kvasieksperimentaalne

Kvasieksperimentaalne is an Estonian term that translates to "quasi-experimental" in English. It refers to research designs that resemble experimental studies but lack one or more of the key characteristics of a true experiment. These designs are often employed when random assignment of participants to different conditions is not feasible or ethical.

In a true experiment, researchers manipulate an independent variable and observe its effect on a dependent variable, with participants randomly assigned to either a treatment group or a control group. This random assignment helps to ensure that the groups are comparable before the intervention, minimizing the influence of confounding variables. Kvasieksperimentaalne designs, however, typically involve pre-existing groups or do not use random assignment.

Common types of kvasieksperimentaalne designs include nonequivalent group designs, time-series designs, and regression discontinuity designs. For instance, a nonequivalent group design might compare the outcomes of two intact groups (e.g., two different classrooms) that receive different interventions, without randomly assigning students to those classrooms. A time-series design involves taking multiple measurements of a dependent variable before and after an intervention is introduced, without a separate control group.

While kvasieksperimentaalne research can provide valuable insights and is often more practical than true experiments, its limitations must be acknowledged. The lack of random assignment means that observed differences between groups may be due to pre-existing differences rather than the intervention itself. Therefore, researchers using these designs must be particularly cautious in interpreting causality and in controlling for potential confounding factors through statistical or design adjustments. Despite these limitations, kvasieksperimentaalne research plays a crucial role in many fields, including education, public health, and social sciences, where true experimental control may be impossible.