Conceptual and Practical Approach to Structural Equations Modeling

Authors

  • Leonardo Adrián Medrano Universidad Nacional de Córdoba
  • Roger Muñoz-Navarro Universidad de Valencia

DOI:

https://doi.org/10.19083/ridu.11.486

Keywords:

Modelos de Ecuaciones Estructurales, AMOS, análisis factorial, regresión múltiple

Abstract

This methodological article explains a conceptual and practical approach to Structural Equation Models or Structural Equation Modeling (SEM). SEMs are considered among the most powerful tools for the study of causal relationships in non-experimental data. They are a combination of factor analysis and multiple regression and are composed of two components: the measurement model and the structural model. The measurement model describes the relationship between a series of observable variables; while in the structural model the relationships between variables are hypothesized; i.e., the relationships between latent variables are described with the use of arrows. Performing a SEM involves five stages: (1) A specification of the Model; (2) Identification of the Model; (3) Estimation of the Model; (4) Evaluation of the Model and (5) Re-specification of the Model. This article provides a series of guidelines on “best practices” for SEM analysis, with examples using the AMOS program.

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Author Biographies

Leonardo Adrián Medrano, Universidad Nacional de Córdoba

Doctor en PsicologíaProfesor. Facultad de Psicología. Universidad Nacional de Córdoba, Argentina.Profesor. Facultad de Psicología. Secretario de Investigación. Universidad Siglo 21, Córdoba, Argentina.

Roger Muñoz-Navarro, Universidad de Valencia

Doctor en PsicologíaProfesor Asociado. Departamento de Personalidad, Evaluación y Tratamientos Psicológicos. Universidad de Valencia, Valencia, España. 

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Published

2017-05-16

How to Cite

Medrano, L. A., & Muñoz-Navarro, R. (2017). Conceptual and Practical Approach to Structural Equations Modeling. Revista Digital De Investigación En Docencia Universitaria [Digital Journal of University Teaching Research], 11(1), 219–239. https://doi.org/10.19083/ridu.11.486