Table 5.

ML and Bayesian Estimation Performances for Single-Level MIMIC Models

					Parameters vs. ML estimates		Parameters vs. Bayesian estimates
Factors	Items	Covariates	Sample size	Parameters	AAB	RMSE	AAB	RMSE
1	3	1	500	9	0.004	0.007	0.008	0.010
			1,000	9	0.005	0.003	0.006	0.007
			3,000	9	0.002	0.003	0.002	0.002
		2	500	10	0.005	0.008	0.007	0.009
			1,000	10	0.003	0.004	0.004	0.005
			3,000	10	0.001	0.001	0.003	0.003
	6	1	500	15	0.003	0.004	0.006	0.009
			1,000	15	0.002	0.003	0.003	0.003
			3,000	15	0.001	0.002	0.001	0.002
		2	500	16	0.003	0.005	0.004	0.005
			1,000	16	0.003	0.003	0.003	0.003
			3,000	16	0.001	0.001	0.001	0.001
2	3	1	500	18	0.004	0.005	0.007	0.009
			1,000	18	0.003	0.005	0.004	0.004
			3,000	18	0.002	0.002	0.001	0.002
		2	500	20	0.005	0.008	0.006	0.008
			1,000	20	0.003	0.003	0.003	0.004
			3,000	20	0.001	0.002	0.001	0.001
	6	1	500	30	0.004	0.006	0.008	0.010
			1,000	30	0.003	0.004	0.005	0.006
			3,000	30	0.001	0.002	0.002	0.002
		2	500	32	0.002	0.003	0.005	0.007
			1,000	32	0.003	0.004	0.003	0.004
			3,000	32	0.001	0.002	0.001	0.002

Note. AAB represents averaged absolute bias; RMSE represents root mean square error.