Table 3.
Goodness of fit statistics, and class frequencies for LPA models from 1 to 5 latent classes.
| LC (1) | LC (2) | LC (3) | LC (4) | LC (5) | |
| Log-L (H0) | -29663.70 | -2636.05 | -2554.08 | -2512.92 | -2507.12 |
| n parameters | 32 | 49 | 66 | 83 | 100 |
| AIC | 5991.4 | 5370.1 | 5240.2 | 5191.8 | 5214.2 |
| BIC | 6073.8 | 5493.7 | 5406.6 | 5401.1 | 5466.4 |
| Entropy | 0.92 | 0.951 | 0.963 | 0.945 | |
| Class frequencies (%) | |||||
| n1 | 92 (100) | 44 (47.8) | 17 (18.5) | 8 (8.7) | 28 (30.4) |
| n2 | 48 (52.2) | 38 (41.3) | 12 (13.0) | 3 (3.3) | |
| n3 | 37 (40.2) | 33 (35.9) | 17 (18.5) | ||
| n4 | 39 (42.4) | 29 (31.5) | |||
| n5 | 15 (16.3) | ||||
LPA: Latent Profile Analysis; LC: Latent Class; Log-L (H0): Log-likelihood of hypothesized model (H0)
AIC: Akaike's Information Criterion (= -2 × model log-likelihood + 2 × number of model parameters); BIC: Bayesian Information Criterion (= -2 × model log-likelihood + log(n) × number of model parameters)