what is an example of experimental method in psychology

Experimental Psychology: 10 Examples & Definition

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Experimental psychology refers to studying psychological phenomena using scientific methods. Originally, the primary scientific method involved manipulating one variable and observing systematic changes in another variable.

Today, psychologists utilize several types of scientific methodologies.

Experimental psychology examines a wide range of psychological phenomena, including: memory, sensation and perception, cognitive processes, motivation, emotion, developmental processes, in addition to the neurophysiological concomitants of each of these subjects.

Studies are conducted on both animal and human participants, and must comply with stringent requirements and controls regarding the ethical treatment of both.

Definition of Experimental Psychology

Experimental psychology is a branch of psychology that utilizes scientific methods to investigate the mind and behavior.

It involves the systematic and controlled study of human and animal behavior through observation and experimentation .

Experimental psychologists design and conduct experiments to understand cognitive processes, perception, learning, memory, emotion, and many other aspects of psychology. They often manipulate variables ( independent variables ) to see how this affects behavior or mental processes (dependent variables).

The findings from experimental psychology research are often used to better understand human behavior and can be applied in a range of contexts, such as education, health, business, and more.

Experimental Psychology Examples

1. The Puzzle Box Studies (Thorndike, 1898) Placing different cats in a box that can only be escaped by pulling a cord, and then taking detailed notes on how long it took for them to escape allowed Edward Thorndike to derive the Law of Effect: actions followed by positive consequences are more likely to occur again, and actions followed by negative consequences are less likely to occur again (Thorndike, 1898).

2. Reinforcement Schedules (Skinner, 1956) By placing rats in a Skinner Box and changing when and how often the rats are rewarded for pressing a lever, it is possible to identify how each schedule results in different behavior patterns (Skinner, 1956). This led to a wide range of theoretical ideas around how rewards and consequences can shape the behaviors of both animals and humans.

3. Observational Learning (Bandura, 1980) Some children watch a video of an adult punching and kicking a Bobo doll. Other children watch a video in which the adult plays nicely with the doll. By carefully observing the children’s behavior later when in a room with a Bobo doll, researchers can determine if television violence affects children’s behavior (Bandura, 1980).

4. The Fallibility of Memory (Loftus & Palmer, 1974) A group of participants watch the same video of two cars having an accident. Two weeks later, some are asked to estimate the rate of speed the cars were going when they “smashed” into each other. Some participants are asked to estimate the rate of speed the cars were going when they “bumped” into each other. Changing the phrasing of the question changes the memory of the eyewitness.

5. Intrinsic Motivation in the Classroom (Dweck, 1990) To investigate the role of autonomy on intrinsic motivation, half of the students are told they are “free to choose” which tasks to complete. The other half of the students are told they “must choose” some of the tasks. Researchers then carefully observe how long the students engage in the tasks and later ask them some questions about if they enjoyed doing the tasks or not.

6. Systematic Desensitization (Wolpe, 1958) A clinical psychologist carefully documents his treatment of a patient’s social phobia with progressive relaxation. At first, the patient is trained to monitor, tense, and relax various muscle groups while viewing photos of parties. Weeks later, they approach a stranger to ask for directions, initiate a conversation on a crowded bus, and attend a small social gathering. The therapist’s notes are transcribed into a scientific report and published in a peer-reviewed journal.

7. Study of Remembering (Bartlett, 1932) Bartlett’s work is a seminal study in the field of memory, where he used the concept of “schema” to describe an organized pattern of thought or behavior. He conducted a series of experiments using folk tales to show that memory recall is influenced by cultural schemas and personal experiences.

8. Study of Obedience (Milgram, 1963) This famous study explored the conflict between obedience to authority and personal conscience. Milgram found that a majority of participants were willing to administer what they believed were harmful electric shocks to a stranger when instructed by an authority figure, highlighting the power of authority and situational factors in driving behavior.

9. Pavlov’s Dog Study (Pavlov, 1927) Ivan Pavlov, a Russian physiologist, conducted a series of experiments that became a cornerstone in the field of experimental psychology. Pavlov noticed that dogs would salivate when they saw food. He then began to ring a bell each time he presented the food to the dogs. After a while, the dogs began to salivate merely at the sound of the bell. This experiment demonstrated the principle of “classical conditioning.”

10, Piaget’s Stages of Development (Piaget, 1958) Jean Piaget proposed a theory of cognitive development in children that consists of four distinct stages: the sensorimotor stage (birth to 2 years), where children learn about the world through their senses and motor activities, through to the the formal operational stage (12 years and beyond), where abstract reasoning and hypothetical thinking develop. Piaget’s theory is an example of experimental psychology as it was developed through systematic observation and experimentation on children’s problem-solving behaviors .

Types of Research Methodologies in Experimental Psychology 

Researchers utilize several different types of research methodologies since the early days of Wundt (1832-1920).

1. The Experiment

The experiment involves the researcher manipulating the level of one variable, called the Independent Variable (IV), and then observing changes in another variable, called the Dependent Variable (DV).

The researcher is interested in determining if the IV causes changes in the DV. For example, does television violence make children more aggressive?

So, some children in the study, called research participants, will watch a show with TV violence, called the treatment group. Others will watch a show with no TV violence, called the control group.

So, there are two levels of the IV: violence and no violence. Next, children will be observed to see if they act more aggressively. This is the DV.

If TV violence makes children more aggressive, then the children that watched the violent show will me more aggressive than the children that watched the non-violent show.

A key requirement of the experiment is random assignment . Each research participant is assigned to one of the two groups in a way that makes it a completely random process. This means that each group will have a mix of children: different personality types, diverse family backgrounds, and range of intelligence levels.

2. The Longitudinal Study

A longitudinal study involves selecting a sample of participants and then following them for years, or decades, periodically collecting data on the variables of interest.

For example, a researcher might be interested in determining if parenting style affects academic performance of children. Parenting style is called the predictor variable , and academic performance is called the outcome variable .

Researchers will begin by randomly selecting a group of children to be in the study. Then, they will identify the type of parenting practices used when the children are 4 and 5 years old.

A few years later, perhaps when the children are 8 and 9, the researchers will collect data on their grades. This process can be repeated over the next 10 years, including through college.

If parenting style has an effect on academic performance, then the researchers will see a connection between the predictor variable and outcome variable.

Children raised with parenting style X will have higher grades than children raised with parenting style Y.

3. The Case Study

The case study is an in-depth study of one individual. This is a research methodology often used early in the examination of a psychological phenomenon or therapeutic treatment.

For example, in the early days of treating phobias, a clinical psychologist may try teaching one of their patients how to relax every time they see the object that creates so much fear and anxiety, such as a large spider.

The therapist would take very detailed notes on how the teaching process was implemented and the reactions of the patient. When the treatment had been completed, those notes would be written in a scientific form and submitted for publication in a scientific journal for other therapists to learn from.

There are several other types of methodologies available which vary different aspects of the three described above. The researcher will select a methodology that is most appropriate to the phenomenon they want to examine.

They also must take into account various practical considerations such as how much time and resources are needed to complete the study. Conducting research always costs money.

People and equipment are needed to carry-out every study, so researchers often try to obtain funding from their university or a government agency. 

Origins and Key Developments in Experimental Psychology

Wilhelm Maximilian Wundt (1832-1920) is considered one of the fathers of modern psychology. He was a physiologist and philosopher and helped establish psychology as a distinct discipline (Khaleefa, 1999).  

In 1879 he established the world’s first psychology research lab at the University of Leipzig. This is considered a key milestone for establishing psychology as a scientific discipline. In addition to being the first person to use the term “psychologist,” to describe himself, he also founded the discipline’s first scientific journal Philosphische Studien in 1883.

Another notable figure in the development of experimental psychology is Ernest Weber . Trained as a physician, Weber studied sensation and perception and created the first quantitative law in psychology.

The equation denotes how judgments of sensory differences are relative to previous levels of sensation, referred to as the just-noticeable difference (jnd). This is known today as Weber’s Law (Hergenhahn, 2009).    

Gustav Fechner , one of Weber’s students, published the first book on experimental psychology in 1860, titled Elemente der Psychophysik. His worked centered on the measurement of psychophysical facets of sensation and perception, with many of his methods still in use today.    

The first American textbook on experimental psychology was Elements of Physiological Psychology, published in 1887 by George Trumball Ladd .

Ladd also established a psychology lab at Yale University, while Stanley Hall and Charles Sanders continued Wundt’s work at a lab at Johns Hopkins University.

In the late 1800s, Charles Pierce’s contribution to experimental psychology is especially noteworthy because he invented the concept of random assignment (Stigler, 1992; Dehue, 1997).

Go Deeper: 15 Random Assignment Examples

This procedure ensures that each participant has an equal chance of being placed in any of the experimental groups (e.g., treatment or control group). This eliminates the influence of confounding factors related to inherent characteristics of the participants.

Random assignment is a fundamental criterion for a study to be considered a valid experiment.

From there, experimental psychology flourished in the 20th century as a science and transformed into an approach utilized in cognitive psychology, developmental psychology, and social psychology .

Today, the term experimental psychology refers to the study of a wide range of phenomena and involves methodologies not limited to the manipulation of variables.

The Scientific Process and Experimental Psychology

The one thing that makes psychology a science and distinguishes it from its roots in philosophy is the reliance upon the scientific process to answer questions. This makes psychology a science was the main goal of its earliest founders such as Wilhelm Wundt.

There are numerous steps in the scientific process, outlined in the graphic below.

1. Observation

First, the scientist observes an interesting phenomenon that sparks a question. For example, are the memories of eyewitnesses really reliable, or are they subject to bias or unintentional manipulation?

2. Hypothesize

Next, this question is converted into a testable hypothesis. For instance: the words used to question a witness can influence what they think they remember.

3. Devise a Study

Then the researcher(s) select a methodology that will allow them to test that hypothesis. In this case, the researchers choose the experiment, which will involve randomly assigning some participants to different conditions.

In one condition, participants are asked a question that implies a certain memory (treatment group), while other participants are asked a question which is phrased neutrally and does not imply a certain memory (control group).

The researchers then write a proposal that describes in detail the procedures they want to use, how participants will be selected, and the safeguards they will employ to ensure the rights of the participants.

That proposal is submitted to an Institutional Review Board (IRB). The IRB is comprised of a panel of researchers, community representatives, and other professionals that are responsible for reviewing all studies involving human participants.

4. Conduct the Study

If the IRB accepts the proposal, then the researchers may begin collecting data. After the data has been collected, it is analyzed using a software program such as SPSS.

Those analyses will either support or reject the hypothesis. That is, either the participants’ memories were affected by the wording of the question, or not.

5. Publish the study

Finally, the researchers write a paper detailing their procedures and results of the statistical analyses. That paper is then submitted to a scientific journal.

The lead editor of that journal will then send copies of the paper to 3-5 experts in that subject. Each of those experts will read the paper and basically try to find as many things wrong with it as possible. Because they are experts, they are very good at this task.

After reading those critiques, most likely, the editor will send the paper back to the researchers and require that they respond to the criticisms, collect more data, or reject the paper outright.

In some cases, the study was so well-done that the criticisms were minimal and the editor accepts the paper. It then gets published in the scientific journal several months later.

That entire process can easily take 2 years, usually more. But, the findings of that study went through a very rigorous process. This means that we can have substantial confidence that the conclusions of the study are valid.

Experimental psychology refers to utilizing a scientific process to investigate psychological phenomenon.

There are a variety of methods employed today. They are used to study a wide range of subjects, including memory, cognitive processes, emotions and the neurophysiological basis of each.

The history of psychology as a science began in the 1800s primarily in Germany. As interest grew, the field expanded to the United States where several influential research labs were established.

As more methodologies were developed, the field of psychology as a science evolved into a prolific scientific discipline that has provided invaluable insights into human behavior.

Bartlett, F. C., & Bartlett, F. C. (1995).  Remembering: A study in experimental and social psychology . Cambridge university press.

Dehue, T. (1997). Deception, efficiency, and random groups: Psychology and the gradual origination of the random group design. Isis , 88 (4), 653-673.

Ebbinghaus, H. (2013). Memory: A contribution to experimental psychology.  Annals of neurosciences ,  20 (4), 155.

Hergenhahn, B. R. (2009). An introduction to the history of psychology. Belmont. CA: Wadsworth Cengage Learning .

Khaleefa, O. (1999). Who is the founder of psychophysics and experimental psychology? American Journal of Islam and Society , 16 (2), 1-26.

Loftus, E. F., & Palmer, J. C. (1974).  Reconstruction of auto-mobile destruction : An example of the interaction between language and memory.  Journal of Verbal Learning and Verbal behavior , 13, 585-589.

Pavlov, I.P. (1927). Conditioned reflexes . Dover, New York.

Piaget, J. (1959).  The language and thought of the child  (Vol. 5). Psychology Press.

Piaget, J., Fraisse, P., & Reuchlin, M. (2014). Experimental psychology its scope and method: Volume I (Psychology Revivals): History and method . Psychology Press.

Skinner, B. F. (1956). A case history in scientlfic method. American Psychologist, 11 , 221-233

Stigler, S. M. (1992). A historical view of statistical concepts in psychology and educational research. American Journal of Education , 101 (1), 60-70.

Thorndike, E. L. (1898). Animal intelligence: An experimental study of the associative processes in animals. Psychological Review Monograph Supplement 2 .

Wolpe, J. (1958). Psychotherapy by reciprocal inhibition. Stanford, CA: Stanford University Press.

Appendix: Images reproduced as Text

Definition: Experimental psychology is a branch of psychology that focuses on conducting systematic and controlled experiments to study human behavior and cognition.

Overview: Experimental psychology aims to gather empirical evidence and explore cause-and-effect relationships between variables. Experimental psychologists utilize various research methods, including laboratory experiments, surveys, and observations, to investigate topics such as perception, memory, learning, motivation, and social behavior .

Example: The Pavlov’s Dog experimental psychology experiment used scientific methods to develop a theory about how learning and association occur in animals. The same concepts were subsequently used in the study of humans, wherein psychology-based ideas about learning were developed. Pavlov’s use of the empirical evidence was foundational to the study’s success.

Experimental Psychology Milestones:

1890: William James publishes “The Principles of Psychology”, a foundational text in the field of psychology.

1896: Lightner Witmer opens the first psychological clinic at the University of Pennsylvania, marking the beginning of clinical psychology.

1913: John B. Watson publishes “Psychology as the Behaviorist Views It”, marking the beginning of Behaviorism.

1920: Hermann Rorschach introduces the Rorschach inkblot test.

1938: B.F. Skinner introduces the concept of operant conditioning .

1967: Ulric Neisser publishes “Cognitive Psychology” , marking the beginning of the cognitive revolution.

1980: The third edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III) is published, introducing a new classification system for mental disorders.

The Scientific Process

• Observe an interesting phenomenon
• Formulate testable hypothesis
• Select methodology and design study
• Submit research proposal to IRB
• Collect and analyzed data; write paper
• Submit paper for critical reviews

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A key aim of psychology is to learn and understand more about psychological phenomena. This is usually done through a process called the experimental method . The experimental method in psychology research attempts to investigate the cause-and-effect relationship between variables . The crucial aspect of the experimental method is that it follows a 'scientific routine' to increase the chances of establishing valid and reliable results. As you can expect with all kinds of research, there are many advantages and disadvantages of the experimental method in psychology research. 

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What do extraneous variables affect? 

Can a research be considered valid if many extraneous variables have been found to affect the study? 

Can the experimental method be used to identify if there is a cause-and-effect relationship between variables? 

Is it acceptable for research following the scientific method to be based on the researchers' subjective opinion? 

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• Approaches in Psychology
• Basic Psychology
• Biological Bases of Behavior
• Biopsychology
• Careers in Psychology
• Clinical Psychology
• Bartlett War of the Ghosts
• Brain Development
• Bruner and Minturn Study of Perceptual Set
• Case Studies Psychology
• Computation
• Conservation of Number Piaget
• Constructive Processes in Memory
• Correlation
• Data handling
• Depth Cues Psychology
• Designing Research
• Developmental Research
• Dweck's Theory of Mindset
• Ethical considerations in research
• Factors Affecting Perception
• Factors Affecting the Accuracy of Memory
• Formulation of Hypothesis
• Gibson's Theory of Direct Perception
• Gregory's Constructivist Theory of Perception
• Gunderson et al 2013 study
• Hughes Policeman Doll Study
• Issues and Debates in Developmental Psychology
• Language and Perception
• McGarrigle and Donaldson Naughty Teddy
• Memory Processes
• Memory recall
• Nature and Nurture in Development
• Normal Distribution Psychology
• Perception Research
• Perceptual Set
• Piagets Theory in Education
• Planning and Conducting Research
• Population Samples
• Primary and Secondary Data
• Quantitative Data
• Quantitative and Qualitative Data
• Quantitative and Qualitative Methods
• Research Procedures
• Serial Position Effect
• Short-term Retention
• Structures of Memory
• Tables, Charts and Graphs
• The Effects of Learning on Development
• The Gilchrist And Nesberg Study Of Motivation
• Three Mountains Task
• Types of Variable
• Types of bias and how to control
• Visual Cues and Constancies
• Visual illusions
• Willingham's Learning Theory
• Cognition and Development
• Cognitive Psychology
• Data Handling and Analysis
• Developmental Psychology
• Eating Behaviour
• Emotion and Motivation
• Famous Psychologists
• Forensic Psychology
• Health Psychology
• Individual Differences Psychology
• Issues and Debates in Psychology
• Personality in Psychology
• Psychological Treatment
• Relationships
• Research Methods in Psychology
• Schizophrenia
• Scientific Foundations of Psychology
• Scientific Investigation
• Sensation and Perception
• Social Context of Behaviour
• Social Psychology

Jump to a key chapter

The experimental method is a research process that involves following scientific guidelines to test hypotheses and establish causal relationships between variables .

• To begin with our learning of the experiment method, we will start with a quick recap covering the elements that make up research.
• We will then move on to discuss the experimental method in psychology research.
• To finish off, we will discuss the advantages and disadvantages of the experimental method in psychology.

Experimental Method of Research

Before we get into the experimental method in psychology research, let's take a quick look at what are the basic components that make up scientific research .

Hypotheses and variables

The hypothesis is an important component of research. The hypothesis is formed at the start of an experiment with the purpose of stating what the researcher expects to find in their study. The hypothesis is important because it is used to identify if the results support or negate psychological theories.

The h ypothesis is a specific, testable statement about the expected outcomes after comparing two (or more) variables.

The hypothesis needs to state the variables being investigated in the research.

The independent variable (IV) is the variable that the researcher manipulates/ changes in their study. This is the variable that the researcher believes these changes in the IV will cause a change in the dependent variable (DV).

The DV is the variable that is being observed and measured. The DV is thought of as the effect that is caused by the changes in the IV.

There are other types of variables, such as extraneous variables, participant variables and situational variables. These are variables that may cause changes in the DV. Ideally, psychology research that follows the scientific method should not have these types of variables. However, it is next to impossible to control for every potential variable that should not affect the DV.

Stages of the experimental method in psychology research

The experimental method has a standardised procedure and has several fixed steps to it that are usually carried out in a lab setting .

• Identify the topic of interest/research and form a hypothesis.
• Identify the IV(s) and DV(s), determine the design and type of experiment, and determine how to measure the IV and DV, e.g. self-report measure, observations, etc.
• Prepare the materials needed in the study and recruit participants through an appropriate sampling method.
• Conduct the experiment in a carefully planned scientific manner, collect the data and statistically analyse the results.
• Write up the lab report, evaluate the study and give suggestions for further research.

Experimental Method Example

A hypothetical study has been described below to show how the experimental method is used in psychology research.

• The researchers researched previously published work on the effects of caffeine on reaction times. The researchers hypothesised that drinking caffeine would affect reaction times based on the previous findings.
• The researchers identified reaction time as the DV and caffeine as the IV; they decided to carry out the study in a lab setting.
• The next stage involved preparing a test that measured reaction times, and participants were randomly assigned to three groups (drink with high levels of caffeine, drink with low levels of caffeine and no caffeine).
• The study was then carried out in a manner to prevent the reliability and validity of the study from being lowered.
• The results of the study were written up in the correct psychology format.

The Features of the Experimental Method

There are three essential requirements of research that follows the experimental method.

We will now discuss each of these and identify how researchers can try and make sure that their research meets these requirements.

Research needs to be considered empirical. Empirical research means that the findings should be reflective of objective facts that the researcher has observed rather than their subjective opinion.

The next requirement, reliability, is important as it makes sure that research findings are consistent across time, in different situations, settings and when applied to other people. When research is found to be reliable, then it is thought that the research findings are representative of the population and can be applied to real-life settings.

Reliability refers to how consistent the results of an experiment are. If the results are similar when the same procedure has been carried out on different occasions, settings or using different participants, then the findings will be considered reliable.

Testing the same study vigorously using the same methodology but on different days, settings, and times or using different samples is used to identify if a study is reliable.

The third requirement of research that follows the experimental method is validity.

Validity is how well a test measures what it intends to.

Validity is important because if the researcher is not in fact measuring what they claim they are measuring, then the results are not accurate and cannot be accurately interpreted or applied. For example, if a test claims it measures personality type but instead measures emotion level, it cannot be a valid test.

The researcher should ensure that their subjective opinion does not influence the research methodology and analysis to ensure research is valid. Researchers can try and combat this through:

Random allocation : Participants are randomly assigned to the experimental or control group; this is used to ensure that individual differences do not cause the results.

Single/Double-blind technique: The researcher is unaware of which experimental condition the participants are in. This prevents the researcher from giving subconscious hints that may influence the participants' behaviour.

Studies that do not use this may measure participants' artificial responses, so the results may not be considered valid.

Types of Experimental Method in Psychology: Experimental Designs

The allocation of participants in experimental/ control conditions is important to ensure that a study is valid. The experimental design is the different ways the participants are split into different conditions/groups of the IV. There are different types of experimental designs .

The independent group's design (IGD)

The IGD is when different participants are assigned to each condition.

When investigating the effect of sleep on reaction times, if using an IGD you would have one group with less sleep (4 hours) and one group with more sleep (11 hours), and the results between the two groups would be compared.

The advantages of this design are that it is less time-consuming than the alternative methods. As different participants are used for each condition there is less chance of participants guessing the hypothesis and altering their behaviour and order effects is not an issue.

However, the disadvantages of this design are that the researcher needs to recruit more participants compared to the other designs. Moreover, there is an increased chance of individual differences influencing the results.

The repeated measures design (RMD)

The RMD is when the same participants are used in all of the conditions.

RMD may be used when investigating if participants are better at memorising information from educational videos or from reading books. The study would involve testing memory after watching an educational video and after reading a book. Each participant would be tested in both conditions.

The advantages of this design are that individual differences will not influence the results of the study as each participant is tested in both conditions and fewer participants may be required to be recruited in comparison to IGD.

In contrast, a disadvantage of this design is that there is a higher risk of order effects influencing the results. This is the idea that the order of conditions tested may influence the study's results.

The matched pairs design (MPD)

The MPD is when participants in each condition are matched on specific variables relevant to the study, e.g. gender, age, IQ, etc.

The advantages of the MPD are that there is no chance of order effects since each condition has a different set of participants and there is less risk of individual differences affecting the results since participants have been matched on such variables.

The disadvantages of this design are that matching participants may be a difficult, costly and time-consuming process.

Advantages and Disadvantages of the Experimental Method in Psychology

Let's move on to discuss the advantages and disadvantages of the experimental method as a whole.

Advantages of the experimental method

• The experimental method gives researchers a high level of control since they choose the IVs and DVs, how to measure them, and the procedure of the study. This means the studies are likely to be high in validity.
• Because of the standardised procedures, experiments can be replicated and their reliability can be tested.

The experimental method allows cause and effect relationships to be determined, which is the goal of psychological research.

The conclusions of these experiments allow useful applications to the real world.

Disadvantages of the experimental method

• Results are subject to human error and subjectivity, e.g. researcher bias, social desirability bias, order effects, etc and so it can be difficult to strictly adhere to the experimental method.
• The procedure of the experimental method can be time-consuming and costly.
• Can include practical problems, e.g. some variables may be hard to manipulate or measure.
• Extraneous variables sometimes can't be controlled, which can lower the validity of a study and its results.
• Participants' behaviour can be influenced by the researcher or the conditions of the experiment.

The Experimental Method - Key Takeaways

• The experimental method is a research process that involves following scientific guidelines to test hypotheses and establish causal relationships between variables.
• There are three important requirements of scientific research that follow the experimental method; these are that research should be empirical, reliable and valid.
• The experimental designs used in psychology research are the independent measures design, repeated measures design and matched pairs design .
• There are advantages and disadvantages of the experimental method in psychology.

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Frequently Asked Questions about Experimental Method

What are the five steps in the experimental method?

The five steps of the experimental method are:

Who used the experimental method?

Some famous researchers who used the experimental method in psychology research are Loftus and Palmer's (1974) experiment on the accuracy of eyewitness testimony, Asch's (1951) Conformity study, and Milgram's (1963) Obedience experiment. 

What is the quasi-experimental method?

The quasi-experimental method is similar to the experimental method in that it tests how changes in the independent variable affect the dependent variable. 

The difference between the two types of the experimental method is that quasi-experimental methods do not randomly assign participants to control and experimental groups, whereas the experimental method does.   

What is the experimental method of psychology?

The experimental method is a research process that involves following scientific guidelines to test hypotheses and establish causal relationships between variables. 

What are the main advantages of the experimental method?

The main advantages of the experimental method are:

• Because of the standardised procedures, experiments can be replicated, and their reliability can be tested.

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7 Important Methods in Psychology With Examples

Psychology is a scientific study of the human mind, mental processes, and behavior. It is called a scientific study because psychologists also do various systematic research and experiments to study and formulate psychological theories like other scientists. Psychological researches involve understanding complex mental processes, human behavior, and collecting different types of data (physiological, psychological, physical, and demographic data), psychologists use various research methods as it is difficult to obtain accurate and reliable results if we use a single research method for collecting research data. The type of method they use depends upon the type of research. Broadly, researches are divide into two types, i.e., experimental and non-experimental researches. Experimental researches involve two or more variables, and it studies the effect of the independent variable on the dependent variable (cause-effect relationship), whereas non-experimental researches do not involve the manipulations of variables. The concept of variables is briefly explained further in this article. Let’s get familiar with some widely used methods of collecting psychology research data.

1. Experimental Method

To understand the experimental method, firstly we need to be familiar with the term ‘variable.’ A variable is an event or stimulus that varies, and its values can be measured. It is to be noted that we can not regard any object as a variable; in fact, the attributes related to that object are called variables. For example, A person is not a variable, but the height of the person is a variable because different people may have different heights. In the experiment method of data collection, we mainly concern with two types of variables, i.e., independent variables and dependent variables. If the value of the variable is manipulated by the researcher to observe its effects, then it is called the independent variable, and the variable that is affected by the change in the independent variable is called the dependent variable. For example, if we want to study the influence of alcohol on the reaction time and driving abilities of the driver, then the amount of alcohol that the driver consumes is the independent variable, and the driving performance of the driver is called the dependent variable. Experimental methods are conducted to establish the relationship between the independent variable (cause) and dependent variables (effect). The experiments are conducted very carefully, and any variables other than the independent variable are kept constant or negligible so that an accurate relationship between the cause and effect can be established. In the above example, other factors like the driver’s stress, anxiety, or mood (extraneous variables) can interfere with the dependent variable (driving ability). It is difficult to avoid these extraneous variables; extraneous variables are the undesired variables that are not studied under the experiments, and their manipulation can alter the results of the study, but we should always try to make them constant or negligible for accurate results.

Control Group and Experimental Group

Experiments generally consist of several research groups that are broadly categorized into control groups and experimental groups. The group that undergoes the manipulation of the independent variable is called the experimental group, whereas the group that does not undergoes the independent variable manipulation, but its other factors or variables are kept the same as the experimental group, is called a control group. The control group basically acts as a comparison group as it is used to measure the changes caused by the independent variable on the experimental group. For example, if a researcher wants to study that how does the conduction of exams affects the learning ability of the student, then, here, the learning ability of the student is the dependent variable and exams are the independent variable. In this experiment, some lectures will be delivered to the students of the same class and of nearly the same learning abilities (based on their previous exam scores or other criteria), and then the students are divide into different groups, one group is not subjected to give the exams, while the other group has to give the exam of what they have learned in the lesson. The group of students that were not subjected to give the exams is called the control group, and the group of students that were subjected to give the exams is called the experimental group. The number of experimental groups can be more than one based on how often does the exams are conducted for each group. At the end of the experiments, the researcher can find the results by comparing the experimental group with the control group.

Types of Experimental Method

Some major types of the experimental method include,

1. Lab Experiments

It is difficult to conduct some experiments in natural settings as many extraneous variables can become a problem for the research. So, researchers conduct the experiments in a controlled manner in laboratories or research centers. It is easy to manage the independent and dependent variables in the controlled settings. For example, if the researcher wants to study the effect of different kinds of music like pop, classical, etc., on the health of the patients, then the researcher will conduct this study in a room rather than in a natural environment as it’s easy to keep extraneous variables constant in the closed settings. Here, music is the independent variable and health is the dependent variable. If the same experiment is conducted outside the lab, then extraneous variables like sunlight, weather, noise, etc., may interfere with the study and manipulate the results of the research.

2. Field Experiments

Sometimes, lab experiment results face criticism for their lack of generalizability as they are not conducted in real-life settings. Field experiments are conducted in the natural environment and real-life settings like schools, industries, hospitals, etc., so they are more ecologically valid than lab experiments. For example, if we want to study whether classroom learning or open environment learning is the best teaching method for students, the researcher would prefer the field experiment over the lab experiment. However, in field experiments, it is very difficult to control the undesired or extraneous variables, which makes it difficult to establish an accurate cause-effect relationship. Moreover, they consume more time than the lab experiments.

3. Quasi Experiments

In lab experiments or fields experiments, sometimes, it is difficult to manipulate some variables due to ethical issues or other constraints. Quasi-experiments are conducted in this situation. In quasi-experiments, the researcher studies that how does a single or many independent variables impact the dependent variable but without manipulating the independent variable. For instance, if the researcher wants to study the effect of terrorism or bomb blasts on the children who have lost their families, then it is difficult to create this situation artificially, so researchers use the quasi-experiments approach. Here, the researcher selects the independent variable instead of manipulating it and compare it with the dependent variable. The researchers will take a group of children who have lost their families (experimental group), and the children who suffered the bomb blast but did not lose their families (control group), and by comparing both these groups, the researcher can analyze the effect of terrorism on the children who lost their families.

2. Observational Method

The observational method is a non-experimental and qualitative research method in which the behavior of the subject under research is observed. An observational method is a great tool for data collection in psychology because the researcher does not require any special types of equipment to collect the research data. We observe several items throughout our day, but psychological researches are different from our daily observations as it involves some important steps such as selection of the area of interest, noting the observations, and analyzing the obtained data. Gathering the data through observation is itself a skill as an observer should be well aware of his actual area of research and he/she should have a clear picture in mind that what qualities or attributes he should observe, and what he should avoid. The researcher should have a good understanding of the correct methods of recording and analyzing the gathered data. The major problem of the observational method is the observer’s biases, there are high chances that the observer may judge the event according to his/her biases rather than interpreting the event in its natural form. We can relate it to a famous saying,

We see things as we are and not as things are”

So, it is the responsibility of the observer to make accurate observations by minimizing his/her biases.

Types of Observations

The observational methods are broadly categorized into the following types,

1. Naturalistic Observation

If the researcher has made the observations in real-life or natural settings such as schools, institutes, homes, open environments, etc., without interfering with the phenomena under observation, then it is known as naturalistic observation. In this type of observation, the researcher does not manipulate or control any situation, and he/she only records the spontaneous behavior of the subject (individual or event under investigation) in their natural environment. Naturalistic observations provide more generalized results because of the natural settings, but it’s difficult to manage the extraneous variables in natural observations and ethical issues of privacy interference and observer bias are some other major problems of naturalistic observations.

2. Controlled Observation

The observations that are conducted in the closed settings, i.e., their various conditions and variable are highly under control, are known as controlled observations. In these observations, variables are manipulated according to the need of the research. For example, if the researcher wants to study the effect of induced workload on the worker’s performance, the research should be conducted in a controlled setting as the researcher can control the independent variable (workload). However, due to the controlled settings approach, these observations are far less to ecological validity than the naturalistic observations, and the behavior of the participants or subjects that are being studied may change because of their awareness of being observed.

3. Participant Observation

The types of observation in which the observer or the researcher itself becomes part of the research are called participant observations. The other participants in the research may or may not be informed about the presence of the observer in the group. However, if the participants are not aware of the observer’s presence, then the results gathered will be more reliable and satisfy ecological validity. In participant observation as the researcher acts as an active member of the observed group, the observer has to be cautious about the fact that other members of the group won’t recognize him/her, and he/she should maintain the proper relationships and a good rapport with the participants under investigation. The strength of the participant observation is that it provides the researcher a holistic approach to understand the process not only from his/her own perspective but also from the participant’s perspective, which reduces the research biases. However, Participant observation is time-consuming, and the findings of this type of observation are usually not generalizable because of the small research groups.

4. Non-Participant Observation

In this type of research, the observer is not present in the research, but he/she uses other means to observe the spontaneous activities or behavior of the individual or group members, this may include installing the camera in the rooms that need to be observed. The main benefit of non-participants’ observation is that the actual behavior of the participants can be observed without making them aware of being under observation. An example of non-participation observation is a school principal who observes the classroom activities of the teacher and students through the CCTV cameras in his/her office.

3. Case Study

In the case study method, the researcher does qualitative research and in-depth analysis of a specific case (subject under investigation). The results obtained from this method are highly reliable; in fact, many famous theories such as the psychoanalytic theory of Sigmund Freud and Jean Piaget’s cognitive development theory are the results of well-structured and proper case studies of the subjects. The case study method allows the researcher to deeply study the psyche of the cases. The researcher does the case studies of the people or events that provide some critical information about the new or less discovered phenomena of the human mind. The number of cases can be one or more, or they are of different or same characteristics, for example, a patient suffering from a mental disorder, a group of people belonging to the same gender, class, or ethnicity, and effect on the people of various natural or man-made disasters such as flood, tsunami, terrorism, and industrialization. Case studies involve the multi-method approach as it uses various other research methods like unstructured interviews, psychological testings, and observations to get detailed information about the subjects. It is the best method to deeply understand and analyze the impact of certain traumatic events on the psychological health of the individual, and it is widely used by clinical psychologists to diagnose various psychological disorders of the patients.

4. Correlational Research

The researcher uses the correlational method if he/she wants to examine the relationship between the two variables. It is to be noted that here researcher does not vary the independent variable as he is only concerned about whether the two variables are linked to each other or not. For example, if you are interested in finding the relation between yoga and the psychological health of the person, then you simply try to find the relationship between these two factors rather than manipulating anything. The degree of the association between the variables is represented by the correlational coefficients ranges from +1.0 to -1.0. The correlation can be of three types, i.e., positive correlation, negative correlation, or zero correlation. If we increase or decrease the value of one variable, the value of another variable also increases or decreases respectively, then it is called a positive correlation, and the value of the correlation coefficient would be near +1.0. If we increase or decrease the value of one variable, the value of another variable decreases or increases respectively, then it is called the negative correlation, and the value of correlational coefficient would be near -1.0, and if the changes in the value of one variable do not affect the other variable, then there does not exist any relationship between the variables, and it is called zero correlation with the correlation value near or equal to zero.

5. Content Analysis

In content analysis research methods, the researcher analyses and quantifies various types of content pieces such as articles, texts, interviews, researches, and other important documents to get useful information about their area of research. Content analyses involve various steps that are data collection, examining the research data, and getting familiar with it, developing ṭhe set of rules for selecting coding units, making coding units (coding unit is the smallest parts of the content that is analyzed) as per the developed rules, and then, finally, analyzing the findings and drawing conclusions. Content analysis is generally of two types, i.e., conceptual analysis, and relational analysis. These are briefly discussed below.

Conceptual Analyses

It involves the selection of the concept (word, phrase, sentence), and then examining the occurrence of the selected concept in the available research data. In conceptual analyses, the researcher selects the sample according to the research question and divides the content into different categories, which makes it easier to focus on the specific data that gives useful information about the research, and then coding and analyzing the results.

Relational Analyses

The initial steps of the relational analyses are the same as the conceptual analyses like selecting the concept, but it’s different from the conceptual analyses because it involves finding the associations or relationships among the concepts. In conceptual analyses, we analyze every concept, but in relational analyses, the individual concepts do not have any importance, instead, the useful information is assessed by finding the associations among the concepts present in the research data.

6. Survey Research Method

Survey research is the most popular mean of data collection in almost every branch of social sciences. It finds its applications in election poll results (election surveys), literacy rate, and population rate analysis. The survey research methods help the researchers understand the actual ground reality of the event by analyzing the social views, attitudes, behavior, and opinions of the people. The researchers use various techniques of survey research methods, which are briefly discussed below.

1. Direct Interviews

An interview process involves direct communication between the interviewer/researcher (who asks the question) and the interviewee/respondent (who answers the questions). Interviews give better in-depth results than any other technique of data collection as the researcher gets first-hand information about the respondent’s mind through communication and observation of his/her behavior. Interviews may be structured or unstructured, when the researcher prepares the sequential list of the questions about when and what questions to be asked in the interview, it is called a structured interview, whereas if the questions to be asked in the interview are not pre-planned, and flexibility is provided to the interviewer to ask questions according to the situation, then it is called the unstructured interview. The responses to the questions in the case of structured interviews are also specified to some extent, such questions are called close-ended questions, while in the case of unstructured interviews, the respondent is free to answer the questions according to his/her desire, and these types of questions are called open-ended questions. For instance, if you ask the respondent whether he/she likes the coffee, then the answer would be either yes or no, i.e., a close-ended question. However, if you ask the respondents about their hobbies, then the respondent will answer it according to his/her will, hence it is an open-ended question. An interview can be of the following types, depending upon the number of interviewers and interviewees involved in the interview. For example,

• One to One Interview: When only the interviewer and one interviewee are present in the interview process.
• Individual to group Interview: When one interviewer interviews a group of people.
• Group to Individual: It is also called group panel interview, in this case, an individual is interviewed by a group of interviewers.
• Group to Group: When a group of interviewers, interviews a group of interviewees.

The most important thing in direct interviews is that the researcher/interviewer should have good interviewing skills, and the ability to build a good rapport with the respondent and making him/her comfortable enough to give accurate answers to the questions asked. The main purpose of conducting an interview is to gather the data about the subject, but the interviewer should be sensitive to the emotions and behavior of the respondent and should not pressurize him/her to give the answers to which he/she is not comfortable enough. The process of the interview is very time-consuming, so it is not much effective as in psychology researches, it would become tedious to take interviews of a large section of society, which is why it is usually preferred for some specific population that may include illiterate or blind people as the interviewer can verbally ask them questions and make sure that whether they understood the questions or not.

2. Telephonic or Digital Surveys

Telephonic surveys involve asking questions about the survey through direct calls or messages. Digital surveys through ‘Google forms’ are also commonly used these days. Telephone and digital surveys are easy to conduct, and they do not consume much time. However, they have many limitations such as the results obtained through them are not much reliable because in this method the researcher does not have proper evidence of certain factors like respondents’ age, gender, and qualifications, etc., and the respondents may have given the manipulative or vague answers.

3. Questionnaires

Questionnaires consist of a well-structured set of questions that are distributed to the people to mark or write the answers. The questions can be open-ended or close-ended, depending upon the type of survey. It is one of the most commonly used survey techniques as it is easy to conduct, less time-consuming, and a cost-effective method to collect research information. It is a better method than the interview for obtaining accurate answers because, in this method, the proper assurance of confidentiality is provided to the respondent, hence the respondent is more likely to mark the accurate answer. Earlier, only paper-based questionnaires were used, but due to the advancement of technology, digital questionnaires, which are sent to people through emails or google forms, are also used these days.

7. Psychological Testing

Psychological testing is also known as psychometrics. Psychological tests are scientifically proven and standardized tests that are constructed by psychologists. These are used to assess the various characteristics of humans such as attitude, aptitude, personality, intelligence quotient, and emotional quotient. There are many psychological tests available these days such as aptitude testing, mental health assessment, educational testing, personality assessment, etc., which are used for different purposes. The multiple-choice questions (MCQs) of the psychological tests are carefully designed, and the factors like gender, age, class, qualification, etc., are considered before conducting these tests. Psychological tests can be conducted offline (pen-paper-based) or online (digital format), depending upon the applicability and availability. The necessary part of the psychological tests is that the participants or the subjects, upon whom the test is conducted, should be properly informed about the testing procedure, and proper instructions about marking or filling the test, time durations of the test, should be verbally provided to them for their better understanding. These tests are constructed by following a systematic approach and three important factors, i.e., validity, reliability, and norms. These are briefly discussed below,

• Validity : The most obvious criterion of constructing the test is that it should be valid. The validity of the test implies that the test should measure what it is designed for. For example, the psychological health assessment test should measure the psychological health of the person rather than the physical health.
• Reliability : The results obtained by the psychological test should be reliable, i.e., there should be almost negligible variations in test scores if the same test is repeated upon the same subjects after some time.
• Norm : For every psychological test, norms are developed, these are the standard values that represent the average performance of the subject or the group of subjects in the tasks that are provided them. Norms enable psychologists to interpret and compare the results obtained by the psychological tests. There are various types of norms for different types of psychological tests such as descriptive norms,  grade norms, age norms, and percentile norms.

Rorschach Psychological Test

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Psychology: Definition, Types, Perspectives

Thank you for sharing this very useful knowledge for a psychology student (in this case). Very explanatory, clear to understand. successes.

Well explained 👏 Thanks

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How Does Experimental Psychology Study Behavior?

Purpose, methods, and history

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Sean is a fact-checker and researcher with experience in sociology, field research, and data analytics.

• Why It Matters

What factors influence people's behaviors and thoughts? Experimental psychology utilizes scientific methods to answer these questions by researching the mind and behavior. Experimental psychologists conduct experiments to learn more about why people do certain things.

Overview of Experimental Psychology

Why do people do the things they do? What factors influence how personality develops? And how do our behaviors and experiences shape our character?

These are just a few of the questions that psychologists explore, and experimental methods allow researchers to create and empirically test hypotheses. By studying such questions, researchers can also develop theories that enable them to describe, explain, predict, and even change human behaviors.

For example, researchers might utilize experimental methods to investigate why people engage in unhealthy behaviors. By learning more about the underlying reasons why these behaviors occur, researchers can then search for effective ways to help people avoid such actions or replace unhealthy choices with more beneficial ones.

Why Experimental Psychology Matters

While students are often required to take experimental psychology courses during undergraduate and graduate school , think about this subject as a methodology rather than a singular area within psychology. People in many subfields of psychology use these techniques to conduct research on everything from childhood development to social issues.

Experimental psychology is important because the findings play a vital role in our understanding of the human mind and behavior.

By better understanding exactly what makes people tick, psychologists and other mental health professionals can explore new approaches to treating psychological distress and mental illness. These are often topics of experimental psychology research.

Experimental Psychology Methods

So how exactly do researchers investigate the human mind and behavior? Because the mind is so complex, it seems like a challenging task to explore the many factors that contribute to how we think, act, and feel.

Experimental psychologists use a variety of different research methods and tools to investigate human behavior. Methods in the experimental psychology category include experiments, case studies, correlational research, and naturalistic observations.

Experiments

Experimentation remains the primary standard in psychological research. In some cases, psychologists can perform experiments to determine if there is a cause-and-effect relationship between different variables.

The basics of conducting a psychology experiment involve:

• Randomly assigning participants to groups
• Operationally defining variables
• Developing a hypothesis
• Manipulating independent variables
• Measuring dependent variables

One experimental psychology research example would be to perform a study to look at whether sleep deprivation impairs performance on a driving test. The experimenter could control other variables that might influence the outcome, varying the amount of sleep participants get the night before.

All of the participants would then take the same driving test via a simulator or on a controlled course. By analyzing the results, researchers can determine if changes in the independent variable (amount of sleep) led to differences in the dependent variable (performance on a driving test).

Case Studies

Case studies allow researchers to study an individual or group of people in great depth. When performing a case study, the researcher collects every single piece of data possible, often observing the person or group over a period of time and in a variety of situations. They also collect detailed information about their subject's background—including family history, education, work, and social life—is also collected.

Such studies are often performed in instances where experimentation is not possible. For example, a scientist might conduct a case study when the person of interest has had a unique or rare experience that could not be replicated in a lab.

Correlational Research

Correlational studies are an experimental psychology method that makes it possible for researchers to look at relationships between different variables. For example, a psychologist might note that as one variable increases, another tends to decrease.

While such studies can look at relationships, they cannot be used to imply causal relationships. The golden rule is that correlation does not equal causation.

Naturalistic Observations

Naturalistic observation gives researchers the opportunity to watch people in their natural environments. This experimental psychology method can be particularly useful in cases where the investigators believe that a lab setting might have an undue influence on participant behaviors.

What Experimental Psychologists Do

Experimental psychologists work in a wide variety of settings, including colleges, universities, research centers, government, and private businesses. Some of these professionals teach experimental methods to students while others conduct research on cognitive processes, animal behavior, neuroscience, personality, and other subject areas.

Those who work in academic settings often teach psychology courses in addition to performing research and publishing their findings in professional journals. Other experimental psychologists work with businesses to discover ways to make employees more productive or to create a safer workplace—a specialty area known as human factors psychology .

Experimental Psychology Research Examples

Some topics that might be explored in experimental psychology research include how music affects motivation, the impact social media has on mental health , and whether a certain color changes one's thoughts or perceptions.

History of Experimental Psychology

To understand how experimental psychology got where it is today, it can be helpful to look at how it originated. Psychology is a relatively young discipline, emerging in the late 1800s. While it started as part of philosophy and biology, it officially became its own field of study when early psychologist Wilhelm Wundt founded the first laboratory devoted to the study of experimental psychology.

Some of the important events that helped shape the field of experimental psychology include:

• 1874 - Wilhelm Wundt published the first experimental psychology textbook, "Grundzüge der physiologischen Psychologie" ("Principles of Physiological Psychology").
• 1875 - William James opened a psychology lab in the United States. The lab was created for the purpose of class demonstrations rather than to perform original experimental research.
• 1879 - The first experimental psychology lab was founded in Leipzig, Germany. Modern experimental psychology dates back to the establishment of the very first psychology lab by pioneering psychologist Wilhelm Wundt during the late nineteenth century.
• 1883 - G. Stanley Hall opened the first experimental psychology lab in the United States at John Hopkins University.
• 1885 - Herman Ebbinghaus published his famous "Über das Gedächtnis" ("On Memory"), which was later translated to English as "Memory: A Contribution to Experimental Psychology." In the work, Ebbinghaus described learning and memory experiments that he conducted on himself.
• 1887 - George Truball Ladd published his textbook "Elements of Physiological Psychology," the first American book to include a significant amount of information on experimental psychology.
• 1887 - James McKeen Cattell established the world's third experimental psychology lab at the University of Pennsylvania.
• 1890 - William James published his classic textbook, "The Principles of Psychology."
• 1891 - Mary Whiton Calkins established an experimental psychology lab at Wellesley College, becoming the first woman to form a psychology lab.
• 1893 - G. Stanley Hall founded the American Psychological Association , the largest professional and scientific organization of psychologists in the United States.
• 1920 - John B. Watson and Rosalie Rayner conducted their now-famous Little Albert Experiment , in which they demonstrated that emotional reactions could be classically conditioned in people.
• 1929 - Edwin Boring's book "A History of Experimental Psychology" was published. Boring was an influential experimental psychologist who was devoted to the use of experimental methods in psychology research.
• 1955 - Lee Cronbach published "Construct Validity in Psychological Tests," which popularized the use of construct validity in psychological studies.
• 1958 - Harry Harlow published "The Nature of Love," which described his experiments with rhesus monkeys on attachment and love.
• 1961 - Albert Bandura conducted his famous Bobo doll experiment, which demonstrated the effects of observation on aggressive behavior.

Experimental Psychology Uses

While experimental psychology is sometimes thought of as a separate branch or subfield of psychology, experimental methods are widely used throughout all areas of psychology.

• Developmental psychologists use experimental methods to study how people grow through childhood and over the course of a lifetime.
• Social psychologists use experimental techniques to study how people are influenced by groups.
• Health psychologists rely on experimentation and research to better understand the factors that contribute to wellness and disease.

A Word From Verywell

The experimental method in psychology helps us learn more about how people think and why they behave the way they do. Experimental psychologists can research a variety of topics using many different experimental methods. Each one contributes to what we know about the mind and human behavior.

Shaughnessy JJ, Zechmeister EB, Zechmeister JS. Research Methods in Psychology . McGraw-Hill.

Heale R, Twycross A. What is a case study? . Evid Based Nurs. 2018;21(1):7-8. doi:10.1136/eb-2017-102845

Chiang IA, Jhangiani RS, Price PC.  Correlational research . In: Research Methods in Psychology, 2nd Canadian edition. BCcampus Open Education.

Pierce T.  Naturalistic observation . Radford University.

Kantowitz BH, Roediger HL, Elmes DG. Experimental Psychology . Cengage Learning.

Weiner IB, Healy AF, Proctor RW. Handbook of Psychology: Volume 4, Experimental Psychology . John Wiley & Sons.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

19+ Experimental Design Examples (Methods + Types)

Ever wondered how scientists discover new medicines, psychologists learn about behavior, or even how marketers figure out what kind of ads you like? Well, they all have something in common: they use a special plan or recipe called an "experimental design."

Imagine you're baking cookies. You can't just throw random amounts of flour, sugar, and chocolate chips into a bowl and hope for the best. You follow a recipe, right? Scientists and researchers do something similar. They follow a "recipe" called an experimental design to make sure their experiments are set up in a way that the answers they find are meaningful and reliable.

Experimental design is the roadmap researchers use to answer questions. It's a set of rules and steps that researchers follow to collect information, or "data," in a way that is fair, accurate, and makes sense.

Long ago, people didn't have detailed game plans for experiments. They often just tried things out and saw what happened. But over time, people got smarter about this. They started creating structured plans—what we now call experimental designs—to get clearer, more trustworthy answers to their questions.

In this article, we'll take you on a journey through the world of experimental designs. We'll talk about the different types, or "flavors," of experimental designs, where they're used, and even give you a peek into how they came to be.

What Is Experimental Design?

Alright, before we dive into the different types of experimental designs, let's get crystal clear on what experimental design actually is.

Imagine you're a detective trying to solve a mystery. You need clues, right? Well, in the world of research, experimental design is like the roadmap that helps you find those clues. It's like the game plan in sports or the blueprint when you're building a house. Just like you wouldn't start building without a good blueprint, researchers won't start their studies without a strong experimental design.

So, why do we need experimental design? Think about baking a cake. If you toss ingredients into a bowl without measuring, you'll end up with a mess instead of a tasty dessert.

Similarly, in research, if you don't have a solid plan, you might get confusing or incorrect results. A good experimental design helps you ask the right questions ( think critically ), decide what to measure ( come up with an idea ), and figure out how to measure it (test it). It also helps you consider things that might mess up your results, like outside influences you hadn't thought of.

For example, let's say you want to find out if listening to music helps people focus better. Your experimental design would help you decide things like: Who are you going to test? What kind of music will you use? How will you measure focus? And, importantly, how will you make sure that it's really the music affecting focus and not something else, like the time of day or whether someone had a good breakfast?

In short, experimental design is the master plan that guides researchers through the process of collecting data, so they can answer questions in the most reliable way possible. It's like the GPS for the journey of discovery!

History of Experimental Design

Around 350 BCE, people like Aristotle were trying to figure out how the world works, but they mostly just thought really hard about things. They didn't test their ideas much. So while they were super smart, their methods weren't always the best for finding out the truth.

Fast forward to the Renaissance (14th to 17th centuries), a time of big changes and lots of curiosity. People like Galileo started to experiment by actually doing tests, like rolling balls down inclined planes to study motion. Galileo's work was cool because he combined thinking with doing. He'd have an idea, test it, look at the results, and then think some more. This approach was a lot more reliable than just sitting around and thinking.

Now, let's zoom ahead to the 18th and 19th centuries. This is when people like Francis Galton, an English polymath, started to get really systematic about experimentation. Galton was obsessed with measuring things. Seriously, he even tried to measure how good-looking people were ! His work helped create the foundations for a more organized approach to experiments.

Next stop: the early 20th century. Enter Ronald A. Fisher , a brilliant British statistician. Fisher was a game-changer. He came up with ideas that are like the bread and butter of modern experimental design.

Fisher invented the concept of the " control group "—that's a group of people or things that don't get the treatment you're testing, so you can compare them to those who do. He also stressed the importance of " randomization ," which means assigning people or things to different groups by chance, like drawing names out of a hat. This makes sure the experiment is fair and the results are trustworthy.

Around the same time, American psychologists like John B. Watson and B.F. Skinner were developing " behaviorism ." They focused on studying things that they could directly observe and measure, like actions and reactions.

Skinner even built boxes—called Skinner Boxes —to test how animals like pigeons and rats learn. Their work helped shape how psychologists design experiments today. Watson performed a very controversial experiment called The Little Albert experiment that helped describe behaviour through conditioning—in other words, how people learn to behave the way they do.

In the later part of the 20th century and into our time, computers have totally shaken things up. Researchers now use super powerful software to help design their experiments and crunch the numbers.

With computers, they can simulate complex experiments before they even start, which helps them predict what might happen. This is especially helpful in fields like medicine, where getting things right can be a matter of life and death.

Also, did you know that experimental designs aren't just for scientists in labs? They're used by people in all sorts of jobs, like marketing, education, and even video game design! Yes, someone probably ran an experiment to figure out what makes a game super fun to play.

So there you have it—a quick tour through the history of experimental design, from Aristotle's deep thoughts to Fisher's groundbreaking ideas, and all the way to today's computer-powered research. These designs are the recipes that help people from all walks of life find answers to their big questions.

Key Terms in Experimental Design

Before we dig into the different types of experimental designs, let's get comfy with some key terms. Understanding these terms will make it easier for us to explore the various types of experimental designs that researchers use to answer their big questions.

Independent Variable : This is what you change or control in your experiment to see what effect it has. Think of it as the "cause" in a cause-and-effect relationship. For example, if you're studying whether different types of music help people focus, the kind of music is the independent variable.

Dependent Variable : This is what you're measuring to see the effect of your independent variable. In our music and focus experiment, how well people focus is the dependent variable—it's what "depends" on the kind of music played.

Control Group : This is a group of people who don't get the special treatment or change you're testing. They help you see what happens when the independent variable is not applied. If you're testing whether a new medicine works, the control group would take a fake pill, called a placebo , instead of the real medicine.

Experimental Group : This is the group that gets the special treatment or change you're interested in. Going back to our medicine example, this group would get the actual medicine to see if it has any effect.

Randomization : This is like shaking things up in a fair way. You randomly put people into the control or experimental group so that each group is a good mix of different kinds of people. This helps make the results more reliable.

Sample : This is the group of people you're studying. They're a "sample" of a larger group that you're interested in. For instance, if you want to know how teenagers feel about a new video game, you might study a sample of 100 teenagers.

Bias : This is anything that might tilt your experiment one way or another without you realizing it. Like if you're testing a new kind of dog food and you only test it on poodles, that could create a bias because maybe poodles just really like that food and other breeds don't.

Data : This is the information you collect during the experiment. It's like the treasure you find on your journey of discovery!

Replication : This means doing the experiment more than once to make sure your findings hold up. It's like double-checking your answers on a test.

Hypothesis : This is your educated guess about what will happen in the experiment. It's like predicting the end of a movie based on the first half.

Steps of Experimental Design

Alright, let's say you're all fired up and ready to run your own experiment. Cool! But where do you start? Well, designing an experiment is a bit like planning a road trip. There are some key steps you've got to take to make sure you reach your destination. Let's break it down:

• Ask a Question : Before you hit the road, you've got to know where you're going. Same with experiments. You start with a question you want to answer, like "Does eating breakfast really make you do better in school?"
• Do Some Homework : Before you pack your bags, you look up the best places to visit, right? In science, this means reading up on what other people have already discovered about your topic.
• Form a Hypothesis : This is your educated guess about what you think will happen. It's like saying, "I bet this route will get us there faster."
• Plan the Details : Now you decide what kind of car you're driving (your experimental design), who's coming with you (your sample), and what snacks to bring (your variables).
• Randomization : Remember, this is like shuffling a deck of cards. You want to mix up who goes into your control and experimental groups to make sure it's a fair test.
• Run the Experiment : Finally, the rubber hits the road! You carry out your plan, making sure to collect your data carefully.
• Analyze the Data : Once the trip's over, you look at your photos and decide which ones are keepers. In science, this means looking at your data to see what it tells you.
• Draw Conclusions : Based on your data, did you find an answer to your question? This is like saying, "Yep, that route was faster," or "Nope, we hit a ton of traffic."
• Share Your Findings : After a great trip, you want to tell everyone about it, right? Scientists do the same by publishing their results so others can learn from them.
• Do It Again? : Sometimes one road trip just isn't enough. In the same way, scientists often repeat their experiments to make sure their findings are solid.

So there you have it! Those are the basic steps you need to follow when you're designing an experiment. Each step helps make sure that you're setting up a fair and reliable way to find answers to your big questions.

Let's get into examples of experimental designs.

1) True Experimental Design

In the world of experiments, the True Experimental Design is like the superstar quarterback everyone talks about. Born out of the early 20th-century work of statisticians like Ronald A. Fisher, this design is all about control, precision, and reliability.

Researchers carefully pick an independent variable to manipulate (remember, that's the thing they're changing on purpose) and measure the dependent variable (the effect they're studying). Then comes the magic trick—randomization. By randomly putting participants into either the control or experimental group, scientists make sure their experiment is as fair as possible.

No sneaky biases here!

True Experimental Design Pros

The pros of True Experimental Design are like the perks of a VIP ticket at a concert: you get the best and most trustworthy results. Because everything is controlled and randomized, you can feel pretty confident that the results aren't just a fluke.

True Experimental Design Cons

However, there's a catch. Sometimes, it's really tough to set up these experiments in a real-world situation. Imagine trying to control every single detail of your day, from the food you eat to the air you breathe. Not so easy, right?

True Experimental Design Uses

The fields that get the most out of True Experimental Designs are those that need super reliable results, like medical research.

When scientists were developing COVID-19 vaccines, they used this design to run clinical trials. They had control groups that received a placebo (a harmless substance with no effect) and experimental groups that got the actual vaccine. Then they measured how many people in each group got sick. By comparing the two, they could say, "Yep, this vaccine works!"

So next time you read about a groundbreaking discovery in medicine or technology, chances are a True Experimental Design was the VIP behind the scenes, making sure everything was on point. It's been the go-to for rigorous scientific inquiry for nearly a century, and it's not stepping off the stage anytime soon.

2) Quasi-Experimental Design

So, let's talk about the Quasi-Experimental Design. Think of this one as the cool cousin of True Experimental Design. It wants to be just like its famous relative, but it's a bit more laid-back and flexible. You'll find quasi-experimental designs when it's tricky to set up a full-blown True Experimental Design with all the bells and whistles.

Quasi-experiments still play with an independent variable, just like their stricter cousins. The big difference? They don't use randomization. It's like wanting to divide a bag of jelly beans equally between your friends, but you can't quite do it perfectly.

In real life, it's often not possible or ethical to randomly assign people to different groups, especially when dealing with sensitive topics like education or social issues. And that's where quasi-experiments come in.

Quasi-Experimental Design Pros

Even though they lack full randomization, quasi-experimental designs are like the Swiss Army knives of research: versatile and practical. They're especially popular in fields like education, sociology, and public policy.

For instance, when researchers wanted to figure out if the Head Start program , aimed at giving young kids a "head start" in school, was effective, they used a quasi-experimental design. They couldn't randomly assign kids to go or not go to preschool, but they could compare kids who did with kids who didn't.

Quasi-Experimental Design Cons

Of course, quasi-experiments come with their own bag of pros and cons. On the plus side, they're easier to set up and often cheaper than true experiments. But the flip side is that they're not as rock-solid in their conclusions. Because the groups aren't randomly assigned, there's always that little voice saying, "Hey, are we missing something here?"

Quasi-Experimental Design Uses

Quasi-Experimental Design gained traction in the mid-20th century. Researchers were grappling with real-world problems that didn't fit neatly into a laboratory setting. Plus, as society became more aware of ethical considerations, the need for flexible designs increased. So, the quasi-experimental approach was like a breath of fresh air for scientists wanting to study complex issues without a laundry list of restrictions.

In short, if True Experimental Design is the superstar quarterback, Quasi-Experimental Design is the versatile player who can adapt and still make significant contributions to the game.

3) Pre-Experimental Design

Now, let's talk about the Pre-Experimental Design. Imagine it as the beginner's skateboard you get before you try out for all the cool tricks. It has wheels, it rolls, but it's not built for the professional skatepark.

Similarly, pre-experimental designs give researchers a starting point. They let you dip your toes in the water of scientific research without diving in head-first.

So, what's the deal with pre-experimental designs?

Pre-Experimental Designs are the basic, no-frills versions of experiments. Researchers still mess around with an independent variable and measure a dependent variable, but they skip over the whole randomization thing and often don't even have a control group.

It's like baking a cake but forgetting the frosting and sprinkles; you'll get some results, but they might not be as complete or reliable as you'd like.

Pre-Experimental Design Pros

Why use such a simple setup? Because sometimes, you just need to get the ball rolling. Pre-experimental designs are great for quick-and-dirty research when you're short on time or resources. They give you a rough idea of what's happening, which you can use to plan more detailed studies later.

A good example of this is early studies on the effects of screen time on kids. Researchers couldn't control every aspect of a child's life, but they could easily ask parents to track how much time their kids spent in front of screens and then look for trends in behavior or school performance.

Pre-Experimental Design Cons

But here's the catch: pre-experimental designs are like that first draft of an essay. It helps you get your ideas down, but you wouldn't want to turn it in for a grade. Because these designs lack the rigorous structure of true or quasi-experimental setups, they can't give you rock-solid conclusions. They're more like clues or signposts pointing you in a certain direction.

Pre-Experimental Design Uses

This type of design became popular in the early stages of various scientific fields. Researchers used them to scratch the surface of a topic, generate some initial data, and then decide if it's worth exploring further. In other words, pre-experimental designs were the stepping stones that led to more complex, thorough investigations.

So, while Pre-Experimental Design may not be the star player on the team, it's like the practice squad that helps everyone get better. It's the starting point that can lead to bigger and better things.

4) Factorial Design

Now, buckle up, because we're moving into the world of Factorial Design, the multi-tasker of the experimental universe.

Imagine juggling not just one, but multiple balls in the air—that's what researchers do in a factorial design.

In Factorial Design, researchers are not satisfied with just studying one independent variable. Nope, they want to study two or more at the same time to see how they interact.

It's like cooking with several spices to see how they blend together to create unique flavors.

Factorial Design became the talk of the town with the rise of computers. Why? Because this design produces a lot of data, and computers are the number crunchers that help make sense of it all. So, thanks to our silicon friends, researchers can study complicated questions like, "How do diet AND exercise together affect weight loss?" instead of looking at just one of those factors.

Factorial Design Pros

This design's main selling point is its ability to explore interactions between variables. For instance, maybe a new study drug works really well for young people but not so great for older adults. A factorial design could reveal that age is a crucial factor, something you might miss if you only studied the drug's effectiveness in general. It's like being a detective who looks for clues not just in one room but throughout the entire house.

Factorial Design Cons

However, factorial designs have their own bag of challenges. First off, they can be pretty complicated to set up and run. Imagine coordinating a four-way intersection with lots of cars coming from all directions—you've got to make sure everything runs smoothly, or you'll end up with a traffic jam. Similarly, researchers need to carefully plan how they'll measure and analyze all the different variables.

Factorial Design Uses

Factorial designs are widely used in psychology to untangle the web of factors that influence human behavior. They're also popular in fields like marketing, where companies want to understand how different aspects like price, packaging, and advertising influence a product's success.

And speaking of success, the factorial design has been a hit since statisticians like Ronald A. Fisher (yep, him again!) expanded on it in the early-to-mid 20th century. It offered a more nuanced way of understanding the world, proving that sometimes, to get the full picture, you've got to juggle more than one ball at a time.

So, if True Experimental Design is the quarterback and Quasi-Experimental Design is the versatile player, Factorial Design is the strategist who sees the entire game board and makes moves accordingly.

5) Longitudinal Design

Alright, let's take a step into the world of Longitudinal Design. Picture it as the grand storyteller, the kind who doesn't just tell you about a single event but spins an epic tale that stretches over years or even decades. This design isn't about quick snapshots; it's about capturing the whole movie of someone's life or a long-running process.

You know how you might take a photo every year on your birthday to see how you've changed? Longitudinal Design is kind of like that, but for scientific research.

With Longitudinal Design, instead of measuring something just once, researchers come back again and again, sometimes over many years, to see how things are going. This helps them understand not just what's happening, but why it's happening and how it changes over time.

This design really started to shine in the latter half of the 20th century, when researchers began to realize that some questions can't be answered in a hurry. Think about studies that look at how kids grow up, or research on how a certain medicine affects you over a long period. These aren't things you can rush.

The famous Framingham Heart Study , started in 1948, is a prime example. It's been studying heart health in a small town in Massachusetts for decades, and the findings have shaped what we know about heart disease.

Longitudinal Design Pros

So, what's to love about Longitudinal Design? First off, it's the go-to for studying change over time, whether that's how people age or how a forest recovers from a fire.

Longitudinal Design Cons

But it's not all sunshine and rainbows. Longitudinal studies take a lot of patience and resources. Plus, keeping track of participants over many years can be like herding cats—difficult and full of surprises.

Longitudinal Design Uses

Despite these challenges, longitudinal studies have been key in fields like psychology, sociology, and medicine. They provide the kind of deep, long-term insights that other designs just can't match.

So, if the True Experimental Design is the superstar quarterback, and the Quasi-Experimental Design is the flexible athlete, then the Factorial Design is the strategist, and the Longitudinal Design is the wise elder who has seen it all and has stories to tell.

6) Cross-Sectional Design

Now, let's flip the script and talk about Cross-Sectional Design, the polar opposite of the Longitudinal Design. If Longitudinal is the grand storyteller, think of Cross-Sectional as the snapshot photographer. It captures a single moment in time, like a selfie that you take to remember a fun day. Researchers using this design collect all their data at one point, providing a kind of "snapshot" of whatever they're studying.

In a Cross-Sectional Design, researchers look at multiple groups all at the same time to see how they're different or similar.

This design rose to popularity in the mid-20th century, mainly because it's so quick and efficient. Imagine wanting to know how people of different ages feel about a new video game. Instead of waiting for years to see how opinions change, you could just ask people of all ages what they think right now. That's Cross-Sectional Design for you—fast and straightforward.

You'll find this type of research everywhere from marketing studies to healthcare. For instance, you might have heard about surveys asking people what they think about a new product or political issue. Those are usually cross-sectional studies, aimed at getting a quick read on public opinion.

Cross-Sectional Design Pros

So, what's the big deal with Cross-Sectional Design? Well, it's the go-to when you need answers fast and don't have the time or resources for a more complicated setup.

Cross-Sectional Design Cons

Remember, speed comes with trade-offs. While you get your results quickly, those results are stuck in time. They can't tell you how things change or why they're changing, just what's happening right now.

Cross-Sectional Design Uses

Also, because they're so quick and simple, cross-sectional studies often serve as the first step in research. They give scientists an idea of what's going on so they can decide if it's worth digging deeper. In that way, they're a bit like a movie trailer, giving you a taste of the action to see if you're interested in seeing the whole film.

So, in our lineup of experimental designs, if True Experimental Design is the superstar quarterback and Longitudinal Design is the wise elder, then Cross-Sectional Design is like the speedy running back—fast, agile, but not designed for long, drawn-out plays.

7) Correlational Design

Next on our roster is the Correlational Design, the keen observer of the experimental world. Imagine this design as the person at a party who loves people-watching. They don't interfere or get involved; they just observe and take mental notes about what's going on.

In a correlational study, researchers don't change or control anything; they simply observe and measure how two variables relate to each other.

The correlational design has roots in the early days of psychology and sociology. Pioneers like Sir Francis Galton used it to study how qualities like intelligence or height could be related within families.

This design is all about asking, "Hey, when this thing happens, does that other thing usually happen too?" For example, researchers might study whether students who have more study time get better grades or whether people who exercise more have lower stress levels.

One of the most famous correlational studies you might have heard of is the link between smoking and lung cancer. Back in the mid-20th century, researchers started noticing that people who smoked a lot also seemed to get lung cancer more often. They couldn't say smoking caused cancer—that would require a true experiment—but the strong correlation was a red flag that led to more research and eventually, health warnings.

Correlational Design Pros

This design is great at proving that two (or more) things can be related. Correlational designs can help prove that more detailed research is needed on a topic. They can help us see patterns or possible causes for things that we otherwise might not have realized.

Correlational Design Cons

But here's where you need to be careful: correlational designs can be tricky. Just because two things are related doesn't mean one causes the other. That's like saying, "Every time I wear my lucky socks, my team wins." Well, it's a fun thought, but those socks aren't really controlling the game.

Correlational Design Uses

Despite this limitation, correlational designs are popular in psychology, economics, and epidemiology, to name a few fields. They're often the first step in exploring a possible relationship between variables. Once a strong correlation is found, researchers may decide to conduct more rigorous experimental studies to examine cause and effect.

So, if the True Experimental Design is the superstar quarterback and the Longitudinal Design is the wise elder, the Factorial Design is the strategist, and the Cross-Sectional Design is the speedster, then the Correlational Design is the clever scout, identifying interesting patterns but leaving the heavy lifting of proving cause and effect to the other types of designs.

8) Meta-Analysis

Last but not least, let's talk about Meta-Analysis, the librarian of experimental designs.

If other designs are all about creating new research, Meta-Analysis is about gathering up everyone else's research, sorting it, and figuring out what it all means when you put it together.

Imagine a jigsaw puzzle where each piece is a different study. Meta-Analysis is the process of fitting all those pieces together to see the big picture.

The concept of Meta-Analysis started to take shape in the late 20th century, when computers became powerful enough to handle massive amounts of data. It was like someone handed researchers a super-powered magnifying glass, letting them examine multiple studies at the same time to find common trends or results.

You might have heard of the Cochrane Reviews in healthcare . These are big collections of meta-analyses that help doctors and policymakers figure out what treatments work best based on all the research that's been done.

For example, if ten different studies show that a certain medicine helps lower blood pressure, a meta-analysis would pull all that information together to give a more accurate answer.

Meta-Analysis Pros

The beauty of Meta-Analysis is that it can provide really strong evidence. Instead of relying on one study, you're looking at the whole landscape of research on a topic.

Meta-Analysis Cons

However, it does have some downsides. For one, Meta-Analysis is only as good as the studies it includes. If those studies are flawed, the meta-analysis will be too. It's like baking a cake: if you use bad ingredients, it doesn't matter how good your recipe is—the cake won't turn out well.

Meta-Analysis Uses

Despite these challenges, meta-analyses are highly respected and widely used in many fields like medicine, psychology, and education. They help us make sense of a world that's bursting with information by showing us the big picture drawn from many smaller snapshots.

So, in our all-star lineup, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, the Factorial Design is the strategist, the Cross-Sectional Design is the speedster, and the Correlational Design is the scout, then the Meta-Analysis is like the coach, using insights from everyone else's plays to come up with the best game plan.

9) Non-Experimental Design

Now, let's talk about a player who's a bit of an outsider on this team of experimental designs—the Non-Experimental Design. Think of this design as the commentator or the journalist who covers the game but doesn't actually play.

In a Non-Experimental Design, researchers are like reporters gathering facts, but they don't interfere or change anything. They're simply there to describe and analyze.

Non-Experimental Design Pros

So, what's the deal with Non-Experimental Design? Its strength is in description and exploration. It's really good for studying things as they are in the real world, without changing any conditions.

Non-Experimental Design Cons

Because a non-experimental design doesn't manipulate variables, it can't prove cause and effect. It's like a weather reporter: they can tell you it's raining, but they can't tell you why it's raining.

The downside? Since researchers aren't controlling variables, it's hard to rule out other explanations for what they observe. It's like hearing one side of a story—you get an idea of what happened, but it might not be the complete picture.

Non-Experimental Design Uses

Non-Experimental Design has always been a part of research, especially in fields like anthropology, sociology, and some areas of psychology.

For instance, if you've ever heard of studies that describe how people behave in different cultures or what teens like to do in their free time, that's often Non-Experimental Design at work. These studies aim to capture the essence of a situation, like painting a portrait instead of taking a snapshot.

One well-known example you might have heard about is the Kinsey Reports from the 1940s and 1950s, which described sexual behavior in men and women. Researchers interviewed thousands of people but didn't manipulate any variables like you would in a true experiment. They simply collected data to create a comprehensive picture of the subject matter.

So, in our metaphorical team of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, and Meta-Analysis is the coach, then Non-Experimental Design is the sports journalist—always present, capturing the game, but not part of the action itself.

10) Repeated Measures Design

Time to meet the Repeated Measures Design, the time traveler of our research team. If this design were a player in a sports game, it would be the one who keeps revisiting past plays to figure out how to improve the next one.

Repeated Measures Design is all about studying the same people or subjects multiple times to see how they change or react under different conditions.

The idea behind Repeated Measures Design isn't new; it's been around since the early days of psychology and medicine. You could say it's a cousin to the Longitudinal Design, but instead of looking at how things naturally change over time, it focuses on how the same group reacts to different things.

Imagine a study looking at how a new energy drink affects people's running speed. Instead of comparing one group that drank the energy drink to another group that didn't, a Repeated Measures Design would have the same group of people run multiple times—once with the energy drink, and once without. This way, you're really zeroing in on the effect of that energy drink, making the results more reliable.

Repeated Measures Design Pros

The strong point of Repeated Measures Design is that it's super focused. Because it uses the same subjects, you don't have to worry about differences between groups messing up your results.

Repeated Measures Design Cons

But the downside? Well, people can get tired or bored if they're tested too many times, which might affect how they respond.

Repeated Measures Design Uses

A famous example of this design is the "Little Albert" experiment, conducted by John B. Watson and Rosalie Rayner in 1920. In this study, a young boy was exposed to a white rat and other stimuli several times to see how his emotional responses changed. Though the ethical standards of this experiment are often criticized today, it was groundbreaking in understanding conditioned emotional responses.

In our metaphorical lineup of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, and Non-Experimental Design is the journalist, then Repeated Measures Design is the time traveler—always looping back to fine-tune the game plan.

11) Crossover Design

Next up is Crossover Design, the switch-hitter of the research world. If you're familiar with baseball, you'll know a switch-hitter is someone who can bat both right-handed and left-handed.

In a similar way, Crossover Design allows subjects to experience multiple conditions, flipping them around so that everyone gets a turn in each role.

This design is like the utility player on our team—versatile, flexible, and really good at adapting.

The Crossover Design has its roots in medical research and has been popular since the mid-20th century. It's often used in clinical trials to test the effectiveness of different treatments.

Crossover Design Pros

The neat thing about this design is that it allows each participant to serve as their own control group. Imagine you're testing two new kinds of headache medicine. Instead of giving one type to one group and another type to a different group, you'd give both kinds to the same people but at different times.

Crossover Design Cons

What's the big deal with Crossover Design? Its major strength is in reducing the "noise" that comes from individual differences. Since each person experiences all conditions, it's easier to see real effects. However, there's a catch. This design assumes that there's no lasting effect from the first condition when you switch to the second one. That might not always be true. If the first treatment has a long-lasting effect, it could mess up the results when you switch to the second treatment.

Crossover Design Uses

A well-known example of Crossover Design is in studies that look at the effects of different types of diets—like low-carb vs. low-fat diets. Researchers might have participants follow a low-carb diet for a few weeks, then switch them to a low-fat diet. By doing this, they can more accurately measure how each diet affects the same group of people.

In our team of experimental designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, and Repeated Measures Design is the time traveler, then Crossover Design is the versatile utility player—always ready to adapt and play multiple roles to get the most accurate results.

12) Cluster Randomized Design

Meet the Cluster Randomized Design, the team captain of group-focused research. In our imaginary lineup of experimental designs, if other designs focus on individual players, then Cluster Randomized Design is looking at how the entire team functions.

This approach is especially common in educational and community-based research, and it's been gaining traction since the late 20th century.

Here's how Cluster Randomized Design works: Instead of assigning individual people to different conditions, researchers assign entire groups, or "clusters." These could be schools, neighborhoods, or even entire towns. This helps you see how the new method works in a real-world setting.

Imagine you want to see if a new anti-bullying program really works. Instead of selecting individual students, you'd introduce the program to a whole school or maybe even several schools, and then compare the results to schools without the program.

Cluster Randomized Design Pros

Why use Cluster Randomized Design? Well, sometimes it's just not practical to assign conditions at the individual level. For example, you can't really have half a school following a new reading program while the other half sticks with the old one; that would be way too confusing! Cluster Randomization helps get around this problem by treating each "cluster" as its own mini-experiment.

Cluster Randomized Design Cons

There's a downside, too. Because entire groups are assigned to each condition, there's a risk that the groups might be different in some important way that the researchers didn't account for. That's like having one sports team that's full of veterans playing against a team of rookies; the match wouldn't be fair.

Cluster Randomized Design Uses

A famous example is the research conducted to test the effectiveness of different public health interventions, like vaccination programs. Researchers might roll out a vaccination program in one community but not in another, then compare the rates of disease in both.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, and Crossover Design is the utility player, then Cluster Randomized Design is the team captain—always looking out for the group as a whole.

13) Mixed-Methods Design

Say hello to Mixed-Methods Design, the all-rounder or the "Renaissance player" of our research team.

Mixed-Methods Design uses a blend of both qualitative and quantitative methods to get a more complete picture, just like a Renaissance person who's good at lots of different things. It's like being good at both offense and defense in a sport; you've got all your bases covered!

Mixed-Methods Design is a fairly new kid on the block, becoming more popular in the late 20th and early 21st centuries as researchers began to see the value in using multiple approaches to tackle complex questions. It's the Swiss Army knife in our research toolkit, combining the best parts of other designs to be more versatile.

Here's how it could work: Imagine you're studying the effects of a new educational app on students' math skills. You might use quantitative methods like tests and grades to measure how much the students improve—that's the 'numbers part.'

But you also want to know how the students feel about math now, or why they think they got better or worse. For that, you could conduct interviews or have students fill out journals—that's the 'story part.'

Mixed-Methods Design Pros

So, what's the scoop on Mixed-Methods Design? The strength is its versatility and depth; you're not just getting numbers or stories, you're getting both, which gives a fuller picture.

Mixed-Methods Design Cons

But, it's also more challenging. Imagine trying to play two sports at the same time! You have to be skilled in different research methods and know how to combine them effectively.

Mixed-Methods Design Uses

A high-profile example of Mixed-Methods Design is research on climate change. Scientists use numbers and data to show temperature changes (quantitative), but they also interview people to understand how these changes are affecting communities (qualitative).

In our team of experimental designs, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, and Cluster Randomized Design is the team captain, then Mixed-Methods Design is the Renaissance player—skilled in multiple areas and able to bring them all together for a winning strategy.

14) Multivariate Design

Now, let's turn our attention to Multivariate Design, the multitasker of the research world.

If our lineup of research designs were like players on a basketball court, Multivariate Design would be the player dribbling, passing, and shooting all at once. This design doesn't just look at one or two things; it looks at several variables simultaneously to see how they interact and affect each other.

Multivariate Design is like baking a cake with many ingredients. Instead of just looking at how flour affects the cake, you also consider sugar, eggs, and milk all at once. This way, you understand how everything works together to make the cake taste good or bad.

Multivariate Design has been a go-to method in psychology, economics, and social sciences since the latter half of the 20th century. With the advent of computers and advanced statistical software, analyzing multiple variables at once became a lot easier, and Multivariate Design soared in popularity.

Multivariate Design Pros

So, what's the benefit of using Multivariate Design? Its power lies in its complexity. By studying multiple variables at the same time, you can get a really rich, detailed understanding of what's going on.

Multivariate Design Cons

But that complexity can also be a drawback. With so many variables, it can be tough to tell which ones are really making a difference and which ones are just along for the ride.

Multivariate Design Uses

Imagine you're a coach trying to figure out the best strategy to win games. You wouldn't just look at how many points your star player scores; you'd also consider assists, rebounds, turnovers, and maybe even how loud the crowd is. A Multivariate Design would help you understand how all these factors work together to determine whether you win or lose.

A well-known example of Multivariate Design is in market research. Companies often use this approach to figure out how different factors—like price, packaging, and advertising—affect sales. By studying multiple variables at once, they can find the best combination to boost profits.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, Cluster Randomized Design is the team captain, and Mixed-Methods Design is the Renaissance player, then Multivariate Design is the multitasker—juggling many variables at once to get a fuller picture of what's happening.

15) Pretest-Posttest Design

Let's introduce Pretest-Posttest Design, the "Before and After" superstar of our research team. You've probably seen those before-and-after pictures in ads for weight loss programs or home renovations, right?

Well, this design is like that, but for science! Pretest-Posttest Design checks out what things are like before the experiment starts and then compares that to what things are like after the experiment ends.

This design is one of the classics, a staple in research for decades across various fields like psychology, education, and healthcare. It's so simple and straightforward that it has stayed popular for a long time.

In Pretest-Posttest Design, you measure your subject's behavior or condition before you introduce any changes—that's your "before" or "pretest." Then you do your experiment, and after it's done, you measure the same thing again—that's your "after" or "posttest."

Pretest-Posttest Design Pros

What makes Pretest-Posttest Design special? It's pretty easy to understand and doesn't require fancy statistics.

Pretest-Posttest Design Cons

But there are some pitfalls. For example, what if the kids in our math example get better at multiplication just because they're older or because they've taken the test before? That would make it hard to tell if the program is really effective or not.

Pretest-Posttest Design Uses

Let's say you're a teacher and you want to know if a new math program helps kids get better at multiplication. First, you'd give all the kids a multiplication test—that's your pretest. Then you'd teach them using the new math program. At the end, you'd give them the same test again—that's your posttest. If the kids do better on the second test, you might conclude that the program works.

One famous use of Pretest-Posttest Design is in evaluating the effectiveness of driver's education courses. Researchers will measure people's driving skills before and after the course to see if they've improved.

16) Solomon Four-Group Design

Next up is the Solomon Four-Group Design, the "chess master" of our research team. This design is all about strategy and careful planning. Named after Richard L. Solomon who introduced it in the 1940s, this method tries to correct some of the weaknesses in simpler designs, like the Pretest-Posttest Design.

Here's how it rolls: The Solomon Four-Group Design uses four different groups to test a hypothesis. Two groups get a pretest, then one of them receives the treatment or intervention, and both get a posttest. The other two groups skip the pretest, and only one of them receives the treatment before they both get a posttest.

Sound complicated? It's like playing 4D chess; you're thinking several moves ahead!

Solomon Four-Group Design Pros

What's the pro and con of the Solomon Four-Group Design? On the plus side, it provides really robust results because it accounts for so many variables.

Solomon Four-Group Design Cons

The downside? It's a lot of work and requires a lot of participants, making it more time-consuming and costly.

Solomon Four-Group Design Uses

Let's say you want to figure out if a new way of teaching history helps students remember facts better. Two classes take a history quiz (pretest), then one class uses the new teaching method while the other sticks with the old way. Both classes take another quiz afterward (posttest).

Meanwhile, two more classes skip the initial quiz, and then one uses the new method before both take the final quiz. Comparing all four groups will give you a much clearer picture of whether the new teaching method works and whether the pretest itself affects the outcome.

The Solomon Four-Group Design is less commonly used than simpler designs but is highly respected for its ability to control for more variables. It's a favorite in educational and psychological research where you really want to dig deep and figure out what's actually causing changes.

17) Adaptive Designs

Now, let's talk about Adaptive Designs, the chameleons of the experimental world.

Imagine you're a detective, and halfway through solving a case, you find a clue that changes everything. You wouldn't just stick to your old plan; you'd adapt and change your approach, right? That's exactly what Adaptive Designs allow researchers to do.

In an Adaptive Design, researchers can make changes to the study as it's happening, based on early results. In a traditional study, once you set your plan, you stick to it from start to finish.

Adaptive Design Pros

This method is particularly useful in fast-paced or high-stakes situations, like developing a new vaccine in the middle of a pandemic. The ability to adapt can save both time and resources, and more importantly, it can save lives by getting effective treatments out faster.

Adaptive Design Cons

But Adaptive Designs aren't without their drawbacks. They can be very complex to plan and carry out, and there's always a risk that the changes made during the study could introduce bias or errors.

Adaptive Design Uses

Adaptive Designs are most often seen in clinical trials, particularly in the medical and pharmaceutical fields.

For instance, if a new drug is showing really promising results, the study might be adjusted to give more participants the new treatment instead of a placebo. Or if one dose level is showing bad side effects, it might be dropped from the study.

The best part is, these changes are pre-planned. Researchers lay out in advance what changes might be made and under what conditions, which helps keep everything scientific and above board.

In terms of applications, besides their heavy usage in medical and pharmaceutical research, Adaptive Designs are also becoming increasingly popular in software testing and market research. In these fields, being able to quickly adjust to early results can give companies a significant advantage.

Adaptive Designs are like the agile startups of the research world—quick to pivot, keen to learn from ongoing results, and focused on rapid, efficient progress. However, they require a great deal of expertise and careful planning to ensure that the adaptability doesn't compromise the integrity of the research.

18) Bayesian Designs

Next, let's dive into Bayesian Designs, the data detectives of the research universe. Named after Thomas Bayes, an 18th-century statistician and minister, this design doesn't just look at what's happening now; it also takes into account what's happened before.

Imagine if you were a detective who not only looked at the evidence in front of you but also used your past cases to make better guesses about your current one. That's the essence of Bayesian Designs.

Bayesian Designs are like detective work in science. As you gather more clues (or data), you update your best guess on what's really happening. This way, your experiment gets smarter as it goes along.

In the world of research, Bayesian Designs are most notably used in areas where you have some prior knowledge that can inform your current study. For example, if earlier research shows that a certain type of medicine usually works well for a specific illness, a Bayesian Design would include that information when studying a new group of patients with the same illness.

Bayesian Design Pros

One of the major advantages of Bayesian Designs is their efficiency. Because they use existing data to inform the current experiment, often fewer resources are needed to reach a reliable conclusion.

Bayesian Design Cons

However, they can be quite complicated to set up and require a deep understanding of both statistics and the subject matter at hand.

Bayesian Design Uses

Bayesian Designs are highly valued in medical research, finance, environmental science, and even in Internet search algorithms. Their ability to continually update and refine hypotheses based on new evidence makes them particularly useful in fields where data is constantly evolving and where quick, informed decisions are crucial.

Here's a real-world example: In the development of personalized medicine, where treatments are tailored to individual patients, Bayesian Designs are invaluable. If a treatment has been effective for patients with similar genetics or symptoms in the past, a Bayesian approach can use that data to predict how well it might work for a new patient.

This type of design is also increasingly popular in machine learning and artificial intelligence. In these fields, Bayesian Designs help algorithms "learn" from past data to make better predictions or decisions in new situations. It's like teaching a computer to be a detective that gets better and better at solving puzzles the more puzzles it sees.

19) Covariate Adaptive Randomization

Now let's turn our attention to Covariate Adaptive Randomization, which you can think of as the "matchmaker" of experimental designs.

Picture a soccer coach trying to create the most balanced teams for a friendly match. They wouldn't just randomly assign players; they'd take into account each player's skills, experience, and other traits.

Covariate Adaptive Randomization is all about creating the most evenly matched groups possible for an experiment.

In traditional randomization, participants are allocated to different groups purely by chance. This is a pretty fair way to do things, but it can sometimes lead to unbalanced groups.

Imagine if all the professional-level players ended up on one soccer team and all the beginners on another; that wouldn't be a very informative match! Covariate Adaptive Randomization fixes this by using important traits or characteristics (called "covariates") to guide the randomization process.

Covariate Adaptive Randomization Pros

The benefits of this design are pretty clear: it aims for balance and fairness, making the final results more trustworthy.

Covariate Adaptive Randomization Cons

But it's not perfect. It can be complex to implement and requires a deep understanding of which characteristics are most important to balance.

Covariate Adaptive Randomization Uses

This design is particularly useful in medical trials. Let's say researchers are testing a new medication for high blood pressure. Participants might have different ages, weights, or pre-existing conditions that could affect the results.

Covariate Adaptive Randomization would make sure that each treatment group has a similar mix of these characteristics, making the results more reliable and easier to interpret.

In practical terms, this design is often seen in clinical trials for new drugs or therapies, but its principles are also applicable in fields like psychology, education, and social sciences.

For instance, in educational research, it might be used to ensure that classrooms being compared have similar distributions of students in terms of academic ability, socioeconomic status, and other factors.

Covariate Adaptive Randomization is like the wise elder of the group, ensuring that everyone has an equal opportunity to show their true capabilities, thereby making the collective results as reliable as possible.

20) Stepped Wedge Design

Let's now focus on the Stepped Wedge Design, a thoughtful and cautious member of the experimental design family.

Imagine you're trying out a new gardening technique, but you're not sure how well it will work. You decide to apply it to one section of your garden first, watch how it performs, and then gradually extend the technique to other sections. This way, you get to see its effects over time and across different conditions. That's basically how Stepped Wedge Design works.

In a Stepped Wedge Design, all participants or clusters start off in the control group, and then, at different times, they 'step' over to the intervention or treatment group. This creates a wedge-like pattern over time where more and more participants receive the treatment as the study progresses. It's like rolling out a new policy in phases, monitoring its impact at each stage before extending it to more people.

Stepped Wedge Design Pros

The Stepped Wedge Design offers several advantages. Firstly, it allows for the study of interventions that are expected to do more good than harm, which makes it ethically appealing.

Secondly, it's useful when resources are limited and it's not feasible to roll out a new treatment to everyone at once. Lastly, because everyone eventually receives the treatment, it can be easier to get buy-in from participants or organizations involved in the study.

Stepped Wedge Design Cons

However, this design can be complex to analyze because it has to account for both the time factor and the changing conditions in each 'step' of the wedge. And like any study where participants know they're receiving an intervention, there's the potential for the results to be influenced by the placebo effect or other biases.

Stepped Wedge Design Uses

This design is particularly useful in health and social care research. For instance, if a hospital wants to implement a new hygiene protocol, it might start in one department, assess its impact, and then roll it out to other departments over time. This allows the hospital to adjust and refine the new protocol based on real-world data before it's fully implemented.

In terms of applications, Stepped Wedge Designs are commonly used in public health initiatives, organizational changes in healthcare settings, and social policy trials. They are particularly useful in situations where an intervention is being rolled out gradually and it's important to understand its impacts at each stage.

21) Sequential Design

Next up is Sequential Design, the dynamic and flexible member of our experimental design family.

Imagine you're playing a video game where you can choose different paths. If you take one path and find a treasure chest, you might decide to continue in that direction. If you hit a dead end, you might backtrack and try a different route. Sequential Design operates in a similar fashion, allowing researchers to make decisions at different stages based on what they've learned so far.

In a Sequential Design, the experiment is broken down into smaller parts, or "sequences." After each sequence, researchers pause to look at the data they've collected. Based on those findings, they then decide whether to stop the experiment because they've got enough information, or to continue and perhaps even modify the next sequence.

Sequential Design Pros

This allows for a more efficient use of resources, as you're only continuing with the experiment if the data suggests it's worth doing so.

One of the great things about Sequential Design is its efficiency. Because you're making data-driven decisions along the way, you can often reach conclusions more quickly and with fewer resources.

Sequential Design Cons

However, it requires careful planning and expertise to ensure that these "stop or go" decisions are made correctly and without bias.

Sequential Design Uses

In terms of its applications, besides healthcare and medicine, Sequential Design is also popular in quality control in manufacturing, environmental monitoring, and financial modeling. In these areas, being able to make quick decisions based on incoming data can be a big advantage.

This design is often used in clinical trials involving new medications or treatments. For example, if early results show that a new drug has significant side effects, the trial can be stopped before more people are exposed to it.

On the flip side, if the drug is showing promising results, the trial might be expanded to include more participants or to extend the testing period.

Think of Sequential Design as the nimble athlete of experimental designs, capable of quick pivots and adjustments to reach the finish line in the most effective way possible. But just like an athlete needs a good coach, this design requires expert oversight to make sure it stays on the right track.

22) Field Experiments

Last but certainly not least, let's explore Field Experiments—the adventurers of the experimental design world.

Picture a scientist leaving the controlled environment of a lab to test a theory in the real world, like a biologist studying animals in their natural habitat or a social scientist observing people in a real community. These are Field Experiments, and they're all about getting out there and gathering data in real-world settings.

Field Experiments embrace the messiness of the real world, unlike laboratory experiments, where everything is controlled down to the smallest detail. This makes them both exciting and challenging.

Field Experiment Pros

On one hand, the results often give us a better understanding of how things work outside the lab.

While Field Experiments offer real-world relevance, they come with challenges like controlling for outside factors and the ethical considerations of intervening in people's lives without their knowledge.

Field Experiment Cons

On the other hand, the lack of control can make it harder to tell exactly what's causing what. Yet, despite these challenges, they remain a valuable tool for researchers who want to understand how theories play out in the real world.

Field Experiment Uses

Let's say a school wants to improve student performance. In a Field Experiment, they might change the school's daily schedule for one semester and keep track of how students perform compared to another school where the schedule remained the same.

Because the study is happening in a real school with real students, the results could be very useful for understanding how the change might work in other schools. But since it's the real world, lots of other factors—like changes in teachers or even the weather—could affect the results.

Field Experiments are widely used in economics, psychology, education, and public policy. For example, you might have heard of the famous "Broken Windows" experiment in the 1980s that looked at how small signs of disorder, like broken windows or graffiti, could encourage more serious crime in neighborhoods. This experiment had a big impact on how cities think about crime prevention.

From the foundational concepts of control groups and independent variables to the sophisticated layouts like Covariate Adaptive Randomization and Sequential Design, it's clear that the realm of experimental design is as varied as it is fascinating.

We've seen that each design has its own special talents, ideal for specific situations. Some designs, like the Classic Controlled Experiment, are like reliable old friends you can always count on.

Others, like Sequential Design, are flexible and adaptable, making quick changes based on what they learn. And let's not forget the adventurous Field Experiments, which take us out of the lab and into the real world to discover things we might not see otherwise.

Choosing the right experimental design is like picking the right tool for the job. The method you choose can make a big difference in how reliable your results are and how much people will trust what you've discovered. And as we've learned, there's a design to suit just about every question, every problem, and every curiosity.

So the next time you read about a new discovery in medicine, psychology, or any other field, you'll have a better understanding of the thought and planning that went into figuring things out. Experimental design is more than just a set of rules; it's a structured way to explore the unknown and answer questions that can change the world.

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