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. Author manuscript; available in PMC: 2023 Feb 8.
Published in final edited form as: Neuropsychology. 2021 Sep 27;35(8):889–903. doi: 10.1037/neu0000775

Table 4.

Model Comparison Results

Model −2LL Par. AIC BIC SSABIC ENTROPY LMR (p-value) BLRT (p-value) RMSEA CFI SRMR

Latent Profile Analysis

One-Class −8739.24 16 17510.48 17584.99 17534.18 - - - - - -
Two-Class −8180.53 25 16411.07 16527.48 16448.10 .77 0 0 - - -
Three-Class −8039.24 34 16146.48 16304.81 16196.84 .74 .001 0 - - -
Four-Class −7956.64 43 15999.28 16199.52 16062.97 .78 .34 0 - - -

Factor Analysis

One-Factor −7969.34 26 15990.69 16111.76 16029.19 - - - .02 .99 .02
Two-factor −7968.55 27 15991.11 16116.84 16031.10 - - - .02 .99 .02

Factor Mixture Analysis

2-Class, 1-Factor
FMM-1 −8180.53 25 16411.07 16527.49 16448.10 .83 .24 0 - - -
FMM-2 −8006.45 27 16066.91 16192.64 16106.90 .32 .002 0 - - -
FMM-3* −7878.58 34 15825.16 15983.49 15875.52 .78 .01 0 - - -
FMM-4* −7841.66 41 15765.33 15956.25 15826.06 .80 0 0 - - -
3-Class, 1-Factor
FMM-1 −8058.47 27 16170.94 16296.67 16210.93 .75 0 0 - - -
FMM-2* −8004.30 29 16066.60 16201.65 16109.56 .59 .04 .03 - - -
FMM-3** −7802.90 44 15693.80 15898.70 15758.98 .79 .002 0 - - -
FMM-4** −7768.64 58 15653.29 15923.38 15739.11 .79 .17 0 - - -
2-Class, 2-Factor
FMM-1 −8180.53 25 16411.07 16527.49 16448.10 .83 0 0 - - -
FMM-2 −7954.23 31 15970.45 16114.81 16016.37 .48 .008 0 - - -
FMM-3* −7849.64 36 15771.88 15938.93 15824.61 .79 .006 0 - - -
FMM-4* a −7798.57 42 15681.14 15876.72 15743.37 .80 0 0 - - -
3-Class, 2-Factor
FMM-1 −8056.01 28 16168.18 16298.57 16209.65 .75 0 0 - - -

Note. FMM-1 = Factor mixture model 1; FMM-2 = Factor mixture model 2; FMM-3 = Factor mixture model 3; FMM-4 = Factor mixture model 4; −2LL = −2 log likelihood; Par. = number of estimated parameters; AIC = Akaike information criterion; BIC = Bayesian information criterion; SSABIC = sample-size adjusted BIC; LMR = Lo-Mendell-Rubin test; BLRT = Bootstrapped Likelihood Ratio Test; RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index; SRMR = Standardized Root Mean Square Residual.

a

Preferred model.

*

Variance fixed in 1 class at 0 for model identification.

**

Variance fixed in 2 classes at 0 for model identification.