Design and Implementation of Reduced Longitudinal Experiments

Longitudinal experiments are essential for investigating the impacts of intervention across time, but are often constrained by significant resource demands, which limit their implementation. This study introduces and evaluates reduced-resource designs to enhance the feasibility of longitudinal experiments. Using Monte Carlo simulations, we assessed the statistical trade-offs of reducing sample size, measurement occasions, and the proportion of experimental units assigned to intervention groups to provide simplified but actionable recommendations for non-experts. We further assessed the robustness of these reductions to attrition, non-sampling errors, and violations to fundamental model assumptions. These findings aim to democratize longitudinal methods and enable exploratory studies in resource-limited contexts such as teacher-led action research or student-driven preliminary scientific investigations.