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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