Best Practices for Your Exploratory Factor Analysis: A Factor Tutorial
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Pablo Rogers
Abstract
Context: exploratory factor analysis (EFA) is one of the statistical methods most widely used in administration; however, its current practice coexists with rules of thumb and heuristics given half a century ago. Objective: the purpose of this article is to present the best practices and recent recommendations for a typical EFA in administration through a practical solution accessible to researchers. Methods: in this sense, in addition to discussing current practices versus recommended practices, a tutorial with real data on Factor is illustrated. The Factor software is still little known in the administration area, but is freeware, easy-to-use (point and click), and powerful. The step-by-step tutorial illustrated in the article, in addition to the discussions raised and an additional example, is also available in the format of tutorial videos. Conclusion: through the proposed didactic methodology (article-tutorial + video-tutorial), we encourage researchers/methodologists who have mastered a particular technique to do the same. Specifically about EFA, we hope that the presentation of the Factor software, as a first solution, can transcend the current outdated rules of thumb and heuristics, by making best practices accessible to administration researchers.
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Rogers, P. (2021). Best Practices for Your Exploratory Factor Analysis: A Factor Tutorial. Journal of Contemporary Administration, 26(6), e210085. https://doi.org/10.1590/1982-7849rac2022210085.en
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Methodological Articles
Since mid-February of 2023, the authors retain the copyright relating to their article and grant the journal RAC, from ANPAD, the right of first publication, with the work simultaneously licensed under the Creative Commons Attribution 4.0 International license (CC BY 4.0), as stated in the article’s PDF document. This license provides that the article published can be shared (allows you to copy and redistribute the material in any medium or format) and adapted (allows you to remix, transform, and create from the material for any purpose, even commercial) by anyone.
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References
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Bido, D. S., Mantovani, D. M. N., & Cohen, E. D. (2018). Destruction of measurement scale through exploratory factor analysis in production and operations research. Gestão & Produção, 25(2), 384–397. https://doi.org/10.1590/0104-530X3391-16
Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). New York, NY: The Guilford Press.
Costello, A. B., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Research, and Evaluation Practical Assessment, Research, and Evaluation, 10, 7. https://doi.org/10.7275/jyj1-4868
Courtney, M., & Gordon, R. (2013). Determining the number of factors to retain in EFA: Using the SPSS R-Menu v2.0 to make more judicious estimations. Practical Assessment, Research and Evaluation, 18(8), 1–14. https://doi.org/10.7275/9cf5-2m72
Damásio, B. F. (2012). Uso da análise fatorial exploratória em psicologia. Avaliação Psicológica, 11(2), 213–228. Retrieved from http://pepsic.bvsalud.org/scielo.php?script=sci_abstract&pid=S1677-04712012000200007
Damásio, B. F., & Dutra, D. de F. (2018). Análise fatorial exploratória: Um tutorial com o Software Factor. In B. F. Damásio & J. C. Borsa (Eds.), Manual de Desenvolvimento de Instrumentos Psicológicos (1a Edição). São Paulo, SP: Vetor Editora.
Dias, P. de S., Silva-Spineli, H. V. R., & Macedo, R. de C. (2019). EstatÃstica multivariadas na Administração: Importância e aplicação da análise fatorial exploratória. Revista Eletrônica de Administração e Turismo, 13(1), 1807–1828. Retrieved from http://www.spell.org.br/documentos/ver/53685/estatisticas-multivariadas-na-administracao--importancia-e-aplicacao-da-analise-fatorial-exploratoria
Fabrigar, L. R., & Wegener, D. T. (2012). Exploratory factor analysis. Oxford, UK: Oxford University Press.
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(3), 272–299. https://doi.org/10.1037/1082-989X.4.3.272
Ferrando, P. J., & Lorenzo-Seva, U. (2014). Exploratory item factor analysis: Additional considerations. Anales de Psicologia, 30(3), 1170–1175. https://doi.org/10.6018/analesps.30.3.199991
Ferrando, P. J., & Lorenzo-Seva, U. (2017). Program FACTOR at 10: Origins, development and future directions. Psicothema, 29(2), 236–241. https://doi.org/10.7334/psicothema2016.304
Ferrando, P. J., & Lorenzo-Seva, U. (2018). Assessing the quality and appropriateness of factor solutions and factor score estimates in exploratory item factor analysis. Educational and Psychological Measurement, 78(5), 762–780. https://doi.org/10.1177/0013164417719308
Ferreira, D. F. (2018). EstatÃstica Multivariada (3rd ed.). Belo Horizonte, MG: UFMG.
Ford, J. K., Maccallum, R. C., & Talt, M. (1986). The application of exploratory factor analysis in applied Psychology: A critical review and analysis. Personnel Psychology, 39(2), 291–314. https://doi.org/10.1111/j.1744-6570.1986.tb00583.x
Gaskin, C. J., & Happell, B. (2014). On exploratory factor analysis: A review of recent evidence, an assessment of current practice, and recommendations for future use. International Journal of Nursing Studies, 51(3), 511–521. https://doi.org/10.1016/j.ijnurstu.2013.10.005
Gignac, G. E. (2009). Partial confirmatory factor analysis: Described and illustrated on the NEO-PI-R. Journal of Personality Assessment, 91(1), 40–47. https://doi.org/10.1080/00223890802484126
Hair, J., Black, W., Babin, B., & Anderson, R. (2014). Multivariate Data Analysis (7th ed.). London, UK: Pearson.
Hair, J. F., Jr, Gabriel, M. L. D. S., Silva, D. da, & Braga, S., Junior. (2019). Development and validation of attitudes measurement scales: fundamental and practical aspects. RAUSP Management Journal, 54(4), 490–507. https://doi.org/10.1108/RAUSP-05-2019-0098
Hancock, G. R., & Mueller, R. O. (2000). Rethinking construct reliability within latent variable systems. In D. F. S. R. Cudek, & S. H. C. DuToit (Eds.), Structural equation modeling: Present and future (pp. 195–216). Lincolnwood, IL: Scientific Software.
Hauck-Filho, N., & Valentini, F. (2020). A análise de componentes principais é útil para selecionar bons itens quando a dimensionalidade dos dados é desconhecida? Avaliação Psicológica, 19(4), A-C. https://doi.org/10.15689/ap.2020.1904.ed
Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66(3), 393–416. https://doi.org/10.1177/0013164405282485
Hoelzle, J. B., & Meyer, G. J. (2013). Exploratory factor analysis: Basics and beyond. In Handbook of Psychology (2nd ed., pp. 164–188). Hoboke, NJ: John Wiley & Sons.
Holgado–Tello, F. P., Chacón–Moscoso, S., Barbero–GarcÃa, I., & Vila–Abad, E. (2010). Polychoric versus Pearson correlations in exploratory and confirmatory factor analysis of ordinal variables. Quality and Quantity, 44(1), 153–166. https://doi.org/10.1007/s11135-008-9190-y
Howard, M. C. (2016). A review of exploratory factor analysis decisions and overview of current practices: What we are doing and how can we improve? International Journal of Human-Computer Interaction, 32(1), 51–62. https://doi.org/10.1080/10447318.2015.1087664
Izquierdo, I., Olea, J., & Abad, F. J. (2014). El análisis factorial exploratorio en estudios de validación: Usos y recomendaciones. Psicothema, 26(3), 395–400. https://doi.org/10.7334/psicothema2013.349
Kline, R. B. (2016). Principles and Pratice of Structural Equation Modeling (4th ed.). New York, NY: The Guilford Press.
Laros, J. A. (2012). O uso da análise fatorial: Algumas diretrizes para pesquisadores. In L. Pasquali (Ed.), Análise Fatorial para pesquisadores (pp. 141–160). LabPAM. Retrieved from https://www.researchgate.net/publication/233735561_O_Uso_da_Analise_Fatorial_Algumas_Diretrizes_para_Pesquisadores
Lloret, S., Ferreres, A., Hernández, A., & Tomás, I. (2014). Exploratory item factor analysis: A practical guide revised and updated. Anales de Psicologia, 30(3), 1151–1169. https://doi.org/10.6018/analesps.30.3.199361
Lloret, S., Ferreres, A., Hernández, A., & Tomás, I. (2017). The exploratory factor analysis of items: Guided analysis based on empirical data and software. Anales de PsicologÃa, 33(2), 417–432. https://doi.org/10.6018/analesps.33.2.270211
Lorenzo-Seva, U. (2003). A factor simplicity index. Psychometrika, 68(1). https://doi.org/10.1007/BF02296652
Lorenzo-Seva, U., & Ferrando, P. J. (2012). TETRA-COM: A comprehensive SPSS program for estimating the tetrachoric correlation. Behavior Research Methods, 44(4), 1191–1196. https://doi.org/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. https://doi.org/10.3758/s13428-014-0511-x
Lorenzo-Seva, U., & Ferrando, P. J. (2019). Robust Promin: A method for diagonally weighted factor rotation. Liberabit: Revista Peruana de PsicologÃa, 25(1), 99–106. https://doi.org/10.24265/liberabit.2019.v25n1.08
Lorenzo-Seva, U., & Ferrando, P. J. (2020). Not Positive Definite Correlation Matrices in Exploratory Item Factor Analysis: Causes, Consequences and a Proposed Solution. Structural Equation Modeling: A Multidisciplinary Journal, 28(1), 1–10. https://doi.org/10.1080/10705511.2020.1735393
Lorenzo-Seva, U., Timmerman, M. E., & Kiers, H. A. L. (2011). The Hull Method for Selecting the Number of Common Factors. Multivariate Behavioral Research, 46(2), 340–364. https://doi.org/10.1080/00273171.2011.564527
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 PsicologÃa, 32(2), 596. https://doi.org/10.6018/analesps.32.2.215161
Marôco, J. (2014). Análise de equações estruturais: Fundamentos teóricos, software e aplicações (2nd ed.). Lisbon, PT: ReportNumber.
Marsh, H., Morin, A., Parker, P., & Kaur, G. (2014). Exploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysis. In Annual Review of Clinical Psychology (Vol. 10, pp. 85–110). https://doi.org/10.1146/annurev-clinpsy-032813-153700
Marsh, H. W., Muthén, B., Morin, Al., Lüdtke, O., Asparouhov, T., & Trautwein, U. (2010). A new look at the big five factor structure through exploratory structural equation modeling. Psychological Assessment, 22(3), 471–491. https://doi.org/10.1037/a0019227
Marsh, Herbert W., Guo, J., Dicke, T., Parker, P. D., & Craven, R. G. (2020). Confirmatory Factor Analysis (CFA), Exploratory Structural Equation Modeling (ESEM), and Set-ESEM: Optimal Balance Between Goodness of Fit and Parsimony. Multivariate Behavioral Research, 55(1), 102–119. https://doi.org/10.1080/00273171.2019.1602503
Marsh, Herbert W., Muthén, B., Asparouhov, T., Lüdtke, O., Robitzsch, A., Morin, A. J. S., & Trautwein, U. (2009). Exploratory structural equation modeling, integrating CFA and EFA: Application to students’ evaluations of university teaching. Structural Equation Modeling, 16(3), 439–476. https://doi.org/10.1080/10705510903008220
Matos, S. D. A., & Rodrigues, E. C. (2019). Análise fatorial. BrasÃlia, DF: Enap.
Mingoti, S. A. (2005). Análise de dados através de métodos de estatÃstica multivariada: Uma abordagem aplicada. Belo Horizonte, MG: UFMG.
Perera, H. N., Izadikhah, Z., O’Connor, P., & McIlveen, P. (2018). Resolving Dimensionality Problems With WHOQOL-BREF Item Responses. Assessment, 25(8), 1014–1025. https://doi.org/10.1177/1073191116678925
Rogers, D., Armada, M. J. da R., & Rogers, P. (2020). Bem-estar financeiro, saúde e qualidade de vida de um investidor. Anais do Encontro da Associação Nacional de Pós-graduação e Pesquisa em Administração, online, 44.
Schreiber, J. B., Stage, F. K., King, J., Nora, A., & Barlow, E. A. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. In Journal of Educational Research, 99(6), 323–338. https://doi.org/10.3200/JOER.99.6.323-338
Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. American Psychological Association. https://doi.org/10.1037/10694-000
Timmerman, M. E., & Lorenzo-Seva, U. (2011). Dimensionality assessment of ordered polytomous items with parallel analysis. Psychological Methods, 16(2), 209–220. https://doi.org/10.1037/a0023353
Vidal, D. F. (2016). Análise fatorial em Administração: Uma aplicação prática com o software SPSS. Revista Eletrônica Estácio Papirus, 3(1), 54–79.
Watkins, M. W. (2018). Exploratory Factor Analysis: A Guide to Best Practice. Journal of Black Psychology, 44(3), 219–246. https://doi.org/10.1177/0095798418771807
Bido, D. S., Mantovani, D. M. N., & Cohen, E. D. (2018). Destruction of measurement scale through exploratory factor analysis in production and operations research. Gestão & Produção, 25(2), 384–397. https://doi.org/10.1590/0104-530X3391-16
Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). New York, NY: The Guilford Press.
Costello, A. B., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Research, and Evaluation Practical Assessment, Research, and Evaluation, 10, 7. https://doi.org/10.7275/jyj1-4868
Courtney, M., & Gordon, R. (2013). Determining the number of factors to retain in EFA: Using the SPSS R-Menu v2.0 to make more judicious estimations. Practical Assessment, Research and Evaluation, 18(8), 1–14. https://doi.org/10.7275/9cf5-2m72
Damásio, B. F. (2012). Uso da análise fatorial exploratória em psicologia. Avaliação Psicológica, 11(2), 213–228. Retrieved from http://pepsic.bvsalud.org/scielo.php?script=sci_abstract&pid=S1677-04712012000200007
Damásio, B. F., & Dutra, D. de F. (2018). Análise fatorial exploratória: Um tutorial com o Software Factor. In B. F. Damásio & J. C. Borsa (Eds.), Manual de Desenvolvimento de Instrumentos Psicológicos (1a Edição). São Paulo, SP: Vetor Editora.
Dias, P. de S., Silva-Spineli, H. V. R., & Macedo, R. de C. (2019). EstatÃstica multivariadas na Administração: Importância e aplicação da análise fatorial exploratória. Revista Eletrônica de Administração e Turismo, 13(1), 1807–1828. Retrieved from http://www.spell.org.br/documentos/ver/53685/estatisticas-multivariadas-na-administracao--importancia-e-aplicacao-da-analise-fatorial-exploratoria
Fabrigar, L. R., & Wegener, D. T. (2012). Exploratory factor analysis. Oxford, UK: Oxford University Press.
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(3), 272–299. https://doi.org/10.1037/1082-989X.4.3.272
Ferrando, P. J., & Lorenzo-Seva, U. (2014). Exploratory item factor analysis: Additional considerations. Anales de Psicologia, 30(3), 1170–1175. https://doi.org/10.6018/analesps.30.3.199991
Ferrando, P. J., & Lorenzo-Seva, U. (2017). Program FACTOR at 10: Origins, development and future directions. Psicothema, 29(2), 236–241. https://doi.org/10.7334/psicothema2016.304
Ferrando, P. J., & Lorenzo-Seva, U. (2018). Assessing the quality and appropriateness of factor solutions and factor score estimates in exploratory item factor analysis. Educational and Psychological Measurement, 78(5), 762–780. https://doi.org/10.1177/0013164417719308
Ferreira, D. F. (2018). EstatÃstica Multivariada (3rd ed.). Belo Horizonte, MG: UFMG.
Ford, J. K., Maccallum, R. C., & Talt, M. (1986). The application of exploratory factor analysis in applied Psychology: A critical review and analysis. Personnel Psychology, 39(2), 291–314. https://doi.org/10.1111/j.1744-6570.1986.tb00583.x
Gaskin, C. J., & Happell, B. (2014). On exploratory factor analysis: A review of recent evidence, an assessment of current practice, and recommendations for future use. International Journal of Nursing Studies, 51(3), 511–521. https://doi.org/10.1016/j.ijnurstu.2013.10.005
Gignac, G. E. (2009). Partial confirmatory factor analysis: Described and illustrated on the NEO-PI-R. Journal of Personality Assessment, 91(1), 40–47. https://doi.org/10.1080/00223890802484126
Hair, J., Black, W., Babin, B., & Anderson, R. (2014). Multivariate Data Analysis (7th ed.). London, UK: Pearson.
Hair, J. F., Jr, Gabriel, M. L. D. S., Silva, D. da, & Braga, S., Junior. (2019). Development and validation of attitudes measurement scales: fundamental and practical aspects. RAUSP Management Journal, 54(4), 490–507. https://doi.org/10.1108/RAUSP-05-2019-0098
Hancock, G. R., & Mueller, R. O. (2000). Rethinking construct reliability within latent variable systems. In D. F. S. R. Cudek, & S. H. C. DuToit (Eds.), Structural equation modeling: Present and future (pp. 195–216). Lincolnwood, IL: Scientific Software.
Hauck-Filho, N., & Valentini, F. (2020). A análise de componentes principais é útil para selecionar bons itens quando a dimensionalidade dos dados é desconhecida? Avaliação Psicológica, 19(4), A-C. https://doi.org/10.15689/ap.2020.1904.ed
Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66(3), 393–416. https://doi.org/10.1177/0013164405282485
Hoelzle, J. B., & Meyer, G. J. (2013). Exploratory factor analysis: Basics and beyond. In Handbook of Psychology (2nd ed., pp. 164–188). Hoboke, NJ: John Wiley & Sons.
Holgado–Tello, F. P., Chacón–Moscoso, S., Barbero–GarcÃa, I., & Vila–Abad, E. (2010). Polychoric versus Pearson correlations in exploratory and confirmatory factor analysis of ordinal variables. Quality and Quantity, 44(1), 153–166. https://doi.org/10.1007/s11135-008-9190-y
Howard, M. C. (2016). A review of exploratory factor analysis decisions and overview of current practices: What we are doing and how can we improve? International Journal of Human-Computer Interaction, 32(1), 51–62. https://doi.org/10.1080/10447318.2015.1087664
Izquierdo, I., Olea, J., & Abad, F. J. (2014). El análisis factorial exploratorio en estudios de validación: Usos y recomendaciones. Psicothema, 26(3), 395–400. https://doi.org/10.7334/psicothema2013.349
Kline, R. B. (2016). Principles and Pratice of Structural Equation Modeling (4th ed.). New York, NY: The Guilford Press.
Laros, J. A. (2012). O uso da análise fatorial: Algumas diretrizes para pesquisadores. In L. Pasquali (Ed.), Análise Fatorial para pesquisadores (pp. 141–160). LabPAM. Retrieved from https://www.researchgate.net/publication/233735561_O_Uso_da_Analise_Fatorial_Algumas_Diretrizes_para_Pesquisadores
Lloret, S., Ferreres, A., Hernández, A., & Tomás, I. (2014). Exploratory item factor analysis: A practical guide revised and updated. Anales de Psicologia, 30(3), 1151–1169. https://doi.org/10.6018/analesps.30.3.199361
Lloret, S., Ferreres, A., Hernández, A., & Tomás, I. (2017). The exploratory factor analysis of items: Guided analysis based on empirical data and software. Anales de PsicologÃa, 33(2), 417–432. https://doi.org/10.6018/analesps.33.2.270211
Lorenzo-Seva, U. (2003). A factor simplicity index. Psychometrika, 68(1). https://doi.org/10.1007/BF02296652
Lorenzo-Seva, U., & Ferrando, P. J. (2012). TETRA-COM: A comprehensive SPSS program for estimating the tetrachoric correlation. Behavior Research Methods, 44(4), 1191–1196. https://doi.org/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. https://doi.org/10.3758/s13428-014-0511-x
Lorenzo-Seva, U., & Ferrando, P. J. (2019). Robust Promin: A method for diagonally weighted factor rotation. Liberabit: Revista Peruana de PsicologÃa, 25(1), 99–106. https://doi.org/10.24265/liberabit.2019.v25n1.08
Lorenzo-Seva, U., & Ferrando, P. J. (2020). Not Positive Definite Correlation Matrices in Exploratory Item Factor Analysis: Causes, Consequences and a Proposed Solution. Structural Equation Modeling: A Multidisciplinary Journal, 28(1), 1–10. https://doi.org/10.1080/10705511.2020.1735393
Lorenzo-Seva, U., Timmerman, M. E., & Kiers, H. A. L. (2011). The Hull Method for Selecting the Number of Common Factors. Multivariate Behavioral Research, 46(2), 340–364. https://doi.org/10.1080/00273171.2011.564527
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 PsicologÃa, 32(2), 596. https://doi.org/10.6018/analesps.32.2.215161
Marôco, J. (2014). Análise de equações estruturais: Fundamentos teóricos, software e aplicações (2nd ed.). Lisbon, PT: ReportNumber.
Marsh, H., Morin, A., Parker, P., & Kaur, G. (2014). Exploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysis. In Annual Review of Clinical Psychology (Vol. 10, pp. 85–110). https://doi.org/10.1146/annurev-clinpsy-032813-153700
Marsh, H. W., Muthén, B., Morin, Al., Lüdtke, O., Asparouhov, T., & Trautwein, U. (2010). A new look at the big five factor structure through exploratory structural equation modeling. Psychological Assessment, 22(3), 471–491. https://doi.org/10.1037/a0019227
Marsh, Herbert W., Guo, J., Dicke, T., Parker, P. D., & Craven, R. G. (2020). Confirmatory Factor Analysis (CFA), Exploratory Structural Equation Modeling (ESEM), and Set-ESEM: Optimal Balance Between Goodness of Fit and Parsimony. Multivariate Behavioral Research, 55(1), 102–119. https://doi.org/10.1080/00273171.2019.1602503
Marsh, Herbert W., Muthén, B., Asparouhov, T., Lüdtke, O., Robitzsch, A., Morin, A. J. S., & Trautwein, U. (2009). Exploratory structural equation modeling, integrating CFA and EFA: Application to students’ evaluations of university teaching. Structural Equation Modeling, 16(3), 439–476. https://doi.org/10.1080/10705510903008220
Matos, S. D. A., & Rodrigues, E. C. (2019). Análise fatorial. BrasÃlia, DF: Enap.
Mingoti, S. A. (2005). Análise de dados através de métodos de estatÃstica multivariada: Uma abordagem aplicada. Belo Horizonte, MG: UFMG.
Perera, H. N., Izadikhah, Z., O’Connor, P., & McIlveen, P. (2018). Resolving Dimensionality Problems With WHOQOL-BREF Item Responses. Assessment, 25(8), 1014–1025. https://doi.org/10.1177/1073191116678925
Rogers, D., Armada, M. J. da R., & Rogers, P. (2020). Bem-estar financeiro, saúde e qualidade de vida de um investidor. Anais do Encontro da Associação Nacional de Pós-graduação e Pesquisa em Administração, online, 44.
Schreiber, J. B., Stage, F. K., King, J., Nora, A., & Barlow, E. A. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. In Journal of Educational Research, 99(6), 323–338. https://doi.org/10.3200/JOER.99.6.323-338
Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. American Psychological Association. https://doi.org/10.1037/10694-000
Timmerman, M. E., & Lorenzo-Seva, U. (2011). Dimensionality assessment of ordered polytomous items with parallel analysis. Psychological Methods, 16(2), 209–220. https://doi.org/10.1037/a0023353
Vidal, D. F. (2016). Análise fatorial em Administração: Uma aplicação prática com o software SPSS. Revista Eletrônica Estácio Papirus, 3(1), 54–79.
Watkins, M. W. (2018). Exploratory Factor Analysis: A Guide to Best Practice. Journal of Black Psychology, 44(3), 219–246. https://doi.org/10.1177/0095798418771807