Table 3.

Estimator performance results with a binary outcome and no covariate.

Binary outcome	Indicators	Sample size	ML			Bayes with Mplus default priors
			NCR (%)	AAB	RMSE	AAB	RMSE
0:60%, 1:40%	3	100	11.8	.087	.150	.120	.154
		200	6.8	.025	.044	.022	.025
		500	1.6	.025	.050	.022	.025
		1,000	0	.007	.010	.013	.016
		5,000	0	.003	.004	.005	.006
	4	100	2.2	.031	.057	.101	.134
		200	2.2	.031	.057	.045	.059
		500	0	.004	.006	.015	.018
		1,000	0	.005	.008	.008	.010
		5,000	0	.002	.003	.002	.003
	7	100	1	.006	.010	.041	.063
		200	0.4	.008	.016	.021	.029
		500	0	.005	.009	.009	.013
		1,000	0	.003	.006	.005	.007
		5,000	0	.001	.001	.001	.002
0:90%, 1:10%	3	100	24.8	.371	.709	.239	.442
		200	8.4	.059	.124	.168	.302
		500	1.4	.056	.104	.087	.149
		1,000	1	.003	.005	.008	.010
		5,000	0	.003	.005	.008	.010
	4	100	5.8	.078	.144	.174	.325
		200	1	.034	.076	.100	.189
		500	0	.034	.076	.100	.189
		1,000	0	.008	.018	.014	.023
		5,000	0	.004	.008	.004	.005
	7	100	1.6	.018	.047	.089	.181
		200	0.2	.011	.030	.042	.073
		500	0	.005	.012	.016	.029
		1,000	0	.002	.003	.007	.013
		5,000	0	.002	.004	.002	.004

Note. ML = maximum likelihood via EM algorithm; EM: expectation and maximization; NCR = non-convergence rate of the ML estimation; AAB: averaged absolute bias; RMSE: root mean square error. The AAB and RMSE estimates are from the converged models. Each and every generated replication of the Bayesian estimation converged based on the potential scale reduction criterion of 1.05 or less.