A quasi-experiment is a type of empirical interventional study used to estimate the causal impact of an intervention on a target population without random assignment. It is a research design that attempts to establish a cause-and-effect relationship between an independent and dependent variable, but unlike a true experiment, it does not rely on random assignment. Instead, subjects are assigned to groups based on non-random criteria. Quasi-experiments are commonly used in social sciences, public health, education, and policy analysis, especially when it is not practical or reasonable to randomize study participants to the treatment condition.

Some key features of quasi-experiments include:

• Pre-post testing: This means that there are tests done before any data are collected to see if there are any person confounds or if any participants have certain tendencies. Then the actual experiment is done with post-test results recorded. This data can be compared as part of the study or the pre-test data can be included in an explanation for the actual experimental data.

• Outcome measures, treatments, and experimental units: Quasi-experiments have these features, but do not use random assignment.

• Types of quasi-experimental designs: Some common types of quasi-experimental designs are regression discontinuity, nonequivalent groups design, and natural experiments.

Quasi-experimental studies have lower internal validity than true experiments and also cannot establish a causal relationship between variables as effectively. However, they are useful in situations where true experiments cannot be used for ethical or practical reasons. For example, it would be unethical to withhold treatment from a subject based on a random basis, and in such situations, a quasi-experimental design can be utilized to avoid any ethical issues. Additionally, quasi-experimental designs are more appropriate when the true experiment design is not feasible due to high expenses or the fact that true experiments generally involve a lot of work to effectively design an experimental intervention for the threshold of subjects required to justify the research as a true experiment.