Table 3.
Classes | Log-Likelihood | Number of Parameters | Entropy | AIC | BIC | Adj. BIC | VLMR |
---|---|---|---|---|---|---|---|
1 | −3056 | 30 | n/a | 6171 | 6307 | 6212 | n/a |
2 | −3002 | 45 | .712 | 6093 | 6298 | 6155 | 108, p = .94 |
3 | −2887 | 60 | .745 | 5893 | 6166 | 6166 | 166, p < .001 |
Note. For entropy, higher numbers (up to 1.0) indicated greater certainty of classification. For AIC, BIC, and sample-size adjusted BIC, lower values indicate better fit, considering parsimony. Vuong-Lo-Mendell-Rubin (VLMR) tests that k classes are needed for better fit, vs. k-l classes being adequate.