Table 2.

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

Model	Log-likelihood	Log-prior	Log-posterior	#pars	BIC	AIC	AIC3	CAIC
1D-ordinal, equal item	−38,016.6	−32.2	−38,048.8	61	76,496.8	76,155.1	76,216.1	76,557.8
1D-ordinal	−37,890.0	−32.5	−37,922.5	80	76,388.2	75,940.1	76,020.1	76,468.2
2D-ordinal, equal item	−37,855.1	−36.1	−37,891.2	62	76,181.5	75,834.3	75,896.3	76,243.5
2D-ordinal	−37,698.1	−37.0	−37,735.1	100	76,156.3	75,596.2	75,696.2	76,256.3
3D-ordinal, equal item	−37,854.0	−36.5	−37,890.5	63	76,186.8	75,834.0	75,897.0	76,249.8
3D-ordinal	−37,685.6	−37.6	−37,723.2	120	76,283.3	75,611.2	75,731.2	76,403.3
1D-nominal, equal itema	−37,976.2	−32.3	−38,008.5	63	76,431.2	76,078.4	76,141.4	76,494.2
1D-nominala	−37,831.3	−32.6	−37,863.9	120	76,574.7	75,902.6	76,022.6	76,694.7
2D-nominal, equal itema	−37,594.0	−36.9	−37,630.9	66	75,689.6	75,320.0	75,386.0	75,756.6
2D-nominala	−37,418.4	−37.5	−37,455.9	173	76,151.8	75,182.8	75,355.8	76,324.8
3D-nominal, equal itema	−37,589.7	−37.2	−37,626.9	69	75,703.9	75,317.4	75,386.4	75,772.9
3D-nominala	−37,367.8	−38.3	−37,406.1	233	76,506.6	75,201.6	75,434.6	76,739.6

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.