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Table 2 CFA-modelling results for latent structure models for MFQ and RCMAS data in adolescents aged 14

From: General and specific components of depression and anxiety in an adolescent population

Estimator robust WLS Chi Squ. (DF) df # parameters CFI TLI RMSEA WRMR SSABIC SSABIC +/-
1 factor model 5345.797 1764 188 0.939 0.937 0.042 1.712 104 332 0
2 factor model aa 4654.275 1758 194 0.951 0.948 0.038 1.565 103 636 - 696
2 factor model bb 5037.228 1763 189 0.944 0.942 0.040 1.647 103 839 -493
3 factor model 4083.833 1752 200 0.960 0.958 0.034 1.424 102 753 - 1579
   - With gender as covariate 4248.097 1810 203 0.957 0.955 0.034 1.450 104 249 -83
   - Correction for differential item functioning 4135.5731 1808 205 0.959 0.957 0.033 1.425 104 073 -259
Estimator robust WLS Chi Squ. (DF) df # parameters CFI TLI RMSEA WRMR SSABIC SSABIC +/-
Bifactor model 3839.960 1724 228 0.964 0.962 0.033 1.350 102 077 -2255
   - With gender as covariate 3951.343 1781 232 0.961 0.959 0.032 1.367 104 651c +319
   - Correction for differential item functioning 4083.833 1752 233 0.960 0.958 0.034 1.424   
  1. aanxiety/depression and somatic factor, following EFA.
  2. btwo-factor model with MFQ items on one factor and RCMAS items on the other factor.
  3. ccomputing the ssaBIC for the bifactor model with adjustments for DIF was computationally unmanageable with MLR.
  4. SSABIC = Sample-size adjusted Bayesian information criterion.
  5. Robust WLS is WLSMV in Mplus i.e. robust Weighted Least Squares for categorical data, mean and variance adjusted.
  6. Robust ML is MLMV in Mplus i.e. Maximum Likelihood covariance structure analysis, mean and variance adjusted.