Quasi-Experimental, True experimental Design


A quasi-experiment is an empirical study used to estimate the causal impact of an intervention on its target population without random assignment. Quasi-experimental research shares similarities with the traditional experimental design or randomized controlled trial, but it specifically lacks the element of random assignment to treatment or control. Instead, quasi-experimental designs typically allow the researcher to control the assignment to the treatment condition, but using some criterion other than random assignment (e.g., an eligibility cut-off mark).

In some cases, the researcher may have control over assignment to treatment. Quasi-experiments are subject to concerns regarding internal validity, because the treatment and control groups may not be comparable at baseline. With random assignment, study participants have the same chance of being assigned to the intervention group or the comparison group. As a result, differences between groups on both observed and unobserved characteristics would be due to chance, rather than to a systematic factor related to treatment (e.g., illness severity). Randomization itself does not guarantee that groups will be equivalent at baseline. Any change in characteristics post-intervention is likely attributable to the intervention. With quasi-experimental studies, it may not be possible to convincingly demonstrate a causal link between the treatment condition and observed outcomes.


The first part of creating a quasi-experimental design is to identify the variables. The quasi-independent variable will be the x-variable, the variable that is manipulated in order to affect a dependent variable. “X” is generally a grouping variable with different levels. Grouping means two or more groups, such as two groups receiving alternative treatments, or a treatment group and a no-treatment group (which may be given a placebo – placebos are more frequently used in medical or physiological experiments). The predicted outcome is the dependent variable, which is the y-variable. In a time series analysis, the dependent variable is observed over time for any changes that may take place. Once the variables have been identified and defined, a procedure should then be implemented and group differences should be examined.

There are several types of quasi-experimental designs, each with different strengths, weaknesses and applications. These designs include:-

  • Difference in differences (pre-post with-without comparison)
  • Non-equivalent control groups design
  • No-treatment control group designs
  • Non-equivalent dependent variables designs
  • Removed treatment group designs
  • Repeated treatment designs
  • Reversed treatment nonequivalent control groups designs
  • Cohort designs
  • Post-test only designs
  • Regression continuity designs
  • Regression discontinuity design
  • Case-control design
  • Time-series designs
  • Multiple time series design
  • Interrupted time series design
  • Propensity score matching
  • Instrumental variables
  • Panel analysis


Since quasi-experimental designs are used when randomization is impractical and/or unethical, they are typically easier to set up than true experimental designs, which require[9] random assignment of subjects. Additionally, utilizing quasi-experimental designs minimizes threats to ecological validity as natural environments do not suffer the same problems of artificiality as compared to a well-controlled laboratory setting.[10] Since quasi-experiments are natural experiments, findings in one may be applied to other subjects and settings, allowing for some generalizations to be made about population. Also, this experimentation method is efficient in longitudinal research that involves longer time periods which can be followed up in different environments.


Quasi-experimental estimates of impact are subject to contamination by confounding variables. In the example above, a variation in the children’s response to spanking is plausibly influenced by factors that cannot be easily measured and controlled, for example the child’s intrinsic wildness or the parent’s irritability. The lack of random assignment in the quasi-experimental design method may allow studies to be more feasible, but this also poses many challenges for the investigator in terms of internal validity. This deficiency in randomization makes it harder to rule out confounding variables and introduces new threats to internal validity.


A true experimental design is one in which the researcher manipulates the Independent Variable (or variables) to observe its effect on some behavior or cognitive process (the dependent variable) while using random assignment of participants to groups in order to control external factors from influencing the results. Without both manipulation of the IV and random assignment of participants, you do not have a true experimental design and, as a result, can’t establish cause and effect.

A true experiment is a type of experimental design and is thought to be the most accurate type of experimental research. This is because a true experiment supports or refutes a hypothesis using statistical analysis. 

There are three criteria that must be met in a true experiment:

  • Control group and experimental group
  • Researcher-manipulated variable
  • Random assignment

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