Table 2.
Fit Statistics for Latent Class Growth Analysis (LCGA) and Growth Mixture Models (GMM) for Data on Depressive Symptoms
| Class sizes | Parameters | BIC | LMR LRT | aLMR LRT | BLRT | Entropy | |
|---|---|---|---|---|---|---|---|
| LCGA | |||||||
| 1-Class | 3,533 | 3 | 42,382 | — | — | — | — |
| 2-Class | 2,610; 923 | 6 | 39,674 | <0.01 | <0.01 | <0.01 | 0.793 |
| 3-Class | 930; 2,263; 340 | 9 | 39,260 | <0.01 | <0.01 | <0.01 | 0.741 |
| 4-Class | 2,156; 343; 441; 593 | 12 | 39,007 | <0.01 | <0.01 | <0.01 | 0.716 |
| 5-Class | 2,105; 391; 558; 326; 152 | 15 | 38,966 | <0.01 | <0.01 | <0.01 | 0.708 |
| LCGA—Count | |||||||
| 1-Class | 3,533 | 2 | 41,618 | — | — | — | — |
| 2-Class | 2,017; 1,516 | 5 | 36,631 | <0.01 | <0.01 | <0.01 | 0.700 |
| 3-Class | 915; 1,098; 1,520 | 8 | 36,223 | <0.01 | <0.01 | <0.01 | 0.570 |
| 4-Class | 862; 937; 971; 762 | 11 | 36,121 | <0.01 | <0.01 | <0.01 | 0.492 |
| 5-Class | 745; 467; 904; 545; 872 | 14 | 36,090 | <0.01 | <0.01 | <0.01 | 0.457 |
| GMM | |||||||
| 1-Class | 3,533 | 6 | 40,021 | — | — | — | — |
| 2-Class | 1,314; 2,219 | 13 | 37,759 | <0.01 | <0.01 | <0.01 | 0.702 |
| 3-Class | 1,285; 527; 1,721 | 14 | 37,204 | <0.01 | <0.01 | <0.01 | 0.675 |
| 4-Class | 231; 1,520; 1,272; 509 | 19 | 37,046 | <0.01 | <0.01 | <0.01 | 0.698 |
| GMM—Count | |||||||
| 1-Class | 3,533 | 5 | 36,370 | — | — | — | — |
| 2-Class | 1,444; 2,089 | 11 | 36,110 | <0.01 | <0.01 | <0.01 | 0.478 |
Note: BIC = Bayesian information criterion; BLRT = bootstrapped likelihood ratio test; CFI = comparative fit index; LMR LRT = Vuong-Lo-Mendell-Rubin likelihood ratio test; aLMR LRT = adjusted Vuong-Lo-Mendell-Rubin likelihood ratio test; RMSEA = root mean square error of approximation. LCGA: model fit for one-class model: CFI = 0.000, RMSEA = 0.256; GMM: model fit for one-class model: CFI = 0.936, RMSEA = 0.075. When taking into consideration the count nature of the data, CFI and RMSEA values are not given. Bold values indicate the model that was selected as being most appropriate or best fitting to the data.
For the three-class solution in the GMM model, the intercept and slope variances and the covariance needed to be set to 0 for the first and second class to allow for model convergence. For the four-class solution in the GMM model, the intercept variance needed to be set to 0 for the second class, the intercept and slope variance needed to be set to 0 for the third and fourth class to ensure for model convergence.