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|Title:||Iteration of partially specified target matrices: Application to the bi-factor case||Autores:||Abad, F. J.
Garrido, Luis Eduardo
Barrada, J. R.
|Researchers (UNIBE):||Garrido, Luis Eduardo||Affiliations:||Decanato de Investigación e Innovación (DII)||Research area:||Ciencias de la Salud||Keywords:||Bi-factor rotation; Exploratory factor analysis; Schmid-Leiman; Target rotation||Issue Date:||2017||Publisher:||Taylor & Francis||Source:||Multivariate Behavioral Research, 52(4), 416-429||Journal:||Multivariate Behavioral Research||Volume:||52||Issue:||4||Start page:||416||End page:||429||Abstract:||
The current study proposes a new bi-factor rotation method, Schmid-Leiman with iterative target rotation (SLi), based on the iteration of partially specified target matrices and an initial target constructed from a Schmid-Leiman (SL) orthogonalization. SLi was expected to ameliorate some of the limitations of the previously presented SL bi-factor rotations, SL and SL with target rotation (SLt), when the factor structure either includes cross-loadings, near-zero loadings, or both. A Monte Carlo simulation was carried out to test the performance of SLi, SL, SLt, and the two analytic bi-factor rotations, bi-quartimin and bi-geomin. The results revealed that SLi accurately recovered the bi-factor structures across the majority of the conditions, and generally outperformed the other rotation methods. SLi provided the biggest improvements over SL and SLt when the bi-factor structures contained cross-loadings and pure indicators of the general factor. Additionally, SLi was superior to bi-quartimin and bi-geomin, which performed inconsistently across the types of factor structures evaluated. No method produced a good recovery of the bi-factor structures when small samples (N = 200) were combined with low factor loadings (0.30-0.50) in the specific factors. Thus, it is recommended that larger samples of at least 500 observations be obtained.
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