2007
DOI: 10.1207/s15328007sem1401_2
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Performance of Factor Mixture Models as a Function of Model Size, Covariate Effects, and Class-Specific Parameters

Abstract: Factor mixture models are designed for the analysis of multivariate data obtained from a population consisting of distinct latent classes. A common factor model is assumed to hold within each of the latent classes. Factor mixture modeling involves obtaining estimates of the model parameters, and may also be used to assign subjects to their most likely latent class. This simulation study investigates aspects of model performance such as parameter coverage and correct class membership assignment and focuses on c… Show more

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Cited by 186 publications
(171 citation statements)
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References 31 publications
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“…Inspection of scree plots (not provided) of the AIC, BIC, and SSA BIC showed a flattening of scores after three classes, corroborating the three-class solution. The inclusion of gender in the high school model also did not substantively change the class proportions, providing further validation of the classes (Lubke & Muthén, 2007). …”
Section: Resultsmentioning
confidence: 84%
See 1 more Smart Citation
“…Inspection of scree plots (not provided) of the AIC, BIC, and SSA BIC showed a flattening of scores after three classes, corroborating the three-class solution. The inclusion of gender in the high school model also did not substantively change the class proportions, providing further validation of the classes (Lubke & Muthén, 2007). …”
Section: Resultsmentioning
confidence: 84%
“…Inspection of scree plots (not provided) of the AIC, BIC, and SSA BIC showed a flattening of scores after four classes, indicating that adding a fifth class did not significantly improve in fit. Next, gender was regressed on class membership, which did not substantively change the class proportions, thus indicating the stability of the classes (Lubke & Muthén, 2007). …”
Section: Resultsmentioning
confidence: 99%
“…Although it is not an index of model fit, it provides a useful assessment of the utility of the extracted classes (Ramaswamy, Desarbo, Reibstein, & Robinson, 1993). Entropy values range from 0 to 1, with higher values indicating greater class separation (Lubke & Muthén, 2007; Petras & Masyn, 2010). …”
Section: Resultsmentioning
confidence: 99%
“…These potential moderator variables might impact class enumeration. However, there is debate about the meaning of moderator variables for class enumeration, so researchers should proceed with care when including moderators and compare results with and without moderator variables (Lubke & Muthén, 2007). Because we were interested in replicating prior studies that did not include moderators, we also chose not to include them.…”
Section: Discussionmentioning
confidence: 99%
“…The first LPA used the eight indicators of objective success (four functional abilities, three indicators of pain, and number of chronic diseases) as dependent variables, specifying a mixture model in Mplus version 5.2 (Lubke & Muthen, 2007;Muthen, 2001;B. Muthen & L. Muthen, 2000;Pastor, Barron, Miller, & Davis, 2007) and allowing the means of indicator variables to freely vary across the latent classes.…”
Section: Four Groups Definedmentioning
confidence: 99%