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Table 2 Comparison of different structural models of sensory symptoms in ASD, fit indices and subgroup proportions (N = 332)

From: Dissecting the phenotypic heterogeneity in sensory features in autism spectrum disorder: a factor mixture modelling approach

Model Log-likelihood Par AIC BIC Adjusted BIC LMR p value BLRT p value Class percentages
Latent class analysis
 Two-subgroup − 19,313 115 38,856 39,294 38,929 .004 < .0001 55%, 45%
 Three-subgroup − 18,812 154 37,932 38,518 38,029 .024 < .0001 27%, 33%, 40%
 Four-subgroup − 18,571 193 37,527 38,262 37,650 .141 < .0001 17%, 30%, 15%, 37%
 Five-subgroup − 18,359 232 37,182 38,064 37,329 .160 a 17%, 14%, 34%, 19%, 16%
 Six-subgroup − 18,245 271 37,032 38,063 37,204 .745 a 17%, 10%, 15%, 21%, 22%, 15%
Factor analysis
 Five-factor − 17,623 124 35,495 35,967 35,574  
 Six-factor − 17,563 129 35,384 35,875 35,466  
 Seven-factor − 17,398 135 35,066 35,580 35,152  
Factor-mixture analysis
 Two-subgroup, seven-factor − 17,362 142 35,009 35,549 35,099 .0021 < .0001 19%, 81%
Three-subgroup, seven-factor − 17,301 150 34,902 35,473 34,997 .01 a 7%, 15%, 77%
 Four-subgroup, seven-factor b        
  1. Par number of estimated parameters, AIC Akaike information criterion, BIC Bayesian information criterion, LMR Lo-Mendell-Rubin test, BLRT Bootstrapped likelihood ratio test,
  2. Best performing FMM (‘three-subgroup, seven-factor’) in font bold
  3. aLog-likelihood was not replicated
  4. bModel did not converge