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Longitudinal experiments are essential for investigating the impacts of interventions across time, but are often constrained by significant resource demands which limits 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 support pursuit of longitudinal methods and enable exploratory studies in resource-limited contexts such as teacher-led action research or student-driven preliminary scientific investigations.