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. Author manuscript; available in PMC: 2016 Mar 1.
Published in final edited form as: Psychol Methods. 2013 Dec 23;20(1):117–141. doi: 10.1037/a0034523

Table 4.

Mean Model Fit for Simulation 1

Model Converge χ2 χ2/df CFI RMSEA
FOCUSR 75.2% 3360.94 (2656.23) 2.00 (1.66) 0.89 (0.08) 0.034 (0.024)
GLLAR 97.0% 3200.51 (2788.80) 1.80 (1.29) 0.90 (0.07) 0.031 (0.021)
MMODR 96.8% 3080.26 (2567.69) 1.74 (1.21) 0.91 (0.07) 0.030 (0.020)

FOCUSF 43.6% 51825.47 (45190.00) 38.70 (26.25) 0.00 (0.00) 0.253 (0.048)
GLLAF 74.7% 32527.02 (34153.45) 16.78 (12.01) 0.07 (0.20) 0.163 (0.038)
MMODF 91.1% 2107.87 (1704.59) 1.06 (0.06) 0.97 (0.05) 0.011 (0.008)

Reference 99.1% 2111.38 (1703.24) 1.06 (0.06) 0.97 (0.05) 0.011 (0.008)

Note. Mean fit statistics, their standard deviations and convergence rates for each of the seven comparison models aggregating across all simulation parameters. Means taken across all converged models with free (unconstrained) factor loadings across timepoints, derivative factors or growth factors. Reported means are taken as grand mean of cell means to prevent models with poor convergence from oversampling simpler (and thus, better fitting) simulation conditions. Models differ in their number of sample sizes and timepoints (and thus, degrees of freedom), the chi2/df column shows the ratio of the chi2 of each model to its degrees of freedom, and thus partially adjusts for this heterogeneity. Proportional and conceptually identical results are found for each combination of degrees of freedom and sample size.