Improving Bi-Factor Exploratory Modeling
Empirical Target Rotation Based on Loading Differences
Abstract
Abstract. Bi-factor exploratory modeling has recently emerged as a promising approach to multidimensional psychological measurement. However, state-of-the-art methods relying on target rotation require researchers to select an arbitrary cut-off for defining the target matrix. Unfortunately, the consequences of such choice on factor recovery remain uninvestigated under realistic conditions (e.g., factors differing in their average loadings). Built upon the iterative target rotation based on Schmid-Leiman algorithm (SLi), a novel method is here introduced (SLiD). SLiD settles an empirical, factor-specific cut-off based on the first prominent one-lagged difference of sorted squared normalized factor loadings. SLiD and SLi with arbitrary cut-off (ranging from .05 to .20) performance were evaluated via Monte Carlo simulation manipulating sample size, number of specific factors, number of indicators, and cross-loading magnitude. Results indicate that SLiD performed the best for all conditions. For SLi, and due to the presence of minor factors, smaller cut-offs (i.e., .05) outperformed higher ones (i.e., .20).
References
2017). Iteration of partially specified target matrices: Application to the bi-factor case. Multivariate Behavioral Research, 52, 416–429. https://doi.org/10.1080/00273171.2017.1301244
(2009). Exploratory structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 16, 397–438. https://doi.org/10.1080/10705510903008204
(2015). What can the Real World do for simulation studies? A comparison of exploratory methods. (Technical Report 181). Retrieved from Department of Statistics, University of Munich https://epub.ub.uni-muenchen.de/24518/
(1972). Orthogonal rotation to a partially specified target. British Journal of Mathematical and Statistical Psychology, 25, 115–120. https://doi.org/10.1111/j.2044-8317.1972.tb00482.x
(2001). The latent structure of memory: A confirmatory factor-analytic study of memory distinctions. Multivariate Behavioral Research, 36, 29–51. https://doi.org/10.1207/S15327906MBR3601
(2006). A comparison of bi-factor and second order models of quality-of-life. Multivariate Behavioral Research, 41, 189–225. https://doi.org/10.1207/s15327906mbr4102_5
(1988). Statistical power analysis for the behavioural sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
(2017). Program FACTOR at 10: Origins, development and future directions. Psicothema, 29, 236–240. https://doi.org/10.7334/psicothema2016.304
(2017). mvtnorm: Multivariate Normal and t Distributions. R package version 1.0-6. Retrieved from https://CRAN.R-project.org/package=mvtnorm
(2018). Empirical underidentification with the bifactor model: A case study. Educational and Psychological Measurement, 78, 717–736. https://doi.org/10.1177/0013164417719947
(2017). Multiple local solutions and geomin rotation. Multivariate Behavioral Research, 52, 720–731. https://doi.org/10.1080/00273171.2017.1361312
(1937). The bi-factor method. Psychometrika, 2, 41–54. https://doi.org/10.1007/BF02287965
(2014). Exploratory factor analysis in validation studies: Uses and recommendations. Psicothema, 26, 395–400. https://doi.org/10.7334/psicothema2013.349
(2018). jamovi. (Version 0.8.1.13) [Computer Software]. Retrieved from https://www.jamovi.org/
. (2004). Rotation to simple loadings using component loss functions: The orthogonal case. Psychometrika, 69, 257–273. https://doi.org/10.1007/S11336-003-1136-B
(2011). Exploratory bi-factor analysis. Psychometrika, 76, 537–549. https://doi.org/10.1007/s11336-011-9218-4
(2012). Exploratory bi-factor analysis: The oblique case. Psychometrika, 77, 442–454. https://doi.org/10.1007/s11336-012-9269-1
(1994). Simplimax: Oblique rotation to an optimal target with simple structure. Psychometrika, 59, 567–579. https://doi.org/10.1007/BF02294392
(1999). Promin: A method for oblique factor rotation. Multivariate Behavioral Research, 34, 347–365. https://doi.org/10.1207/S15327906MBR3403_3
(2006). Tucker’s congruence coefficient as a meaningful index of factor similarity. Methodology, 2, 57–64. https://doi.org/10.1027/1614-2241.2.2.57
(2016). Exploratory bifactor analysis: The Schmid-Leiman orthogonalization and Jennrich-Bentler analytic rotations. Multivariate Behavioral Research, 51, 698–717. https://doi.org/10.1080/00273171.2016.1215898
(2008, April). Power of AFIs to detect CFA model misfit. Paper presented at the 23rd Annual Conference of the Society for Industrial and Organizational Psychology, San Francisco, CA
(2015). Iteration of partially specified target matrices: Applications in exploratory and Bayesian confirmatory factor analysis. Multivariate Behavioral Research, 50, 149–161. https://doi.org/10.1080/00273171.2014.973990
(2015). A bifactor exploratory structural equation modeling framework for the identification of distinct sources of construct-relevant psychometric multidimensionality. Structural Equation Modeling: A Multidisciplinary Journal, 23, 116–139. https://doi.org/10.1080/10705511.2014.961800
(2013). Rotation to a partially specified target matrix in exploratory factor analysis: How many targets? Structural Equation Modeling: A Multidisciplinary Journal, 20, 131–147. https://doi.org/10.1080/10705511.2013.742399
(2015). Rotation to a partially specified target matrix in exploratory factor analysis in practice. Behavior Research Methods, 47, 494–505. https://doi.org/10.3758/s13428-014-0486-7
(2012). Rotatitional uniqueness conditions under oblique factor correlation metric. Psychometrika, 77, 288–292. https://doi.org/10.1007/S11336-012-9259-3
(1999). A note on procrustean rotation in exploratory factor analysis: A computer intensive approach to goodness-of-fit evaluation. Educational and Psychological Measurement, 59, 47–57. https://doi.org/10.1177/00131649921969730
(2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47, 667–696. https://doi.org/10.1080/00273171.2012.715555
(2010). Bifactor models and rotations: Exploring the extent to which multidimensional data yield univocal scale scores. Journal of Personality Assessment, 92, 544–559. https://doi.org/10.1080/00223891.2010.496477.Bifactor
(2011). Target rotations and assessing the impact of model violations on the parameters of unidimensional item response theory models. Educational and Psychological Measurement, 71, 684–711. https://doi.org/10.1177/0013164410378690
(2017). Psych: Procedures for personality and psychological research. Northwestern University: Evanston, IL. (Version 1.7.8). Retrieved from https://CRAN.R-project.org/package =psch
(2017). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.r-project.org/
. (1957). The development of hierarchical factor solutions. Psychometrika, 22, 53–61. https://doi.org/10.1007/BF02289209
(1994). A simple method for procrustean rotation in factor analysis using majorization theory. Multivariate Behavioral Research, 29, 385–408. https://doi.org/10.1207/s15327906mbr2904_4
(1951). A method for synthesis of factor analysis studies (Personal Research Section Report No. 984) . Washington, DC: Department of the Army
(2017). Direct Schmid-Leiman transformations and rank-deficient loadings matrices. Psychometrika, 83, 858–870. https://doi.org/10.1007/s11336-017-9599-0
(