Table 5.

Estimator performance results with no binary outcome.

Covariate	Indicators	Sample size	ML			EAP
			NCR (%)	AAB	RMSE	AAB	RMSE
No	3	100	35.2	.015	.016	.055	.058
		200	12.8	.006	.007	.034	.034
		500	1.6	.003	.003	.014	.014
		1,000	0	.003	.003	.006	.006
		5,000	0	.002	.002	.003	.003
	4	100	5.6	.008	.009	.050	.063
		200	0.4	.002	.002	.024	.030
		500	0	.003	.003	.008	.009
		1,000	0	.002	.002	.005	.005
		5,000	0	.001	.001	.001	.001
	7	100	0.8	.004	.005	.044	.056
		200	0.2	.002	.003	.021	.026
		500	0	.002	.002	.008	.011
		1,000	0	.001	.002	.005	.007
		5,000	0	.001	.001	.001	.001
Yes	3	100	38.4	.006	.008	.052	.066
		200	15.2	.003	.004	.028	.034
		500	1.6	.004	.004	.010	.013
		1,000	0.2	.003	.004	.009	.011
		5,000	0	.001	.001	.001	.002
	4	100	5	.010	.013	.042	.059
		200	0.4	.006	.007	.020	.028
		500	0	.001	.002	.008	.010
		1,000	0	.001	.001	.004	.006
		5,000	0	.000	.001	.001	.002
	7	100	1.2	.004	.007	.037	.055
		200	0.2	.002	.003	.017	.026
		500	0	.002	.002	.007	.009
		1,000	0	.001	.002	.004	.005
		5,000	0	.001	.001	.001	.002

Note. ML = maximum likelihood via EM algorithm; EM: expectation and maximization; EAP = Bayesian with diffuse priors (posterior means); 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.