The exploratory factor analysis of items: guided analysis based on empirical data and software.

Susana Lloret, Adoración Ferreres, Ana Hernández, Inés Tomás


The aim of the present study is to illustrate how the appropriate or inappropriate application of exploratory factor analysis (EFA) can lead to quite different conclusions. To reach this goal, we evaluated the degree to which four different programs used to perform an EFA, specifically SPSS, FACTOR, PRELIS and MPlus, allow or limit the application of the currently recommended standards. In addition, we analyze and compare the results offered by the four programs when factor analyzing empirical data from scales that fit the assumptions of the classic linear EFA modeling adequately, ambiguously, or optimally, depending on the case, through the possibilities the different programs offer. The results of the comparison show the consequences of choosing one program or another; and the consequences of selecting some options or others within the same program, depending on the nature of the data. Finally, the study offers practical recommendations for applied researchers with a methodological orientation.


Exploratory Factor Analysis; SPSS; FACTOR; PRELIS; MPlus.

Full Text:

PDF PDF (Español)


Anstey, E. (1959). Test de Dominós. Buenos Aires: Paidós.

Bock, R. D., Gibbons, R., & Muraki, E. (1988). Full-information item factor analysis. Applied Psychological Measurement, 12, 261-280. doi: 10.1177/014662168801200305

Browne, M. W. (1972a). Orthogonal rotation to a partially specified target. British Journal of Mathematical and Statistical Psychology, 25, 115-120. doi: 10.1111/j.2044-8317.1972.tb00482.x

Browne, M. W. (1972b). Oblique rotation to a partially specified target. British Journal of Mathematical and Statistical Psychology, 25, 207-212. doi: 10.1111/j.2044-8317.1972.tb00492.x

Browne, M. W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36, 111-150. doi: 10.1207/S15327906MBR3601_05

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. 136-136). Sage Publications

Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14, 464-504. doi: 10.1080/10705510701301834

Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9, 233-255. doi: 10.1207/S15328007SEM0902_5

Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4, 272-299. doi: 10.1037/1082-989X.4.3.272

Ferrando, P. J. (1994). El problema del factor de dificultad: una revisión y algunas consideraciones prácticas [The problem of difficult factor: A revisión and some practice considerations]. Psicológica, 15, 275-283.

Ferrando, P. J., & Anguiano-Carrasco, C. (2010). El análisis factorial como técnica de investigación en psicología [The factor analysis as method of research in Psychology]. Papeles del Psicólogo, 31, 18-33.

Ferrando, P. J., & Lorenzo-Seva, U. (2013). Unrestricted item factor analysis and some relations with item response theory. Technical Report. Retrieved from

Ferrando, P. J., & Lorenzo-Seva, U. (2014). El análisis factorial exploratorio de los ítems: algunas consideraciones adicionales [Exploratory item factor analysis: Some additional considerations]. Anales de Psicología, 30, 1170-1175.

Flora, D. B., LaBrish, C., & Chalmers, R. P. (2012). Old and new ideas for data screening and assumption testing for exploratory and confirmatory factor analysis. Frontiers in Quantitative Psychology and Measurement, 3, 1-21. doi: 10.3389/fpsyg.2012.00055

Harman, H. H. (1980). Análisis factorial modern [Modern factor analysis]. Madrid: Saltés.

Hendrickson, A. E., & White, P. O. (1964). Promax: A quick method for rotation to a simple structure. British Journal of Mathematical and Statistical Psychology, 17, 65-70. doi: 10.1111/j.2044-8317.1964.tb00244.x

Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179-185. doi:10.1007/BF02289447

Izquierdo, I., Olea, J., & Abad, F. J. (2014). El análisis factorial exploratorio en estudios de validación: usos y recomendaciones [Exploratory factors analysis in validation studies: Uses and recommendations]. Psicothema, 26 (3), 395-400. doi: 10.7334/psicothema2013.349

Jöreskog, K. G. (2002). Structural equation modeling with ordinal variables using LISREL (updated in 2004). Technical report. Available on

Jöreskog, K. G. (2003). Factor analysis by MINRES. Technical report. Available on

Jöreskog, K. G., & Sörbom, D. (2007). LISREL 8.80. [Computer Software]. Lincolnwood, IL: Scientific Software International, Inc.

Jöreskog, K. G., Sörbom, D., Du Toit, S., & Du Doit, M. (1999). LISREL 8: New statistical features. Chicago: Scientific Software International.

Kaiser, H. F. (1958). The varimax criterion for analytical rotation in factor analysis. Psychometrika, 23, 187-200. doi:10.1007/BF02289233

Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39, 31-36. doi:10.1007/BF02291575

Kiers, H. A. L. (1994). Simplimax: Oblique rotation to an optimal target with simple structure. Psychometrika, 59, 567-579. doi:10.1007/BF02294392

Lloret, S., Ferreres, A., Hernández, A., & Tomás, I. (2014). El análisis factorial exploratorio de los ítems: una guía práctica, revisada y actualizada [Exploratory item factor analysis: A practical guide revised and updated]. Anales de Psicología, 30, 1151-1169. doi: 10.5018/analesps.30.3.199361

Lorenzo-Seva, U. (1999). Promin: a method for oblique factor rotation. Multivariate Behavioral Research, 34, 347-356. doi: 10.1207/S15327906MBR3403_3

Lorenzo-Seva, U., & Ferrando, P. J. (2006). FACTOR: a computer program to fit the exploratory factor analysis model. Behavioral Research Methods, 38, 88-91. doi:10.3758/BF03192753

Lorenzo-Seva, U., & Ferrando, P. J. (2013). FACTOR 9.2. A comprehensive program for fitting exploratory and semiconfirmatory factor analysis and IRT models. Applied Psychological Measurement, 37, 497-498. doi: 10.1177/0146621613487794

Lorenzo-Seva, U., & Ferrando, P. J. (2012). TETRA-COM: A comprehensive SPSS program for estimating the tetrachoric correlation. Behavioral Research, 44, 1191-1196. doi:10.3758/s13428-012-0200-6

Lorenzo-Seva, U., & Ferrando, P. J. (2015). POLYMAT-C: A comprehensive SPSS program for computing the polychoric correlation matrix. Behavior Research Methods, 47(3), 884-889. doi:10.3758/s13428-014-0511-x

Lorenzo-Seva, U., & Van Ginkel, J. R. (2016). Multiple imputation of missing values in exploratory factor analysis of multidimensional scales: estimating latent trait scores. Anales de Psicología, 32, 596-608. doi: 10.6018/analesps.32.2.215161

Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57, 519-530. doi: 10.1093/biomet/57.3.519

Marsh, H. W., Richards, G. E., Johnson, S., Roche, S., & Tremayne, P. (1994). Physical Self-Description Questionnaire: Psychometric properties and a multitrait-multimethod analysis of relations to existing instruments. Journal of Sport and Exercise Psychology, 16, 270-305.

Muthén, B., & Kaplan D. (1985). A comparison of some methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematical and Statistical Psychology, 38, 171-189. doi: 10.1111/j.2044.8317.1985.tb00832.x

Muthén, B., & Asparouhov, T. (2010). Bayesian SEM: A more flexible representation of substantive theory. Psychological Methods, 17, 313-335. doi : 10.1037/a0026802

Muthén, L. K., & Muthén, B. O. (1998-2012). Mplus user’s guide (7th ed.) Los Angeles, CA: Muthén & Muthén.

Muthén, L. K., & Muthén, B. O. (2007). Mplus user’s guide (5th ed.) Los Angeles, CA: Muthén & Muthén.

O’Connor, B. (2000). SPSS and SAS programs for determining the number of components using parallel analysis and Velicer's MAP test. Behavior Research Methods, Instruments, & Computers, 32, 396-402. doi:10.3758/BF03200807

Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66, 507-514. doi:10.1007/BF02296192

Trendafilov, N. (1994). A simple method for procrustean rotation in factor analysis using majorization theory. Multivariate Behavioral Research, 29, 385-408. doi: 10.1207/s15327906mbr2904_4

Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41, 321–327. doi:10.1007/BF02293557

Widaman, K. F. (1985). Hierarchically nested covariance structure models for multitrait-multimethod data. Applied Psychological Measurement, 9, 1-26. doi: 10.1177/014662168500900101



  • There are currently no refbacks.

Copyright (c) 2017 Servicio de Publicaciones, Universidad de Murcia (Spain)

Open AccessSello de Calidad FECyT 2013ClarivAnaliticsWJ.jpgScielo-Españadoajscimago