008). Factor correlations are set to 0 because what is shared HMPL-013 mechanism of action between factors is already captured by the common factor (Chen, 2006; Muth Muth , 2012). This bifactor parameterization has two advantages over hierarchical models in which first-level factors load onto a higher-level factor. First, from a practical standpoint, convergence problems are very common for hierarchical models, whereas this is not frequently a problem for bifactor models. Second, from a conceptual perspective, bifactor models allow examination of how other variables are related to both the common and specific aspects of a construct (Chen, 2006; Chen et al., 2012; Friedman et al., 2008), about which there are frequently distinct hypotheses (e.g., general NE vs. what is unique to depressed mood). Full bifactor models were tested first and then modified based on the significance of factor variances and pattern of loadings.4 If variance was not significant for a specific factor, this was taken as evidence that the specific factor was not needed to account for variance on the items in that subscale (i.e., they are fully accounted for by the common factor), and the specific factor was eliminated. In contrast, if items from a subscale loaded strongly on their specific factor but had any non-significant and/or negative JC-1 chemical information loadings on the common factor, this suggested that that subscale was best considered a separate factor, and loadings on the common factor were eliminated. Model fit for these final models was then compared to that of the correlated factor and one-factor models. In all cases, the bifactor models fit the data best. Thus, only the final bifactor models are reported here. Results from the individual subscale models and correlated subscale models are reported in Supplemental Materials. Correlations with Adolescent Functioning–After development of final EATQ-R models, correlations were tested between the factors in each EATQ-R dimension model (EC, NE and PE) and each of the adolescent functioning measures (CDI, MASC, SNAP, RPEQ antisocial behavior towards peers, REPQ victimization towards peers, school behavior, grades) in samples 1 and 2, for which these measures were available. As school behavior and grades were assessed with a single question each, they were analyzed as manifest variables. All other measures were analyzed as latent variables, based on their established subscale structure. The MASC and SNAP have correlated subscales that have been supported by previous factor-analyses analysis (March et al., 1997; Pillow, Pelham, Hoza, Molina Stulz, 1998). Thus, they were analyzed using bifactor models, with common and subscale-specific factors. The MASC model included a common factor and specific factors for Physical Symptoms, Separation/Panic and Harm avoidance subscales; the Social Anxiety subscale was fully accounted for by the common factor (i.e., there was not significant variance associated with the specific factor, so it was eliminated.) The SNAP model included a common factor and Inattention, Hyperactivity and Impulsivity specific factors. The CDI, RPEQ antisocial behavior towards peers, and REPQ victimization towards peers scales were each analyzed as single factors.5 Finally, we compared the findings regarding links to adolescent functioning using our final EATQ-R models to those obtained using the traditional method of computing temperamentAuthor Manuscript Author Manuscript Author Manuscript Author Manuscript4To test the significance of factor.008). Factor correlations are set to 0 because what is shared between factors is already captured by the common factor (Chen, 2006; Muth Muth , 2012). This bifactor parameterization has two advantages over hierarchical models in which first-level factors load onto a higher-level factor. First, from a practical standpoint, convergence problems are very common for hierarchical models, whereas this is not frequently a problem for bifactor models. Second, from a conceptual perspective, bifactor models allow examination of how other variables are related to both the common and specific aspects of a construct (Chen, 2006; Chen et al., 2012; Friedman et al., 2008), about which there are frequently distinct hypotheses (e.g., general NE vs. what is unique to depressed mood). Full bifactor models were tested first and then modified based on the significance of factor variances and pattern of loadings.4 If variance was not significant for a specific factor, this was taken as evidence that the specific factor was not needed to account for variance on the items in that subscale (i.e., they are fully accounted for by the common factor), and the specific factor was eliminated. In contrast, if items from a subscale loaded strongly on their specific factor but had any non-significant and/or negative loadings on the common factor, this suggested that that subscale was best considered a separate factor, and loadings on the common factor were eliminated. Model fit for these final models was then compared to that of the correlated factor and one-factor models. In all cases, the bifactor models fit the data best. Thus, only the final bifactor models are reported here. Results from the individual subscale models and correlated subscale models are reported in Supplemental Materials. Correlations with Adolescent Functioning–After development of final EATQ-R models, correlations were tested between the factors in each EATQ-R dimension model (EC, NE and PE) and each of the adolescent functioning measures (CDI, MASC, SNAP, RPEQ antisocial behavior towards peers, REPQ victimization towards peers, school behavior, grades) in samples 1 and 2, for which these measures were available. As school behavior and grades were assessed with a single question each, they were analyzed as manifest variables. All other measures were analyzed as latent variables, based on their established subscale structure. The MASC and SNAP have correlated subscales that have been supported by previous factor-analyses analysis (March et al., 1997; Pillow, Pelham, Hoza, Molina Stulz, 1998). Thus, they were analyzed using bifactor models, with common and subscale-specific factors. The MASC model included a common factor and specific factors for Physical Symptoms, Separation/Panic and Harm avoidance subscales; the Social Anxiety subscale was fully accounted for by the common factor (i.e., there was not significant variance associated with the specific factor, so it was eliminated.) The SNAP model included a common factor and Inattention, Hyperactivity and Impulsivity specific factors. The CDI, RPEQ antisocial behavior towards peers, and REPQ victimization towards peers scales were each analyzed as single factors.5 Finally, we compared the findings regarding links to adolescent functioning using our final EATQ-R models to those obtained using the traditional method of computing temperamentAuthor Manuscript Author Manuscript Author Manuscript Author Manuscript4To test the significance of factor.
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