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. Author manuscript; available in PMC: 2018 Sep 1.
Published in final edited form as: J Pain Symptom Manage. 2017 Jul 15;54(3):263–272. doi: 10.1016/j.jpainsymman.2017.07.018

Table 1.

Latent Profile Analysis Solutions and Fit Indices for One- Through Four-Classes for SF-12 Physical Component Scores

Model LL AIC BIC VLMR Entropy
1 Class −1927.82 3897.64 3979.42 n/a n/a
2 Class −1800.19 3656.37 3765.41 255.27** .79
3 Classa −1725.57 3521.14 3657.44 149.23* .82
4 Class −1684.82 3453.64 3617.20 81.50ns .81
ns

Not significant;

*

p < .05;

**

p < .001

a

The 3-class solution was selected because the VLMR was significant, indicating that three classes fit the data better than two classes, and the VLMR was not significant for the 4-class solution, indicating that too many classes had been extracted.

Note. AIC = Akaike Information Criterion, BIC = Bayesian Information Criterion, LL = log-likelihood, VLMR = Vuong-Lo-Mendell-Rubin likelihood ratio test for the K vs. K-1 model