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Experiments and quasi-experiments.
This page includes an explanation of the types, key components, validity, ethics, and advantages and disadvantages of experimental design.
An experiment is a study in which the researcher manipulates the level of some independent variable and then measures the outcome. Experiments are powerful techniques for evaluating cause-and-effect relationships. Many researchers consider experiments the "gold standard" against which all other research designs should be judged. Experiments are conducted both in the laboratory and in real life situations.
Types of Experimental Design
There are two basic types of research design:
- True experiments
- Quasi-experiments
The purpose of both is to examine the cause of certain phenomena.
True experiments, in which all the important factors that might affect the phenomena of interest are completely controlled, are the preferred design. Often, however, it is not possible or practical to control all the key factors, so it becomes necessary to implement a quasi-experimental research design.
Similarities between true and quasi-experiments:
- Study participants are subjected to some type of treatment or condition
- Some outcome of interest is measured
- The researchers test whether differences in this outcome are related to the treatment
Differences between true experiments and quasi-experiments:
- In a true experiment, participants are randomly assigned to either the treatment or the control group, whereas they are not assigned randomly in a quasi-experiment
- In a quasi-experiment, the control and treatment groups differ not only in terms of the experimental treatment they receive, but also in other, often unknown or unknowable, ways. Thus, the researcher must try to statistically control for as many of these differences as possible
- Because control is lacking in quasi-experiments, there may be several "rival hypotheses" competing with the experimental manipulation as explanations for observed results
Key Components of Experimental Research Design
The manipulation of predictor variables.
In an experiment, the researcher manipulates the factor that is hypothesized to affect the outcome of interest. The factor that is being manipulated is typically referred to as the treatment or intervention. The researcher may manipulate whether research subjects receive a treatment (e.g., antidepressant medicine: yes or no) and the level of treatment (e.g., 50 mg, 75 mg, 100 mg, and 125 mg).
Suppose, for example, a group of researchers was interested in the causes of maternal employment. They might hypothesize that the provision of government-subsidized child care would promote such employment. They could then design an experiment in which some subjects would be provided the option of government-funded child care subsidies and others would not. The researchers might also manipulate the value of the child care subsidies in order to determine if higher subsidy values might result in different levels of maternal employment.
Random Assignment
- Study participants are randomly assigned to different treatment groups
- All participants have the same chance of being in a given condition
- Participants are assigned to either the group that receives the treatment, known as the "experimental group" or "treatment group," or to the group which does not receive the treatment, referred to as the "control group"
- Random assignment neutralizes factors other than the independent and dependent variables, making it possible to directly infer cause and effect
Random Sampling
Traditionally, experimental researchers have used convenience sampling to select study participants. However, as research methods have become more rigorous, and the problems with generalizing from a convenience sample to the larger population have become more apparent, experimental researchers are increasingly turning to random sampling. In experimental policy research studies, participants are often randomly selected from program administrative databases and randomly assigned to the control or treatment groups.
Validity of Results
The two types of validity of experiments are internal and external. It is often difficult to achieve both in social science research experiments.
Internal Validity
- When an experiment is internally valid, we are certain that the independent variable (e.g., child care subsidies) caused the outcome of the study (e.g., maternal employment)
- When subjects are randomly assigned to treatment or control groups, we can assume that the independent variable caused the observed outcomes because the two groups should not have differed from one another at the start of the experiment
- For example, take the child care subsidy example above. Since research subjects were randomly assigned to the treatment (child care subsidies available) and control (no child care subsidies available) groups, the two groups should not have differed at the outset of the study. If, after the intervention, mothers in the treatment group were more likely to be working, we can assume that the availability of child care subsidies promoted maternal employment
One potential threat to internal validity in experiments occurs when participants either drop out of the study or refuse to participate in the study. If particular types of individuals drop out or refuse to participate more often than individuals with other characteristics, this is called differential attrition. For example, suppose an experiment was conducted to assess the effects of a new reading curriculum. If the new curriculum was so tough that many of the slowest readers dropped out of school, the school with the new curriculum would experience an increase in the average reading scores. The reason they experienced an increase in reading scores, however, is because the worst readers left the school, not because the new curriculum improved students' reading skills.
External Validity
- External validity is also of particular concern in social science experiments
- It can be very difficult to generalize experimental results to groups that were not included in the study
- Studies that randomly select participants from the most diverse and representative populations are more likely to have external validity
- The use of random sampling techniques makes it easier to generalize the results of studies to other groups
For example, a research study shows that a new curriculum improved reading comprehension of third-grade children in Iowa. To assess the study's external validity, you would ask whether this new curriculum would also be effective with third graders in New York or with children in other elementary grades.
Glossary terms related to validity:
- internal validity
- external validity
- differential attrition
It is particularly important in experimental research to follow ethical guidelines. Protecting the health and safety of research subjects is imperative. In order to assure subject safety, all researchers should have their project reviewed by the Institutional Review Boards (IRBS). The National Institutes of Health supplies strict guidelines for project approval. Many of these guidelines are based on the Belmont Report (pdf).
The basic ethical principles:
- Respect for persons -- requires that research subjects are not coerced into participating in a study and requires the protection of research subjects who have diminished autonomy
- Beneficence -- requires that experiments do not harm research subjects, and that researchers minimize the risks for subjects while maximizing the benefits for them
- Justice -- requires that all forms of differential treatment among research subjects be justified
Advantages and Disadvantages of Experimental Design
The environment in which the research takes place can often be carefully controlled. Consequently, it is easier to estimate the true effect of the variable of interest on the outcome of interest.
Disadvantages
It is often difficult to assure the external validity of the experiment, due to the frequently nonrandom selection processes and the artificial nature of the experimental context.
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True Experimental Design - Types & How to Conduct
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True-experimental research is often considered the most accurate research. A researcher has complete control over the process which helps reduce any error in the result. This also increases the confidence level of the research outcome.
In this blog, we will explore in detail what it is, its various types, and how to conduct it in 7 steps.
What is a true experimental design?
True experimental design is a statistical approach to establishing a cause-and-effect relationship between variables. This research method is the most accurate forms which provides substantial backing to support the existence of relationships.
There are three elements in this study that you need to fulfill in order to perform this type of research:
1. The existence of a control group: The sample of participants is subdivided into 2 groups – one that is subjected to the experiment and so, undergoes changes and the other that does not.
2. The presence of an independent variable: Independent variables that influence the working of other variables must be there for the researcher to control and observe changes.
3. Random assignment: Participants must be randomly distributed within the groups.
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An example of true experimental design
A study to observe the effects of physical exercise on productivity levels can be conducted using a true experimental design.
Suppose a group of 300 people volunteer for a study involving office workers in their 20s. These 300 participants are randomly distributed into 3 groups.
- 1st Group: A control group that does not participate in exercising and has to carry on with their everyday schedule.
- 2nd Group: Asked to indulge in home workouts for 30-45 minutes every day for one month.
- 3rd Group: Has to work out 2 hours every day for a month. Both groups have to take one rest day per week.
In this research, the level of physical exercise acts as an independent variable while the performance at the workplace is a dependent variable that varies with the change in exercise levels.
Before initiating the true experimental research, each participant’s current performance at the workplace is evaluated and documented. As the study goes on, a progress report is generated for each of the 300 participants to monitor how their physical activity has impacted their workplace functioning.
At the end of two weeks, participants from the 2nd and 3rd groups that are able to endure their current level of workout, are asked to increase their daily exercise time by half an hour. While those that aren’t able to endure, are suggested to either continue with the same timing or fix the timing to a level that is half an hour lower.
So, in this true experimental design a participant who at the end of two weeks is not able to put up with 2 hours of workout, will now workout for 1 hour and 30 minutes for the remaining tenure of two weeks while someone who can endure the 2 hours, will now push themselves towards 2 hours and 30 minutes.
In this manner, the researcher notes the timings of each member from the two active groups for the first two weeks and the remaining two weeks after the change in timings and also monitors their corresponding performance levels at work.
The above example can be categorized as true experiment research since now we have:
- Control group: Group 1 carries on with their schedule without being conditioned to exercise.
- Independent variable : The duration of exercise each day.
- Random assignment: 300 participants are randomly distributed into 3 groups and as such, there are no criteria for the assignment.
What is the purpose of conducting true experimental research?
Both the primary usage and purpose of a true experimental design lie in establishing meaningful relationships based on quantitative surveillance.
True experiments focus on connecting the dots between two or more variables by displaying how the change in one variable brings about a change in another variable. It can be as small a change as having enough sleep improves retention or as large scale as geographical differences affect consumer behavior.
The main idea is to ensure the presence of different sets of variables to study with some shared commonality.
Beyond this, the research is used when the three criteria of random distribution, a control group, and an independent variable to be manipulated by the researcher, are met.
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What are the advantages of true experimental design?
Let’s take a look at some advantages that make this research design conclusive and accurate research.
Concrete method of research:
The statistical nature of the experimental design makes it highly credible and accurate. The data collected from the research is subjected to statistical tools.
This makes the results easy to understand, objective and actionable. This makes it a better alternative to observation-based studies that are subjective and difficult to make inferences from.
Easy to understand and replicate:
Since the research provides hard figures and a precise representation of the entire process, the results presented become easily comprehensible for any stakeholder.
Further, it becomes easier for future researchers conducting studies around the same subject to get a grasp of prior takes on the same and replicate its results to supplement their own research.
Establishes comparison:
The presence of a control group in true experimental research allows researchers to compare and contrast. The degree to which a methodology is applied to a group can be studied with respect to the end result as a frame of reference.
Conclusive:
The research combines observational and statistical analysis to generate informed conclusions. This directs the flow of follow-up actions in a definite direction, thus, making the research process fruitful.
What are the disadvantages of true experimental design?
We should also learn about the disadvantages it can pose in research to help you determine when and how you should use this type of research.
This research design is costly. It takes a lot of investment in recruiting and managing a large number of participants which is necessary for the sample to be representative.
The high resource investment makes it highly important for the researcher to plan each aspect of the process to its minute details.
Too idealistic:
The research takes place in a completely controlled environment. Such a scenario is not representative of real-world situations and so the results may not be authentic.
T his is one of the main limitation why open-field research is preferred over lab research, wherein the researcher can influence the study.
Time-consuming:
Setting up and conducting a true experiment is highly time-consuming. This is because of the processes like recruiting a large enough sample, gathering respondent data, random distribution into groups, monitoring the process over a span of time, tracking changes, and making adjustments.
The amount of processes, although essential to the entire model, is not a feasible option to go for when the results are required in the near future.
Now that we’ve learned about the advantages and disadvantages let’s look at its types.
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What are the 3 types of true experimental design?
The research design is categorized into three types based on the way you should conduct the research. Each type has its own procedure and guidelines, which you should be aware of to achieve reliable data.
The three types are:
1) Post-test-only control group design.
2) Pre-test post-test control group design.
3) Solomon four group control design.
Let’s see how these three types differ.
1) Post-test-only control group design:
In this type of true experimental research, the control as well as the experimental group that has been formed using random allocation, are not tested before applying the experimental methodology. This is so as to avoid affecting the quality of the study.
The participants are always on the lookout to identify the purpose and criteria for assessment. Pre-test conveys to them the basis on which they are being judged which can allow them to modify their end responses, compromising the quality of the entire research process.
However, this can hinder your ability to establish a comparison between the pre-experiment and post-experiment conditions which weighs in on the changes that have taken place over the course of the research.
2) Pre-test post-test control group design:
It is a modification of the post-test control group design with an additional test carried out before the implementation of the experimental methodology.
This two-way testing method can help in noticing significant changes brought in the research groups as a result of the experimental intervention. There is no guarantee that the results present the true picture as post-testing can be affected due to the exposure of the respondents to the pre-test.
3) Solomon four group control design:
This type of true experimental design involves the random distribution of sample members into 4 groups. These groups consist of 2 control groups that are not subjected to the experiments and changes and 2 experimental groups that the experimental methodology applies to.
Out of these 4 groups, one control and one experimental group is used for pre-testing while all four groups are subjected to post-tests.
This way researcher gets to establish pre-test post-test contrast while there remains another set of respondents that have not been exposed to pre-tests and so, provide genuine post-test responses, thus, accounting for testing effects.
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What is the difference between pre-experimental & true experimental research design.
Pre-experimental research helps determine the researchers’ intervention on a group of people. It is a step where you design the proper experiment to address a research question.
True experiment defines that you are conducting the research. It helps establish a cause-and-effect relationship between the variables.
We’ll discuss the differences between the two based on four categories, which are:
- Observatory Vs. Statistical.
- Absence Vs. Presence of control groups.
- Non-randomization Vs. Randomization.
- Feasibility test Vs. Conclusive test.
Let’s find the differences to better understand the two experiments.
Observatory vs Statistical:
Pre-experimental research is an observation-based model i.e. it is highly subjective and qualitative in nature.
The true experimental design offers an accurate analysis of the data collected using statistical data analysis tools.
Absence vs Presence of control groups:
Pre-experimental research designs do not usually employ a control group which makes it difficult to establish contrast.
While all three types of true experiments employ control groups.
Non-randomization vs Randomization:
Pre-experimental research doesn’t use randomization in certain cases whereas
True experimental research always adheres to a randomization approach to group distribution.
Feasibility test vs Conclusive test:
Pre-tests are used as a feasibility mechanism to see if the methodology being applied is actually suitable for the research purpose and whether it will have an impact or not.
While true experiments are conclusive in nature.
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7 Steps to conduct a true experimental research
It’s important to understand the steps/guidelines of research in order to maintain research integrity and gather valid and reliable data.
We have explained 7 steps to conducting this research in detail. The TL;DR version of it is:
1) Identify the research objective.
2) Identify independent and dependent variables.
3) Define and group the population.
4) Conduct Pre-tests.
5) Conduct the research.
6) Conduct post-tests.
7) Analyse the collected data.
Now let’s explore these seven steps in true experimental design.
1) Identify the research objective:
Identify the variables which you need to analyze for a cause-and-effect relationship. Deliberate which particular relationship study will help you make effective decisions and frame this research objective in one of the following manners:
- Determination of the impact of X on Y
- Studying how the usage/application of X causes Y
2) Identify independent and dependent variables:
Establish clarity as to what would be your controlling/independent variable and what variable would change and would be observed by the researcher. In the above samples, for research purposes, X is an independent variable & Y is a dependent variable.
3) Define and group the population:
Define the targeted audience for the true experimental design. It is out of this target audience that a sample needs to be selected for accurate research to be carried out. It is imperative that the target population gets defined in as much detail as possible.
To narrow the field of view, a random selection of individuals from the population is carried out. These are the selected respondents that help the researcher in answering their research questions. Post their selection, this sample of individuals gets randomly subdivided into control and experimental groups.
4) Conduct Pre-tests:
Before commencing with the actual study, pre-tests are to be carried out wherever necessary. These pre-tests take an assessment of the condition of the respondent so that an effective comparison between the pre and post-tests reveals the change brought about by the research.
5) Conduct the research:
Implement your experimental procedure with the experimental group created in the previous step in the true experimental design. Provide the necessary instructions and solve any doubts or queries that the participants might have. Monitor their practices and track their progress. Ensure that the intervention is being properly complied with, otherwise, the results can be tainted.
6) Conduct post-tests:
Gauge the impact that the intervention has had on the experimental group and compare it with the pre-tests. This is particularly important since the pre-test serves as a starting point from where all the changes that have been measured in the post-test, are the effect of the experimental intervention.
So for example: If the pre-test in the above example shows that a particular customer service employee was able to solve 10 customer problems in two hours and the post-test conducted after a month of 2-hour workouts every day shows a boost of 5 additional customer problems being solved within those 2 hours, the additional 5 customer service calls that the employee makes is the result of the additional productivity gained by the employee as a result of putting in the requisite time
7) Analyse the collected data:
Use appropriate statistical tools to derive inferences from the data observed and collected. Correlational data analysis tools and tests of significance are highly effective relationship-based studies and so are highly applicable for true experimental research.
This step also includes differentiating between the pre and the post-tests for scoping in on the impact that the independent variable has had on the dependent variable. A contrast between the control group and the experimental groups sheds light on the change brought about within the span of the experiment and how much change is brought intentionally and is not caused by chance.
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Wrapping up;
This sums up everything about true experimental design. While it’s often considered complex and expensive, it is also one of the most accurate research.
The true experiment uses statistical analysis which ensures that your data is reliable and has a high confidence level. Curious to learn how you can use survey software to conduct your experimental research, book a meeting with us .
- What is true experimental research design?
True experimental research design helps investigate the cause-and-effect relationships between the variables under study. The research method requires manipulating an independent variable, random assignment of participants to different groups, and measuring the dependent variable.
- How does true experiment research differ from other research designs?
The true experiment uses random selection/assignment of participants in the group to minimize preexisting differences between groups. It allows researchers to make causal inferences about the influence of independent variables. This is the factor that makes it different from other research designs like correlational research.
- What are the key components of true experimental research designs?
The following are the important factors of a true experimental design:
- Manipulation of the independent variable.
- Control groups.
- Experiment groups.
- Dependent variable.
- Random assignment.
- What are some advantages of true experiment design?
It enables you to establish causal relationships between variables and offers control over the confounding variables. Moreover, you can generalize the research findings to the target population.
- What ethical considerations are important in a true experimental research design?
When conducting this research method, you must obtain informed consent from the participants. It’s important to ensure the confidentiality and privacy of the participants to minimize any risk or harm.
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Understanding True Experiments in Psychology: Principles and Applications
True experiments in psychology are a crucial method used by researchers to test hypotheses, establish cause and effect relationships, and advance scientific knowledge. In this article, we will delve into the principles of true experiments, such as random assignment and manipulation of variables.
We will also explore how true experiments differ from other types of studies, the ethical considerations involved, and the limitations researchers face. Stay tuned to discover how researchers can improve the validity of true experiments and their applications in the field of psychology.
- Random assignment is a crucial principle in true experiments as it ensures that participants are assigned to groups without bias, increasing the validity of the study.
- Manipulation of the independent variable and control group are key elements in establishing cause and effect relationships in true experiments.
- True experiments have various applications such as testing hypotheses, identifying effective treatments, and advancing scientific knowledge in psychology.
- 1 What Are True Experiments in Psychology?
- 2.1 Random Assignment
- 2.2 Control Group
- 2.3 Manipulation of Independent Variable
- 2.4 Measurement of Dependent Variable
- 2.5 Elimination of Confounding Variables
- 3.1 Correlational Studies
- 3.2 Quasi-Experiments
- 3.3 Observational Studies
- 4.1 Testing Hypotheses
- 4.2 Establishing Cause and Effect Relationships
- 4.3 Replicating Previous Findings
- 4.4 Identifying Effective Treatments
- 4.5 Advancing Scientific Knowledge
- 5 What Are the Ethical Considerations in True Experiments?
- 6 What Are the Limitations of True Experiments?
- 7 How Can Researchers Improve the Validity of True Experiments?
- 8.1 What is a true experiment in psychology?
- 8.2 What are the principles of true experiments in psychology?
- 8.3 Why is random assignment important in true experiments?
- 8.4 How does manipulation of an independent variable work in true experiments?
- 8.5 How are extraneous variables controlled in true experiments?
- 8.6 What are some applications of true experiments in psychology?
What Are True Experiments in Psychology?
True experiments in psychology refer to research studies that involve the manipulation of an independent variable to observe the effects on a dependent variable within a controlled environment.
By manipulating the independent variable, researchers aim to determine causality relationships between variables, testing hypotheses and theories. This controlled setting ensures that any changes in the dependent variable are a direct result of the manipulated independent variable, helping to eliminate extraneous factors and increase the internal validity of the study.
Researchers carefully design these experiments, often using random assignment to assign participants to different conditions, minimizing the influence of individual differences. This rigorous methodology allows psychologists to draw reliable conclusions about human behavior and cognition.
What Are the Principles of True Experiments?
The principles of true experiments in psychology encompass key elements such as random assignment , control group implementation, and the manipulation of independent variables.
Random assignment is a crucial aspect of experimental design that involves assigning participants to different groups by chance to reduce biases and ensure that each participant has an equal chance of being in any group. This helps in establishing a cause-and-effect relationship between variables.
Control group practices involve creating a group that does not receive the experimental treatment to serve as a benchmark for comparison, allowing researchers to assess the impact of the treatment.
The manipulation of variables refers to intentionally changing one variable to observe its effect on another, allowing researchers to test hypotheses and draw conclusions based on the results.
Random Assignment
Random assignment is a crucial aspect of true experiments, ensuring that participants are assigned to groups without bias or influence.
By randomly assigning individuals to different experimental groups, researchers can be more confident that any differences observed in the outcomes are due to the manipulation of the independent variable rather than individual characteristics.
This process helps eliminate selection bias, where certain characteristics of participants may influence group allocation, ultimately leading to distorted results.
The importance of random assignment lies in its ability to create comparable groups, making the conclusions drawn from the study more credible and generalizable.
Control Group
The control group in true experiments serves as a baseline for comparison, allowing researchers to assess the impact of the independent variable.
By isolating the variable of interest and comparing it to a group that does not receive the treatment or intervention, researchers can determine whether the observed effects are truly due to the independent variable. This group provides a standard of reference against which the experimental group is measured, ensuring that any changes or outcomes can be attributed accurately. Control groups play a crucial role in reducing bias and ensuring the validity of study results, helping researchers draw meaningful conclusions from their experiments.
Manipulation of Independent Variable
Manipulating the independent variable is a core component of true experiments, enabling researchers to assess its causal impact on the dependent variable.
By systematically varying the independent variable, researchers can observe how changes in the manipulated factor lead to changes in the dependent variable, thereby establishing a cause-and-effect relationship. This manipulation allows for controlling and testing different conditions to study the direct influence of the independent variable on the outcome. Through this process, researchers can deduce the extent to which the independent variable influences the dependent variable, aiding in drawing valid conclusions about the relationship between the two variables.
Measurement of Dependent Variable
The measurement of the dependent variable in true experiments involves assessing the outcome or response that is influenced by the manipulation of the independent variable.
In research studies, the process of measuring the dependent variable plays a crucial role in determining the effectiveness of the experimental manipulation. Researchers use various techniques to quantify and record the responses of participants to different conditions. This measurement step is essential for comparing the outcomes across different groups and evaluating the impact of the independent variable on the dependent variable. Data collection tools such as surveys, questionnaires, observations, and physiological measurements are commonly employed to capture the relevant information accurately.
Elimination of Confounding Variables
Eliminating confounding variables is essential in true experiments to ensure that the observed effects are attributed to the manipulated variables rather than external influences.
To address confounding variables effectively, researchers employ various strategies to control and minimize the impact of extraneous factors.
One common approach is randomization, where participants are assigned to different experimental conditions randomly to distribute potential confounders evenly across groups.
Another method involves matching participants based on certain variables to ensure comparability between groups.
Researchers may use statistical techniques such as regression analysis to control for confounding variables during data analysis.
How Are True Experiments Different from Other Types of Studies?
True experiments in psychology differ from other types of studies such as correlational studies and quasi-experiments by their ability to establish causal relationships through controlled manipulation.
While correlational studies aim to identify relationships between variables without manipulating them, true experiments involve the deliberate manipulation of one or more factors to observe the effect on another variable. This manipulation allows researchers to assess causality effectively by controlling for confounding variables and random assignment of participants to experimental conditions.
With experimental designs, researchers can establish a cause-and-effect relationship, providing stronger evidence of the effect of an independent variable on a dependent variable. This rigorous approach helps in drawing more definitive conclusions compared to observational or correlational studies.
Correlational Studies
Correlational studies in psychology focus on identifying relationships between variables without manipulating them, offering insights into associations and predictive patterns.
These studies are significant in understanding how variables interact in the natural world, providing crucial data for researchers to analyze and draw conclusions from. By examining the relationship between variables, researchers can determine if changes in one variable correspond with changes in another, revealing potential predictive patterns. This type of research design is valuable when it is not possible or ethical to manipulate certain variables, allowing researchers to observe and interpret connections without intervening. Through statistical analysis, correlational studies help establish the strength and direction of relationships between different factors, contributing to a deeper understanding of complex phenomena.
Quasi-Experiments
Quasi-experiments in psychology resemble true experiments but lack random assignment or full control over variables, often utilized when strict experimental conditions are challenging to implement.
This type of research design shares many similarities with traditional experiments, such as having a treatment group and a control group to analyze the effects of an independent variable. Without random assignment, researchers cannot ensure that participants are equally distributed between groups based on relevant factors, potentially impacting the internal validity of the study. The limitation in control and randomization in quasi-experiments leads to difficulties in establishing a cause-and-effect relationship definitively.
Observational Studies
Observational studies in psychology involve the systematic observation of behaviors, perceptions, or phenomena without intervention or manipulation, providing valuable insights into natural settings.
In these studies, researchers carefully examine how people interact with their environment and how certain stimuli influence their actions and reactions. Behavioral analysis plays a crucial role in understanding human nature and decision-making processes. By closely monitoring individuals in their everyday routines and environments, psychologists can uncover patterns, trends, and subconscious influences that might not be apparent in experimental settings.
What Are the Applications of True Experiments in Psychology?
True experiments play a vital role in psychology by testing hypotheses, establishing cause-and-effect relationships, replicating findings, and advancing scientific knowledge through systematic procedures.
One of the key elements of true experiments is hypothesis testing , where researchers formulate a clear statement to be tested through experimentation. This process allows them to investigate and validate theories in a controlled environment.
These experiments are essential for assessing causality , as they help determine if changes in one variable directly cause changes in another. This ability to establish cause-and-effect relationships is crucial for understanding the intricacies of human behavior and mental processes.
By focusing on these rigorous methods, psychologists can push the boundaries of scientific advancement and contribute valuable insights to the field.
Testing Hypotheses
One of the primary applications of true experiments in psychology is testing hypotheses by manipulating variables and analyzing the resulting data for empirical support.
Formulating a hypothesis is the initial step in this process. Researchers conceptualize a testable statement that predicts the relationship between variables. For instance, in a study examining the impact of music on concentration, the hypothesis could be that participants exposed to classical music will perform better on a cognitive task compared to those in a silent environment.
After establishing the hypothesis, the experiment is designed to manipulate the independent variable (in this case, music exposure) and measure the dependent variable (concentration level). Data collection methods, such as observation or surveys, are then employed to gather information.
Establishing Cause and Effect Relationships
True experiments excel in establishing cause-and-effect relationships, particularly in cognitive processing studies, by systematically manipulating variables to determine their impact on outcomes.
In cognitive psychology, researchers use true experiments to delve into the intricate mechanisms of cognitive processes, such as memory, attention, and perception. By carefully manipulating independent variables and observing changes in dependent variables, they can infer causal relationships and better understand the underlying cognitive mechanisms. This method allows researchers to draw reliable conclusions about how specific variables influence mental processes, contributing valuable insights to the field.
Replicating Previous Findings
True experiments contribute to replicating previous findings in psychology, especially in memory research, by confirming the reliability and validity of established results through rigorous experimentation.
In memory-related studies, replicating research findings becomes crucial to strengthen the scientific foundation and ensure the credibility of existing knowledge in cognitive processes. Through meticulous experimental design and methodological precision, researchers aim to validate the consistency of outcomes, providing a solid basis for generalizing findings to broader populations or contexts.
Result validation processes often involve conducting multiple trials, controlling for confounding variables, and utilizing statistical analyses to determine the robustness and significance of the obtained results. By meticulously following these steps, scientists can enhance the overall understanding of memory mechanisms and contribute to the advancement of psychological theories.
Identifying Effective Treatments
True experiments aid in identifying effective treatments and interventions in psychology, enabling researchers to evaluate the efficacy of therapeutic approaches or behavioral modifications through controlled studies.
By establishing control over variables and random assignment of participants, true experiments establish a cause-and-effect relationship between the intervention and outcomes. These experiments often involve an experimental group receiving the treatment and a control group that does not, allowing researchers to compare results objectively.
The results obtained from true experiments provide valuable insights into the effectiveness of different therapeutic techniques and help psychologists tailor interventions to suit individual client needs. This evidence-based approach ensures that psychological interventions are not only well-founded but also have a higher chance of success.
Advancing Scientific Knowledge
True experiments are pivotal in advancing scientific knowledge in psychology, with EEG data studies exemplifying how controlled experimentation enhances our understanding of cognitive processes and brain functions.
Through true experiments, researchers are able to systematically manipulate variables to establish cause-and-effect relationships in their studies, providing valuable insights into complex neural mechanisms. The meticulous process of EEG data analysis in experimental settings allows scientists to observe real-time brain activity, monitor cognitive responses, and pinpoint specific brain regions involved in various tasks.
The applications of cognitive research are vast and impactful, ranging from exploring memory formation, attentional processes, language comprehension, to investigating neurological disorders such as Alzheimer’s disease and schizophrenia. Understanding the intricacies of the brain through empirical research is crucial for developing effective interventions and treatments for cognitive impairments.
What Are the Ethical Considerations in True Experiments?
Ethical considerations in true experiments involve ensuring the well-being and rights of participants, obtaining informed consent, maintaining confidentiality, and adhering to ethical guidelines set by psychological associations.
Central to the ethical framework of true experiments is the fundamental principle of beneficence, where researchers must prioritize the welfare and best interests of those participating in the study. This encompasses not only physical health but also psychological and emotional aspects to ensure a holistic approach to participant protection. Informed consent plays a pivotal role, granting individuals the autonomy to make voluntary decisions regarding their involvement after being provided with all pertinent information. This transparent exchange is crucial for fostering trust and respect.
What Are the Limitations of True Experiments?
The limitations of true experiments in psychology include challenges related to controlling extraneous variables , generalizing findings to real-world scenarios, and addressing ethical constraints in experimental designs.
Extraneous variables, also known as confounding variables, can significantly impact the results of an experiment by introducing unintended influences that distort the relationships being studied. These variables are crucial to be managed effectively to ensure the internal validity of the study. Complete control over all extraneous variables is often impractical, especially in complex human behaviors where numerous factors can interact. This limitation highlights the need for researchers to carefully design experiments and utilize statistical techniques to minimize their effects.
How Can Researchers Improve the Validity of True Experiments?
Researchers can enhance the validity of true experiments in psychology by implementing rigorous scientific methods, conducting thorough data analysis, ensuring result reliability, and adhering to established research protocols.
One of the key strategies for enhancing experiment validity is to prioritize random assignment of participants to experimental groups, which helps control for confounding variables and increases the likelihood of establishing cause and effect relationships.
Researchers should carefully design control conditions to accurately measure the impact of the independent variable. Data validation processes such as peer review and replication studies play a vital role in confirming the robustness of experimental findings.
Frequently Asked Questions
What is a true experiment in psychology.
A true experiment in psychology is a research method in which an independent variable is manipulated to determine its effect on a dependent variable, while controlling for other variables that may influence the results.
What are the principles of true experiments in psychology?
The three main principles of true experiments in psychology are random assignment, manipulation of an independent variable, and control of extraneous variables.
Why is random assignment important in true experiments?
Random assignment helps to ensure that participants are assigned to different experimental groups by chance, reducing the likelihood of pre-existing differences between the groups that could affect the results.
How does manipulation of an independent variable work in true experiments?
The independent variable, often referred to as the treatment, is manipulated by the researcher to observe its effects on the dependent variable. This allows for the establishment of cause-and-effect relationships.
How are extraneous variables controlled in true experiments?
Extraneous variables, which are factors that may influence the results but are not the focus of the study, are controlled through various methods such as random assignment, use of control groups, and experimental design.
What are some applications of true experiments in psychology?
True experiments are commonly used in psychology to study the effects of various interventions or treatments on behavior or mental processes. They can also be used to test theories and hypotheses, and to determine the effectiveness of different strategies or programs.
Dr. Emily Tan is a researcher in the field of psychological assessment and testing. Her expertise includes the development and validation of psychological measures, with a particular interest in personality assessment. Dr. Tan’s work aims to improve the accuracy and ethical application of psychological tests in various settings, from clinical diagnostics to organizational hiring processes.
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30 8.1 Experimental design: What is it and when should it be used?
Learning objectives.
- Define experiment
- Identify the core features of true experimental designs
- Describe the difference between an experimental group and a control group
- Identify and describe the various types of true experimental designs
Experiments are an excellent data collection strategy for social workers wishing to observe the effects of a clinical intervention or social welfare program. Understanding what experiments are and how they are conducted is useful for all social scientists, whether they actually plan to use this methodology or simply aim to understand findings from experimental studies. An experiment is a method of data collection designed to test hypotheses under controlled conditions. In social scientific research, the term experiment has a precise meaning and should not be used to describe all research methodologies.
Experiments have a long and important history in social science. Behaviorists such as John Watson, B. F. Skinner, Ivan Pavlov, and Albert Bandura used experimental design to demonstrate the various types of conditioning. Using strictly controlled environments, behaviorists were able to isolate a single stimulus as the cause of measurable differences in behavior or physiological responses. The foundations of social learning theory and behavior modification are found in experimental research projects. Moreover, behaviorist experiments brought psychology and social science away from the abstract world of Freudian analysis and towards empirical inquiry, grounded in real-world observations and objectively-defined variables. Experiments are used at all levels of social work inquiry, including agency-based experiments that test therapeutic interventions and policy experiments that test new programs.
Several kinds of experimental designs exist. In general, designs considered to be true experiments contain three basic key features:
- random assignment of participants into experimental and control groups
- a “treatment” (or intervention) provided to the experimental group
- measurement of the effects of the treatment in a post-test administered to both groups
Some true experiments are more complex. Their designs can also include a pre-test and can have more than two groups, but these are the minimum requirements for a design to be a true experiment.
Experimental and control groups
In a true experiment, the effect of an intervention is tested by comparing two groups: one that is exposed to the intervention (the experimental group , also known as the treatment group) and another that does not receive the intervention (the control group ). Importantly, participants in a true experiment need to be randomly assigned to either the control or experimental groups. Random assignment uses a random number generator or some other random process to assign people into experimental and control groups. Random assignment is important in experimental research because it helps to ensure that the experimental group and control group are comparable and that any differences between the experimental and control groups are due to random chance. We will address more of the logic behind random assignment in the next section.
Treatment or intervention
In an experiment, the independent variable is receiving the intervention being tested—for example, a therapeutic technique, prevention program, or access to some service or support. It is less common in of social work research, but social science research may also have a stimulus, rather than an intervention as the independent variable. For example, an electric shock or a reading about death might be used as a stimulus to provoke a response.
In some cases, it may be immoral to withhold treatment completely from a control group within an experiment. If you recruited two groups of people with severe addiction and only provided treatment to one group, the other group would likely suffer. For these cases, researchers use a control group that receives “treatment as usual.” Experimenters must clearly define what treatment as usual means. For example, a standard treatment in substance abuse recovery is attending Alcoholics Anonymous or Narcotics Anonymous meetings. A substance abuse researcher conducting an experiment may use twelve-step programs in their control group and use their experimental intervention in the experimental group. The results would show whether the experimental intervention worked better than normal treatment, which is useful information.
The dependent variable is usually the intended effect the researcher wants the intervention to have. If the researcher is testing a new therapy for individuals with binge eating disorder, their dependent variable may be the number of binge eating episodes a participant reports. The researcher likely expects her intervention to decrease the number of binge eating episodes reported by participants. Thus, she must, at a minimum, measure the number of episodes that occur after the intervention, which is the post-test . In a classic experimental design, participants are also given a pretest to measure the dependent variable before the experimental treatment begins.
Types of experimental design
Let’s put these concepts in chronological order so we can better understand how an experiment runs from start to finish. Once you’ve collected your sample, you’ll need to randomly assign your participants to the experimental group and control group. In a common type of experimental design, you will then give both groups your pretest, which measures your dependent variable, to see what your participants are like before you start your intervention. Next, you will provide your intervention, or independent variable, to your experimental group, but not to your control group. Many interventions last a few weeks or months to complete, particularly therapeutic treatments. Finally, you will administer your post-test to both groups to observe any changes in your dependent variable. What we’ve just described is known as the classical experimental design and is the simplest type of true experimental design. All of the designs we review in this section are variations on this approach. Figure 8.1 visually represents these steps.
An interesting example of experimental research can be found in Shannon K. McCoy and Brenda Major’s (2003) study of people’s perceptions of prejudice. In one portion of this multifaceted study, all participants were given a pretest to assess their levels of depression. No significant differences in depression were found between the experimental and control groups during the pretest. Participants in the experimental group were then asked to read an article suggesting that prejudice against their own racial group is severe and pervasive, while participants in the control group were asked to read an article suggesting that prejudice against a racial group other than their own is severe and pervasive. Clearly, these were not meant to be interventions or treatments to help depression, but were stimuli designed to elicit changes in people’s depression levels. Upon measuring depression scores during the post-test period, the researchers discovered that those who had received the experimental stimulus (the article citing prejudice against their same racial group) reported greater depression than those in the control group. This is just one of many examples of social scientific experimental research.
In addition to classic experimental design, there are two other ways of designing experiments that are considered to fall within the purview of “true” experiments (Babbie, 2010; Campbell & Stanley, 1963). The posttest-only control group design is almost the same as classic experimental design, except it does not use a pretest. Researchers who use posttest-only designs want to eliminate testing effects , in which participants’ scores on a measure change because they have already been exposed to it. If you took multiple SAT or ACT practice exams before you took the real one you sent to colleges, you’ve taken advantage of testing effects to get a better score. Considering the previous example on racism and depression, participants who are given a pretest about depression before being exposed to the stimulus would likely assume that the intervention is designed to address depression. That knowledge could cause them to answer differently on the post-test than they otherwise would. In theory, as long as the control and experimental groups have been determined randomly and are therefore comparable, no pretest is needed. However, most researchers prefer to use pretests in case randomization did not result in equivalent groups and to help assess change over time within both the experimental and control groups.
Researchers wishing to account for testing effects but also gather pretest data can use a Solomon four-group design. In the Solomon four-group design , the researcher uses four groups. Two groups are treated as they would be in a classic experiment—pretest, experimental group intervention, and post-test. The other two groups do not receive the pretest, though one receives the intervention. All groups are given the post-test. Table 8.1 illustrates the features of each of the four groups in the Solomon four-group design. By having one set of experimental and control groups that complete the pretest (Groups 1 and 2) and another set that does not complete the pretest (Groups 3 and 4), researchers using the Solomon four-group design can account for testing effects in their analysis.
Group 1 | X | X | X |
Group 2 | X | X | |
Group 3 | X | X | |
Group 4 | X |
Solomon four-group designs are challenging to implement in the real world because they are time- and resource-intensive. Researchers must recruit enough participants to create four groups and implement interventions in two of them.
Overall, true experimental designs are sometimes difficult to implement in a real-world practice environment. It may be impossible to withhold treatment from a control group or randomly assign participants in a study. In these cases, pre-experimental and quasi-experimental designs–which we will discuss in the next section–can be used. However, the differences in rigor from true experimental designs leave their conclusions more open to critique.
Experimental design in macro-level research
You can imagine that social work researchers may be limited in their ability to use random assignment when examining the effects of governmental policy on individuals. For example, it is unlikely that a researcher could randomly assign some states to implement decriminalization of recreational marijuana and some states not to in order to assess the effects of the policy change. There are, however, important examples of policy experiments that use random assignment, including the Oregon Medicaid experiment. In the Oregon Medicaid experiment, the wait list for Oregon was so long, state officials conducted a lottery to see who from the wait list would receive Medicaid (Baicker et al., 2013). Researchers used the lottery as a natural experiment that included random assignment. People selected to be a part of Medicaid were the experimental group and those on the wait list were in the control group. There are some practical complications macro-level experiments, just as with other experiments. For example, the ethical concern with using people on a wait list as a control group exists in macro-level research just as it does in micro-level research.
Key Takeaways
- True experimental designs require random assignment.
- Control groups do not receive an intervention, and experimental groups receive an intervention.
- The basic components of a true experiment include a pretest, posttest, control group, and experimental group.
- Testing effects may cause researchers to use variations on the classic experimental design.
- Classic experimental design- uses random assignment, an experimental and control group, as well as pre- and posttesting
- Control group- the group in an experiment that does not receive the intervention
- Experiment- a method of data collection designed to test hypotheses under controlled conditions
- Experimental group- the group in an experiment that receives the intervention
- Posttest- a measurement taken after the intervention
- Posttest-only control group design- a type of experimental design that uses random assignment, and an experimental and control group, but does not use a pretest
- Pretest- a measurement taken prior to the intervention
- Random assignment-using a random process to assign people into experimental and control groups
- Solomon four-group design- uses random assignment, two experimental and two control groups, pretests for half of the groups, and posttests for all
- Testing effects- when a participant’s scores on a measure change because they have already been exposed to it
- True experiments- a group of experimental designs that contain independent and dependent variables, pretesting and post testing, and experimental and control groups
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Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
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True, Natural and Field Experiments An easy lesson idea for learning about experiments.
Travis Dixon September 29, 2016 Research Methodology
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There is a difference between a “true experiment” a “field experiment” and a “natural experiment”. These separate experimental methods are commonly used in psychological research and they each have their strengths and limitations.
True Experiments
Berry’s classic study compared two cultures in order to understand how economics, parenting and cultural values can influence behaviour. But what type of method would we call this?
A true experiment is one where:
- have randomly assigned participants to a condition (if using independent samples)
Repeated measures designs don’t need random allocation because there is no allocation as all participants do both conditions.
One potential issue in laboratory experiments is that they are conducted in environments that are not natural for the participants, so the behaviour might not reflect what happens in real life.
Field Experiments
A field experiment is one where:
- the researcher conducts an experiment by manipulating an IV,
- …and measuring the effects on the DV in a natural environment.
They still try to minimize the effects of other variables and to control for these, but it’s just happening in a natural environment: the field.
- Natural Experiment
A natural experiment is one where:
- the independent variable is naturally occurring. i.e. it hasn’t been manipulated by the researcher.
There are many instances where naturally occurring events or phenomenon may interest researchers. The issue with natural experiments is that it can’t be guaranteed that it is the independent variable that is having an effect on the dependent variable.
- Quantitative Research Methods Glossary
- Let’s STOP the research methods madness!
- What makes an experiment “quasi”?
Activity Idea
Students can work with a partner to decide if the following are true, field or natural experiments.
If you cant’ decide, what other information do you need?
- Berry’s cross-cultural study on conformity ( Key Study: Conformity Across Cultures (Berry, 1967)
- Bandura’s bobo doll study ( Key Study: Bandura’s Bobo Doll (1963)
- Rosenzweig’s rat study ( Key Study: Animal research on neuroplasticity (Rosenzweig and Bennett, 1961)
Let’s make it a bit trickier:
- Key Study: London Taxi Drivers vs. Bus Drivers (Maguire, 2006)
- Key Study: Evolution of Gender Differences in Sexual Behaviour (Clark and Hatfield, 1989)
- Key Study: Serotonin, tryptophan and the brain (Passamonti et al., 2012)
- Saint Helena Study : television was introduced on the island of Saint Helena in the Atlantic ocean and the researchers measured the behaviour of the kids before and after TV was introduced.
- Light Therapy : the researchers randomly assigned patients with depression into three different groups. The three groups received different forms of light therapy to treat depression (red light, bright light, soft light). The lights were installed in the participants’ bedrooms and were timed to come on naturally. The effects on depression were measured via interviews.
What are the strengths and limitations of:
- True Experiment
- Field Experiment
Travis Dixon is an IB Psychology teacher, author, workshop leader, examiner and IA moderator.
Psychology Sorted
Psychology for all, experimental methods explained.
The easiest one to define is the true experiment.
Often called a ‘laboratory/lab’ experiment, this does not have to take place in a lab, but can be conducted in a classroom, office, waiting room, or even outside, providing it meets the criteria. These are that allocation of participants to the two or more experimental (or experimental and control) groups or conditions is random and that the independent variable (IV) is manipulated by the researcher in order to measure the effect on the dependent variable (DV). Other variables are carefully controlled, such as location, temperature, time of day, time taken for experiment, materials used, etc. This should result in a cause and effect relationship between the IV and the DV. Examples are randomised controlled drug trials or many of the cognitive experiments into memory, such as Glanzer and Cunitz_1966.
A field experiment is similar, in that individuals are usually randomly assigned to groups, where this is possible, and the IV is manipulated by the researcher. However, as this takes place in the participants’ natural surroundings, the extraneous variables that could confound the findings of the research are somewhat more difficult to control. The implications for causation depend on how well these variables are controlled, and on the random allocation of participants. Examples are bystander effect studies, and also research into the effect of digital technology on learning, such as that conducted by Hembrooke and Gay_2003 .
A quasi-experiment is similar to either or both of the above, but the participants are not randomly allocated to groups. Instead they are allocated on the basis of self-selection as male/female; left or right-handed; preference for coffee or tea; young/old, etc. or researcher selection as scoring above or below and certain level on a pre-test; measured socio-economic status; psychology student or biology student, etc. These are therefore, non-equivalent groups. The IV is often manipulated and the DV measured as before, but the nature of the groups is a potential confounding variable. If testing the effect of a new reading scheme on the reading ages of 11 year olds, a quasi-experimental design would allocate one class of 11 year olds to read using the scheme, and another to continue with the old scheme (control group), and then measure reading ages after a set period of time. But there may have been other differences between the groups that mean a cause and effect relationship cannot be reliably established: those in the first class may also have already been better readers, or several months older, than those in the control group. Baseline pre-testing is one way around this, in which the students’ improvement is measured against their own earlier reading age, in a pre-test/post-test design. In some quasi-experiments, the allocation to groups by certain criteria itself forms the IV, and the effects of gender, age or handedness on memory, for example, are measured. Examples are research into the efficacy of anti-depressants, when some participants are taking one anti-depressant and some another, or Caspi et al._2003 , who investigated whether a polymorphism on the serotonin transporter gene is linked to a higher or lower risk of individual depression in the face of different levels of perceived stress.
Finally, natural experiments are those in which there is no manipulation of the IV, because it is a naturally-occurring variable. It may be an earthquake (IV) and measurement of people’s fear levels (DV) at living on a fault line before and after the event, or an increase in unemployment as a large factory closes (IV) and measurement of depression levels amongst adults of working age before and after the factory closure (DV). As with field experiments, many of the extraneous variables are difficult to control as the research takes place in people’s natural environment. A good example of a natural experiment is Charlton (1975) research into the effect of the introduction of television to the remote island of St. Helena.
The differences between quasi experiments and correlational research, and between natural experiments and case studies are sometimes hard to determine, so I would always encourage students to explain exactly why they are designating something as one or the other. We can’t always trust the original article either – Bartlett was happy to describe his studies as experiments, which they were not! Here’s hoping these examples have helped. The following texts are super-useful, and were referred to while writing this post.:
Campbell, D.T. & Stanley J.C . (1963). Experimental and Quasi-Experimental Designs for Research. Boston: Houghton Mifflin (ISBN 9780528614002)
Coolican, H. (2009, 5th ed.). Research Methods and Statistics in Psychology. UK: Hodder (ISBN 9780340983447)
Shadish, W.R., Cook, T.D. & Campbell, D.T. (2001, 2nd ed.). Experimental and Quasi-experimental Designs for Generalized Causal Inference. UK: Wadsworth (ISBN 9780395615560)
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Experimental Design
- Living reference work entry
- First Online: 28 August 2020
- Cite this living reference work entry
- Kim Koh 2
75 Accesses
Experiments ; Randomized clinical trial ; Randomized trial
In quality-of-life and well-being research specifically, and in medical, nursing, social, educational, and psychological research more generally, experimental design can be used to test cause-and-effect relationships between the independent and dependent variables.
Description
Experimental design was pioneered by R. A. Fisher in the fields of agriculture and education (Fisher 1935 ). In studies that use experimental design, the independent variables are manipulated or controlled by researchers, which enables the testing of the cause-and-effect relationship between the independent and dependent variables. An experimental design can control many threats to internal validity by using random assignment of participants to different treatment/intervention and control/comparison groups. Therefore, it is considered one of the most statistically robust designs in quality-of-life and well-being research, as well as in...
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Werklund School of Education, University of Calgary, Calgary, AB, Canada
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Correspondence to Kim Koh .
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Dipartimento di Scienze Statistiche, Sapienza Università di Roma, Roma, Italy
Filomena Maggino
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Department of ECPS & Intitute of Applied Mathematics, University of British Columbia, Vancouver, BC, Canada
Bruno Zumbo
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Koh, K. (2020). Experimental Design. In: Maggino, F. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Cham. https://doi.org/10.1007/978-3-319-69909-7_967-2
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DOI : https://doi.org/10.1007/978-3-319-69909-7_967-2
Received : 11 October 2019
Accepted : 02 December 2019
Published : 28 August 2020
Publisher Name : Springer, Cham
Print ISBN : 978-3-319-69909-7
Online ISBN : 978-3-319-69909-7
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