Table 1.

Relative Fit of One-Class Versus Two-Class Models (of 500 Samples, N = 200).

Fit statistic	Mean differencea	Mean % changea	Class membership
Skewness 0, kurtosis 0
AIC	−0.75	−0.02
BIC	−10.65	−0.26
SBIC	−1.14	−0.03
Skewness 0, kurtosis 2
AIC	14.45	0.35	Class 1 = 28 (14.10)
BIC	4.55	0.11	Class 2 = 172 (85.90)
SBIC	14.06	0.34
Skewness 0, kurtosis 4
AIC	14.52	0.36	Class 1 = 33 (16.65)
BIC	−11.87	−0.29	Class 2 = 167 (83.35)
SBIC	13.48	0.33
Skewness 1, kurtosis 0
AIC	16.93	0.41	Class 1 = 53 (26.65)
BIC	−9.45	−0.23	Class 2 = 147 (73.40)
SBIC	15.89	0.39
Skewness 1, kurtosis 2
AIC	17.98	0.44	Class 1 = 61 (30.26)
BIC	8.08	0.20	Class 2 = 139 (69.74)
SBIC	17.59	0.43
Skewness 1, kurtosis 4
AIC	21.42	0.53	Class 1 = 56 (28.03)
BIC	11.52	0.28	Class 2 = 144 (71.97)
SBIC	21.03	0.51
Skewness 1.6, kurtosis 0
AIC	49.13	1.20	Class 1 = 29 (14.50)
BIC	39.23	0.95	Class 2 = 171 (85.50)
SBIC	7.55	0.18
Skewness 1.6, kurtosis 2
AIC	31.55	0.77	Class 1 = 20 (10.20)
BIC	21.66	0.52	Class 2 = 180 (89.80)
SBIC	31.16	0.76
Skewness 1.6, kurtosis 4
AIC	31.52	0.77	Class 1 = 15 (7.57)
BIC	21.63	0.52	Class 2 = 180 (92.43)
SBIC	31.13	0.76

Note. AIC = Akaike’s information criterion; BIC = Bayesian information criterion; SBIC = sample-corrected BIC.

^aMean difference = Fit1 − Fit2, and percentage change = (1 − Fit2/Fit1) × 100.