random sampling is a process for obtaining a sample that accurately represents a population .
![random assignment establish cause and effect Photo of a coin toss to represent how we can incorporate random assignment in our experiment.](https://i0.wp.com/statisticsbyjim.com/wp-content/uploads/2018/01/coin_toss.png?resize=300%2C300&ssl=1)
Random assignment uses a chance process to assign subjects to experimental groups. Using random assignment requires that the experimenters can control the group assignment for all study subjects. For our study, we must be able to assign our participants to either the control group or the supplement group. Clearly, if we don’t have the ability to assign subjects to the groups, we can’t use random assignment!
Additionally, the process must have an equal probability of assigning a subject to any of the groups. For example, in our vitamin supplement study, we can use a coin toss to assign each subject to either the control group or supplement group. For more complex experimental designs, we can use a random number generator or even draw names out of a hat.
Random Assignment Distributes Confounders Equally
The random assignment process distributes confounding properties amongst your experimental groups equally. In other words, randomness helps eliminate systematic differences between groups. For our study, flipping the coin tends to equalize the distribution of subjects with healthier habits between the control and treatment group. Consequently, these two groups should start roughly equal for all confounding variables, including healthy habits!
Random assignment is a simple, elegant solution to a complex problem. For any given study area, there can be a long list of confounding variables that you could worry about. However, using random assignment, you don’t need to know what they are, how to detect them, or even measure them. Instead, use random assignment to equalize them across your experimental groups so they’re not a problem.
Because random assignment helps ensure that the groups are comparable when the experiment begins, you can be more confident that the treatments caused the post-study differences. Random assignment helps increase the internal validity of your study.
Comparing the Vitamin Study With and Without Random Assignment
Let’s compare two scenarios involving our hypothetical vitamin study. We’ll assume that the study obtains statistically significant results in both cases.
Scenario 1: We don’t use random assignment and, unbeknownst to us, subjects with healthier habits disproportionately end up in the supplement treatment group. The experimental groups differ by both healthy habits and vitamin consumption. Consequently, we can’t determine whether it was the habits or vitamins that improved the outcomes.
Scenario 2: We use random assignment and, consequently, the treatment and control groups start with roughly equal levels of healthy habits. The intentional introduction of vitamin supplements in the treatment group is the primary difference between the groups. Consequently, we can more confidently assert that the supplements caused an improvement in health outcomes.
For both scenarios, the statistical results could be identical. However, the methodology behind the second scenario makes a stronger case for a causal relationship between vitamin supplement consumption and health outcomes.
How important is it to use the correct methodology? Well, if the relationship between vitamins and health outcomes is not causal, then consuming vitamins won’t cause your health outcomes to improve regardless of what the study indicates. Instead, it’s probably all the other healthy habits!
Learn more about Randomized Controlled Trials (RCTs) that are the gold standard for identifying causal relationships because they use random assignment.
Drawbacks of Random Assignment
Random assignment helps reduce the chances of systematic differences between the groups at the start of an experiment and, thereby, mitigates the threats of confounding variables and alternative explanations. However, the process does not always equalize all of the confounding variables. Its random nature tends to eliminate systematic differences, but it doesn’t always succeed.
Sometimes random assignment is impossible because the experimenters cannot control the treatment or independent variable. For example, if you want to determine how individuals with and without depression perform on a test, you cannot randomly assign subjects to these groups. The same difficulty occurs when you’re studying differences between genders.
In other cases, there might be ethical issues. For example, in a randomized experiment, the researchers would want to withhold treatment for the control group. However, if the treatments are vaccinations, it might be unethical to withhold the vaccinations.
Other times, random assignment might be possible, but it is very challenging. For example, with vitamin consumption, it’s generally thought that if vitamin supplements cause health improvements, it’s only after very long-term use. It’s hard to enforce random assignment with a strict regimen for usage in one group and non-usage in the other group over the long-run. Or imagine a study about smoking. The researchers would find it difficult to assign subjects to the smoking and non-smoking groups randomly!
Fortunately, if you can’t use random assignment to help reduce the problem of confounding variables, there are different methods available. The other primary approach is to perform an observational study and incorporate the confounders into the statistical model itself. For more information, read my post Observational Studies Explained .
Read About Real Experiments that Used Random Assignment
I’ve written several blog posts about studies that have used random assignment to make causal inferences. Read studies about the following:
- Flu Vaccinations
- COVID-19 Vaccinations
Sullivan L. Random assignment versus random selection . SAGE Glossary of the Social and Behavioral Sciences, SAGE Publications, Inc.; 2009.
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Reader Interactions
November 13, 2019 at 4:59 am
Hi Jim, I have a question of randomly assigning participants to one of two conditions when it is an ongoing study and you are not sure of how many participants there will be. I am using this random assignment tool for factorial experiments. http://methodologymedia.psu.edu/most/rannumgenerator It asks you for the total number of participants but at this point, I am not sure how many there will be. Thanks for any advice you can give me, Floyd
May 28, 2019 at 11:34 am
Jim, can you comment on the validity of using the following approach when we can’t use random assignments. I’m in education, we have an ACT prep course that we offer. We can’t force students to take it and we can’t keep them from taking it either. But we want to know if it’s working. Let’s say that by senior year all students who are going to take the ACT have taken it. Let’s also say that I’m only including students who have taking it twice (so I can show growth between first and second time taking it). What I’ve done to address confounders is to go back to say 8th or 9th grade (prior to anyone taking the ACT or the ACT prep course) and run an analysis showing the two groups are not significantly different to start with. Is this valid? If the ACT prep students were higher achievers in 8th or 9th grade, I could not assume my prep course is effecting greater growth, but if they were not significantly different in 8th or 9th grade, I can assume the significant difference in ACT growth (from first to second testing) is due to the prep course. Yes or no?
May 26, 2019 at 5:37 pm
Nice post! I think the key to understanding scientific research is to understand randomization. And most people don’t get it.
May 27, 2019 at 9:48 pm
Thank you, Anoop!
I think randomness in an experiment is a funny thing. The issue of confounding factors is a serious problem. You might not even know what they are! But, use random assignment and, voila, the problem usually goes away! If you can’t use random assignment, suddenly you have a whole host of issues to worry about, which I’ll be writing about in more detail in my upcoming post about observational experiments!
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Random Assignment in Psychology (Definition + 40 Examples)
![random assignment establish cause and effect practical psychology logo](https://practicalpie.com/wp-content/uploads/2023/09/practical-psychology-logo-60x60.jpg)
Have you ever wondered how researchers discover new ways to help people learn, make decisions, or overcome challenges? A hidden hero in this adventure of discovery is a method called random assignment, a cornerstone in psychological research that helps scientists uncover the truths about the human mind and behavior.
Random Assignment is a process used in research where each participant has an equal chance of being placed in any group within the study. This technique is essential in experiments as it helps to eliminate biases, ensuring that the different groups being compared are similar in all important aspects.
By doing so, researchers can be confident that any differences observed are likely due to the variable being tested, rather than other factors.
In this article, we’ll explore the intriguing world of random assignment, diving into its history, principles, real-world examples, and the impact it has had on the field of psychology.
History of Random Assignment
![random assignment establish cause and effect two women in different conditions](https://practicalpie.com/wp-content/uploads/2023/09/two-women-in-different-conditions.png)
Stepping back in time, we delve into the origins of random assignment, which finds its roots in the early 20th century.
The pioneering mind behind this innovative technique was Sir Ronald A. Fisher , a British statistician and biologist. Fisher introduced the concept of random assignment in the 1920s, aiming to improve the quality and reliability of experimental research .
His contributions laid the groundwork for the method's evolution and its widespread adoption in various fields, particularly in psychology.
Fisher’s groundbreaking work on random assignment was motivated by his desire to control for confounding variables – those pesky factors that could muddy the waters of research findings.
By assigning participants to different groups purely by chance, he realized that the influence of these confounding variables could be minimized, paving the way for more accurate and trustworthy results.
Early Studies Utilizing Random Assignment
Following Fisher's initial development, random assignment started to gain traction in the research community. Early studies adopting this methodology focused on a variety of topics, from agriculture (which was Fisher’s primary field of interest) to medicine and psychology.
The approach allowed researchers to draw stronger conclusions from their experiments, bolstering the development of new theories and practices.
One notable early study utilizing random assignment was conducted in the field of educational psychology. Researchers were keen to understand the impact of different teaching methods on student outcomes.
By randomly assigning students to various instructional approaches, they were able to isolate the effects of the teaching methods, leading to valuable insights and recommendations for educators.
Evolution of the Methodology
As the decades rolled on, random assignment continued to evolve and adapt to the changing landscape of research.
Advances in technology introduced new tools and techniques for implementing randomization, such as computerized random number generators, which offered greater precision and ease of use.
The application of random assignment expanded beyond the confines of the laboratory, finding its way into field studies and large-scale surveys.
Researchers across diverse disciplines embraced the methodology, recognizing its potential to enhance the validity of their findings and contribute to the advancement of knowledge.
From its humble beginnings in the early 20th century to its widespread use today, random assignment has proven to be a cornerstone of scientific inquiry.
Its development and evolution have played a pivotal role in shaping the landscape of psychological research, driving discoveries that have improved lives and deepened our understanding of the human experience.
Principles of Random Assignment
Delving into the heart of random assignment, we uncover the theories and principles that form its foundation.
The method is steeped in the basics of probability theory and statistical inference, ensuring that each participant has an equal chance of being placed in any group, thus fostering fair and unbiased results.
Basic Principles of Random Assignment
Understanding the core principles of random assignment is key to grasping its significance in research. There are three principles: equal probability of selection, reduction of bias, and ensuring representativeness.
The first principle, equal probability of selection , ensures that every participant has an identical chance of being assigned to any group in the study. This randomness is crucial as it mitigates the risk of bias and establishes a level playing field.
The second principle focuses on the reduction of bias . Random assignment acts as a safeguard, ensuring that the groups being compared are alike in all essential aspects before the experiment begins.
This similarity between groups allows researchers to attribute any differences observed in the outcomes directly to the independent variable being studied.
Lastly, ensuring representativeness is a vital principle. When participants are assigned randomly, the resulting groups are more likely to be representative of the larger population.
This characteristic is crucial for the generalizability of the study’s findings, allowing researchers to apply their insights broadly.
Theoretical Foundation
The theoretical foundation of random assignment lies in probability theory and statistical inference .
Probability theory deals with the likelihood of different outcomes, providing a mathematical framework for analyzing random phenomena. In the context of random assignment, it helps in ensuring that each participant has an equal chance of being placed in any group.
Statistical inference, on the other hand, allows researchers to draw conclusions about a population based on a sample of data drawn from that population. It is the mechanism through which the results of a study can be generalized to a broader context.
Random assignment enhances the reliability of statistical inferences by reducing biases and ensuring that the sample is representative.
Differentiating Random Assignment from Random Selection
It’s essential to distinguish between random assignment and random selection, as the two terms, while related, have distinct meanings in the realm of research.
Random assignment refers to how participants are placed into different groups in an experiment, aiming to control for confounding variables and help determine causes.
In contrast, random selection pertains to how individuals are chosen to participate in a study. This method is used to ensure that the sample of participants is representative of the larger population, which is vital for the external validity of the research.
While both methods are rooted in randomness and probability, they serve different purposes in the research process.
Understanding the theories, principles, and distinctions of random assignment illuminates its pivotal role in psychological research.
This method, anchored in probability theory and statistical inference, serves as a beacon of reliability, guiding researchers in their quest for knowledge and ensuring that their findings stand the test of validity and applicability.
Methodology of Random Assignment
![random assignment establish cause and effect woman sleeping with a brain monitor](https://practicalpie.com/wp-content/uploads/2023/09/woman-sleeping-with-a-brain-monitor.png)
Implementing random assignment in a study is a meticulous process that involves several crucial steps.
The initial step is participant selection, where individuals are chosen to partake in the study. This stage is critical to ensure that the pool of participants is diverse and representative of the population the study aims to generalize to.
Once the pool of participants has been established, the actual assignment process begins. In this step, each participant is allocated randomly to one of the groups in the study.
Researchers use various tools, such as random number generators or computerized methods, to ensure that this assignment is genuinely random and free from biases.
Monitoring and adjusting form the final step in the implementation of random assignment. Researchers need to continuously observe the groups to ensure that they remain comparable in all essential aspects throughout the study.
If any significant discrepancies arise, adjustments might be necessary to maintain the study’s integrity and validity.
Tools and Techniques Used
The evolution of technology has introduced a variety of tools and techniques to facilitate random assignment.
Random number generators, both manual and computerized, are commonly used to assign participants to different groups. These generators ensure that each individual has an equal chance of being placed in any group, upholding the principle of equal probability of selection.
In addition to random number generators, researchers often use specialized computer software designed for statistical analysis and experimental design.
These software programs offer advanced features that allow for precise and efficient random assignment, minimizing the risk of human error and enhancing the study’s reliability.
Ethical Considerations
The implementation of random assignment is not devoid of ethical considerations. Informed consent is a fundamental ethical principle that researchers must uphold.
Informed consent means that every participant should be fully informed about the nature of the study, the procedures involved, and any potential risks or benefits, ensuring that they voluntarily agree to participate.
Beyond informed consent, researchers must conduct a thorough risk and benefit analysis. The potential benefits of the study should outweigh any risks or harms to the participants.
Safeguarding the well-being of participants is paramount, and any study employing random assignment must adhere to established ethical guidelines and standards.
Conclusion of Methodology
The methodology of random assignment, while seemingly straightforward, is a multifaceted process that demands precision, fairness, and ethical integrity. From participant selection to assignment and monitoring, each step is crucial to ensure the validity of the study’s findings.
The tools and techniques employed, coupled with a steadfast commitment to ethical principles, underscore the significance of random assignment as a cornerstone of robust psychological research.
Benefits of Random Assignment in Psychological Research
The impact and importance of random assignment in psychological research cannot be overstated. It is fundamental for ensuring the study is accurate, allowing the researchers to determine if their study actually caused the results they saw, and making sure the findings can be applied to the real world.
Facilitating Causal Inferences
When participants are randomly assigned to different groups, researchers can be more confident that the observed effects are due to the independent variable being changed, and not other factors.
This ability to determine the cause is called causal inference .
This confidence allows for the drawing of causal relationships, which are foundational for theory development and application in psychology.
Ensuring Internal Validity
One of the foremost impacts of random assignment is its ability to enhance the internal validity of an experiment.
Internal validity refers to the extent to which a researcher can assert that changes in the dependent variable are solely due to manipulations of the independent variable , and not due to confounding variables.
By ensuring that each participant has an equal chance of being in any condition of the experiment, random assignment helps control for participant characteristics that could otherwise complicate the results.
Enhancing Generalizability
Beyond internal validity, random assignment also plays a crucial role in enhancing the generalizability of research findings.
When done correctly, it ensures that the sample groups are representative of the larger population, so can allow researchers to apply their findings more broadly.
This representative nature is essential for the practical application of research, impacting policy, interventions, and psychological therapies.
Limitations of Random Assignment
Potential for implementation issues.
While the principles of random assignment are robust, the method can face implementation issues.
One of the most common problems is logistical constraints. Some studies, due to their nature or the specific population being studied, find it challenging to implement random assignment effectively.
For instance, in educational settings, logistical issues such as class schedules and school policies might stop the random allocation of students to different teaching methods .
Ethical Dilemmas
Random assignment, while methodologically sound, can also present ethical dilemmas.
In some cases, withholding a potentially beneficial treatment from one of the groups of participants can raise serious ethical questions, especially in medical or clinical research where participants' well-being might be directly affected.
Researchers must navigate these ethical waters carefully, balancing the pursuit of knowledge with the well-being of participants.
Generalizability Concerns
Even when implemented correctly, random assignment does not always guarantee generalizable results.
The types of people in the participant pool, the specific context of the study, and the nature of the variables being studied can all influence the extent to which the findings can be applied to the broader population.
Researchers must be cautious in making broad generalizations from studies, even those employing strict random assignment.
Practical and Real-World Limitations
In the real world, many variables cannot be manipulated for ethical or practical reasons, limiting the applicability of random assignment.
For instance, researchers cannot randomly assign individuals to different levels of intelligence, socioeconomic status, or cultural backgrounds.
This limitation necessitates the use of other research designs, such as correlational or observational studies , when exploring relationships involving such variables.
Response to Critiques
In response to these critiques, people in favor of random assignment argue that the method, despite its limitations, remains one of the most reliable ways to establish cause and effect in experimental research.
They acknowledge the challenges and ethical considerations but emphasize the rigorous frameworks in place to address them.
The ongoing discussion around the limitations and critiques of random assignment contributes to the evolution of the method, making sure it is continuously relevant and applicable in psychological research.
While random assignment is a powerful tool in experimental research, it is not without its critiques and limitations. Implementation issues, ethical dilemmas, generalizability concerns, and real-world limitations can pose significant challenges.
However, the continued discourse and refinement around these issues underline the method's enduring significance in the pursuit of knowledge in psychology.
By being careful with how we do things and doing what's right, random assignment stays a really important part of studying how people act and think.
Real-World Applications and Examples
![random assignment establish cause and effect man on a treadmill](https://practicalpie.com/wp-content/uploads/2023/09/man-on-a-treadmill.png)
Random assignment has been employed in many studies across various fields of psychology, leading to significant discoveries and advancements.
Here are some real-world applications and examples illustrating the diversity and impact of this method:
- Medicine and Health Psychology: Randomized Controlled Trials (RCTs) are the gold standard in medical research. In these studies, participants are randomly assigned to either the treatment or control group to test the efficacy of new medications or interventions.
- Educational Psychology: Studies in this field have used random assignment to explore the effects of different teaching methods, classroom environments, and educational technologies on student learning and outcomes.
- Cognitive Psychology: Researchers have employed random assignment to investigate various aspects of human cognition, including memory, attention, and problem-solving, leading to a deeper understanding of how the mind works.
- Social Psychology: Random assignment has been instrumental in studying social phenomena, such as conformity, aggression, and prosocial behavior, shedding light on the intricate dynamics of human interaction.
Let's get into some specific examples. You'll need to know one term though, and that is "control group." A control group is a set of participants in a study who do not receive the treatment or intervention being tested , serving as a baseline to compare with the group that does, in order to assess the effectiveness of the treatment.
- Smoking Cessation Study: Researchers used random assignment to put participants into two groups. One group received a new anti-smoking program, while the other did not. This helped determine if the program was effective in helping people quit smoking.
- Math Tutoring Program: A study on students used random assignment to place them into two groups. One group received additional math tutoring, while the other continued with regular classes, to see if the extra help improved their grades.
- Exercise and Mental Health: Adults were randomly assigned to either an exercise group or a control group to study the impact of physical activity on mental health and mood.
- Diet and Weight Loss: A study randomly assigned participants to different diet plans to compare their effectiveness in promoting weight loss and improving health markers.
- Sleep and Learning: Researchers randomly assigned students to either a sleep extension group or a regular sleep group to study the impact of sleep on learning and memory.
- Classroom Seating Arrangement: Teachers used random assignment to place students in different seating arrangements to examine the effect on focus and academic performance.
- Music and Productivity: Employees were randomly assigned to listen to music or work in silence to investigate the effect of music on workplace productivity.
- Medication for ADHD: Children with ADHD were randomly assigned to receive either medication, behavioral therapy, or a placebo to compare treatment effectiveness.
- Mindfulness Meditation for Stress: Adults were randomly assigned to a mindfulness meditation group or a waitlist control group to study the impact on stress levels.
- Video Games and Aggression: A study randomly assigned participants to play either violent or non-violent video games and then measured their aggression levels.
- Online Learning Platforms: Students were randomly assigned to use different online learning platforms to evaluate their effectiveness in enhancing learning outcomes.
- Hand Sanitizers in Schools: Schools were randomly assigned to use hand sanitizers or not to study the impact on student illness and absenteeism.
- Caffeine and Alertness: Participants were randomly assigned to consume caffeinated or decaffeinated beverages to measure the effects on alertness and cognitive performance.
- Green Spaces and Well-being: Neighborhoods were randomly assigned to receive green space interventions to study the impact on residents’ well-being and community connections.
- Pet Therapy for Hospital Patients: Patients were randomly assigned to receive pet therapy or standard care to assess the impact on recovery and mood.
- Yoga for Chronic Pain: Individuals with chronic pain were randomly assigned to a yoga intervention group or a control group to study the effect on pain levels and quality of life.
- Flu Vaccines Effectiveness: Different groups of people were randomly assigned to receive either the flu vaccine or a placebo to determine the vaccine’s effectiveness.
- Reading Strategies for Dyslexia: Children with dyslexia were randomly assigned to different reading intervention strategies to compare their effectiveness.
- Physical Environment and Creativity: Participants were randomly assigned to different room setups to study the impact of physical environment on creative thinking.
- Laughter Therapy for Depression: Individuals with depression were randomly assigned to laughter therapy sessions or control groups to assess the impact on mood.
- Financial Incentives for Exercise: Participants were randomly assigned to receive financial incentives for exercising to study the impact on physical activity levels.
- Art Therapy for Anxiety: Individuals with anxiety were randomly assigned to art therapy sessions or a waitlist control group to measure the effect on anxiety levels.
- Natural Light in Offices: Employees were randomly assigned to workspaces with natural or artificial light to study the impact on productivity and job satisfaction.
- School Start Times and Academic Performance: Schools were randomly assigned different start times to study the effect on student academic performance and well-being.
- Horticulture Therapy for Seniors: Older adults were randomly assigned to participate in horticulture therapy or traditional activities to study the impact on cognitive function and life satisfaction.
- Hydration and Cognitive Function: Participants were randomly assigned to different hydration levels to measure the impact on cognitive function and alertness.
- Intergenerational Programs: Seniors and young people were randomly assigned to intergenerational programs to study the effects on well-being and cross-generational understanding.
- Therapeutic Horseback Riding for Autism: Children with autism were randomly assigned to therapeutic horseback riding or traditional therapy to study the impact on social communication skills.
- Active Commuting and Health: Employees were randomly assigned to active commuting (cycling, walking) or passive commuting to study the effect on physical health.
- Mindful Eating for Weight Management: Individuals were randomly assigned to mindful eating workshops or control groups to study the impact on weight management and eating habits.
- Noise Levels and Learning: Students were randomly assigned to classrooms with different noise levels to study the effect on learning and concentration.
- Bilingual Education Methods: Schools were randomly assigned different bilingual education methods to compare their effectiveness in language acquisition.
- Outdoor Play and Child Development: Children were randomly assigned to different amounts of outdoor playtime to study the impact on physical and cognitive development.
- Social Media Detox: Participants were randomly assigned to a social media detox or regular usage to study the impact on mental health and well-being.
- Therapeutic Writing for Trauma Survivors: Individuals who experienced trauma were randomly assigned to therapeutic writing sessions or control groups to study the impact on psychological well-being.
- Mentoring Programs for At-risk Youth: At-risk youth were randomly assigned to mentoring programs or control groups to assess the impact on academic achievement and behavior.
- Dance Therapy for Parkinson’s Disease: Individuals with Parkinson’s disease were randomly assigned to dance therapy or traditional exercise to study the effect on motor function and quality of life.
- Aquaponics in Schools: Schools were randomly assigned to implement aquaponics programs to study the impact on student engagement and environmental awareness.
- Virtual Reality for Phobia Treatment: Individuals with phobias were randomly assigned to virtual reality exposure therapy or traditional therapy to compare effectiveness.
- Gardening and Mental Health: Participants were randomly assigned to engage in gardening or other leisure activities to study the impact on mental health and stress reduction.
Each of these studies exemplifies how random assignment is utilized in various fields and settings, shedding light on the multitude of ways it can be applied to glean valuable insights and knowledge.
Real-world Impact of Random Assignment
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Random assignment is like a key tool in the world of learning about people's minds and behaviors. It’s super important and helps in many different areas of our everyday lives. It helps make better rules, creates new ways to help people, and is used in lots of different fields.
Health and Medicine
In health and medicine, random assignment has helped doctors and scientists make lots of discoveries. It’s a big part of tests that help create new medicines and treatments.
By putting people into different groups by chance, scientists can really see if a medicine works.
This has led to new ways to help people with all sorts of health problems, like diabetes, heart disease, and mental health issues like depression and anxiety.
Schools and education have also learned a lot from random assignment. Researchers have used it to look at different ways of teaching, what kind of classrooms are best, and how technology can help learning.
This knowledge has helped make better school rules, develop what we learn in school, and find the best ways to teach students of all ages and backgrounds.
Workplace and Organizational Behavior
Random assignment helps us understand how people act at work and what makes a workplace good or bad.
Studies have looked at different kinds of workplaces, how bosses should act, and how teams should be put together. This has helped companies make better rules and create places to work that are helpful and make people happy.
Environmental and Social Changes
Random assignment is also used to see how changes in the community and environment affect people. Studies have looked at community projects, changes to the environment, and social programs to see how they help or hurt people’s well-being.
This has led to better community projects, efforts to protect the environment, and programs to help people in society.
Technology and Human Interaction
In our world where technology is always changing, studies with random assignment help us see how tech like social media, virtual reality, and online stuff affect how we act and feel.
This has helped make better and safer technology and rules about using it so that everyone can benefit.
The effects of random assignment go far and wide, way beyond just a science lab. It helps us understand lots of different things, leads to new and improved ways to do things, and really makes a difference in the world around us.
From making healthcare and schools better to creating positive changes in communities and the environment, the real-world impact of random assignment shows just how important it is in helping us learn and make the world a better place.
So, what have we learned? Random assignment is like a super tool in learning about how people think and act. It's like a detective helping us find clues and solve mysteries in many parts of our lives.
From creating new medicines to helping kids learn better in school, and from making workplaces happier to protecting the environment, it’s got a big job!
This method isn’t just something scientists use in labs; it reaches out and touches our everyday lives. It helps make positive changes and teaches us valuable lessons.
Whether we are talking about technology, health, education, or the environment, random assignment is there, working behind the scenes, making things better and safer for all of us.
In the end, the simple act of putting people into groups by chance helps us make big discoveries and improvements. It’s like throwing a small stone into a pond and watching the ripples spread out far and wide.
Thanks to random assignment, we are always learning, growing, and finding new ways to make our world a happier and healthier place for everyone!
Related posts:
- 19+ Experimental Design Examples (Methods + Types)
- Cluster Sampling vs Stratified Sampling
- 41+ White Collar Job Examples (Salary + Path)
- 47+ Blue Collar Job Examples (Salary + Path)
- McDonaldization of Society (Definition + Examples)
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2.3 Analyzing Findings
Learning objectives.
By the end of this section, you will be able to:
- Explain what a correlation coefficient tells us about the relationship between variables
- Recognize that correlation does not indicate a cause-and-effect relationship between variables
- Discuss our tendency to look for relationships between variables that do not really exist
- Explain random sampling and assignment of participants into experimental and control groups
- Discuss how experimenter or participant bias could affect the results of an experiment
- Identify independent and dependent variables
Did you know that as sales in ice cream increase, so does the overall rate of crime? Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone? There is no question that a relationship exists between ice cream and crime (e.g., Harper, 2013), but it would be pretty foolish to decide that one thing actually caused the other to occur.
It is much more likely that both ice cream sales and crime rates are related to the temperature outside. When the temperature is warm, there are lots of people out of their houses, interacting with each other, getting annoyed with one another, and sometimes committing crimes. Also, when it is warm outside, we are more likely to seek a cool treat like ice cream. How do we determine if there is indeed a relationship between two things? And when there is a relationship, how can we discern whether it is attributable to coincidence or causation?
Correlational Research
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between variables. The correlation coefficient is usually represented by the letter r .
The number portion of the correlation coefficient indicates the strength of the relationship. The closer the number is to 1 (be it negative or positive), the more strongly related the variables are, and the more predictable changes in one variable will be as the other variable changes. The closer the number is to zero, the weaker the relationship, and the less predictable the relationship between the variables becomes. For instance, a correlation coefficient of 0.9 indicates a far stronger relationship than a correlation coefficient of 0.3. If the variables are not related to one another at all, the correlation coefficient is 0. The example above about ice cream and crime is an example of two variables that we might expect to have no relationship to each other.
The sign—positive or negative—of the correlation coefficient indicates the direction of the relationship ( Figure 2.12 ). A positive correlation means that the variables move in the same direction. Put another way, it means that as one variable increases so does the other, and conversely, when one variable decreases so does the other. A negative correlation means that the variables move in opposite directions. If two variables are negatively correlated, a decrease in one variable is associated with an increase in the other and vice versa.
The example of ice cream and crime rates is a positive correlation because both variables increase when temperatures are warmer. Other examples of positive correlations are the relationship between an individual’s height and weight or the relationship between a person’s age and number of wrinkles. One might expect a negative correlation to exist between someone’s tiredness during the day and the number of hours they slept the previous night: the amount of sleep decreases as the feelings of tiredness increase. In a real-world example of negative correlation, student researchers at the University of Minnesota found a weak negative correlation ( r = -0.29) between the average number of days per week that students got fewer than 5 hours of sleep and their GPA (Lowry, Dean, & Manders, 2010). Keep in mind that a negative correlation is not the same as no correlation. For example, we would probably find no correlation between hours of sleep and shoe size.
As mentioned earlier, correlations have predictive value. Imagine that you are on the admissions committee of a major university. You are faced with a huge number of applications, but you are able to accommodate only a small percentage of the applicant pool. How might you decide who should be admitted? You might try to correlate your current students’ college GPA with their scores on standardized tests like the SAT or ACT. By observing which correlations were strongest for your current students, you could use this information to predict relative success of those students who have applied for admission into the university.
Link to Learning
Manipulate this interactive scatterplot to practice your understanding of positive and negative correlation.
Correlation Does Not Indicate Causation
Correlational research is useful because it allows us to discover the strength and direction of relationships that exist between two variables. However, correlation is limited because establishing the existence of a relationship tells us little about cause and effect . While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable , is actually causing the systematic movement in our variables of interest. In the ice cream/crime rate example mentioned earlier, temperature is a confounding variable that could account for the relationship between the two variables.
Even when we cannot point to clear confounding variables, we should not assume that a correlation between two variables implies that one variable causes changes in another. This can be frustrating when a cause-and-effect relationship seems clear and intuitive. Think back to our discussion of the research done by the American Cancer Society and how their research projects were some of the first demonstrations of the link between smoking and cancer. It seems reasonable to assume that smoking causes cancer, but if we were limited to correlational research , we would be overstepping our bounds by making this assumption.
Unfortunately, people mistakenly make claims of causation as a function of correlations all the time. Such claims are especially common in advertisements and news stories. For example, research found that people who eat certain breakfast cereal may have a reduced risk of heart disease (Anderson, Hanna, Peng, & Kryscio, 2000). Cereal companies are likely to share this information in a way that maximizes and perhaps overstates the positive aspects of eating cereal. But does cereal really cause better health, or are there other possible explanations for the health of those who eat cereal? While correlational research is invaluable in identifying relationships among variables, a major limitation is the inability to establish causality. Psychologists want to make statements about cause and effect, but the only way to do that is to conduct an experiment to answer a research question. The next section describes how scientific experiments incorporate methods that eliminate, or control for, alternative explanations, which allow researchers to explore how changes in one variable cause changes in another variable.
Illusory Correlations
The temptation to make erroneous cause-and-effect statements based on correlational research is not the only way we tend to misinterpret data. We also tend to make the mistake of illusory correlations, especially with unsystematic observations. Illusory correlations , or false correlations, occur when people believe that relationships exist between two things when no such relationship exists. One well-known illusory correlation is the supposed effect that the moon’s phases have on human behavior. Many people passionately assert that human behavior is affected by the phase of the moon, and specifically, that people act strangely when the moon is full ( Figure 2.14 ).
There is no denying that the moon exerts a powerful influence on our planet. The ebb and flow of the ocean’s tides are tightly tied to the gravitational forces of the moon. Many people believe, therefore, that it is logical that we are affected by the moon as well. After all, our bodies are largely made up of water. A meta-analysis of nearly 40 studies consistently demonstrated, however, that the relationship between the moon and our behavior does not exist (Rotton & Kelly, 1985). While we may pay more attention to odd behavior during the full phase of the moon, the rates of odd behavior remain constant throughout the lunar cycle.
Why are we so apt to believe in illusory correlations like this? Often we read or hear about them and simply accept the information as valid. Or, we have a hunch about how something works and then look for evidence to support that hunch, ignoring evidence that would tell us our hunch is false; this is known as confirmation bias . Other times, we find illusory correlations based on the information that comes most easily to mind, even if that information is severely limited. And while we may feel confident that we can use these relationships to better understand and predict the world around us, illusory correlations can have significant drawbacks. For example, research suggests that illusory correlations—in which certain behaviors are inaccurately attributed to certain groups—are involved in the formation of prejudicial attitudes that can ultimately lead to discriminatory behavior (Fiedler, 2004).
Causality: Conducting Experiments and Using the Data
As you’ve learned, the only way to establish that there is a cause-and-effect relationship between two variables is to conduct a scientific experiment . Experiment has a different meaning in the scientific context than in everyday life. In everyday conversation, we often use it to describe trying something for the first time, such as experimenting with a new hair style or a new food. However, in the scientific context, an experiment has precise requirements for design and implementation.
The Experimental Hypothesis
In order to conduct an experiment, a researcher must have a specific hypothesis to be tested. As you’ve learned, hypotheses can be formulated either through direct observation of the real world or after careful review of previous research. For example, if you think that the use of technology in the classroom has negative impacts on learning, then you have basically formulated a hypothesis—namely, that the use of technology in the classroom should be limited because it decreases learning. How might you have arrived at this particular hypothesis? You may have noticed that your classmates who take notes on their laptops perform at lower levels on class exams than those who take notes by hand, or those who receive a lesson via a computer program versus via an in-person teacher have different levels of performance when tested ( Figure 2.15 ).
These sorts of personal observations are what often lead us to formulate a specific hypothesis, but we cannot use limited personal observations and anecdotal evidence to rigorously test our hypothesis. Instead, to find out if real-world data supports our hypothesis, we have to conduct an experiment.
Designing an Experiment
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested (in this case, the use of technology)—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.
In our example of how the use of technology should be limited in the classroom, we have the experimental group learn algebra using a computer program and then test their learning. We measure the learning in our control group after they are taught algebra by a teacher in a traditional classroom. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation.
We also need to precisely define, or operationalize, how we measure learning of algebra. An operational definition is a precise description of our variables, and it is important in allowing others to understand exactly how and what a researcher measures in a particular experiment. In operationalizing learning, we might choose to look at performance on a test covering the material on which the individuals were taught by the teacher or the computer program. We might also ask our participants to summarize the information that was just presented in some way. Whatever we determine, it is important that we operationalize learning in such a way that anyone who hears about our study for the first time knows exactly what we mean by learning. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.
Once we have operationalized what is considered use of technology and what is considered learning in our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants spend 45 minutes learning algebra (either through a computer program or with an in-person math teacher) and then give them a test on the material covered during the 45 minutes.
Ideally, the people who score the tests are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how they interpret ambiguous responses, such as sloppy handwriting or minor computational mistakes. By being blind to which child is in which group, we protect against those biases. This situation is a single-blind study , meaning that one of the groups (participants) are unaware as to which group they are in (experiment or control group) while the researcher who developed the experiment knows which participants are in each group.
In a double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase placebo effect , you already have some idea as to why this is an important consideration. The placebo effect occurs when people's expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.
The placebo effect is commonly described in terms of testing the effectiveness of a new medication. Imagine that you work in a pharmaceutical company, and you think you have a new drug that is effective in treating depression. To demonstrate that your medication is effective, you run an experiment with two groups: The experimental group receives the medication, and the control group does not. But you don’t want participants to know whether they received the drug or not.
Why is that? Imagine that you are a participant in this study, and you have just taken a pill that you think will improve your mood. Because you expect the pill to have an effect, you might feel better simply because you took the pill and not because of any drug actually contained in the pill—this is the placebo effect.
To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case a sugar pill). Now everyone gets a pill, and once again neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations ( Figure 2.16 ).
Independent and Dependent Variables
In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An independent variable is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how technology use in the classroom affects learning, the independent variable is the type of learning by participants in the study ( Figure 2.17 ). A dependent variable is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the learning exhibited by our participants.
We expect that the dependent variable will change as a function of the independent variable. In other words, the dependent variable depends on the independent variable. A good way to think about the relationship between the independent and dependent variables is with this question: What effect does the independent variable have on the dependent variable? Returning to our example, what is the effect of being taught a lesson through a computer program versus through an in-person instructor?
Selecting and Assigning Experimental Participants
Now that our study is designed, we need to obtain a sample of individuals to include in our experiment. Our study involves human participants so we need to determine whom to include. Participants are the subjects of psychological research, and as the name implies, individuals who are involved in psychological research actively participate in the process. Often, psychological research projects rely on college students to serve as participants. In fact, the vast majority of research in psychology subfields has historically involved students as research participants (Sears, 1986; Arnett, 2008). But are college students truly representative of the general population? College students tend to be younger, more educated, more liberal, and less diverse than the general population. Although using students as test subjects is an accepted practice, relying on such a limited pool of research participants can be problematic because it is difficult to generalize findings to the larger population.
Our hypothetical experiment involves high school students, and we must first generate a sample of students. Samples are used because populations are usually too large to reasonably involve every member in our particular experiment ( Figure 2.18 ). If possible, we should use a random sample (there are other types of samples, but for the purposes of this chapter, we will focus on random samples). A random sample is a subset of a larger population in which every member of the population has an equal chance of being selected. Random samples are preferred because if the sample is large enough we can be reasonably sure that the participating individuals are representative of the larger population. This means that the percentages of characteristics in the sample—sex, ethnicity, socioeconomic level, and any other characteristics that might affect the results—are close to those percentages in the larger population.
In our example, let’s say we decide our population of interest is algebra students. But all algebra students is a very large population, so we need to be more specific; instead we might say our population of interest is all algebra students in a particular city. We should include students from various income brackets, family situations, races, ethnicities, religions, and geographic areas of town. With this more manageable population, we can work with the local schools in selecting a random sample of around 200 algebra students who we want to participate in our experiment.
In summary, because we cannot test all of the algebra students in a city, we want to find a group of about 200 that reflects the composition of that city. With a representative group, we can generalize our findings to the larger population without fear of our sample being biased in some way.
Now that we have a sample, the next step of the experimental process is to split the participants into experimental and control groups through random assignment. With random assignment , all participants have an equal chance of being assigned to either group. There is statistical software that will randomly assign each of the algebra students in the sample to either the experimental or the control group.
Random assignment is critical for sound experimental design . With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. So, for instance, it would be very unlikely that we would get one group composed entirely of males, a given ethnic identity, or a given religious ideology. This is important because if the groups were systematically different before the experiment began, we would not know the origin of any differences we find between the groups: Were the differences preexisting, or were they caused by manipulation of the independent variable? Random assignment allows us to assume that any differences observed between experimental and control groups result from the manipulation of the independent variable.
Use this online random number generator to learn more about random sampling and assignments.
Issues to Consider
While experiments allow scientists to make cause-and-effect claims, they are not without problems. True experiments require the experimenter to manipulate an independent variable, and that can complicate many questions that psychologists might want to address. For instance, imagine that you want to know what effect sex (the independent variable) has on spatial memory (the dependent variable). Although you can certainly look for differences between males and females on a task that taps into spatial memory, you cannot directly control a person’s sex. We categorize this type of research approach as quasi-experimental and recognize that we cannot make cause-and-effect claims in these circumstances.
Experimenters are also limited by ethical constraints. For instance, you would not be able to conduct an experiment designed to determine if experiencing abuse as a child leads to lower levels of self-esteem among adults. To conduct such an experiment, you would need to randomly assign some experimental participants to a group that receives abuse, and that experiment would be unethical.
Interpreting Experimental Findings
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely it is that any difference found is due to chance (and thus not meaningful). For example, if an experiment is done on the effectiveness of a nutritional supplement, and those taking a placebo pill (and not the supplement) have the same result as those taking the supplement, then the experiment has shown that the nutritional supplement is not effective. Generally, psychologists consider differences to be statistically significant if there is less than a five percent chance of observing them if the groups did not actually differ from one another. Stated another way, psychologists want to limit the chances of making “false positive” claims to five percent or less.
The greatest strength of experiments is the ability to assert that any significant differences in the findings are caused by the independent variable. This occurs because random selection, random assignment, and a design that limits the effects of both experimenter bias and participant expectancy should create groups that are similar in composition and treatment. Therefore, any difference between the groups is attributable to the independent variable, and now we can finally make a causal statement. If we find that watching a violent television program results in more violent behavior than watching a nonviolent program, we can safely say that watching violent television programs causes an increase in the display of violent behavior.
Reporting Research
When psychologists complete a research project, they generally want to share their findings with other scientists. The American Psychological Association (APA) publishes a manual detailing how to write a paper for submission to scientific journals. Unlike an article that might be published in a magazine like Psychology Today, which targets a general audience with an interest in psychology, scientific journals generally publish peer-reviewed journal articles aimed at an audience of professionals and scholars who are actively involved in research themselves.
The Online Writing Lab (OWL) at Purdue University can walk you through the APA writing guidelines.
A peer-reviewed journal article is read by several other scientists (generally anonymously) with expertise in the subject matter. These peer reviewers provide feedback—to both the author and the journal editor—regarding the quality of the draft. Peer reviewers look for a strong rationale for the research being described, a clear description of how the research was conducted, and evidence that the research was conducted in an ethical manner. They also look for flaws in the study's design, methods, and statistical analyses. They check that the conclusions drawn by the authors seem reasonable given the observations made during the research. Peer reviewers also comment on how valuable the research is in advancing the discipline’s knowledge. This helps prevent unnecessary duplication of research findings in the scientific literature and, to some extent, ensures that each research article provides new information. Ultimately, the journal editor will compile all of the peer reviewer feedback and determine whether the article will be published in its current state (a rare occurrence), published with revisions, or not accepted for publication.
Peer review provides some degree of quality control for psychological research. Poorly conceived or executed studies can be weeded out, and even well-designed research can be improved by the revisions suggested. Peer review also ensures that the research is described clearly enough to allow other scientists to replicate it, meaning they can repeat the experiment using different samples to determine reliability. Sometimes replications involve additional measures that expand on the original finding. In any case, each replication serves to provide more evidence to support the original research findings. Successful replications of published research make scientists more apt to adopt those findings, while repeated failures tend to cast doubt on the legitimacy of the original article and lead scientists to look elsewhere. For example, it would be a major advancement in the medical field if a published study indicated that taking a new drug helped individuals achieve better health without changing their behavior. But if other scientists could not replicate the results, the original study’s claims would be questioned.
In recent years, there has been increasing concern about a “replication crisis” that has affected a number of scientific fields, including psychology. Some of the most well-known studies and scientists have produced research that has failed to be replicated by others (as discussed in Shrout & Rodgers, 2018). In fact, even a famous Nobel Prize-winning scientist has recently retracted a published paper because she had difficulty replicating her results (Nobel Prize-winning scientist Frances Arnold retracts paper, 2020 January 3). These kinds of outcomes have prompted some scientists to begin to work together and more openly, and some would argue that the current “crisis” is actually improving the ways in which science is conducted and in how its results are shared with others (Aschwanden, 2018).
The Vaccine-Autism Myth and Retraction of Published Studies
Some scientists have claimed that routine childhood vaccines cause some children to develop autism, and, in fact, several peer-reviewed publications published research making these claims. Since the initial reports, large-scale epidemiological research has indicated that vaccinations are not responsible for causing autism and that it is much safer to have your child vaccinated than not. Furthermore, several of the original studies making this claim have since been retracted.
A published piece of work can be rescinded when data is called into question because of falsification, fabrication, or serious research design problems. Once rescinded, the scientific community is informed that there are serious problems with the original publication. Retractions can be initiated by the researcher who led the study, by research collaborators, by the institution that employed the researcher, or by the editorial board of the journal in which the article was originally published. In the vaccine-autism case, the retraction was made because of a significant conflict of interest in which the leading researcher had a financial interest in establishing a link between childhood vaccines and autism (Offit, 2008). Unfortunately, the initial studies received so much media attention that many parents around the world became hesitant to have their children vaccinated ( Figure 2.19 ). Continued reliance on such debunked studies has significant consequences. For instance, between January and October of 2019, there were 22 measles outbreaks across the United States and more than a thousand cases of individuals contracting measles (Patel et al., 2019). This is likely due to the anti-vaccination movements that have risen from the debunked research. For more information about how the vaccine/autism story unfolded, as well as the repercussions of this story, take a look at Paul Offit’s book, Autism’s False Prophets: Bad Science, Risky Medicine, and the Search for a Cure.
Reliability and Validity
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways. There are a number of different types of reliability. Some of these include inter-rater reliability (the degree to which two or more different observers agree on what has been observed), internal consistency (the degree to which different items on a survey that measure the same thing correlate with one another), and test-retest reliability (the degree to which the outcomes of a particular measure remain consistent over multiple administrations).
Unfortunately, being consistent in measurement does not necessarily mean that you have measured something correctly. To illustrate this concept, consider a kitchen scale that would be used to measure the weight of cereal that you eat in the morning. If the scale is not properly calibrated, it may consistently under- or overestimate the amount of cereal that’s being measured. While the scale is highly reliable in producing consistent results (e.g., the same amount of cereal poured onto the scale produces the same reading each time), those results are incorrect. This is where validity comes into play. Validity refers to the extent to which a given instrument or tool accurately measures what it’s supposed to measure, and once again, there are a number of ways in which validity can be expressed. Ecological validity (the degree to which research results generalize to real-world applications), construct validity (the degree to which a given variable actually captures or measures what it is intended to measure), and face validity (the degree to which a given variable seems valid on the surface) are just a few types that researchers consider. While any valid measure is by necessity reliable, the reverse is not necessarily true. Researchers strive to use instruments that are both highly reliable and valid.
Everyday Connection
How valid are the sat and act.
Standardized tests like the SAT and ACT are supposed to measure an individual’s aptitude for a college education, but how reliable and valid are such tests? Research conducted by the College Board suggests that scores on the SAT have high predictive validity for first-year college students’ GPA (Kobrin, Patterson, Shaw, Mattern, & Barbuti, 2008). In this context, predictive validity refers to the test’s ability to effectively predict the GPA of college freshmen. Given that many institutions of higher education require the SAT or ACT for admission, this high degree of predictive validity might be comforting.
However, the emphasis placed on SAT or ACT scores in college admissions is changing based on a number of factors. For one, some researchers assert that these tests are biased, and students from historically marginalized populations are at a disadvantage that unfairly reduces the likelihood of being admitted into a college (Santelices & Wilson, 2010). Additionally, some research has suggested that the predictive validity of these tests is grossly exaggerated in how well they are able to predict the GPA of first-year college students. In fact, it has been suggested that the SAT’s predictive validity may be overestimated by as much as 150% (Rothstein, 2004). Many institutions of higher education are beginning to consider de-emphasizing the significance of SAT scores in making admission decisions (Rimer, 2008).
Recent examples of high profile cheating scandals both domestically and abroad have only increased the scrutiny being placed on these types of tests, and as of March 2019, more than 1000 institutions of higher education have either relaxed or eliminated the requirements for SAT or ACT testing for admissions (Strauss, 2019, March 19).
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Experimental Method In Psychology
Saul McLeod, PhD
Editor-in-Chief for Simply Psychology
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
Learn about our Editorial Process
Olivia Guy-Evans, MSc
Associate Editor for Simply Psychology
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
On This Page:
The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups .
What is an Experiment?
An experiment is an investigation in which a hypothesis is scientifically tested. An independent variable (the cause) is manipulated in an experiment, and the dependent variable (the effect) is measured; any extraneous variables are controlled.
An advantage is that experiments should be objective. The researcher’s views and opinions should not affect a study’s results. This is good as it makes the data more valid and less biased.
There are three types of experiments you need to know:
1. Lab Experiment
A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions.
A laboratory experiment is conducted under highly controlled conditions (not necessarily a laboratory) where accurate measurements are possible.
The researcher uses a standardized procedure to determine where the experiment will take place, at what time, with which participants, and in what circumstances.
Participants are randomly allocated to each independent variable group.
Examples are Milgram’s experiment on obedience and Loftus and Palmer’s car crash study .
- Strength : It is easier to replicate (i.e., copy) a laboratory experiment. This is because a standardized procedure is used.
- Strength : They allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.
- Limitation : The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e., low ecological validity. This means it would not be possible to generalize the findings to a real-life setting.
- Limitation : Demand characteristics or experimenter effects may bias the results and become confounding variables .
2. Field Experiment
A field experiment is a research method in psychology that takes place in a natural, real-world setting. It is similar to a laboratory experiment in that the experimenter manipulates one or more independent variables and measures the effects on the dependent variable.
However, in a field experiment, the participants are unaware they are being studied, and the experimenter has less control over the extraneous variables .
Field experiments are often used to study social phenomena, such as altruism, obedience, and persuasion. They are also used to test the effectiveness of interventions in real-world settings, such as educational programs and public health campaigns.
An example is Holfing’s hospital study on obedience .
- Strength : behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e., higher ecological validity than a lab experiment.
- Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied. This occurs when the study is covert.
- Limitation : There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.
3. Natural Experiment
A natural experiment in psychology is a research method in which the experimenter observes the effects of a naturally occurring event or situation on the dependent variable without manipulating any variables.
Natural experiments are conducted in the day (i.e., real life) environment of the participants, but here, the experimenter has no control over the independent variable as it occurs naturally in real life.
Natural experiments are often used to study psychological phenomena that would be difficult or unethical to study in a laboratory setting, such as the effects of natural disasters, policy changes, or social movements.
For example, Hodges and Tizard’s attachment research (1989) compared the long-term development of children who have been adopted, fostered, or returned to their mothers with a control group of children who had spent all their lives in their biological families.
Here is a fictional example of a natural experiment in psychology:
Researchers might compare academic achievement rates among students born before and after a major policy change that increased funding for education.
In this case, the independent variable is the timing of the policy change, and the dependent variable is academic achievement. The researchers would not be able to manipulate the independent variable, but they could observe its effects on the dependent variable.
- Strength : behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e., very high ecological validity.
- Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied.
- Strength : It can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g., researching stress .
- Limitation : They may be more expensive and time-consuming than lab experiments.
- Limitation : There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.
Key Terminology
Ecological validity.
The degree to which an investigation represents real-life experiences.
Experimenter effects
These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.
Demand characteristics
The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).
Independent variable (IV)
The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.
Dependent variable (DV)
Variable the experimenter measures. This is the outcome (i.e., the result) of a study.
Extraneous variables (EV)
All variables which are not independent variables but could affect the results (DV) of the experiment. EVs should be controlled where possible.
Confounding variables
Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.
Random Allocation
Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of participating in each condition.
The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.
Order effects
Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:
(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;
(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.
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What Is Random Assignment in Psychology?
Categories Research Methods
Random assignment means that every participant has the same chance of being chosen for the experimental or control group. It involves using procedures that rely on chance to assign participants to groups. Doing this means that every participant in a study has an equal opportunity to be assigned to any group.
For example, in a psychology experiment, participants might be assigned to either a control or experimental group. Some experiments might only have one experimental group, while others may have several treatment variations.
Using random assignment means that each participant has the same chance of being assigned to any of these groups.
Table of Contents
How to Use Random Assignment
So what type of procedures might psychologists utilize for random assignment? Strategies can include:
- Flipping a coin
- Assigning random numbers
- Rolling dice
- Drawing names out of a hat
How Does Random Assignment Work?
A psychology experiment aims to determine if changes in one variable lead to changes in another variable. Researchers will first begin by coming up with a hypothesis. Once researchers have an idea of what they think they might find in a population, they will come up with an experimental design and then recruit participants for their study.
Once they have a pool of participants representative of the population they are interested in looking at, they will randomly assign the participants to their groups.
- Control group : Some participants will end up in the control group, which serves as a baseline and does not receive the independent variables.
- Experimental group : Other participants will end up in the experimental groups that receive some form of the independent variables.
By using random assignment, the researchers make it more likely that the groups are equal at the start of the experiment. Since the groups are the same on other variables, it can be assumed that any changes that occur are the result of varying the independent variables.
After a treatment has been administered, the researchers will then collect data in order to determine if the independent variable had any impact on the dependent variable.
Random Assignment vs. Random Selection
It is important to remember that random assignment is not the same thing as random selection , also known as random sampling.
Random selection instead involves how people are chosen to be in a study. Using random selection, every member of a population stands an equal chance of being chosen for a study or experiment.
So random sampling affects how participants are chosen for a study, while random assignment affects how participants are then assigned to groups.
Examples of Random Assignment
Imagine that a psychology researcher is conducting an experiment to determine if getting adequate sleep the night before an exam results in better test scores.
Forming a Hypothesis
They hypothesize that participants who get 8 hours of sleep will do better on a math exam than participants who only get 4 hours of sleep.
Obtaining Participants
The researcher starts by obtaining a pool of participants. They find 100 participants from a local university. Half of the participants are female, and half are male.
Randomly Assign Participants to Groups
The researcher then assigns random numbers to each participant and uses a random number generator to randomly assign each number to either the 4-hour or 8-hour sleep groups.
Conduct the Experiment
Those in the 8-hour sleep group agree to sleep for 8 hours that night, while those in the 4-hour group agree to wake up after only 4 hours. The following day, all of the participants meet in a classroom.
Collect and Analyze Data
Everyone takes the same math test. The test scores are then compared to see if the amount of sleep the night before had any impact on test scores.
Why Is Random Assignment Important in Psychology Research?
Random assignment is important in psychology research because it helps improve a study’s internal validity. This means that the researchers are sure that the study demonstrates a cause-and-effect relationship between an independent and dependent variable.
Random assignment improves the internal validity by minimizing the risk that there are systematic differences in the participants who are in each group.
Key Points to Remember About Random Assignment
- Random assignment in psychology involves each participant having an equal chance of being chosen for any of the groups, including the control and experimental groups.
- It helps control for potential confounding variables, reducing the likelihood of pre-existing differences between groups.
- This method enhances the internal validity of experiments, allowing researchers to draw more reliable conclusions about cause-and-effect relationships.
- Random assignment is crucial for creating comparable groups and increasing the scientific rigor of psychological studies.
![random assignment establish cause and effect Logo for University of Central Florida Pressbooks](https://pressbooks.online.ucf.edu/app/uploads/2018/08/TabLockup_horizontal_KGrgb_300ppi.png)
Psychological Research
Experiments
Learning objectives.
- Describe the experimental process, including ways to control for bias
- Identify and differentiate between independent and dependent variables
Causality: Conducting Experiments and Using the Data
Experimental hypothesis.
In order to conduct an experiment, a researcher must have a specific hypothesis to be tested. As you’ve learned, hypotheses can be formulated either through direct observation of the real world or after careful review of previous research. For example, if you think that children should not be allowed to watch violent programming on television because doing so would cause them to behave more violently, then you have basically formulated a hypothesis—namely, that watching violent television programs causes children to behave more violently. How might you have arrived at this particular hypothesis? You may have younger relatives who watch cartoons featuring characters using martial arts to save the world from evildoers, with an impressive array of punching, kicking, and defensive postures. You notice that after watching these programs for a while, your young relatives mimic the fighting behavior of the characters portrayed in the cartoon (Figure 1).
![random assignment establish cause and effect A photograph shows a child pointing a toy gun.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224519/CNX_Psych_02_05_toygun.jpg)
These sorts of personal observations are what often lead us to formulate a specific hypothesis, but we cannot use limited personal observations and anecdotal evidence to rigorously test our hypothesis. Instead, to find out if real-world data supports our hypothesis, we have to conduct an experiment.
Designing an Experiment
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested (in this case, violent TV images)—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.
In our example of how violent television programming might affect violent behavior in children, we have the experimental group view violent television programming for a specified time and then measure their violent behavior. We measure the violent behavior in our control group after they watch nonviolent television programming for the same amount of time. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation. Therefore, we have the control group watch non-violent television programming for the same amount of time as the experimental group.
We also need to precisely define, or operationalize, what is considered violent and nonviolent. An operational definition is a description of how we will measure our variables, and it is important in allowing others understand exactly how and what a researcher measures in a particular experiment. In operationalizing violent behavior, we might choose to count only physical acts like kicking or punching as instances of this behavior, or we also may choose to include angry verbal exchanges. Whatever we determine, it is important that we operationalize violent behavior in such a way that anyone who hears about our study for the first time knows exactly what we mean by violence. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.
Once we have operationalized what is considered violent television programming and what is considered violent behavior from our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants watch a 30-minute television program (either violent or nonviolent, depending on their group membership) before sending them out to a playground for an hour where their behavior is observed and the number and type of violent acts is recorded.
Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how much attention they paid to each child’s behavior as well as how they interpreted that behavior. By being blind to which child is in which group, we protect against those biases. This situation is a single-blind study , meaning that one of the groups (participants) are unaware as to which group they are in (experiment or control group) while the researcher who developed the experiment knows which participants are in each group.
![random assignment establish cause and effect A photograph shows three glass bottles of pills labeled as placebos.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224520/CNX_Psych_02_05_placebo.jpg)
In a double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase placebo effect, you already have some idea as to why this is an important consideration. The placebo effect occurs when people’s expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.
The placebo effect is commonly described in terms of testing the effectiveness of a new medication. Imagine that you work in a pharmaceutical company, and you think you have a new drug that is effective in treating depression. To demonstrate that your medication is effective, you run an experiment with two groups: The experimental group receives the medication, and the control group does not. But you don’t want participants to know whether they received the drug or not.
Why is that? Imagine that you are a participant in this study, and you have just taken a pill that you think will improve your mood. Because you expect the pill to have an effect, you might feel better simply because you took the pill and not because of any drug actually contained in the pill—this is the placebo effect.
To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case a sugar pill). Now everyone gets a pill, and once again neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations (Figure 2).
Independent and Dependent Variables
In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An independent variable is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how violent television programs affect children’s display of violent behavior, the independent variable is the type of program—violent or nonviolent—viewed by participants in the study (Figure 3). A dependent variable is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the number of violent acts displayed by the experimental participants.
![random assignment establish cause and effect A box labeled “independent variable: type of television programming viewed” contains a photograph of a person shooting an automatic weapon. An arrow labeled “influences change in the…” leads to a second box. The second box is labeled “dependent variable: violent behavior displayed” and has a photograph of a child pointing a toy gun.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224522/CNX_Psych_02_05_variables.jpg)
We expect that the dependent variable will change as a function of the independent variable. In other words, the dependent variable depends on the independent variable. A good way to think about the relationship between the independent and dependent variables is with this question: What effect does the independent variable have on the dependent variable? Returning to our example, what effect does watching a half hour of violent television programming or nonviolent television programming have on the number of incidents of physical aggression displayed on the playground?
Selecting and Assigning Experimental Participants
Now that our study is designed, we need to obtain a sample of individuals to include in our experiment. Our study involves human participants so we need to determine who to include. Participants are the subjects of psychological research, and as the name implies, individuals who are involved in psychological research actively participate in the process. Often, psychological research projects rely on college students to serve as participants. In fact, the vast majority of research in psychology subfields has historically involved students as research participants (Sears, 1986; Arnett, 2008). But are college students truly representative of the general population? College students tend to be younger, more educated, more liberal, and less diverse than the general population. Although using students as test subjects is an accepted practice, relying on such a limited pool of research participants can be problematic because it is difficult to generalize findings to the larger population.
Our hypothetical experiment involves children, and we must first generate a sample of child participants. Samples are used because populations are usually too large to reasonably involve every member in our particular experiment (Figure 4). If possible, we should use a random sample (there are other types of samples, but for the purposes of this section, we will focus on random samples). A random sample is a subset of a larger population in which every member of the population has an equal chance of being selected. Random samples are preferred because if the sample is large enough we can be reasonably sure that the participating individuals are representative of the larger population. This means that the percentages of characteristics in the sample—sex, ethnicity, socioeconomic level, and any other characteristics that might affect the results—are close to those percentages in the larger population.
In our example, let’s say we decide our population of interest is fourth graders. But all fourth graders is a very large population, so we need to be more specific; instead we might say our population of interest is all fourth graders in a particular city. We should include students from various income brackets, family situations, races, ethnicities, religions, and geographic areas of town. With this more manageable population, we can work with the local schools in selecting a random sample of around 200 fourth graders who we want to participate in our experiment.
In summary, because we cannot test all of the fourth graders in a city, we want to find a group of about 200 that reflects the composition of that city. With a representative group, we can generalize our findings to the larger population without fear of our sample being biased in some way.
![random assignment establish cause and effect (a) A photograph shows an aerial view of crowds on a street. (b) A photograph shows s small group of children.](https://s3-us-west-2.amazonaws.com/courses-images-archive-read-only/wp-content/uploads/sites/902/2015/02/23224523/CNX_Psych_02_05_sample.jpg)
Now that we have a sample, the next step of the experimental process is to split the participants into experimental and control groups through random assignment. With random assignment , all participants have an equal chance of being assigned to either group. There is statistical software that will randomly assign each of the fourth graders in the sample to either the experimental or the control group.
Random assignment is critical for sound experimental design . With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. So, for instance, it would be very unlikely that we would get one group composed entirely of males, a given ethnic identity, or a given religious ideology. This is important because if the groups were systematically different before the experiment began, we would not know the origin of any differences we find between the groups: Were the differences preexisting, or were they caused by manipulation of the independent variable? Random assignment allows us to assume that any differences observed between experimental and control groups result from the manipulation of the independent variable.
Issues to Consider
While experiments allow scientists to make cause-and-effect claims, they are not without problems. True experiments require the experimenter to manipulate an independent variable, and that can complicate many questions that psychologists might want to address. For instance, imagine that you want to know what effect sex (the independent variable) has on spatial memory (the dependent variable). Although you can certainly look for differences between males and females on a task that taps into spatial memory, you cannot directly control a person’s sex. We categorize this type of research approach as quasi-experimental and recognize that we cannot make cause-and-effect claims in these circumstances.
Experimenters are also limited by ethical constraints. For instance, you would not be able to conduct an experiment designed to determine if experiencing abuse as a child leads to lower levels of self-esteem among adults. To conduct such an experiment, you would need to randomly assign some experimental participants to a group that receives abuse, and that experiment would be unethical.
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group designed to answer the research question; experimental manipulation is the only difference between the experimental and control groups, so any differences between the two are due to experimental manipulation rather than chance
description of what actions and operations will be used to measure the dependent variables and manipulate the independent variables
researcher expectations skew the results of the study
experiment in which the researcher knows which participants are in the experimental group and which are in the control group
experiment in which both the researchers and the participants are blind to group assignments
people's expectations or beliefs influencing or determining their experience in a given situation
variable that is influenced or controlled by the experimenter; in a sound experimental study, the independent variable is the only important difference between the experimental and control group
variable that the researcher measures to see how much effect the independent variable had
subjects of psychological research
using a probability-based method to select a subset of individuals for the sample from the population.
using a probability-based method to divide a sample into treatment groups.
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How the Experimental Method Works in Psychology
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The Experimental Process
Types of experiments, potential pitfalls of the experimental method.
The experimental method is a type of research procedure that involves manipulating variables to determine if there is a cause-and-effect relationship. The results obtained through the experimental method are useful but do not prove with 100% certainty that a singular cause always creates a specific effect. Instead, they show the probability that a cause will or will not lead to a particular effect.
At a Glance
While there are many different research techniques available, the experimental method allows researchers to look at cause-and-effect relationships. Using the experimental method, researchers randomly assign participants to a control or experimental group and manipulate levels of an independent variable. If changes in the independent variable lead to changes in the dependent variable, it indicates there is likely a causal relationship between them.
What Is the Experimental Method in Psychology?
The experimental method involves manipulating one variable to determine if this causes changes in another variable. This method relies on controlled research methods and random assignment of study subjects to test a hypothesis.
For example, researchers may want to learn how different visual patterns may impact our perception. Or they might wonder whether certain actions can improve memory . Experiments are conducted on many behavioral topics, including:
The scientific method forms the basis of the experimental method. This is a process used to determine the relationship between two variables—in this case, to explain human behavior .
Positivism is also important in the experimental method. It refers to factual knowledge that is obtained through observation, which is considered to be trustworthy.
When using the experimental method, researchers first identify and define key variables. Then they formulate a hypothesis, manipulate the variables, and collect data on the results. Unrelated or irrelevant variables are carefully controlled to minimize the potential impact on the experiment outcome.
History of the Experimental Method
The idea of using experiments to better understand human psychology began toward the end of the nineteenth century. Wilhelm Wundt established the first formal laboratory in 1879.
Wundt is often called the father of experimental psychology. He believed that experiments could help explain how psychology works, and used this approach to study consciousness .
Wundt coined the term "physiological psychology." This is a hybrid of physiology and psychology, or how the body affects the brain.
Other early contributors to the development and evolution of experimental psychology as we know it today include:
- Gustav Fechner (1801-1887), who helped develop procedures for measuring sensations according to the size of the stimulus
- Hermann von Helmholtz (1821-1894), who analyzed philosophical assumptions through research in an attempt to arrive at scientific conclusions
- Franz Brentano (1838-1917), who called for a combination of first-person and third-person research methods when studying psychology
- Georg Elias Müller (1850-1934), who performed an early experiment on attitude which involved the sensory discrimination of weights and revealed how anticipation can affect this discrimination
Key Terms to Know
To understand how the experimental method works, it is important to know some key terms.
Dependent Variable
The dependent variable is the effect that the experimenter is measuring. If a researcher was investigating how sleep influences test scores, for example, the test scores would be the dependent variable.
Independent Variable
The independent variable is the variable that the experimenter manipulates. In the previous example, the amount of sleep an individual gets would be the independent variable.
A hypothesis is a tentative statement or a guess about the possible relationship between two or more variables. In looking at how sleep influences test scores, the researcher might hypothesize that people who get more sleep will perform better on a math test the following day. The purpose of the experiment, then, is to either support or reject this hypothesis.
Operational definitions are necessary when performing an experiment. When we say that something is an independent or dependent variable, we must have a very clear and specific definition of the meaning and scope of that variable.
Extraneous Variables
Extraneous variables are other variables that may also affect the outcome of an experiment. Types of extraneous variables include participant variables, situational variables, demand characteristics, and experimenter effects. In some cases, researchers can take steps to control for extraneous variables.
Demand Characteristics
Demand characteristics are subtle hints that indicate what an experimenter is hoping to find in a psychology experiment. This can sometimes cause participants to alter their behavior, which can affect the results of the experiment.
Intervening Variables
Intervening variables are factors that can affect the relationship between two other variables.
Confounding Variables
Confounding variables are variables that can affect the dependent variable, but that experimenters cannot control for. Confounding variables can make it difficult to determine if the effect was due to changes in the independent variable or if the confounding variable may have played a role.
Psychologists, like other scientists, use the scientific method when conducting an experiment. The scientific method is a set of procedures and principles that guide how scientists develop research questions, collect data, and come to conclusions.
The five basic steps of the experimental process are:
- Identifying a problem to study
- Devising the research protocol
- Conducting the experiment
- Analyzing the data collected
- Sharing the findings (usually in writing or via presentation)
Most psychology students are expected to use the experimental method at some point in their academic careers. Learning how to conduct an experiment is important to understanding how psychologists prove and disprove theories in this field.
There are a few different types of experiments that researchers might use when studying psychology. Each has pros and cons depending on the participants being studied, the hypothesis, and the resources available to conduct the research.
Lab Experiments
Lab experiments are common in psychology because they allow experimenters more control over the variables. These experiments can also be easier for other researchers to replicate. The drawback of this research type is that what takes place in a lab is not always what takes place in the real world.
Field Experiments
Sometimes researchers opt to conduct their experiments in the field. For example, a social psychologist interested in researching prosocial behavior might have a person pretend to faint and observe how long it takes onlookers to respond.
This type of experiment can be a great way to see behavioral responses in realistic settings. But it is more difficult for researchers to control the many variables existing in these settings that could potentially influence the experiment's results.
Quasi-Experiments
While lab experiments are known as true experiments, researchers can also utilize a quasi-experiment. Quasi-experiments are often referred to as natural experiments because the researchers do not have true control over the independent variable.
A researcher looking at personality differences and birth order, for example, is not able to manipulate the independent variable in the situation (personality traits). Participants also cannot be randomly assigned because they naturally fall into pre-existing groups based on their birth order.
So why would a researcher use a quasi-experiment? This is a good choice in situations where scientists are interested in studying phenomena in natural, real-world settings. It's also beneficial if there are limits on research funds or time.
Field experiments can be either quasi-experiments or true experiments.
Examples of the Experimental Method in Use
The experimental method can provide insight into human thoughts and behaviors, Researchers use experiments to study many aspects of psychology.
A 2019 study investigated whether splitting attention between electronic devices and classroom lectures had an effect on college students' learning abilities. It found that dividing attention between these two mediums did not affect lecture comprehension. However, it did impact long-term retention of the lecture information, which affected students' exam performance.
An experiment used participants' eye movements and electroencephalogram (EEG) data to better understand cognitive processing differences between experts and novices. It found that experts had higher power in their theta brain waves than novices, suggesting that they also had a higher cognitive load.
A study looked at whether chatting online with a computer via a chatbot changed the positive effects of emotional disclosure often received when talking with an actual human. It found that the effects were the same in both cases.
One experimental study evaluated whether exercise timing impacts information recall. It found that engaging in exercise prior to performing a memory task helped improve participants' short-term memory abilities.
Sometimes researchers use the experimental method to get a bigger-picture view of psychological behaviors and impacts. For example, one 2018 study examined several lab experiments to learn more about the impact of various environmental factors on building occupant perceptions.
A 2020 study set out to determine the role that sensation-seeking plays in political violence. This research found that sensation-seeking individuals have a higher propensity for engaging in political violence. It also found that providing access to a more peaceful, yet still exciting political group helps reduce this effect.
While the experimental method can be a valuable tool for learning more about psychology and its impacts, it also comes with a few pitfalls.
Experiments may produce artificial results, which are difficult to apply to real-world situations. Similarly, researcher bias can impact the data collected. Results may not be able to be reproduced, meaning the results have low reliability .
Since humans are unpredictable and their behavior can be subjective, it can be hard to measure responses in an experiment. In addition, political pressure may alter the results. The subjects may not be a good representation of the population, or groups used may not be comparable.
And finally, since researchers are human too, results may be degraded due to human error.
What This Means For You
Every psychological research method has its pros and cons. The experimental method can help establish cause and effect, and it's also beneficial when research funds are limited or time is of the essence.
At the same time, it's essential to be aware of this method's pitfalls, such as how biases can affect the results or the potential for low reliability. Keeping these in mind can help you review and assess research studies more accurately, giving you a better idea of whether the results can be trusted or have limitations.
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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
Experimental Research
- First Online: 25 February 2021
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- C. George Thomas 2
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Experiments are part of the scientific method that helps to decide the fate of two or more competing hypotheses or explanations on a phenomenon. The term ‘experiment’ arises from Latin, Experiri, which means, ‘to try’. The knowledge accrues from experiments differs from other types of knowledge in that it is always shaped upon observation or experience. In other words, experiments generate empirical knowledge. In fact, the emphasis on experimentation in the sixteenth and seventeenth centuries for establishing causal relationships for various phenomena happening in nature heralded the resurgence of modern science from its roots in ancient philosophy spearheaded by great Greek philosophers such as Aristotle.
The strongest arguments prove nothing so long as the conclusions are not verified by experience. Experimental science is the queen of sciences and the goal of all speculation . Roger Bacon (1214–1294)
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- Random Assignment in Experiments | Introduction & Examples
Random Assignment in Experiments | Introduction & Examples
Published on 6 May 2022 by Pritha Bhandari . Revised on 13 February 2023.
In experimental research, random assignment is a way of placing participants from your sample into different treatment groups using randomisation.
With simple random assignment, every member of the sample has a known or equal chance of being placed in a control group or an experimental group. Studies that use simple random assignment are also called completely randomised designs .
Random assignment is a key part of experimental design . It helps you ensure that all groups are comparable at the start of a study: any differences between them are due to random factors.
Table of contents
Why does random assignment matter, random sampling vs random assignment, how do you use random assignment, when is random assignment not used, frequently asked questions about random assignment.
Random assignment is an important part of control in experimental research, because it helps strengthen the internal validity of an experiment.
In experiments, researchers manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables. To do so, they often use different levels of an independent variable for different groups of participants.
This is called a between-groups or independent measures design.
You use three groups of participants that are each given a different level of the independent variable:
- A control group that’s given a placebo (no dosage)
- An experimental group that’s given a low dosage
- A second experimental group that’s given a high dosage
Random assignment to helps you make sure that the treatment groups don’t differ in systematic or biased ways at the start of the experiment.
If you don’t use random assignment, you may not be able to rule out alternative explanations for your results.
- Participants recruited from pubs are placed in the control group
- Participants recruited from local community centres are placed in the low-dosage experimental group
- Participants recruited from gyms are placed in the high-dosage group
With this type of assignment, it’s hard to tell whether the participant characteristics are the same across all groups at the start of the study. Gym users may tend to engage in more healthy behaviours than people who frequent pubs or community centres, and this would introduce a healthy user bias in your study.
Although random assignment helps even out baseline differences between groups, it doesn’t always make them completely equivalent. There may still be extraneous variables that differ between groups, and there will always be some group differences that arise from chance.
Most of the time, the random variation between groups is low, and, therefore, it’s acceptable for further analysis. This is especially true when you have a large sample. In general, you should always use random assignment in experiments when it is ethically possible and makes sense for your study topic.
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Random sampling and random assignment are both important concepts in research, but it’s important to understand the difference between them.
Random sampling (also called probability sampling or random selection) is a way of selecting members of a population to be included in your study. In contrast, random assignment is a way of sorting the sample participants into control and experimental groups.
While random sampling is used in many types of studies, random assignment is only used in between-subjects experimental designs.
Some studies use both random sampling and random assignment, while others use only one or the other.
![random assignment establish cause and effect Random sample vs random assignment](https://www.scribbr.co.uk/wp-content/uploads/2023/02/random-sample-vs-random-assignment.webp)
Random sampling enhances the external validity or generalisability of your results, because it helps to ensure that your sample is unbiased and representative of the whole population. This allows you to make stronger statistical inferences .
You use a simple random sample to collect data. Because you have access to the whole population (all employees), you can assign all 8,000 employees a number and use a random number generator to select 300 employees. These 300 employees are your full sample.
Random assignment enhances the internal validity of the study, because it ensures that there are no systematic differences between the participants in each group. This helps you conclude that the outcomes can be attributed to the independent variable .
- A control group that receives no intervention
- An experimental group that has a remote team-building intervention every week for a month
You use random assignment to place participants into the control or experimental group. To do so, you take your list of participants and assign each participant a number. Again, you use a random number generator to place each participant in one of the two groups.
To use simple random assignment, you start by giving every member of the sample a unique number. Then, you can use computer programs or manual methods to randomly assign each participant to a group.
- Random number generator: Use a computer program to generate random numbers from the list for each group.
- Lottery method: Place all numbers individually into a hat or a bucket, and draw numbers at random for each group.
- Flip a coin: When you only have two groups, for each number on the list, flip a coin to decide if they’ll be in the control or the experimental group.
- Use a dice: When you have three groups, for each number on the list, roll a die to decide which of the groups they will be in. For example, assume that rolling 1 or 2 lands them in a control group; 3 or 4 in an experimental group; and 5 or 6 in a second control or experimental group.
This type of random assignment is the most powerful method of placing participants in conditions, because each individual has an equal chance of being placed in any one of your treatment groups.
Random assignment in block designs
In more complicated experimental designs, random assignment is only used after participants are grouped into blocks based on some characteristic (e.g., test score or demographic variable). These groupings mean that you need a larger sample to achieve high statistical power .
For example, a randomised block design involves placing participants into blocks based on a shared characteristic (e.g., college students vs graduates), and then using random assignment within each block to assign participants to every treatment condition. This helps you assess whether the characteristic affects the outcomes of your treatment.
In an experimental matched design , you use blocking and then match up individual participants from each block based on specific characteristics. Within each matched pair or group, you randomly assign each participant to one of the conditions in the experiment and compare their outcomes.
Sometimes, it’s not relevant or ethical to use simple random assignment, so groups are assigned in a different way.
When comparing different groups
Sometimes, differences between participants are the main focus of a study, for example, when comparing children and adults or people with and without health conditions. Participants are not randomly assigned to different groups, but instead assigned based on their characteristics.
In this type of study, the characteristic of interest (e.g., gender) is an independent variable, and the groups differ based on the different levels (e.g., men, women). All participants are tested the same way, and then their group-level outcomes are compared.
When it’s not ethically permissible
When studying unhealthy or dangerous behaviours, it’s not possible to use random assignment. For example, if you’re studying heavy drinkers and social drinkers, it’s unethical to randomly assign participants to one of the two groups and ask them to drink large amounts of alcohol for your experiment.
When you can’t assign participants to groups, you can also conduct a quasi-experimental study . In a quasi-experiment, you study the outcomes of pre-existing groups who receive treatments that you may not have any control over (e.g., heavy drinkers and social drinkers).
These groups aren’t randomly assigned, but may be considered comparable when some other variables (e.g., age or socioeconomic status) are controlled for.
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.
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.
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.
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A Consumer’s Guide to Causal Modeling: Part I
RESEARCH IS the tool that scientists use to further their knowledge of the world. Scientists ask questions about phenomena and design research studies to answer those questions. In many instances, scientists are interested in what causes certain outcomes to occur. The most powerful and direct way to establish cause and effect is to design an experiment. In order to be considered an experiment, the study must have a control (or comparison) group and at least one experimental (or treatment) group; subjects must be assigned to groups randomly, not by choice of the subject or the investigator; and the researcher must manipulate the independent variable so that the experimental group gets the treatment and the control group does not ( Polit & Hungler, 1991 ).
When random assignment to group is not feasible, a quasi-experimental design that requires a comparison group and manipulation of the independent variable may be used; however, in some circumstances, manipulation of the independent variable is not possible, forcing the researcher to use naturally occurring groups. For example, suppose the researcher is interested in the effects of a mother’s employment on her child’s development. One way to study this would be to compare children of employed mothers (“treatment group”) with children of nonemployed mothers (“comparison group”). Although there are two groups, random assignment of mothers to employment or nonemployment would raise both practical and ethical concerns. Manipulating either the mother’s employment status or the number of hours the mother is employed (the independent variable) also raises these concerns. Thus, the researcher must use naturally occurring groups, a design that provides very weak evidence of causality. Causal modeling, a methodological and analytical technique, may provide stronger evidence about the direction and strength of cause-and-effect relationships hypothesized by the researcher. The purpose of this two-part series is to discuss the conceptual basis for causal modeling and to provide the consumer of nursing research with guidelines for evaluating published reports of research that used causal modeling techniques.
Nursing phenomena are often complex with many variables in the causal chain. One shortcoming of the experimental design study is that it fails to provide information about the effects of other variables on the independent or dependent variables because random assignment of subjects to groups breaks any relationships between these other variables and the independent variable. Removal of these relationships strengthens the evidence for causality, but it does not represent the complexity of the process by which an outcome occurs in the real world. Some statistical techniques allow the researcher to analyze the effect of independent variables on a dependent variable while controlling for the effects of other variables on the process. These techniques tell us how much influence a variable exerts directly on the dependent variable (direct effect). However, sometimes a variable’s influence is transmitted through another variable (indirect effect). In that case, the total amount of influence the variable has on the dependent variable is underestimated by these techniques because the indirect effect is not considered. Causal modeling can be used to represent very complex relationships among a set of variables, including direct and indirect effects, which increases the model’s correspondence with reality.
CONSTRUCTING A CAUSAL MODEL
Causal modeling requires the researcher to construct a model to explain the relationships among concepts related to a specific phenomenon ( Asher, 1983 ). A causal model is a diagram of the relationships between independent, control, and dependent variables. A path model is a less complex type of causal model. Causal models are also known as “structural equation models,” “covariance structure models,” or “LISREL models.” Each of these terms has a slightly different meaning based on the specific analysis technique used but are often used interchangeably in the literature.
Construction of a causal model can be accomplished in two basic ways. The preferred method is to build the model before the study begins based on theoretical expectations. In this method, the researcher considers the relationship between the independent and dependent variables of interest. Then variables that could affect either the independent or the dependent variable or the relationship between them are added to the model, and the direction of their effects is specified. Each linkage must be supported by expectations from other theories (theoretical evidence), by results of research (empirical evidence), or by experiential knowledge. An example from the author’s research is provided to illustrate how support for the proposed linkages is described.
The causal model in Figure 1 is a simplified version* of the expected system of relationships between maternal employment status and child development that is currently being used to guide a study of this phenomenon with preschool children, some born prematurely and some born at term, and their families. Each of the linkages can be supported theoretically or empirically. Research suggests that employment has a negative effect on mother-child interaction, at least in infancy ( Barglow, Vaughn, & Molitor, 1987 ; Belsky & Rovine, 1988 ), although other studies find no effect or a positive effect ( Schubert, Bradley-Johnson, & Nuttal, 1980 ). Because of these conflicting findings, we are positing a relationship between employment and mother-child interaction, but we are not specifying whether this will be a positive or a negative relationship. Consistency between employment attitudes and employment status repeatedly has been found to have a positive effect on mother-child interaction ( Benn, 1986 ; Bronfenbrenner, Alvarez, & Henderson, 1984 ; Hock, 1980 ).
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Causal model diagram
Family functioning is defined as the quality of relationships among family members. Again, research findings are conflicting but support the importance of investigating the effect of employment on family functioning. Some researchers find no adverse effects on marital satisfaction ( Locksley, 1980 ), and others find negative effects ( Hardesty & Betz, 1980 ). In a series of studies, Youngblut, Loveland-Cherry, and Horan ( 1991 , 1993 , in press ) found that parents in nonemployed-mother families were more satisfied with their family’s relationships than parents in employed-mother families when the family’s preterm infant was 18 months old (in press), but not at 3 months (1991) or at 9 and 12 months (1993). The path from consistency to family functioning is supported by a positive relationship between degree of choice in the employment decision and parents’ perceptions of family relationships and family cohesion ( Youngblut et al., in press ).
The link between mother-child relationship and child development is strongly supported by the work of many researchers (see Sameroff & Chandler, 1975 , for review of the full-term literature; or Meisels & Plunkett, 1988 , for review of the preterm literature). Because research on the effects of family functioning on preterm infant development is sparse, this link is supported with theoretical literature. Family systems theory ( Friedman, 1986 ) suggests that quality of family relationships will affect the individual members of the family. Therefore, if the mother-child relationship has an effect on child development, it is likely that family relationships will also have an effect on the child’s development. In addition, both Bronfenbrenner (1985) and Hoffman (1989) have advocated broadening inquiry beyond the mother-child relationship to include the family. Finally, the effect of mother’s education level on child outcomes is included as a control variable with empirical support by many researchers (Heynes, 1982; Hoffman, 1989 ). When constructed theoretically, the causal model will serve as a guide for study design and implementation and for data analysis.
Causal models can also be built empirically. In this case, construction of the model is based on the correlations among the variables that were actually obtained in the study. Although this may be more appealing and perhaps simpler, there are several issues that need to be considered. First is the issue of relying on simple (bivariate) correlations to indicate relationships. A correlation between two variables indicates the total amount of overlap between the two variables. This overlap represents the combination of direct effects, indirect effects, and other effects. Direct effects are those that are due to one variable when the effects of other variables are controlled. Indirect effects are transmitted through intervening, or mediating, variables. Other effects could be due to sampling or measurement error or to the effect of another variable on both of the variables being correlated ( Pedhazur, 1982 ).
For example, if the correlation between employment and child development is high, one might consider putting a direct path between the two variables ( Figure 2 , solid line, top diagram). However, it may be that the quality of the mother-child relationship mediates the effect of employment on child development ( Figure 2 , broken line, top diagram). If so, controlling for quality of mother-child interaction would show that there is really no correlation between employment and development. Or it may be that the correlation between employment and development ( Figure 2 , curved broken line, bottom diagram) is due to the effect of neonatal health status on both employment and development ( Figure 2 , solid lines, bottom diagram); in other words, the correlation is a coincidence (known as a spurious correlation) that is due to the impact of a third variable on the other two. Thus, relying on the bivariate relationships would be misleading and would obscure the “true” path of the effect.
![Click on image to zoom An external file that holds a picture, illustration, etc.
Object name is nihms-211819-f0002.jpg](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2905793/bin/nihms-211819-f0002.jpg)
Diagram of direct, indirect, and spurious effects.
Importantly, using the correlations obtained with the study’s data changes the analysis from hypothesis testing to a fishing expedition. Causal models represent a system of hypotheses about the interrelationships of variables when the model is constructed theoretically before the data are collected. Testing the model then represents testing each of the implied hypotheses. However, if the model is constructed from the data, it can no longer be considered to represent a system of hypotheses and the analysis becomes post hoc exploration of the data. This practice of data snooping often yields results that are not theoretically defensible, do not make sense conceptually, and are less likely to be replicated in future research.
In summary, causal modeling is an important tool for knowledge development in nursing, and its use is increasing. Therefore, consumers of nursing research must have some familiarity with the conceptual underpinnings of causal modeling and an appreciation of how the technique may be used. In addition, the readers’ ability to critique published reports of model testing is crucial so that they can evaluate the results of these studies. The first part of this two-part series was aimed at increasing the reader’s level of sophistication regarding causal modeling techniques. The second part will provide guidelines for evaluating published reports of research.
Acknowledgments
Supported in part by a grant from the National Institute for Nursing Research (No. ROI-NR02707) and by an administrative supplement from the Office of Research on Women’s Health through the National Institute for Nursing Research.
Address reprint requests to JoAnne M. Youngblut. PhD. RN. Frances Payne Bolton School of Nursing. Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106-4904.
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Frequently asked questions
What is random assignment.
In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.
Frequently asked questions: Methodology
Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.
Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .
Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.
Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.
Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.
A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”
To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.
Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.
While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.
Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.
Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.
- Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
- Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .
You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.
- Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related
Content validity shows you how accurately a test or other measurement method taps into the various aspects of the specific construct you are researching.
In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.
The higher the content validity, the more accurate the measurement of the construct.
If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.
Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.
When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.
For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).
On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.
A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.
Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.
Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.
Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .
This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .
Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.
Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .
Snowball sampling is best used in the following cases:
- If there is no sampling frame available (e.g., people with a rare disease)
- If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
- If the research focuses on a sensitive topic (e.g., extramarital affairs)
The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.
Reproducibility and replicability are related terms.
- Reproducing research entails reanalyzing the existing data in the same manner.
- Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data .
- A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
- A successful replication shows that the reliability of the results is high.
Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.
The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).
Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.
A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.
The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.
Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.
On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.
Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.
However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.
In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.
A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.
Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.
Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .
A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.
The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .
An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .
It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.
While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.
Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.
Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.
Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.
Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.
You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .
When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.
Construct validity is often considered the overarching type of measurement validity , because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.
Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.
There are two subtypes of construct validity.
- Convergent validity : The extent to which your measure corresponds to measures of related constructs
- Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs
Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.
The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.
Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.
You can think of naturalistic observation as “people watching” with a purpose.
A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.
In statistics, dependent variables are also called:
- Response variables (they respond to a change in another variable)
- Outcome variables (they represent the outcome you want to measure)
- Left-hand-side variables (they appear on the left-hand side of a regression equation)
An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.
Independent variables are also called:
- Explanatory variables (they explain an event or outcome)
- Predictor variables (they can be used to predict the value of a dependent variable)
- Right-hand-side variables (they appear on the right-hand side of a regression equation).
As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.
Overall, your focus group questions should be:
- Open-ended and flexible
- Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
- Unambiguous, getting straight to the point while still stimulating discussion
- Unbiased and neutral
A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:
- You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
- You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
- Your research question depends on strong parity between participants, with environmental conditions held constant.
More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .
Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .
Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.
This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.
The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.
There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.
A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:
- You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
- Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.
An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.
Unstructured interviews are best used when:
- You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
- Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
- You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
- Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.
The four most common types of interviews are:
- Structured interviews : The questions are predetermined in both topic and order.
- Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
- Unstructured interviews : None of the questions are predetermined.
- Focus group interviews : The questions are presented to a group instead of one individual.
Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .
In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.
Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.
Deductive reasoning is also called deductive logic.
There are many different types of inductive reasoning that people use formally or informally.
Here are a few common types:
- Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
- Statistical generalization: You use specific numbers about samples to make statements about populations.
- Causal reasoning: You make cause-and-effect links between different things.
- Sign reasoning: You make a conclusion about a correlational relationship between different things.
- Analogical reasoning: You make a conclusion about something based on its similarities to something else.
Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.
Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.
In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.
Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.
Inductive reasoning is also called inductive logic or bottom-up reasoning.
A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.
A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).
Triangulation can help:
- Reduce research bias that comes from using a single method, theory, or investigator
- Enhance validity by approaching the same topic with different tools
- Establish credibility by giving you a complete picture of the research problem
But triangulation can also pose problems:
- It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
- Your results may be inconsistent or even contradictory.
There are four main types of triangulation :
- Data triangulation : Using data from different times, spaces, and people
- Investigator triangulation : Involving multiple researchers in collecting or analyzing data
- Theory triangulation : Using varying theoretical perspectives in your research
- Methodological triangulation : Using different methodologies to approach the same topic
Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.
However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.
Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.
Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.
Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.
In general, the peer review process follows the following steps:
- First, the author submits the manuscript to the editor.
- Reject the manuscript and send it back to author, or
- Send it onward to the selected peer reviewer(s)
- Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
- Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.
Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.
You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.
Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.
Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.
Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.
Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.
Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.
Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.
Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.
For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.
After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.
Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.
These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.
Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.
Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.
Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.
In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.
Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.
These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.
Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .
You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.
You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.
Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.
Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.
Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .
These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.
In multistage sampling , you can use probability or non-probability sampling methods .
For a probability sample, you have to conduct probability sampling at every stage.
You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.
Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.
But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .
These are four of the most common mixed methods designs :
- Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions.
- Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
- Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
- Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.
Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.
Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.
In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.
This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.
No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.
To find the slope of the line, you’ll need to perform a regression analysis .
Correlation coefficients always range between -1 and 1.
The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.
The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.
These are the assumptions your data must meet if you want to use Pearson’s r :
- Both variables are on an interval or ratio level of measurement
- Data from both variables follow normal distributions
- Your data have no outliers
- Your data is from a random or representative sample
- You expect a linear relationship between the two variables
Quantitative research designs can be divided into two main categories:
- Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
- Experimental and quasi-experimental designs are used to test causal relationships .
Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.
A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.
The priorities of a research design can vary depending on the field, but you usually have to specify:
- Your research questions and/or hypotheses
- Your overall approach (e.g., qualitative or quantitative )
- The type of design you’re using (e.g., a survey , experiment , or case study )
- Your sampling methods or criteria for selecting subjects
- Your data collection methods (e.g., questionnaires , observations)
- Your data collection procedures (e.g., operationalization , timing and data management)
- Your data analysis methods (e.g., statistical tests or thematic analysis )
A research design is a strategy for answering your research question . It defines your overall approach and determines how you will collect and analyze data.
Questionnaires can be self-administered or researcher-administered.
Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.
Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.
You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.
Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.
Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.
A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.
The third variable and directionality problems are two main reasons why correlation isn’t causation .
The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.
The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.
Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.
Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.
While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .
Controlled experiments establish causality, whereas correlational studies only show associations between variables.
- In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
- In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.
In general, correlational research is high in external validity while experimental research is high in internal validity .
A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.
A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.
Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.
A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .
A correlation reflects the strength and/or direction of the association between two or more variables.
- A positive correlation means that both variables change in the same direction.
- A negative correlation means that the variables change in opposite directions.
- A zero correlation means there’s no relationship between the variables.
Random error is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .
You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.
Systematic error is generally a bigger problem in research.
With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.
Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.
Random and systematic error are two types of measurement error.
Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).
Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).
On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.
- If you have quantitative variables , use a scatterplot or a line graph.
- If your response variable is categorical, use a scatterplot or a line graph.
- If your explanatory variable is categorical, use a bar graph.
The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.
Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.
The difference between explanatory and response variables is simple:
- An explanatory variable is the expected cause, and it explains the results.
- A response variable is the expected effect, and it responds to other variables.
In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:
- A control group that receives a standard treatment, a fake treatment, or no treatment.
- Random assignment of participants to ensure the groups are equivalent.
Depending on your study topic, there are various other methods of controlling variables .
There are 4 main types of extraneous variables :
- Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
- Experimenter effects : unintentional actions by researchers that influence study outcomes.
- Situational variables : environmental variables that alter participants’ behaviors.
- Participant variables : any characteristic or aspect of a participant’s background that could affect study results.
An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.
A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.
In a factorial design, multiple independent variables are tested.
If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.
Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .
Advantages:
- Only requires small samples
- Statistically powerful
- Removes the effects of individual differences on the outcomes
Disadvantages:
- Internal validity threats reduce the likelihood of establishing a direct relationship between variables
- Time-related effects, such as growth, can influence the outcomes
- Carryover effects mean that the specific order of different treatments affect the outcomes
While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .
- Prevents carryover effects of learning and fatigue.
- Shorter study duration.
- Needs larger samples for high power.
- Uses more resources to recruit participants, administer sessions, cover costs, etc.
- Individual differences may be an alternative explanation for results.
Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.
In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.
In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.
The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.
Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.
In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.
To implement random assignment , assign a unique number to every member of your study’s sample .
Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.
Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.
In contrast, random assignment is a way of sorting the sample into control and experimental groups.
Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.
“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.
Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.
Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .
If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .
A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.
Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.
Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.
If something is a mediating variable :
- It’s caused by the independent variable .
- It influences the dependent variable
- When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.
A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.
A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.
There are three key steps in systematic sampling :
- Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
- Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
- Choose every k th member of the population as your sample.
Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .
Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.
For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.
You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.
Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.
For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.
In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).
Once divided, each subgroup is randomly sampled using another probability sampling method.
Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.
However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.
There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.
- In single-stage sampling , you collect data from every unit within the selected clusters.
- In double-stage sampling , you select a random sample of units from within the clusters.
- In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.
Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.
The clusters should ideally each be mini-representations of the population as a whole.
If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,
If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.
The American Community Survey is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.
Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.
Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .
Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity as they can use real-world interventions instead of artificial laboratory settings.
A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.
Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .
If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.
- In a single-blind study , only the participants are blinded.
- In a double-blind study , both participants and experimenters are blinded.
- In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.
Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .
A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.
However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).
For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.
An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.
Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.
Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.
The type of data determines what statistical tests you should use to analyze your data.
A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.
To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.
In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).
The process of turning abstract concepts into measurable variables and indicators is called operationalization .
There are various approaches to qualitative data analysis , but they all share five steps in common:
- Prepare and organize your data.
- Review and explore your data.
- Develop a data coding system.
- Assign codes to the data.
- Identify recurring themes.
The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .
There are five common approaches to qualitative research :
- Grounded theory involves collecting data in order to develop new theories.
- Ethnography involves immersing yourself in a group or organization to understand its culture.
- Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
- Phenomenological research involves investigating phenomena through people’s lived experiences.
- Action research links theory and practice in several cycles to drive innovative changes.
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.
Operationalization means turning abstract conceptual ideas into measurable observations.
For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.
Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.
When conducting research, collecting original data has significant advantages:
- You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
- You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )
However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.
Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.
There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.
In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.
In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .
In statistical control , you include potential confounders as variables in your regression .
In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.
A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.
Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.
To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.
Yes, but including more than one of either type requires multiple research questions .
For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.
You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .
To ensure the internal validity of an experiment , you should only change one independent variable at a time.
No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!
You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .
- The type of soda – diet or regular – is the independent variable .
- The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.
Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.
In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.
Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .
Probability sampling means that every member of the target population has a known chance of being included in the sample.
Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .
Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .
Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.
Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.
A sampling error is the difference between a population parameter and a sample statistic .
A statistic refers to measures about the sample , while a parameter refers to measures about the population .
Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.
Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.
There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.
The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).
The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.
Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .
Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.
Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.
Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.
The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .
Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.
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 study | Cross-sectional study |
---|---|
observations | Observations at a in time |
Observes the multiple times | Observes (a “cross-section”) in the population |
Follows in participants over time | Provides of society at a given point |
There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .
Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.
In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .
The research methods you use depend on the type of data you need to answer your research question .
- If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
- If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
- If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.
A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.
A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.
In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.
Discrete and continuous variables are two types of quantitative variables :
- Discrete variables represent counts (e.g. the number of objects in a collection).
- Continuous variables represent measurable amounts (e.g. water volume or weight).
Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).
Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).
You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .
You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .
In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:
- The independent variable is the amount of nutrients added to the crop field.
- The dependent variable is the biomass of the crops at harvest time.
Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .
Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:
- A testable hypothesis
- At least one independent variable that can be precisely manipulated
- At least one dependent variable that can be precisely measured
When designing the experiment, you decide:
- How you will manipulate the variable(s)
- How you will control for any potential confounding variables
- How many subjects or samples will be included in the study
- How subjects will be assigned to treatment levels
Experimental design is essential to the internal and external validity of your experiment.
I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .
External validity is the extent to which your results can be generalized to other contexts.
The validity of your experiment depends on your experimental design .
Reliability and validity are both about how well a method measures something:
- Reliability refers to the consistency of a measure (whether the results can be reproduced under the same conditions).
- Validity refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure).
If you are doing experimental research, you also have to consider the internal and external validity of your experiment.
A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.
In statistics, sampling allows you to test a hypothesis about the characteristics of a population.
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.
Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.
Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).
In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .
In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.
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Home » Experimental Design – Types, Methods, Guide
Experimental Design – Types, Methods, Guide
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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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14.1 What is experimental design and when should you use it?
Learning objectives.
Learners will be able to…
- Describe the purpose of experimental design research
- Describe nomethetic causality and the logic of experimental design
- Identify the characteristics of a basic experiment
- Discuss the relationship between dependent and independent variables in experiments
- Identify the three major types of experimental designs
Pre-awareness check (Knowledge)
What are your thoughts on the phrase ‘experiment’ in the realm of social sciences? In an experiment, what is the independent variable?
The basics of experiments
In social work research, experimental design is used to test the effects of treatments, interventions, programs, or other conditions to which individuals, groups, organizations, or communities may be exposed to. There are a lot of experiments social work researchers can use to explore topics such as treatments for depression, impacts of school-based mental health on student outcomes, or prevention of abuse of people with disabilities. The American Psychological Association defines an experiment as:
a series of observations conducted under controlled conditions to study a relationship with the purpose of drawing causal inferences about that relationship. An experiment involves the manipulation of an independent variable , the measurement of a dependent variable , and the exposure of various participants to one or more of the conditions being studied. Random selection of participants and their random assignment to conditions also are necessary in experiments .
In experimental design, the independent variable is the intervention, treatment, or condition that is being investigated as a potential cause of change (i.e., the experimental condition ). The effect, or outcome, of the experimental condition is the dependent variable. Trying out a new restaurant, dating a new person – we often call these things “experiments.” However, a true social science experiment would include recruitment of a large enough sample, random assignment to control and experimental groups, exposing those in the experimental group to an experimental condition, and collecting observations at the end of the experiment.
Social scientists use this level of rigor and control to maximize the internal validity of their research. Internal validity is the confidence researchers have about whether the independent variable (e.g, treatment) truly produces a change in the dependent, or outcome, variable. The logic and features of experimental design are intended to help establish causality and to reduce threats to internal validity , which we will discuss in Section 14.5 .
Experiments attempt to establish a nomothetic causal relationship between two variables—the treatment and its intended outcome. We discussed the four criteria for establishing nomothetic causality in Section 4.3 :
- plausibility,
- covariation,
- temporality, and
- nonspuriousness.
Experiments should establish plausibility , having a plausible reason why their intervention would cause changes in the dependent variable. Usually, a theory framework or previous empirical evidence will indicate the plausibility of a causal relationship.
Covariation can be established for causal explanations by showing that the “cause” and the “effect” change together. In experiments, the cause is an intervention, treatment, or other experimental condition. Whether or not a research participant is exposed to the experimental condition is the independent variable. The effect in an experiment is the outcome being assessed and is the dependent variable in the study. When the independent and dependent variables covary, they can have a positive association (e.g., those exposed to the intervention have increased self-esteem) or a negative association (e.g., those exposed to the intervention have reduced anxiety).
Since researcher controls when the intervention is administered, they can be assured that changes in the independent variable (the treatment) happens before changes in the dependent variable (the outcome). In this way, experiments assure temporality .
Finally, one of the most important features of experiments is that they allow researchers to eliminate spurious variables to support the criterion of nonspuriousness . True experiments are usually conducted under strictly controlled conditions. The intervention is given in the same way to each person, with a minimal number of other variables that might cause their post-test scores to change.
The logic of experimental design
How do we know that one phenomenon causes another? The complexity of the social world in which we practice and conduct research means that causes of social problems are rarely cut and dry. Uncovering explanations for social problems is key to helping clients address them, and experimental research designs are one road to finding answers.
Just because two phenomena are related in some way doesn’t mean that one causes the other. Ice cream sales increase in the summer, and so does the rate of violent crime; does that mean that eating ice cream is going to make me violent? Obviously not, because ice cream is great. The reality of that association is far more complex—it could be that hot weather makes people more irritable and, at times, violent, while also making people want ice cream. More likely, though, there are other social factors not accounted for in the way we just described this association.
As we have discussed, experimental designs can help clear up at least some of this fog by allowing researchers to isolate the effect of interventions on dependent variables by controlling extraneous variables . In true experimental design (discussed in the next section) and quasi-experimental design, researchers accomplish this w ith a control group or comparison group and the experimental group . The experimental group is sometimes called the treatment group because people in the experimental group receive the treatment or are exposed to the experimental condition (but we will call it the experimental group in this chapter.) The control/comparison group does not receive the treatment or intervention. Instead they may receive what is known as “treatment as usual” or perhaps no treatment at all.
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In a well-designed experiment, the control group should look almost identical to the experimental group in terms of demographics and other relevant factors. What if we want to know the effect of CBT on social anxiety, but we have learned in prior research that men tend to have a more difficult time overcoming social anxiety? We would want our control and experimental groups to have a similar portions of men, since ostensibly, both groups’ results would be affected by the men in the group. If your control group has 5 women, 6 men, and 4 non-binary people, then your experimental group should be made up of roughly the same gender balance to help control for the influence of gender on the outcome of your intervention. (In reality, the groups should be similar along other dimensions, as well, and your group will likely be much larger.) The researcher will use the same outcome measures for both groups and compare them, and assuming the experiment was designed correctly, get a pretty good answer about whether the intervention had an effect on social anxiety.
Random assignment [/pb_glossary], also called randomization, entails using a random process to decide which participants are put into the control or experimental group (which participants receive an intervention and which do not). By randomly assigning participants to a group, you can reduce the effect of extraneous variables on your research because there won’t be a systematic difference between the groups.
Do not confuse random assignment with random sampling . Random sampling is a method for selecting a sample from a population and is rarely used in psychological research. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other related fields. Random sampling helps a great deal with external validity, or generalizability , whereas random assignment increases internal validity .
Other Features of Experiments that Help Establish Causality
To control for spuriousness (as well as meeting the three other criteria for establishing causality), experiments try to control as many aspects of the research process as possible: using control groups, having large enough sample sizes, standardizing the treatment, etc. Researchers in large experiments often employ clinicians or other research staff to help them. Researchers train their staff members exhaustively, provide pre-scripted responses to common questions, and control the physical environment of the experiment so each person who participates receives the exact same treatment. Experimental researchers also document their procedures, so that others can review them and make changes in future research if they think it will improve on the ability to control for spurious variables.
An interesting example is Bruce Alexander’s (2010) Rat Park experiments. Much of the early research conducted on addictive drugs, like heroin and cocaine, was conducted on animals other than humans, usually mice or rats. The scientific consensus up until Alexander’s experiments was that cocaine and heroin were so addictive that rats, if offered the drugs, would consume them repeatedly until they perished. Researchers claimed this behavior explained how addiction worked in humans, but Alexander was not so sure. He knew rats were social animals and the experimental procedure from previous experiments did not allow them to socialize. Instead, rats were kept isolated in small cages with only food, water, and metal walls. To Alexander, social isolation was a spurious variable, causing changes in addictive behavior not due to the drug itself. Alexander created an experiment of his own, in which rats were allowed to run freely in an interesting environment, socialize and mate with other rats, and of course, drink from a solution that contained an addictive drug. In this environment, rats did not become hopelessly addicted to drugs. In fact, they had little interest in the substance. To Alexander, the results of his experiment demonstrated that social isolation was more of a causal factor for addiction than the drug itself.
One challenge with Alexander’s findings is that subsequent researchers have had mixed success replicating his findings (e.g., Petrie, 1996; Solinas, Thiriet, El Rawas, Lardeux, & Jaber, 2009). Replication involves conducting another researcher’s experiment in the same manner and seeing if it produces the same results. If the causal relationship is real, it should occur in all (or at least most) rigorous replications of the experiment.
Replicability
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To allow for easier replication, researchers should describe their experimental methods diligently. Researchers with the Open Science Collaboration (2015) [1] conducted the Reproducibility Project , which caused a significant controversy regarding the validity of psychological studies. The researchers with the project attempted to reproduce the results of 100 experiments published in major psychology journals since 2008. What they found was shocking. Although 97% of the original studies reported significant results, only 36% of the replicated studies had significant findings. The average effect size in the replication studies was half that of the original studies. The implications of the Reproducibility Project are potentially staggering, and encourage social scientists to carefully consider the validity of their reported findings and that the scientific community take steps to ensure researchers do not cherry-pick data or change their hypotheses simply to get published.
Generalizability
Let’s return to Alexander’s Rat Park study and consider the implications of his experiment for substance use professionals. The conclusions he drew from his experiments on rats were meant to be generalized to the population. If this could be done, the experiment would have a high degree of external validity , which is the degree to which conclusions generalize to larger populations and different situations. Alexander argues his conclusions about addiction and social isolation help us understand why people living in deprived, isolated environments may become addicted to drugs more often than those in more enriching environments. Similarly, earlier rat researchers argued their results showed these drugs were instantly addictive to humans, often to the point of death.
Neither study’s results will match up perfectly with real life. There are clients in social work practice who may fit into Alexander’s social isolation model, but social isolation is complex. Clients can live in environments with other sociable humans, work jobs, and have romantic relationships; does this mean they are not socially isolated? On the other hand, clients may face structural racism, poverty, trauma, and other challenges that may contribute to their social environment. Alexander’s work helps understand clients’ experiences, but the explanation is incomplete. Human existence is more complicated than the experimental conditions in Rat Park.
Effectiveness versus Efficacy
Social workers are especially attentive to how social context shapes social life. This consideration points out a potential weakness of experiments. They can be rather artificial. When an experiment demonstrates causality under ideal, controlled circumstances, it establishes the efficacy of an intervention.
How often do real-world social interactions occur in the same way that they do in a controlled experiment? Experiments that are conducted in community settings by community practitioners are less easily controlled than those conducted in a lab or with researchers who adhere strictly to research protocols delivering the intervention. When an experiment demonstrates causality in a real-world setting that is not tightly controlled, it establishes the effectiveness of the intervention.
The distinction between efficacy and effectiveness demonstrates the tension between internal and external validity. Internal validity and external validity are conceptually linked. Internal validity refers to the degree to which the intervention causes its intended outcomes, and external validity refers to how well that relationship applies to different groups and circumstances than the experiment. However, the more researchers tightly control the environment to ensure internal validity, the more they may risk external validity for generalizing their results to different populations and circumstances. Correspondingly, researchers whose settings are just like the real world will be less able to ensure internal validity, as there are many factors that could pollute the research process. This is not to suggest that experimental research findings cannot have high levels of both internal and external validity, but that experimental researchers must always be aware of this potential weakness and clearly report limitations in their research reports.
Types of Experimental Designs
Experimental design is an umbrella term for a research method that is designed to test hypotheses related to causality under controlled conditions. Table 14.1 describes the three major types of experimental design (pre-experimental, quasi-experimental, and true experimental) and presents subtypes for each. As we will see in the coming sections, some types of experimental design are better at establishing causality than others. It’s also worth considering that true experiments, which most effectively establish causality , are often difficult and expensive to implement. Although the other experimental designs aren’t perfect, they still produce useful, valid evidence and may be more feasible to carry out.
) | ||
A. One-group pretest posttest | A. Pre- and posttests are administered, but no comparison group | XXXX |
B. One-shot case study | B. No pretest | What is the average level of loneliness among graduates of a peer support training program? What percent of graduates rate their social support as “good” or “excellent”? |
) | ||
C. Nonequivalent comparison group design | C. Similar to classical experimental design only without random assignment | XXXX |
D. Static-group design | D. No pretest, posttest administered after the intervention
| |
E. Natural experiments | E. Naturally occurring event becomes “experimental condition”; observational study in which some cases are exposed to condition (which becomes the “experimental condition”) and others are not; changes in “experimental” group can be assessed; | |
( ) | XXXX | |
F. Classical experimental design | F. Pre- and posttest; control group | |
G. Posttest only control group | G. Does not use a pretest and assumes random assignment results in equivalent groups | |
H. Solomon four group design | H. Random assignment, two experimental and two control groups, pretests for half of the groups and posttests for all |
Key Takeaways
- Experimental designs are useful for establishing causality, but some types of experimental design do this better than others.
- Experiments help researchers isolate the effect of the independent variable on the dependent variable by controlling for the effect of extraneous variables .
- Experiments use a control/comparison group and an experimental group to test the effects of interventions. These groups should be as similar to each other as possible in terms of demographics and other relevant factors.
- True experiments have control groups with randomly assigned participants; quasi-experimental types of experiments have comparison groups to which participants are not randomly assigned; pre-experimental designs do not have a comparison group.
TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS):
- Think about the research project you’ve been designing so far. How might you use a basic experiment to answer your question? If your question isn’t explanatory, try to formulate a new explanatory question and consider the usefulness of an experiment.
- Why is establishing a simple relationship between two variables not indicative of one causing the other?
TRACK 2 (IF YOU AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS):
Imagine you are interested in studying child welfare practice. You are interested in learning more about community-based programs aimed to prevent child maltreatment and to prevent out-of-home placement for children.
- Think about the research project stated above. How might you use a basic experiment to look more into this research topic? Try to formulate an explanatory question and consider the usefulness of an experiment.
- Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349 (6251), aac4716. Doi: 10.1126/science.aac4716 ↵
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.
treatment, intervention, or experience that is being tested in an experiment (the independent variable) that is received by the experimental group and not by the control group.
Ability to say that one variable "causes" something to happen to another variable. Very important to assess when thinking about studies that examine causation such as experimental or quasi-experimental designs.
circumstances or events that may affect the outcome of an experiment, resulting in changes in the research participants that are not a result of the intervention, treatment, or experimental condition being tested
causal explanations that can be universally applied to groups, such as scientific laws or universal truths
as a criteria for causal relationship, the relationship must make logical sense and seem possible
when the values of two variables change at the same time
as a criteria for causal relationship, the cause must come before the effect
an association between two variables that is NOT caused by a third variable
variables and characteristics that have an effect on your outcome, but aren't the primary variable whose influence you're interested in testing.
the group of participants in our study who do not receive the intervention we are researching in experiments with random assignment
the group of participants in our study who do not receive the intervention we are researching in experiments without random assignment
in experimental design, the group of participants in our study who do receive the intervention we are researching
The ability to apply research findings beyond the study sample to some broader population,
This is a synonymous term for generalizability - the ability to apply the findings of a study beyond the sample to a broader population.
performance of an intervention under ideal and controlled circumstances, such as in a lab or delivered by trained researcher-interventionists
The performance of an intervention under "real-world" conditions that are not closely controlled and ideal
the idea that one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief
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In contrast, random assignment is a way of sorting the sample participants into control and experimental groups. While random sampling is used in many types of studies, random assignment is only used in between-subjects experimental designs. ... In a cause-and-effect study, a confounding variable is an unmeasured variable that influences both ...
Explain why each of the following would be used in this experiment to help establish cause and effect. • Random assignment • Placebo condition . Part C . Professor Menendez presented the research at an international conference. After the presentation, audience members met in small groups to discuss the research.
3.6 Causation and Random Assignment. Medical researchers may be interested in showing that a drug helps improve people's health (the cause of improvement is the drug), while educational researchers may be interested in showing a curricular innovation improves students' learning (the curricular innovation causes improved learning).
Correlation, Causation, and Confounding Variables. Random assignment helps you separate causation from correlation and rule out confounding variables. As a critical component of the scientific method, experiments typically set up contrasts between a control group and one or more treatment groups. The idea is to determine whether the effect, which is the difference between a treatment group and ...
Materio / Getty Images. Random assignment refers to the use of chance procedures in psychology experiments to ensure that each participant has the same opportunity to be assigned to any given group in a study to eliminate any potential bias in the experiment at the outset. Participants are randomly assigned to different groups, such as the ...
In response to these critiques, people in favor of random assignment argue that the method, despite its limitations, remains one of the most reliable ways to establish cause and effect in experimental research. They acknowledge the challenges and ethical considerations but emphasize the rigorous frameworks in place to address them.
Random selection (also called probability sampling or random sampling) is a way of randomly selecting members of a population to be included in your study. On the other hand, random assignment is a way of sorting the sample participants into control and treatment groups. Random selection ensures that everyone in the population has an equal ...
If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, ... Quasi-experimental design attempts to establish a cause-and-effect relationship by using criteria other than randomization. 1127. How to write a lab report A lab report conveys the aim, methods, results, and conclusions of a ...
Explain random sampling and assignment of participants into experimental and control groups; ... As you've learned, the only way to establish that there is a cause-and-effect relationship between two variables is to conduct a scientific experiment. Experiment has a different meaning in the scientific context than in everyday life.
The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups. What is an Experiment? An experiment is an investigation in which a hypothesis is scientifically tested. An ...
Random assignment in psychology involves each participant having an equal chance of being chosen for any of the groups, including the control and experimental groups. It helps control for potential confounding variables, reducing the likelihood of pre-existing differences between groups. This method enhances the internal validity of experiments ...
As you've learned, the only way to establish that there is a cause-and-effect relationship between two variables is to conduct a scientific experiment. ... Random assignment is critical for sound experimental design. With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. ...
The experimental method involves manipulating one variable to determine if this causes changes in another variable. This method relies on controlled research methods and random assignment of study subjects to test a hypothesis. For example, researchers may want to learn how different visual patterns may impact our perception.
In other words, you are trying to establish cause and effect relationships between events or phenomena by forming hypotheses. 5.1 Cause and Effect Relationships ... Random assignment is the most feasible practical strategy to ensure that groups are statistically equivalent. Note that random assignment is different from random sampling.
Random sampling (also called probability sampling or random selection) is a way of selecting members of a population to be included in your study. In contrast, random assignment is a way of sorting the sample participants into control and experimental groups. While random sampling is used in many types of studies, random assignment is only used ...
Random assignment reduces any pre-existing variables we can't account for. We randomly assign participants to either eat a lot of fruit, or eat a ... establish cause and effect relationships. Think cause, think experiments have Higher cholesterol . LP 1G Experiments 14 03/14/21
The most powerful and direct way to establish cause and effect is to design an experiment. In order to be considered an experiment, the study must have a control (or comparison) ... When random assignment to group is not feasible, a quasi-experimental design that requires a comparison group and manipulation of the independent variable may be ...
Random assignment helps ensure that the groups are comparable. ... 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. What is a confounding variable?
Random assignment allows cause-and-effect relationships to be identified but it is only one part of the process. Random assignment produces groups that are, in theory, equal at the start of the ...
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. ... Random assignment: Experimental design often involves randomly assigning participants to conditions ...
2. Explain HOW you (the researcher) will conduct random assignment. 3. Argue WHY you (the researcher) will conduct random assignment. In your answer, be sure to discuss at least one confounding variable that is equally distributed between the control and experimental groups. Underline the confounding variable.
Random selection of participants and their random assignment to conditions also are necessary in experiments. In experimental design, the independent variable is the intervention, treatment, or condition that is being investigated as a potential cause of change (i.e., the experimental condition). The effect, or outcome, of the experimental ...
Instructors can use the activity introduced in this chapter to demonstrate how random assignment controls for extraneous variables, which strengthens cause-and-effect interpretations of the results. The instructor shuffles and distributes playing cards to students in class to simulate random assignment to an experimental group (e.g., "red card") and control group (e.g., "black card ...