Table 1.

Model Comparison Indices for Simulation Datasets 1 and 2: Simulation Dataset 1.

Model	Log-likelihood	Log-prior	Log-posterior	#pars	BIC	AIC	AIC3	CAIC
1D-ordinal, equal item	−38,320.9	−23.7	−38,344.6	41	76,953.4	76,723.7	76,764.7	76,994.4
1D-ordinal	−38,166.4	−24.0	−38,190.3	60	76,788.8	76,452.7	76,512.7	76,848.8
2D-ordinal, equal item	−38,320.5	−25.1	−38,345.6	42	76,960.2	76,725.0	76,767.0	77,002.2
2D-ordinal	−38,117.8	−25.7	−38,143.5	80	76,843.7	76,395.6	76,475.6	76,923.7
3D-ordinal, equal item	−38,320.5	−25.6	−38,346.0	43	76,967.8	76,726.9	76,769.9	77,010.8
3D-ordinal	−38,025.5	−35.0	−38,060.5	100	76,811.0	76,250.9	76,350.9	76,911.0
1D-nominal, equal itema	−37,686.3	−24.2	−37,710.6	41	75,684.3	75,454.7	75,495.7	75,725.3
1D-nominala	−37,533.6	−24.5	−37,558.1	61	75,530.9	75,189.3	75,250.3	75,591.9
2D-nominal, equal itema	−36,313.8	−28.1	−36,341.9	42	72,946.9	72,711.6	72,753.6	72,988.9
2D-nominala	−36,080.6	−28.5	−36,109.1	82	72,784.6	72,325.3	72,407.3	72,866.6
3D-nominal, equal itema	−36,329.6	−28.4	−36,357.9	43	72,986.0	72,745.1	72,788.1	73,029.0
3D-nominala	−36,083.6	−28.9	−36,112.5	101	72,934.8	72,369.1	72,470.1	73,035.8

Note. Boldfaced values indicate best model. BIC = Bayesian Information Criterion; AIC = Akaike Information Criterion; CAIC = Consistent version of the AIC.

^aWith monotonicity constraints applied. pars = Number of parameters.