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Cited by 263Meta-analysis and systematic reviewsPsychometric Methodologies and TestingPsychology Research and Bibliometrics

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Meta-analytic structural equation modeling with moderating effects on SEM parameters.

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Jak, Suzanne, Cheung, Mike W. -L.·2020·Psychological Methods
DOI 10.1037/met0000245
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Abstract

Meta-analytic structural equation modeling (MASEM) is an increasingly popular meta-analytic technique that combines the strengths of meta-analysis and structural equation modeling. MASEM facilitates the evaluation of complete theoretical models (e.g., path models or factor analytic models), accounts for sampling covariance between effect sizes, and provides measures of overall fit of the hypothesized model on meta-analytic data. We propose a novel MASEM method, one-stage MASEM, which is better suitable to explain study-level heterogeneity than existing methods. One-stage MASEM allows researchers to incorporate continuous or categorical moderators into the MASEM, in which any parameter in the structural equation model (e.g., path coefficients and factor loadings) can be modeled by the moderator variable, while the method does not require complete data for the primary studies included in the meta-analysis. We illustrate the new method on two real data sets, evaluate its empirical performance via a computer simulation study, and provide user-friendly R-functions and annotated syntax to assist researchers in applying one-stage MASEM. We close the article by presenting several future research directions. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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Research works printed but not linked: 4

  1. Becker, B. J. (2009). Model-based meta-analysis. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta-analysis (2nd ed., pp. 377-395). New York, NY: Russell Sage Foundation.
  2. Cooper, H. M. (2010). Research synthesis and meta-analysis: A step-by- step approach (4th ed.). Los Angeles, CA: Sage.
  3. Olkin, I., & Siotani, M. (1976). Asymptotic distribution of functions of a correlation matrix. In S. Ideka (Ed.), Essays in probability and statistics (pp. 235-251). Tokyo, Japan: Shinko Tsusho.
  4. Pigott, T. D. (2009). Handling missing data. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta-analysis (2nd ed., pp. 399 -416). New York, NY: Russell Sage Foundation.

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Metadata

Authors
Jak, S., & Cheung, M. W. L.
Affiliations
National University of Singapore (SG)
ORCID
Venue
Psychological Methods
Publisher
American Psychological Association (APA)
Published
2020-08
Volume
25
Issue
4
Pages
430-455
ISSN
  • 1082-989X (ISSN-L)
  • 1939-1463
DOI
10.1037/met0000245
OpenAlex
W2947143056
Cited by
262
References
108
Citations by year
2026: 47 · 2025: 70 · 2024: 43 · 2023: 36 · 2022: 28 · 2021: 28 · 2020: 9
FWCI
11.03
Type
journal article
Language
en
Open access
Licence
other-oa
Locations
  • Psychological Methods (publishedVersion)
  • PubMed (publishedVersion)
  • UvA-DARE (University of Amsterdam) (submittedVersion, other-oa)
  • PsyArXiv (OSF Preprints) (submittedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Funders
  • Netherlands Organisation for Scientific Research (451-16-001)
  • Ministry of Education (FY2017-FRC1-008)
Topics
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
Keywords
meta-analytic structural equation modeling, moderating effect, model fit, path coefficients, factor loadings, R functions
MeSH terms
Latent Class Analysis, Computer Simulation, Humans, Psychology, Meta-Analysis as Topic, Models, Statistical

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