DOI

  • Jiesi Guo
  • Herbert W. Marsh
  • Philip Parker
  • Theresa Dicke
  • Oliver Lüdtke
  • Thierno M. O. Diallo
In this study, we contrast two competing approaches, not previously compared, that balance the rigor of CFA/SEM with the flexibility to fit realistically complex data. Exploratory SEM (ESEM) is claimed to provide an optimal compromise between EFA and CFA/SEM. Alternatively, a family of three Bayesian SEMs (BSEMs) replace fixed-zero estimates with informative, small-variance priors for different subsets of parameters: cross-loadings (CL), residual covariances (RC), or CLs and RCs (CLRC). In Study 1, using three simulation studies, results showed that (1) BSEM-CL performed more closely to ESEM; (2) BSEM-CLRC did not provide more accurate model estimation compared with BSEM-CL; (3) BSEM-RC provided unstable estimation; and (4) different specifications of targeted values in ESEM and informative priors in BSEM have significant impacts on model estimation. The real data analysis (Study 2) showed that the differences in estimation between different models were largely consistent with those in Study1 but somewhat smaller.
OriginalspracheEnglisch
ZeitschriftStructural Equation Modeling: A Multidisciplinary Journal
Jahrgang26
Ausgabenummer4
Seiten (von - bis)529-556
Seitenumfang28
ISSN1070-5511
DOIs
PublikationsstatusVeröffentlicht - 04.07.2019

    Fachgebiete

  • Methodenforschung und -entwicklung

ID: 966262