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. 2022 Jul 14:1–17. Online ahead of print. doi: 10.1007/s12144-022-03377-4

Table 1.

Latent class analysis models fit statistics

k LL #FP Scaling AIC CAIC BIC SABIC AWE LMR-LRT (p) BF cmP Entropy
1 -24409.318 32 2.000 48882.635 48883.580 49065.888 48964.218 49409.142 NA 0.000 0.000 NA
2 -22701.662 65 1.962 45533.324 45537.220 45905.557 45699.041 46602.789 0.000 0.000 0.000 0.810
3 -22104.333 98 1.967 44404.667 44413.613 44965.879 44654.516 46017.090 0.000 0.000 0.000 0.795
4 -21916.712 131 1.949 44095.424 44111.615 44845.615 44429.406 46250.807 0.695 0.000 0.000 0.809
5 -21754.983 164 1.960 43837.965 43863.700 44777.137 44256.080 46536.308 0.766 0.019 0.019 0.758
6 -21623.551 197 2.005 43641.103 43678.790 44769.254 44143.351 46882.404 0.760 >150.000 0.981 0.758
7 -21531.882 230 1.959 43523.763 43575.928 44840.894 44110.144 47308.025 0.809 NA 0.000 0.765

k: number of classes, LL log-likelihood, #FP Number of free parameters, Scaling Scaling factor associated with MLR loglikelihood estimates, AIC Akaike information criterion, CAIC Consistent AIC, BIC Bayesian information criterion, SABIC Sample-size adjusted BIC, AWE Approximate weight of evidence, BF Bayes factor, cmP Approximate correct model probability, LMR-LRT Adjusted Lo-Mendel-Rubin likelihood ratio test. Bolded values indicate best fit for each respective statistic