Quasi-Experimental Design
What is Quasi-Experimental Design?
Quasi-Experimental Designs are used to study cause-and-effect relationships. In a quasi-experiment, the researcher manipulates one or more independent variables and observes the effect on a dependent variable. However, unlike in a true experiment, the researcher does not randomly assign participants to different conditions.
Quasi-Experimental Research
Quasi-experimental Research is a type of research that is conducted in order to determine the cause and effect relationship between two variables. This type of research is often used when it is not possible to conduct a true experiment, due to ethical reasons or practical constraints. Quasi-experimental research designs typically involve the use of control groups and/or comparison groups in order to improve the validity of the findings.
Quasi-Experimental Research Types
There are three common types of quasi-experimental research designs:
- Pre-test post-test design
- Solomon four-group design
- Interrupted time series design
Pre-test post-test design
A pre-test post-test design is a research design that involves administering a pretest to all participants, followed by intervention, and then comparing the results of the posttest.
Solomon four-group design
In a Solomon four-group design, there are four groups of participants, with two groups receiving the treatment and two groups receiving no treatment (or a placebo). This design is used to control for confounding variables and to ensure that any effects of the treatment are not due to chance. This design is often used in clinical trials.
Interrupted time series design
An interrupted time series design is a research design that involves collecting data at regular intervals before and after a treatment is introduced. The treatment can be any type of intervention, such as a change in policy or the implementation of a new program. The data are analyzed to see if there is a statistically significant difference in the outcome variable before and after the treatment.
Quasi-Experimental Data Analysis Methods
There are three Quasi-Experimental Data Analysis Methods:
- Regression Discontinuity
- Difference-in-differences
- Instrumental variables
Regression Discontinuity
Regression discontinuity is a method that can be used to estimate the causal effect of an intervention by looking at the difference in outcomes between individuals who just barely qualify for the intervention and those who just barely do not qualify.
Difference-in-differences
Difference-in-differences is a method that can be used to compare the before and after outcomes of two groups that have been affected by an intervention. This method looks at the difference in outcomes between the two groups before and after the intervention has been implemented.
Instrumental variables
Instrumental variables is a method that can be used to identify the causal effect of an explanatory variable on an outcome variable. This method uses the variation in an explanatory variable to identify the causal relationship between two variables.
Quasi-Experimental Research Example
- One example of quasi-experimental research is a study that looks at the effects of a new medication on a group of people who are taking it. The researchers would not be able to randomly assign people to take the medication or not, so they would instead use a quasi-experimental design.
- Another example of quasi-experimental research is a study that looks at the effects of a new teaching method on a group of students. The researchers would not be able to randomly assign students to use the new method or not, so they would instead use a quasi-experimental design.
Also see Qualitative Research
How to Conduct Quasi-Experimental Research
Follow the steps in order to conduct Quasi-Experimental Research.
- The researcher must identify a treatment and control group.
- The researcher must select a sample of subjects from each group.
- The researcher must collect data from the subjects and analyze it.
When to use Quasi-Experimental Research
Quasi-experimental research is used when the researcher wants to study a phenomenon but they can’t manipulate the independent variable.
For example, in a study on how diets affect weight, the researcher might randomly assign participants to one of two groups – one group would get a high-fat diet, while the other would get a low-fat diet. But because participants cannot be randomly assigned to these diets, this type of study is considered quasi-experimental.
Advantages of Quasi-Experimental Design
There are many advantages to using quasi-experimental designs in research.
- The most important is that they allow researchers to study causal relationships in natural settings, which is something that cannot be done with experimental designs.
- Quasi-experimental designs are also more realistic and ecologically valid than experimental designs, and they allow for the study of variables that cannot be manipulated.
- They are less expensive and time-consuming than experimental designs, making them a more feasible option for many researchers.
Also see Correlational Research Design
Disadvantages of Quasi-Experimental Design
There are several disadvantages to using quasi-experimental designs.
- Quasi-experimental designs do not involve random assignment, they are less likely to produce statistically significant results.
- It has a lack of control group, it can be difficult to determine whether the results are due to the independent variable or some other factor.
- They do not always involve randomization, they can be more susceptible to selection bias than true experimental designs.
Also see Descriptive Research Design
