Rationale and Applicability of Exploratory Structural Equation Modeling (ESEM) in psychoeducational contexts

  1. Cristiano Mauro Assis Gomes 1
  2. Leandro S. Almeida 2
  3. José Carlos Núñez 3
  1. 1 Universidade Federal de Minas Gerais
    info

    Universidade Federal de Minas Gerais

    Belo Horizonte, Brasil

    ROR https://ror.org/0176yjw32

  2. 2 Universidade do Minho
    info

    Universidade do Minho

    Braga, Portugal

    ROR https://ror.org/037wpkx04

  3. 3 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Show affiliations +
Journal:
Psicothema

ISSN: 0214-9915

Year of publication: 2017

Volume: 29

Issue: 3

Pages: 396-401

Type: Article

DOI: 10.7334/PSICOTHEMA2016.369 DIALNET GOOGLE SCHOLAR

More publications in: Psicothema

Sustainable development goals

Abstract

Background: In last few years, the use of confirmatory factor analysis (CFA) has become dominant in structural validation of psychological tests. However, the requirement of latent variables only loading on specific target items introduces some constraints on the solutions found, namely a factor solution that links some items only in one specific dimension. The most recent use of exploratory structural equation modeling (ESEM), which allows items to be predominantly related to a factor, with non-zero loadings on other factors, has been identified as the one that best respects the proper functioning of the assessed psychological attributes. Method: In this study we compared the two approaches to structural validity using the answers of a sample of 2,478 first-year higher education students to a multidimensional questionnaire of academic expectations. Results: The results show clear gains in information collected when combining CFA and ESEM. Conclusions: In conclusion, some implications are highlighted for research and practice of psychological assessment.

Bibliographic References

  • Almeida, L. S., Deaño, M., Araújo, A. M., Costa, A. R., Conde, A., & Alfonso, S. (2012). Questionário de Perceções Académicas: Versão Expectativas (QPA-E) [Questionnaire of Academic Perceptions: Expectations Version QPA-E]. Braga: Universidad de Vigo-Ourense.
  • Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 16(3), 397-438. http://dx.doi.org/10.1080/10705510903008204
  • Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246. http://dx.doi. org/10.1037/0033-2909.107.2.238
  • Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Newbury Park, CA: Sage.
  • Deaño, M., Diniz, A., Almeida, L. S., Alfonso, S., Costa, A. R., García-Señorán, M., Conde, A., Araújo, A., Iglesias-Sarmiento, V., Gonçalves, P., & Tellado, F. (2015). Propiedades psicométricas del Cuestionario de Percepciones Académicas para la evaluación de las expectativas de los estudiantes de primer año en Enseñanza Superior [Psychometric properties of the Questionnaire of Academic Perceptions to assess 1st-year higher education students’ expectations]. Anales de Psicología, 31(1), 964-973. http://dx.doi.org/10.6018/analesps.31.1.161641
  • Ferrando, P. J., & Lorenzo-Seva, U. (2000). Unrestricted versus restricted factor analysis of multidimensional test items: Some aspects of the problem and some suggestions. Psicológica, 21(3), 301-323.
  • Furnham, A., Guenole, N., Levine, S. Z., & Chamorro-Premuzic, T. (2013). The NEO Personality Inventory-Revised: Factor structure and gender invariance from exploratory structural equation modeling analyses in a high-stakes setting. Assessment, 20(1), 14-23. http://dx.doi.org/10.1177/1073191112448213
  • García, J. A. M., & Caro, L. M. (2009). El análisis factorial confirmatorio y la validez de escalas en modelos causales [Confirmatory factor analysis and the validity of the measurement scales within a causal modelling framework]. Anales de Psicología, 25(2), 368-374.
  • Howard, J. L., Gagné, M., Morin, A. J. S., & Forest, J. (2016). Using bifactor exploratory structural equation modeling to test for a continuum structure of motivation. Journal of Management, 1-27. http://dx.doi. org/10.1177/0149206316645653
  • Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55. http://dx.doi. org/10.1080/10705519909540118
  • Marsh, H. W. (2007). Application of confirmatory factor analysis and structural equation modeling in sport/exercise psychology. In G. Tenenbaum & R. C. Eklund (Eds.), Handbook of sport psychology (3rd edition, pp. 774-798). New York: Wiley.
  • Marsh, H. W., Nagengast, B., & Morin, A. J. S. (2013). Measurement invariance of Big-Five Factors over the life span: ESEM tests of gender, age, plasticity, maturity and la dolce vita effects. Developmental Psychology, 49(6), 1194-1218. http://dx.doi.org/10.1037/a0026913
  • Marsh, H. W., Morin, A. J. S., Parker, P. D., & Kaur, G. (2014). Exploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysis. Annual Review of Clinical Psychology, 1, 85-110. http://dx.doi.org/10.1146/annurev-clinpsy-032813-153700
  • Morin, A. J. S., & Maïano, C. (2011). Cross-validation of the short form of the Physical Self-Inventory (PSI-S) using exploratory structural equation modeling (ESEM). Psychology of Sport and Exercise, 12(5), 540-554. http://dx.doi.org/10.1016/j.psychsport.2011.04.003
  • Muthén, L. K., & Muthén, B. O. (1998-2014). Mplus User’s Guide (7th ed.). Los Angeles, CA: Muthén & Muthén.
  • Schumacker, R. E., & Lomax, R. G. (2004). A beginner’s guide to structural equation modeling. London: Erlbaum.
Data source: Dialnet