Definition of Quasi Experimental Design

Quasi Experimental Design is a research method used in social sciences and other fields to study cause-and-effect relationships between different variables. It is called “quasi” experimental because it resembles an experimental design but lacks some key elements, such as random assignment.

Characteristics of Quasi Experimental Design

  • Comparison Groups: Quasi experimental design involves at least two groups that are compared to determine the impact of an independent variable on the dependent variable. These groups can be pre-existing or created by the researchers, but they are not randomly assigned.
  • Independent Variable: The researcher manipulates or selects an independent variable to observe its effect on the dependent variable.
  • Dependent Variable: The variable that is measured or observed to determine changes or differences caused by the independent variable.
  • Lack of Randomization: Unlike experimental designs, quasi experimental designs do not involve random assignment of participants to groups. Instead, participants are assigned based on criteria such as convenience, availability, or pre-existing characteristics.
  • Real-World Settings: Quasi experimental designs are often conducted in real-world settings, such as schools, communities, or organizations, where it may be difficult or impractical to control all variables.
  • Data Collection: Researchers collect data using various methods, such as surveys, observations, or existing records, to evaluate the impact of the independent variable.
  • Data Analysis: Statistical techniques, such as regression analysis or analysis of variance (ANOVA), are commonly employed to analyze the data and determine the relationship between the independent and dependent variables.

Advantages and Limitations of Quasi Experimental Design


  • Allows researchers to study cause-and-effect relationships that may be unethical or impractical to investigate through experimental designs.
  • Provides a middle ground between experimental and purely observational designs.
  • Offers high external validity as it can be conducted in real-world settings.


  • Lack of randomization limits the ability to establish strong causal relationships.
  • Potential for selection bias, as participants are not randomly assigned.
  • Difficulty in ruling out alternative explanations for observed results.
  • May be less precise due to the absence of control over all variables.