meta-analysis
Definition
Meta-analysis refers to a statistical method for synthesizing and pooling results across multiple primary studies to obtain integrated estimates of relationships between variables. Meta-analyses reflect the limitations and characteristics of the meta-analyzed literature, including measurement approaches, sample populations, and methodological choices made in constituent studies. Modern meta-analytical approaches, particularly meta-analytical structural equation modeling (MASEM), enable testing of individual-level interactions by analyzing individual participant data across studies, which differs fundamentally from study-level moderation that operates on aggregated sample means and is susceptible to ecological fallacy. Meta-analyses can reveal both average effect sizes and sources of heterogeneity across studies.
Related Terms
Applications
Meta-analysis and Interaction Effects
Meta-analyses testing interaction-based hypotheses have traditionally relied on bivariate approximations that do not properly control for main effects and thus yield inflated or inaccurate estimates of true interactions. Recent advances in meta-analytical structural equation modeling (MASEM) enable rigorous multivariate testing of individual-level interactions using individual participant data across studies, eliminating ecological fallacy concerns and providing substantially different effect estimates compared to previous bivariate methods.
Sources: Vu & Bierwiaczonek (2025)
Meta-analysis and Heterogeneity
Meta-analytical findings reveal heterogeneity in effect sizes across studies, which can be attributed to multiple sources including methodological differences between studies and measurement choices. Reanalysis of meta-analytical data shows that country-level factors typically account for only a modest portion of effect variability, with larger variation arising from methodological and between-study differences.
Sources: Bierwiaczonek (2025)
Meta-analysis and Individual Differences
Meta-analyses synthesizing relationships between individual difference variables and outcomes employ effect size guidelines to interpret correlation magnitudes and identify moderators of those relationships.
Sources: Bowes & Fazio (2024)
