Exploring the possibility of meta-analysis in exploratory factor analysis: A methodological commentary
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Abstract
Meta-analysis is a vital statistical tool in psychometric research, enabling the synthesis of multiple studies to enhance the reliability and validity of measurement instruments. This study applies meta-analytic techniques to exploratory factor analysis (EFA) to establish a structured framework for aggregating factor structures across psychological and health-related assessments. Given the variations in factor solutions due to methodological and sample differences, a systematic synthesis is essential. The study outlines key methodological considerations, including data extraction, effect size computation using Epsilon-Squared (ω²), heterogeneity analysis, and statistical synthesis via a random-effects model. Findings indicate that meta-analysis can improve the generalizability of factor structures, with Factor 1 accounting for an average ω² of 0.72 across studies. The results highlight the importance of refining statistical approaches to address factor heterogeneity and enhance psychometric meta-analytic practices. This research contributes to the advancement of valid and reliable measurement frameworks in psychological and health sciences.
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| Issue | Articles in Press | |
| Section | Commentary(s) | |
| Keywords | ||
| factor analysis; statistical; psychometrics; meta-analysis; research design; validation | ||
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