Quasi-Experimental Studies
Quasi-experimental studies are research designs used to estimate causal relationships when random assignment of participants to treatment and control groups is not feasible or ethical. They involve comparing outcomes between groups that receive different interventions or exposures, but without the strict randomization of true experiments, often relying on natural or pre-existing conditions. These designs are common in social sciences, education, public health, and policy evaluation where controlled experiments are impractical.
Developers should learn quasi-experimental methods when working in data science, analytics, or research roles that involve evaluating the impact of interventions, such as A/B testing in tech products, assessing policy changes, or analyzing observational data. They are crucial for making causal inferences from non-randomized data, helping to inform decisions in fields like user experience research, public health informatics, or educational technology where ethical or logistical constraints prevent full randomization.