Table 2.
Characteristics of statistical inference and estimation methods.
| Method of estimation | |
| Bayesian | 13 (20.0%) |
| GEE | 1 (1.5%) |
| GLS | 2 (3.1%) |
| MLE | 12 (18.5%) |
| PQL | 1 (1.5%) |
| ReML | 3 (4.6%) |
| ReML and Bayesian | 1 (1.5%) |
| ReML and MLE | 3 (4.6%) |
| Weighted least squares | 2 (3.1%) |
| NI | 27 (41.5%) |
| Distribution of response | |
| Binary | 24 (36.9%) |
| Multinomial | 1 (1.5%) |
| Normal | 31 (47.7%) |
| Normal and binary | 2 (3.1%) |
| Ordinal and binary | 1 (1.5%) |
| Ordinal | 5 (7.7%) |
| Poisson | 1 (1.5%) |
| Model validation measures | |
| AIC | 3 (4.6%) |
| BIC | 2 (3.1%) |
| AIC and BIC | 7 (10.8%) |
| AIC, BIC, and LRT | 11 (16.9%) |
| DIC | 7 (10.8%) |
| R-square | 3 (4.6%) |
| LRT | 5 (7.7%) |
| RMSEA/MSE | 6 (9.2%) |
| HDI/QIC | 2 (3.1%) |
| NI | 19 (29.2%) |
| Statistical software | |
| HLM | 6 (9.2%) |
| Mlwin | 8 (12.3%) |
| Mplus | 4 (6.2%) |
| R | 13 (20.0%) |
| SAS | 9 (13.8%) |
| SAS and Mlwin | 1 (1.5%) |
| SPSS | 5 (7.7%) |
| Stata | 9 (13.8%) |
| Stata and Mlwin | 2 (3.1%) |
| WinBUGS | 2 (3.1%) |
| NI | 6 (9.2%) |
| Statistical packages | |
| GLIMMIX | 5 (7.7%) |
| GLLAMM | 1 (1.5%) |
| HSAR | 1 (1.5%) |
| JAGS (rjags, runjags, and coda) | 2 (3.1%) |
| lme4/lmer | 4 (6.2%) |
| lme4, spdep, and sjstats | 2 (3.1%) |
| Nmle | 2 (3.1%) |
| PROC MIXED/NLMIXED | 4 (6.2%) |
| VCMMR estimation | 1 (1.5%) |
| xtlogit | 1 (1.5%) |
| xtmixed | 1 (1.5%) |
| NI | 41 (63.1%) |