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. Author manuscript; available in PMC: 2016 Dec 15.
Published in final edited form as: Psychiatry Res. 2015 Oct 3;230(2):553–560. doi: 10.1016/j.psychres.2015.10.003

Table 4.

Model fit results

log Likelihood # parameters BIC saBIC
LCA C2 −18259.270 62 37003.581 36806.592
LCA C3 −17690.645 94 36116.675 35818.013
LCA C4 −17365.716 126 35717.161 35316.827
F1C1 −17376.632 41 35074.033 34943.766
F2C1 −17164.978 45 34558.756 34415.780
F2C2 −16945.583 78 34360.309 34112.483
F2C3 −16845.291 113 34603.874 34244.845
F2C4 −16784.063 148 34723.727 34253.494
F3C1 −16947.075 46 34254.038 34107.885
F3C2 −16816.382 81 34266.480 34009.122
F3C3 −16822.285 119 34482.748 34104.655
F3C4 −16818.046 157 34698.028 34199.199

Note: Models were fitted to a subsample of N=2,499 to separate model selection from the estimation of regressions on comorbid behaviors in order to avoid inflated type 1 error. LCA C# indicates results for latent class models with # latent classes. F#C# are factor mixture models where # indicates the number of factors within class and the number of classes (e.g., F2C3 is a 2-factor 3-class model). BIC stands for Bayesian Information Criterion, with smaller values indicating better model fit. SaBIC is a BIC with a different sample size adjustment.31