Meta-Analytic Effect Size Synthesis, Subgroup Disaggregation, and Forest Plot Visualization: An SPSS Empirical Evaluation of Behavioral Interventions

This project conducted a quantitative meta-analysis in IBM SPSS Statistics to synthesize empirical effect sizes across behavioral intervention studies and visualize pooled outcome metrics using high-resolution Forest plots. Utilizing SPSS meta-analytic procedures, the analysis processed study-level effect estimates from primary research outputs (.spv viewer data) to evaluate cumulative effect magnitudes across target behavioral constructs ("L behaviors").The analytical pipeline applied study selection filtering to isolate positive-effect studies and executed sensitivity analyses across disaggregated outcome categories. Statistical synthesis involved calculating weighted pooled effect sizes under fixed-effects and random-effects assumptions, alongside variance metrics and 95% confidence intervals. Heterogeneity across individual studies was quantified using standard diagnostic statistics, including Cochran’s $Q$, $I^2$ inconsistency metrics, and tau-squared ($\tau^2$) inter-study variance estimates.To support clear data communication and scientific reporting, the synthesized outputs were mapped into customized SPSS Forest plots. These visual diagrams displayed individual study weights, effect size point estimates with error bars, and diamond summary markers for overall pooled effects as well as disaggregated behavioral sub-domains, providing robust evidence-based insights for research and decision-making.