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. Author manuscript; available in PMC: 2017 Nov 1.
Published in final edited form as: J Pain Symptom Manage. 2016 Sep 21;52(5):695–708.e4. doi: 10.1016/j.jpainsymman.2016.04.014

Table 1.

Fit Indices for the Lee Fatigue Scale and Lee Energy Scale GMM Class Solutions

Fatigue
GMM LL AIC BIC Entropy BLRT VLMR
1-Classa −5068.45 10168.90 10232.65 n/a n/a n/a
2-Classb −5035.46 10106.93 10178.64 0.61 68.73* 68.73**
3-Class −5026.22 10096.44 10184.09 0.71 18.48ns 18.48ns
Energy
GMM LL AIC BIC Entropy BLRT VLMR
1-Classa −5454.43 10930.87 10974.69 n/a n/a n/a
2-Classb −5430.78 10893.56 10957.30 0.53 47.31* 47.31ns
3-Class −5422.49 10886.98 10970.64 0.53 16.58ns 16.58*
*

p < .05;

**

p < .01

a

Latent growth curve with linear and quadratic components; Chi2=34.60, 19 df, p < .02, CFI = .99, RMSEA = .045

b

2-class model was selected. The BIC was smaller than for the 1-class and 3-class models, and the BLRT indicated that the 2-class solution fit the data better than the 1-class solution.

*

p< .05

a

Latent growth curve with linear and quadratic components; Chi2 =50.99, 24 df, p = .001, CFI = .964, RMSEA = .053

b

2-class model was selected. The BIC was smaller than for the 1-class and 3-class models, and the BLRT indicated that the 2-class solution fit the data better than the 1-class solution.

Abbreviations: AIC = Akaike Information Criterion, BIC = Bayesian Information Criterion, BLRT = parametric bootstrapped likelihood ratio test for K-1 (H0) vs K classes, CFI = Comparative Fit Index, GMM = growth mixture model, LL = loglikelihood, ns = not significant, RMSEA = Root Mean Squared Error of Approximattion, VLMR = Vuong-Lo-Mendell-Rubin likelihood ratio test for K-1 (H0) vs K classes