2022
DOI: 10.1037/met0000529
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Assessing the fitting propensity of factor models.

Abstract: Model selection is an omnipresent issue in structural equation modeling (SEM). When deciding among competing theories instantiated as formal statistical models, a trade-off is often sought between goodness-of-fit and model parsimony. Whereas traditional fit assessment in SEM quantifies parsimony solely as the number of free parameters, the ability of a model to account for diverse data patterns-known as fitting propensity-also depends on the functional form of a model. The present investigation provides a syst… Show more

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Cited by 13 publications
(14 citation statements)
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“…Values of CFI ≥ .97, NNFI ≥ .97, RMSEA ≤ .05, and SRMR ≤ .05 were considered indicators for good model fit (Schermelleh-Engel et al, 2003). Following Bader and Moshagen (2022), we used the NNFI and RMSEA for model comparisons. In contrast to fit indices that do not take the model complexity into account or information criteria that are strongly affected by sample size, parsimony-adjusted goodness of fit indices more accurately identify the best relative fit for model selection.…”
Section: Methodsmentioning
confidence: 99%
“…Values of CFI ≥ .97, NNFI ≥ .97, RMSEA ≤ .05, and SRMR ≤ .05 were considered indicators for good model fit (Schermelleh-Engel et al, 2003). Following Bader and Moshagen (2022), we used the NNFI and RMSEA for model comparisons. In contrast to fit indices that do not take the model complexity into account or information criteria that are strongly affected by sample size, parsimony-adjusted goodness of fit indices more accurately identify the best relative fit for model selection.…”
Section: Methodsmentioning
confidence: 99%
“…Intercorrelations between factors were set to zero to allow for clear partitioning of variance. Based on recent recommendations (Bader & Moshagen, 2022), the optimal number of exploratory factors was determined using the Tucker-Lewis index (TLI) and the root-mean-square error of approximation (RMSEA), with additional consideration given to deriving a maximum number of interpretable factors. For the tasks, we used bifactor confirmatory factor analysis (bifactor CFA), as shown in Supplemental Table S2.…”
Section: Internal Structurementioning
confidence: 99%
“…The main focus, however, is on testing the competing measurement models by means of confirmatory factor analysis with a weighted least square estimator using the asymptotic variance-covariance matrix of the pooled correlations from the first step as weights (Cheung & Chan, 2005). In line with conventional standards (see Schermelleh-Engel et al, 2003) and current recommendations (Bader & Moshagen, 2022), the following cut-off criteria were used as an indication of acceptable model fit: Comparative fit index (CFI) ! .95, non-normed fit index (NNFI; also known as Tucker-Lewis Index) !…”
Section: Meta-analytic Proceduresmentioning
confidence: 99%