Design a Quasi-Experimental Evaluation

You are a senior MEAL specialist with expertise in impact evaluation and quantitative research design. Your task is to design a quasi-experimental evaluation that assesses the program's causal impact on its primary outcome. The program is implemented in designated treatment areas, and comparison areas are available in neighboring locations with similar demographic and economic profiles. Baseline data may or may not be available from a pre-program survey. **Develop the following components:** 1. **Research Design Selection:** Justify the choice of quasi-experimental design. Compare at least three options (difference-in-differences, propensity score matching, regression discontinuity, instrumental variables) and explain why the selected design is most appropriate given the program context. Address: * Why randomization was not feasible * Key assumptions of the chosen design * Conditions under which the design would fail 2. **Comparison Group Strategy:** * Matching methodology: Specify the matching approach (propensity score matching, coarsened exact matching, or nearest-neighbor matching) * Matching variables: List 8-12 observable characteristics for matching * Balance diagnostics: Describe how you will assess and report covariate balance (standardized mean differences, variance ratios) * Minimum acceptable balance thresholds 3. **Difference-in-Differences Analysis Plan:** * Model specification (including the interaction term and its interpretation) * Parallel trends assumption: How you will test and present evidence for the parallel trends assumption using pre-treatment data * Fixed effects and control variables to include * Clustering strategy for standard errors (at what level and why) * Heterogeneous treatment effects: Subgroup analyses planned by relevant demographic and socioeconomic categories 4. **Sample Size and Power Calculation:** * Minimum detectable effect size (with justification) * Required sample size for treatment and comparison groups * Assumptions: intra-cluster correlation, significance level, power, expected attrition * Sensitivity analysis for different effect sizes 5. **Threats to Validity Assessment:** Create a table with columns: Threat, Type (internal/external), Severity (high/medium/low), Mitigation Strategy, and Residual Risk. Address at minimum: * Selection bias * Attrition/differential dropout * Spillover effects * Contamination * Regression to the mean * Hawthorne effects * Time-varying confounders 6. **Data Collection Plan:** * Instruments needed (household survey, administrative data, qualitative complement) * Timing of data collection rounds * Quality assurance procedures * Ethical considerations and IRB requirements 7. **Analysis Timeline:** A phased plan from data collection through final impact report, including sensitivity analyses and robustness checks. **Output Format:** Deliver all components as clearly labeled sections. The threats to validity assessment should be a formatted table. Include the model specification as a clearly written equation with variable definitions.