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Does Cluster-Robust Estimation Provide Within-Study Effects? A Comparison of Individual Participant Data Methods in MASEM

Title: Does Cluster-Robust Estimation Provide Within-Study Effects? A Comparison of Individual Participant Data Methods in MASEM
Language: English
Authors: Lennert J. Groot (ORCID 0000-0002-8711-6086); Kees Jan Kan (ORCID 0000-0003-0088-9906); Suzanne Jak (ORCID 0000-0002-2223-5594)
Source: Structural Equation Modeling: A Multidisciplinary Journal. 2025 32(5):801-813.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 13
Publication Date: 2025
Document Type: Journal Articles; Reports - Research
Descriptors: Structural Equation Models; Meta Analysis; Computation; Data Analysis; Path Analysis; Correlation; Matrices; Statistical Bias; Error of Measurement; Goodness of Fit
DOI: 10.1080/10705511.2025.2505995
ISSN: 1070-5511; 1532-8007
Abstract: Researchers conducting meta-analytical structural equation modeling (MASEM) with individual participant data can choose from several methods, including cluster-robust estimation, two-level SEM, multivariate meta-analysis of path coefficients, and One-Stage MASEM (OSMASEM). While two-level SEM and OSMASEM model within- and between-study effects separately, cluster-robust estimation combines them, estimating an overall path coefficient. Despite its popularity, cluster-robust estimation often yields results that differ from other methods. Simulations using factor models and real-world comparisons using path models show that it may not accurately reflect within-study estimates and can produce biased standard errors. This study compares IPD MASEM methods using simulated data, varying intraclass correlations, parameter equality across levels, number of studies, and missing data. Results reveal that cluster-robust estimation frequently misrepresents within-study estimates, produces biased standard errors, and tends to incorrectly reject model fit, highlighting the need for careful method selection in IPD MASEM applications.
Abstractor: As Provided
Notes: https://osf.io/qh4jn
Entry Date: 2026
Accession Number: EJ1501502
Database: ERIC