Table 2.
Selecting an optimal profile solution: model fit and class enumeration
| Mean posterior probabilities | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Profiles | BIC | BSLRT | Entropy | Smallest, n (%) | 1 | 2 | 3 | 4 | 5 |
| 1 | 1714.67 | – | – | – | – | – | – | – | – |
| 2 | 1387.44 | <0.001 | 0.78 | 98 (49) | 0.95 | 0.93 | – | – | – |
| 3 | 1333.05 | <0.001 | 0.99 | 38 (19) | 1.00 | 0.99 | 1.00 | – | – |
| 4 | 1535.94 | 0.15 | 0.98 | 8 (4) | 1.00 | 0.99 | 0.98 | 0.93 | – |
| 5 | 1673.45 | 0.50 | 0.88 | 9 (4) | 1.00 | 0.94 | 0.83 | 0.97 | 0.94 |
Bold items correspond with the solution selected as optimal
BIC Bayesian Information Criterion, BSLRT Bootstrap LRT, LRT likelihood ratio test