Embracing Fallibility in Quantitative Research: Thoughts and Remarks on Exploratory Factor Analysis and beyond
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Abstract
Objective: errors are inevitable in the scholarly pursuit of truth, yet they are often seen as flaws rather than growth opportunities. This paper examines the tension between scholars’ inherent fallibility and rigorous academic research standards, particularly concerning quantitative methods such as exploratory factor analysis (EFA) and partial least squares structural equation modeling (PLS-SEM). The focus is on whether the academic community effectively balances the acceptance of errors as part of the learning process, with the relentless pursuit of truth and how this balance influences the advancement of knowledge within the context of evolving statistical tools needed to improve our understanding of complex global relationships. Provocations: if errors are fundamental to scientific progress, why does the academic community approach them with apprehension? This fear of mistakes may inhibit innovation, especially in fields such as quantitative methods research, where the stakes are high. Another question is whether the accessibility of user-friendly statistical software has led to a superficial understanding of complex methodologies, prioritizing convenience over depth. Conclusions: we advocate for a shift in how the academic community perceives errors toward viewing them as essential to the research process rather than as fatal flaws. Embracing a humble approach to pointing out mistakes and limitations, particularly with quantitative methods such as EFA and SEM, can create a more innovative and progressive research environment. We call for a cultural shift where constructive critiques are balanced with understanding our collective fallibility, with the ultimate goal of producing more impactful scholarship.
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References
Cattell, R. B. (1966). The scree test is used to measure the number of factors. Multivariate Behavioral Research, 1(2), 245- 276. https://doi.org/10.1207/s15327906mbr0102_10
Cooperman, A. W., & Waller, N. G. (2022). Heywood you go away! Examining causes, effects, and treatments for Heywood cases in exploratory factor analysis. Psychological Methods, 27(2), 156-176. https://psycnet.apa.org/doi/10.1037/met0000384
Cudeck, R., & MacCallum, R. C. (Eds.). (2007). Factor analysis at 100: Historical developments and future directions. Routledge.
Goretzko, D. (2023). Regularized exploratory factor analysis as an alternative to factor rotation. European Journal of Psychological Assessment. Advanced online publication. https://doi.org/10.1027/1015-5759/a000792
Goretzko, D., Pham, T. T. H., & Bühner, M. (2021). Exploratory factor analysis: Current use, methodological developments and recommendations for good practice. Current Psychology, 40, 3510-3521. https://doi.org/10.1007/s12144-019-00300-2
Hair, J. F., Jr., Anderson, R., Babin, B., & Black, W. (2019). Multivariate Data Analysis (8th ed.). Andover, England: Cengage Learning EMEA.
Hair, J. F., Jr., Gabriel, M. L. D. S., Silva, D., & Braga Jr., S. S. (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
Hair, J. F., Jr., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101-110. https://doi.org/10.1016/j.jbusres.2019.11.069
Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications.
Hair, J. F., Jr., Sarstedt, M., Ringle, C. M., & Gudergan, S. (2024). Advanced Issues in Partial Least Squares Structural Equation Modeling (2nd ed.). Sage.
Latan, H., Hair, Jr., J.F., Noonan, R., & Sabol, M. (2023). Introduction to Partial Least Squares Path modeling: Basic concepts and recent methodological enhancements. In H. Latan, J. F. Hair, Jr., & R. Noonan (Eds.), Partial Least Squares path modeling: Basic concepts, methodological issues and applications (pp. 3-21). Springer.
Lorenzo-Seva, U., & Ferrando, P. J. (2024). Determining sample size requirements in EFA solutions: A simple empirical proposal. Multivariate Behavioral Research, 59(5), 899- 912. https://doi.org/10.1080/00273171.2024.2342324
Mulaik, S. A. (1987). A brief history of the philosophical foundations of exploratory factor analysis. Multivariate Behavioral Research, 22(3), 267-305. https://doi.org/10.1207/s15327906mbr2203_3
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill. O’Connor, B. P. (2023). EFA.dimensions: Exploratory factor analysis functions for assessing dimensionality. R package version 0.1.8.1. https://doi.org/10.32614/CRAN.package.EFA.dimensions
Pedhazur, E. J., & Schmelkin, L. P. (1991). Measurement, design, and analysis: An integrated approach. Lawrence Erlbaum Associates.
Revelle, W. (2024). psych: Procedures for personality and psychological research, version = 2.4.1. Northwestern University, Evanston, Illinois, USA. https://doi.org/10.32614/CRAN.package.psych
Rogers, P. (2022). Best practices for your exploratory factor analysis: A factor tutorial. Revista de Administração Contemporânea, 26(6), e210085. https://doi.org/10.1590/1982-7849rac2022210085.en
Schreiber, J. B. (2021). Issues and recommendations for exploratory factor analysis and principal component analysis. Research in Social and Administrative Pharmacy, 17(5), 1004- 1011. https://doi.org/10.1016/j.sapharm.2020.07.027
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322- 2347. https://doi.org/10.1108/EJM-02-2019-0189
Spearman, C. (1904). “General intelligence” objectively determined and measured. The American Journal of Psychology, 15(2), 201-292. https://doi.org/10.2307/1412107
Spearman, C. (1927). The abilities of man. MacMillan. Steiner, M. D., & Grieder, S. G. (2020). EFAtools: An R package with fast and flexible implementations of exploratory factor analysis tools. Journal of Open Source Software, 5(53), 2521. https://doi.org/10.21105/joss.02521
Vogt, W. P., Vogt, E. R., Gardner, D. C., & Haeffele, L. M. (2014.) Selecting the right analysis for your data: quantitative, qualitative and mixed methods. The Guilford Press.
Williams, B., Onsman, A., & Brown, T. (2010). Exploratory factor analysis: A five-step guide for novices. Australasian Journal of Paramedicine, 8, 1-13. https://doi.org/10.33151/ajp.8.3.93
Zhang, G., Jiang, G., Hattori, M., & Trichtinger, L. (2023). _EFAutilities: Utility Functions for Exploratory Factor Analysis_. R package version 2.1.3. https://doi.org/10.32614/CRAN.package.EFAutilities