QUASI-EXPERIMENTAL RESEARCH

QUASI-EXPERIMENTAL RESEARCH

Definition of the method/technique 

A quasi-experimental design aims to establish a cause-and-effect relationship between an independent and dependent variable. However, unlike a true experiment, a quasi-experiment does not rely on random assignment. Instead, subjects are assigned to groups based on non-random criteria. Quasi-experimental design is a useful tool in situations where true experiments cannot be used for ethical or practical reasons.

Characteristics of the method/technique 

● Quasi-Experimental Design is a unique research methodology because it is characterized by what it        lacks. 

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

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

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

Main uses of the method/technique 

Although true experiments have higher internal validity, you might choose to use a quasi-experimental design for ethical or practical reasons. 

➢ Ethical. Sometimes it would be unethical to provide or withhold a treatment on a random basis, so a true experiment is not feasible. In this case, a quasi-experiment can allow you to study the same causal relationship without the ethical issues. 

➢ Practical. True experimental design may be infeasible to implement or simply too expensive, particularly for researchers without access to large funding streams. 

Advantages and Disadvantages of using the method/technique 

Advantages: 

★ 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.

Disadvantages: 

❖ 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.