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Under certain conditions, researchers often turn to field experiments, also known as quasi-experiment. These conditions usually occur when it is not possible to randomly assign participants to treatment and control groups (White & Sabarwal, 2014). Rather, selection to a group is by the participants, the researcher, or both the participant and the researcher (White & Sabarwal, 2014).
In a quasi-experiment, the independent variable is manipulated and similar to an experiment, it tests causal hypothesis (Campbell & Stanley, 1963).
Quasi-experiments allow researchers to infer causality by using the logic behind the experiment in a different way; however, there are three criteria that must be satisfied for causality to be inferred:
- The independent variable (X) comes before the dependent variable (Y) in time.
- X and Y are related to each other (i.e., they occur together).
- The relationship between X and Y aren’t explained by other causal agents (Crump, Price, Jhangiani, Chiang, & Leighton, 2017).
In a quasi-experiment the researcher identifies a comparison group that is as similar as is possible to the treatment group, as it relates to baseline (pre-intervention) characteristics. There are techniques for reducing selection bias when creating a comparison group. These techniques are regression discontinuity design and propensity score matching (White & Sabarwal, 2014); available at https://www.unicef-irc.org/publications/pdf/brief_8_quasi-experimental%20design_eng.pdf for more detail on these techniques.
