Class | AIC | ∆AIC | BIC | ∆BIC | Adj LR | Entrophy |
|---|
1 | 5412 | | 5444 | | | |
2 | 5043 | (369) | 5095 | (349) | 367.17*** | .81 |
3 | 4990 | (53) | 5063 | (32) | 60.67 | .73 |
4
|
4906
|
(84)
|
4998
|
(65)
|
91.31**
|
.75
|
5 | 4880 | (26) | 4994 | (4) | 34.47 | .79 |
6 | 4858 | (22) | 4991 | (3) | 31.45 | .79 |
- Note. AIC = Akaike’s Information Criterion, BIC=Bayesian Information Criterion. ∆ refers to the difference of an information criterion from a model with k classes compared with the information criterion from a model with k+1 classes. For AIC and BIC, lower values indicate better trade-off between the complexity and the goodness of fit of the model. LR = Lo-Rubin Test. Bold refers to the model that was chosen for further analyses. Levels of significance are ** p < .01, *** p <.001
