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Design strong quasi-experiments when randomization impossible. Design types: 1. Non-equivalent groups: compare treatment and comparison groups without random assignment. 2. Interrupted time series: multiple observations before and after intervention. 3. Regression discontinuity: treatment assigned based on cutoff score. 4. Instrumental variables: use natural randomization to estimate causal effects. Strengthening designs: 1. Propensity score matching: match treatment and control on likelihood of receiving treatment. 2. Difference-in-differences: compare changes over time between treatment and control areas. 3. Multiple baselines: stagger intervention across participants or sites. Threats to validity: 1. Selection bias: groups differ systematically. 2. History: events coinciding with intervention. 3. Maturation: natural change over time. Analysis considerations: intention-to-treat vs. per-protocol analysis, sensitivity analysis for unobserved confounders, report effect sizes and confidence intervals.
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