Skip to main content
. 2012 Jul 23;7(7):e40560. doi: 10.1371/journal.pone.0040560

Table 2. Fit indices for general sleep disturbance scale gmm solutions over seven assessments, with dyad as a clustering variable.

GMM LL AIC BIC Entropy VLMRc
1-Classa −6238.023 12508.047 12564.581 n/a n/a
2-Classb −6208.505 12463.011 12544.279 0.856 59.036**
3-Class −6193.223 12444.445 12546.914 0.811 30.565n.s.
*

p<.05, p** <.01, ***p<.001, n.s.  =  p>.05.

a

Random coefficients latent growth curve model with linear and quadratic components; Chi2 = 108.81, 26 df, p<0.001, CFI = 0.921, RMSEA = 0.112.

b

2-class model was selected, based on its having the smallest BIC, the largest entropy, and a significant VLMR. Further, the VLMR is not significant for the 3-class model, and the 3-class model estimated a class with only 4% of the sample – a class size that is unlikely to be reliable.

c

This value is the Chi2 statistic for the VLMR. When significant, the VLMR test provides evidence that the K-class model fits the data better than the K-1-class model.

Abbreviations: GMM  =  Growth mixture model; LL  =  log likelihood; AIC  =  Akaike Information Criteria; BIC  =  Bayesian Information Criterion; VLMR  =  Vuong-Lo-Mendell-Rubin likelihood ratio test; CFI  =  comparative fit index; RMSEA  =  root mean square error of approximation.