Table 4.

Estimator performance results with a binary outcome and a covariate.

Binary outcome	Indicators	Sample size	ML			Bayes with Mplus default priors
			NCR (%)	AAB	RMSE	AAB	RMSE
0:60%, 1:40%	3	100	4.4	0.020	0.028	.053	.082
		200	1.2	0.012	0.017	.022	.034
		500	0.2	0.006	0.008	.010	.014
		1,000	0	0.003	0.003	.005	.007
		5,000	0	0.001	0.002	.001	.002
	4	100	1	0.013	0.022	.052	.078
		200	0.4	0.005	0.008	.019	.031
		500	0	0.010	0.028	.007	.012
		1,000	0	0.002	0.004	.004	.005
		5,000	0	0.001	0.001	.001	.001
	7	100	0.2	0.011	0.020	.035	.057
		200	0	0.006	0.008	.016	.026
		500	0	0.003	0.004	.007	.011
		1,000	0	0.003	0.007	.003	.006
		5,000	0	0.001	0.001	.001	.002
0:90%, 1:10%	3	100	5.2	2.098*	5.323*	.134	.272
		200	1	0.034	0.075	.078	.145
		500	0.2	0.006	0.009	.026	.043
		1,000	0	0.004	0.006	.013	.021
		5,000	0	0.002	0.004	.003	.006
	4	100	2	0.042	0.104	.114	.223
		200	0.6	0.015	0.038	.057	.107
		500	0	0.006	0.012	.021	.035
		1,000	0	0.004	0.006	.010	.018
		5,000	0	0.001	0.002	.003	.007
	7	100	0.6	0.033	0.090	.079	.146
		200	0	0.014	0.035	.040	.075
		500	0	0.004	0.006	.015	.026
		1,000	0	0.002	0.003	.006	.011
		5,000	0	0.001	0.002	.002	.003

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.

^*A condition where parameter(s) had an unacceptable estimate(s), that is, extremely large or small parameter estimates.