Introducción al metaanálisis multivariado con modelos de ecuaciones estructurales
DOI:
https://doi.org/10.5944/ap.22.1.43279Palabras clave:
Metaanálisis, Modelos de ecuaciones estructurales, Síntesis de la evidencia , MASEMResumen
El meta-análisis con modelos de ecuaciones estructurales (Meta-Analytic Structural Equation Modeling, MASEM) es una metodología novedosa de síntesis de la evidencia que combina las ventajas del metaanálisis tradicional con los modelos de ecuaciones estructurales, permitiendo explorar relaciones complejas entre variables a través de la integración de estudios independientes. Este tutorial ofrece una introducción accesible a la metodología MASEM, incluyendo los conceptos fundamentales, los pasos metodológicos para su implementación, y una aplicación práctica usando software estadístico libre. El objetivo es proporcionar a los investigadores una comprensión sólida y las herramientas necesarias para aplicar MASEM en sus propios estudios. Para ello se aborda desde la preparación de datos hasta la interpretación de resultados, destacando tanto las fortalezas como las limitaciones de esta metodología.
Descargas
Citas
Aguayo-Estremera, R., Cañadas-De la Fuente, G. R., Ariza-Castilla, T., Ortega-Campos, E., Gómez-Urquiza, J. L., Romero-Béjar, J. L. y De la Fuente-Solana, E. I. (2024). A Comparison of Univariate and meta-Analytic Structural Equation Modelling Approaches to Reliability Generalization Applied to the Maslach Burnout Inventory. Frontiers in Psychology, 15, Artículo 1383619. https://doi.org/10.3389/fpsyg.2024.1383619
Becker, B. J. (1992). Using Results from Replicated Studies to Estimate Linear Models. Journal of Educational Statistics, 17, 341–362. https://doi.org/10.3102/10769986017004341
Becker, B. J. (1995). Corrections to “Using results from Replicated Studies to Estimate Linear Models”. Journal of Educational and Behavioral Statistics, 20, 100–102. https://doi.org/10.3102/10769986020001100
Becker, B. J. y Aloe, A. M. (2019). Model-based Meta-analysis. En H. Cooper, L. V. Hedges y J. C. Valentine, The Handbook of Research Synthesis and Meta-analysis (3ª ed, pp. 339-363). Russell Sage.
Bilici, Z. Ş., Van den Noortgate, W. y Jak, S. (2025). Six ways to handle dependent effect sizes in meta-analytic structural equation modeling: Is there a gold standard? Research Synthesis Methods, 0, 1–27. https://doi.org/10.1017/rsm.2024.10
Borenstein, M., Hedges, L. V., Higgins, J. P. T. y Rothstein, H. R. (2010). A basic introduction to fixed‐effect and random‐effects models for meta‐analysis. Research Synthesis Methods, 1, 97-111. https://doi.org/10.1002/jrsm.12
Borenstein, M., Higgins, J. P. T., Hedges, L. V. y Rothstein, H. R. (2017). Basics of meta‐analysis: I2 is not an absolute measure of heterogeneity. Research Synthesis Methods, 8, 5–18. https://doi.org/10.1002/jrsm.1230
Botella, J. y Meca, J. S. (2015). Metaanálisis en ciencias sociales y de la salud [Meta-analysis in Social and Health Sciences]. Síntesis.
Cano-López, J. B., Garcia-Sancho, E., Fernández-Castilla, B. y Salguero, J. M. (2022). Empirical Evidence of the Metacognitive Model of Rumination and Depression in Clinical and Nonclinical Samples: A Systematic Review and Meta-Analysis. Cognitive Therapy and Research, 46(6), 1–26. https://doi.org/10.1007/s10608-021-10260-2
Cooper, H., Hedges, L. V. y Valentine, J. C. (Eds.). (2019). The Handbook of Research Synthesis and Meta-Analysis. Sage.
Cheung, M. W.-L. (2013). Multivariate Meta-Analysis as Structural Equation Models. Structural Equation Modeling, 20, 429–454. https://doi.org/10.1080/10705511.2013.797827
Cheung, M. W.-L. (2015). Meta-analysis: A Structural Equation Modeling Approach. Wiley.
Cheung, M. W.-L. (2015). metaSEM: an R Package for Meta-Analysis Using Structural Equation Modeling. Frontiers in Psychology, 5, Artículo 1521. https://doi.org/10.3389/fpsyg.2014.01521
Cheung, M. W.-L. (2009). Constructing Approximate Confidence Intervals for Parameters with Structural Equation Models. Structural Equation Modeling: A Multidisciplinary Journal, 16, 267–294. https://doi.org/10.1080/10705510902751291
Cheung, M. W.-L. (2019). Some Reflections on Combining Meta-Analysis and Structural Equation Modeling. Research Synthesis Methods, 10, 15–22. https://doi.org/10.1002/jrsm.1321
Cheung, M. W.-L. y Chan, W. (2005). Meta-analytic Structural Equation Modeling: A Two-Stage Approach. Psychological Methods, 10, 40–64. https://doi.org/10.1037/1082-989X.10.1.40
Cheung, M. W.-L. y Cheung, S. F. (2016). Random-Effects Models for Meta-Analytic Structural Equation Modeling: Review, Issues, and Illustrations. Research Synthesis Methods, 7, 140–155. https://doi.org/10.1002/jrsm.1166
Cronbach, L. J. (1951). Coefficient Alpha and the Internal Structure Of Tests. Psychometrika, 16, 297–334. https://doi.org/10.1007/BF02310555
De Jonge, H., Jak, S. y Kan, K. J. (2020). Dealing with Artificially Dichotomized Variables in Meta-Analytic Structural Equation Modeling. Zeitschrift für Psychologie, 228, 25–35. https://doi.org/10.1027/2151-2604/a000395
Glass, G. V. (1976). Primary, Secondary, and Meta-Analysis of Research. Educational Researcher, 5, 3–8. https://doi.org/10.3102/0013189X005010003
Grissom, R. J. y Kim, J. J. (2012). Effect Sizes for Research: Univariate and Multivariate Applications (2ª Ed.). Routledge.
Groot, L. J., Kan, K. J. y Jak, S. (2024). Checking the Inventory: Illustrating Different Methods for Individual Participant Data Meta‐Analytic Structural Equation Modeling. Research Synthesis Methods, 15, 872–895. https://doi.org/10.1002/jrsm.1735
Hattie, J. (2008). Visible Learning: A Synthesis of over 800 Meta-Analyses Relating to Achievement. Routledge.
Hedges, L. V. y Vevea, J. L. (1998). Fixed-and Random-Effects Models in Meta-Analysis. Psychological Methods, 3, 486–504. https://doi.org/10.1037/1082-989X.3.4.486
Higgins, J. P. T. y Thompson, S. G. (2002). Quantifying Heterogeneity in a Meta‐Analysis. Statistics in Medicine, 21, 1539–1558. https://doi.org/10.1002/sim.1186
Hu, L. T. y Bentler, P. M. (1999). Cutoff Criteria for fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Jak, S. (2015). Meta-analytic Structural Equation Modelling. Springer https://doi.org/10.1007/978-3-319-27174-3
Jak, S. y Cheung, M. W.-L. (2018a). Accounting for Missing Correlation Coefficients in Fixed-Effects MASEM. Multivariate Behavioral Research, 53, 1–14. https://doi.org/10.1080/00273171.2017.1375886
Jak, S. y Cheung, M. W.-L. (2018b). Testing Moderator Hypotheses in Meta-Analytic Structural Equation Modeling using Subgroup Analysis. Behavior Research Methods, 50, 1359–1373. https://doi.org/10.3758/s13428-018-1046-3
Jak, S. y Cheung, M. W.-L. (2020). Meta-analytic Structural Equation Modeling with Moderating Effects on SEM Parameters. Psychological Methods, 25, 430–455. https://doi.org/10.1037/met0000245
Jak, S., Li, H., Kolbe, L., de Jonge, H. y Cheung, M. W.-L. (2021). Meta‐analytic Structural Equation Modeling Made Easy: A Tutorial and Web Application for One‐Stage MASEM. Research Synthesis Methods, 12, 590–606. https://doi.org/10.1002/jrsm.1498
Kline, R. B. (2023). Principles and Practice of Structural Equation Modeling (5ª ed.). Guilford.
Lei, P. W. y Wu, Q. (2007). Introduction to Structural Equation Modeling: Issues and Practical Considerations. Educational Measurement: Issues and Practice, 26, 33–43. https://doi.org/10.1111/j.1745-3992.2007.00099.x
López‐López, J. A., Page, M. J., Lipsey, M. W. y Higgins, J. P. T. (2018). Dealing with Effect Size Multiplicity in Systematic Reviews and Meta‐Analyses. Research Synthesis Methods, 9, 336–351. https://doi.org/10.1002/jrsm.1310
Maslach, C. y Jackson, S. E. (1981). The Measurement of Experienced Burnout. Journal of Organizational Behavior, 2, 99–113. https://doi.org/10.1002/job.4030020205
McArdle, J. J. y McDonald, R. P. (1984). Some Algebraic Properties of the Reticular Action Model for Moment Structures. British Journal of Mathematical and Statistical Psychology, 37, 234–251. https://doi.org/10.1111/j.2044-8317.1984.tb00802.x
McDonald, R. P. (1999). Test Theory: A Unified Treatment. L. Erlbaum.
McNeish, D. (2018). Thanks coefficient alpha, we’ll take it from here. Psychological Methods, 23, 412–433. https://doi.org/10.1037/met0000144
Muñiz, J. (2018). Introducción a la Psicometría. Teoría Clásica y TRI. [Introduction to Psychometrics. Classical Theory and IRT]. Pirámide.
Papageorgiou, C. y Wells, A. (2003). An Empirical Test of a Clinical Metacognitive Model of Rumination and Depression. Cognitive Therapy and Research, 27, 261–273. https://doi.org/10.1023/A:1023962332399
Raykov, T. y Marcoulides, G. A. (2013). Meta-analysis of Scale Reliability Using Latent Variable Modeling. Structural Equation Modeling, 20, Artículo 338353. https://doi.org/10.1080/10705511.2013.769396
Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. https://doi.org/10.18637/jss.v048.i02
Ruiz, M. A., Pardo, A. y San Martín, R. (2010). Modelos de ecuaciones estructurales [Structural Equation Models]. Papeles del Psicólogo, 31, 34–45. https://www.redalyc.org/pdf/778/77812441004.pdf
Scherer, R. y Teo, T. (2020). A Tutorial on the Meta-Analytic Structural Equation Modeling of Reliability Coefficients. Psychological Methods, 25, 747–775. https://doi.org/10.1037/met0000261
Viswesvaran, C. y Ones, D. S. (1995). Theory Testing: Combining Psychometric Meta-Analysis and Structural Equations Modeling. Personnel Psychology, 48, 865–885. https://doi.org/10.1111/j.1744-6570.1995.tb01784.x.
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2025 Facultad de Psicología

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
La revista Acción Psicológica se publica bajo licencia Creative Commons Reconocimiento – NoComercial (CC BY-NC). Las opiniones y contenidos de los artículos publicados en Acción Psicológica son de responsabilidad exclusiva de los autores y no comprometen la opinión y política científica de la revista. También serán responsables de proporcionar copias de los datos en bruto, puntuaciones, y, en general, material experimental relevante a los lectores interesados.
Copyright Note
Acción Psicológica is published under Creative Commons Attribution-Non Commercial (CC BY-NC). The opinions and contents of the articles published in Acción Psicológica are responsibility of the authors and do not compromise the scientific and political opinion of the journal. Authors are also responsible for providing copies of the raw data, ratings, and, in general, relevant experimental material to interested readers.




