Much like an actual experiment, quasi-experimental research tries to demonstrate a cause-and-effect link between a dependent and an independent variable. A quasi-experiment, on the other hand, does not depend on random assignment, unlike an actual experiment. The subjects are sorted into groups based on non-random variables.
What is Quasi-Experimental Research?
“Resemblance” is the definition of “quasi.” Individuals are not randomly allocated to conditions or orders of conditions, even though the regression analysis is changed. As a result, quasi-experimental research is research that appears to be experimental but is not.
The directionality problem is avoided in quasi-experimental research since the regression analysis is altered before the multiple regression is assessed. However, because individuals are not randomized at random, there are likely to be additional disparities across conditions in quasi-experimental research.
As a result, in terms of internal consistency, quasi-experiments fall somewhere between correlational research and actual experiments.
The key component of a true experiment is randomly allocated groups. This means that each person has an equivalent chance of being assigned to the experimental group or the control group, depending on whether they are manipulated or not.
Simply put, a quasi-experiment is not a real experiment. A quasi-experiment does not feature randomly allocated groups since the main component of a real experiment is randomly assigned groups. Why is it so crucial to have randomly allocated groups, given that they constitute the only distinction between quasi-experimental and actual experimental research?
Let’s use an example to illustrate our point. Let’s assume we want to discover how new psychological therapy affects depressed patients. In a genuine trial, you’d split half of the psych ward into treatment groups, With half getting the new psychotherapy therapy and the other half receiving standard depression treatment.
And the physicians compare the outcomes of this treatment to the results of standard treatments to see if this treatment is more effective. Doctors, on the other hand, are unlikely to agree with this genuine experiment since they believe it is unethical to treat one group while leaving another untreated.
A quasi-experimental study will be useful in this case. Instead of allocating these patients at random, you uncover pre-existing psychotherapist groups in the hospitals. Clearly, there’ll be counselors who are eager to undertake these trials as well as others who prefer to stick to the old ways.
These pre-existing groups can be used to compare the symptom development of individuals who received the novel therapy with those who received the normal course of treatment, even though the groups weren’t chosen at random.
If any substantial variations between them can be well explained, you may be very assured that any differences are attributable to the treatment but not to other extraneous variables.
As we mentioned before, quasi-experimental research entails manipulating an independent variable by randomly assigning people to conditions or sequences of conditions. Non-equivalent group designs, pretest-posttest designs, and regression discontinuity designs are only a few of the essential types.
What are quasi-experimental research designs?
Quasi-experimental research designs are a type of research design that is similar to experimental designs but doesn’t give full control over the independent variable(s) like true experimental designs do.
In a quasi-experimental design, the researcher changes or watches an independent variable, but the participants are not put into groups at random. Instead, people are put into groups based on things they already have in common, like their age, gender, or how many times they have seen a certain stimulus.
Because the assignments are not random, it is harder to draw conclusions about cause and effect than in a real experiment. However, quasi-experimental designs are still useful when randomization is not possible or ethical.
The true experimental design may be impossible to accomplish or just too expensive, especially for researchers with few resources. Quasi-experimental designs enable you to investigate an issue by utilizing data that has already been paid for or gathered by others (often the government).
Because they allow better control for confounding variables than other forms of studies, they have higher external validity than most genuine experiments and higher internal validity (less than true experiments) than other non-experimental research.
Is quasi-experimental research quantitative or qualitative?
Quasi-experimental research is a quantitative research method. It involves numerical data collection and statistical analysis. Quasi-experimental research compares groups with different circumstances or treatments to find cause-and-effect links.
It draws statistical conclusions from quantitative data. Qualitative data can enhance quasi-experimental research by revealing participants’ experiences and opinions, but quantitative data is the method’s foundation.
Quasi-experimental research types
There are many different sorts of quasi-experimental designs. Three of the most popular varieties are described below: Design of non-equivalent groups, Discontinuity in regression, and Natural experiments.
Design of Non-equivalent GroupsThe researcher picks existing groups that look comparable, but only one of the groups receives the therapy in a non-equivalent group design.
When employing this design, researchers attempt to accommodate for any confounding factors by adjusting for them in their study or selecting groups that are as comparable as feasible. The most prevalent sort of quasi-experimental design is this one.
Example: Design of Non-equivalent GroupsYou believe that the new after-school activity will result in improved academic performance. You pick two comparable groups of students from separate classes, one of which uses the new program and the other does not.
You can see if the program influences grades by comparing students who participate to those who do not.
Discontinuity in regressionMany of the prospective therapies that researchers want to investigate are based on a basic arbitrary cutoff, with those who fall over the threshold receiving treatment and those who fall below it not. At this point, the group differences are frequently so minor that they are almost non-existent.
As a result, researchers can utilize people who are under the limit as a reference group and people who are just beyond it as an intervention group.
Example: Discontinuity in regressionIn the United States, certain high schools are reserved for pupils who achieve a specified level of achievement on a test. Those who succeed in this exam are likely to vary from those who do not in a systematic way.
However, because the precise cutoff number is arbitrary, students near the limit who barely pass exams and those who fail by a razor-thin margin tend to be extremely similar, with the minute variations in their results owing primarily to chance. As a result, any disparities in outcomes must be due to their educational experiences.
You may look at the long-term outcomes of these two groups of kids to see how attending a selective school affects them.
Natural experimentsResearchers usually choose which group the individuals are allocated to in both lab and outdoor tests. A random or irregular assignment of patients to the control treatment occurs in a natural experiment because of an external occurrence or scenario (“nature”).
Natural experiments are not actual experiments since they are observational, even though some employ random assignments.
Example: Natural experimentsOne of the best-known natural experiments is the Oregon Health Study. In 2008, Oregon voted to increase the number of low-income people enrolled in Medicaid, America’s low-income public health care program.
However, because they couldn’t afford to pay everyone who qualified for the program, they had to use a random lottery to distribute slots.
Experts were able to investigate the program’s impact by utilizing enrolled people as a treatment group and those who were qualified but did not play the jackpot as an experimental group.
How QuestionPro helps in quasi-experimental research?
QuestionPro can be a useful tool in quasi-experimental research because it includes features that can assist you in designing and analyzing your research study. Here are some ways in which QuestionPro can help in quasi-experimental research:
Design surveysQuestionPro lets you create questionnaires to collect data from study participants. Multiple choice, open-ended, and Likert scale questions are available. Quantitative and qualitative data can be collected this way.
Randomize participantsQuasi-experimental research does not randomly assign people to groups, yet some components of the study may need to be randomized. QuestionPro can randomize question and answer in order to reduce bias.
Collect data over timeQuasi-experimental research generally collects data over time to assess an intervention or treatment. QuestionPro lets you send periodic surveys to collect data. You can also use features like reminders to get more people to reply.
Analyze dataCross-tabulation and statistical analysis are among QuestionPro’s data analysis options. This might help you find data patterns and evaluate the intervention or treatment’s efficacy.
Collaborate with your teamQuestionPro lets your team access surveys and statistics. This improves collaboration and data-driven decision-making.
With QuestionPro, you have access to the most mature market research platform and tool that helps you collect and analyze the insights that matter the most. By leveraging InsightsHub, the unified hub for data management, you can leverage the consolidated platform to organize, explore, search, and discover your research data in one organized data repository.
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