Table 2.
Growth Mixture Model: Optimization of Trajectory Number
| Model | Latent Classes | Covariates | Free parameters | Loglikelihood | BIC | Entropy | BLRT (parameter difference), p - value |
|---|---|---|---|---|---|---|---|
| 1 | 2 | No | 8 | -2926.37 | 5908.52 | .90 | 1122.09(3), p <.0001 |
| 2 | 3 | No | 11 | -2844.65 | 5766.01 | .78 | 163.440(3), p <.0001 |
| 3 | 4 | No | 14 | -2817.08 | 5731.79 | .73 | 55.137(3), p <.0001 |
| 4 | 5 | No | 17 | -2802.60 | 5723.75 | .76 | 28.96(3), p =.0312 |
| 5 | 6 | No | 20 | -2790.85 | 5721.17 | .77 | 23508(3), p =.19 |
| 6 | 4 | Yes | 50 | -2230.96 | 4802.54 | .78 | 267.98 (19), p =.67 |
| 7 | 3 | Yes | 35 | -2276.35 | 4791.14 | .77 | 213.18 (15), p <.0001 |
Note. BIC, Bayesian information criterion (-2logL + r ln n), where L is the model’s maximum likelihood value, n is the sample size, and r is the number of free model parameters; BLRT, Bootstrap Likelihood Ratio Test for k (H0) versus k-1 classes; Entropy (EK=1-(piklog(pik))/nlog(K)), where pik is the conditional probability of individual i in class K, is a measure of classification quality, with values closer to one representing better classification.