A Model Implied Instrumental Variable Approach to Exploratory Factor Analysis (MIIV-EFA)

Kenneth A. Bollen (), Kathleen M. Gates and Lan Luo
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Kenneth A. Bollen: University of North Carolina at Chapel Hill
Kathleen M. Gates: University of North Carolina at Chapel Hill
Lan Luo: University of North Carolina at Chapel Hill

Psychometrika, 2024, vol. 89, issue 2, No 13, 687-716

Abstract: Abstract Spearman (Am J Psychol 15(1):201–293, 1904. https://doi.org/10.2307/1412107 ) marks the birth of factor analysis. Many articles and books have extended his landmark paper in permitting multiple factors and determining the number of factors, developing ideas about simple structure and factor rotation, and distinguishing between confirmatory and exploratory factor analysis (CFA and EFA). We propose a new model implied instrumental variable (MIIV) approach to EFA that allows intercepts for the measurement equations, correlated common factors, correlated errors, standard errors of factor loadings and measurement intercepts, overidentification tests of equations, and a procedure for determining the number of factors. We also permit simpler structures by removing nonsignificant loadings. Simulations of factor analysis models with and without cross-loadings demonstrate the impressive performance of the MIIV-EFA procedure in recovering the correct number of factors and in recovering the primary and secondary loadings. For example, in nearly all replications MIIV-EFA finds the correct number of factors when N is 100 or more. Even the primary and secondary loadings of the most complex models were recovered when the sample sizes were at least 500. We discuss limitations and future research areas. Two appendices describe alternative MIIV-EFA algorithms and the sensitivity of the algorithm to cross-loadings.

Keywords: exploratory factor analysis; latent variables; model implied instrumental variables; miiv; number of factors; exploratory analysis (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11336-024-09949-6

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