posttestonly

Posttestonly, short for posttest-only design, is an experimental design in which participants are randomly assigned to treatment and control groups and a dependent variable is measured only after the intervention, with no pretest measurement. This design contrasts with pretest-posttest designs that include measurements before the intervention.

Purpose and usage: Posttest-only designs are used when baseline data collection is impractical, could influence participant responses, or when researchers want to avoid testing effects. They are common in education, psychology, and field evaluations where a quick, cost-effective assessment of the intervention’s impact is desired.

Design features: The typical setup involves at least two groups—one receiving the intervention and one not. The primary analysis compares posttest scores between groups, often using a t-test or ANOVA, with effect size reported (for example, Cohen’s d). Random assignment helps ensure equivalence across groups on unmeasured variables, though this relies on sample size and proper implementation.

Advantages: Simplicity and cost-effectiveness; reduces potential reactivity and testing effects associated with pretesting; avoids contamination of pretest measures affecting outcomes.

Disadvantages: Inability to verify baseline equivalence for the groups; reliance on successful randomization to balance confounds; vulnerability to attrition or differential dropout that can bias results; cannot measure change from baseline or explore preintervention conditions.

Variants and related designs: Nonrandomized posttest-only designs exist (nonequivalent control group posttest design), though randomized posttest-only designs are more robust. The Solomon four-group design combines posttests with pretests to evaluate potential testing effects, but it is more complex and resource-intensive.

Overall, posttest-only designs offer a straightforward approach for estimating intervention effects when pretesting is undesirable or impractical, with careful attention to randomization and attrition risks.