Table 2.
Model fit statistics (1).
| Models | LLa | BICb (LL) | AICc (LL) | AIC3 (LL) | Npard | L²e | df e | P valuef |
| 1-cluster | −3470.26 | 7010.65 | 6962.51 | 6973.51 | 11.00 | 6940.51 | 577.00 | <.001 |
| 2-cluster | −3261.06 | 7076.90 | 6696.12 | 6783.12 | 87.00 | 6522.12 | 501.00 | <.001 |
| 3-cluster | −3066.08 | 7171.57 | 6458.16 | 6621.16 | 163.00 | 6132.16 | 425.00 | <.001 |
| 4-cluster | −2954.16 | 7432.35 | 6386.31 | 6625.31 | 239.00 | 5908.31 | 349.00 | <.001 |
| 5-cluster | −2866.60 | 7741.86 | 6363.19 | 6678.19 | 315.00 | 5733.19 | 273.00 | <.001 |
| 6-cluster | −2747.72 | 7988.73 | 6277.43 | 6668.43 | 391.00 | 5495.43 | 197.00 | <.001 |
| 7-cluster | −2690.39 | 8358.71 | 6314.78 | 6781.78 | 467.00 | 5380.78 | 121.00 | <.001 |
aLL: log-likelihood (the smaller the absolute value of log-likelihood, the better the model fit).
bBIC: Bayesian information criterion (values closer to 0 indicate better fit).
cAIC: Akaike information criterion (values closer to 0 indicate better fit).
dNpar: number of estimated parameters.
eL2, df: the sample size–adjusted BIC (SABIC) based on the L2 and df, which is the more common formulation in the analysis of frequency tables. They are defined as:
fAll P values <.01.