QUASI-EXPERIMENTAL RESEARCH

 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

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 Groups

The 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.

Discontinuity in regression

Natural experiments

Many 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.

Researchers 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”).

Characteristics of the Quasi-experimental Research

·   The researcher often does not have control over the treatment, but instead studies pre-existing groups that received different treatments after the fact.

·     Some other, non-random method is used to assign subjects to groups

·     Control groups are not required (although they are commonly used).

·  Participants are categorized and then put into respective experimental groups.

·     Researchers study the existing groups of treatments received.

Advantages of the Quasi-experimental Research

·  Higher external validity than most true experiments, because they often involve real-world interventions instead of artificial laboratory settings.

·  Higher internal validity than other non-experimental types of research, because they allow you to better control for confounding variables than other types of studies do.

·  The quasi-experiment method can be combined with other experimental methods too.

·  Offers better control over the third variable known as the confounding variable which influences the cause and effect.

 

Disadvantages of the Quasi-experimental Research

·   Lower internal validity than true experiments—without randomization, it can be difficult to verify that all confounding variables have been accounted for.

·  The use of retrospective data that has already been collected for other purposes can be inaccurate, incomplete, or difficult to access.

·     It has scope for human errors.

·    Human responses are difficult to measure, hence, there is a chance that the results are produced artificially.