. Author manuscript; available in PMC: 2016 Mar 1.

Published in final edited form as: Behav Genet. 2014 Dec 25;45(2):200–214. doi: 10.1007/s10519-014-9698-y

Table III.

Model-fitting results of Block #2: AICcs (underlined) and Akaike weights.

		Age-Moderation Effects
		AC	None
SES-Moderation Effects	ACE	38612.34 0.1188 (Model #5)^b	38613.93 0.0538 (Model #13)^b
	AC	38612.75 0.0972 (Model #6)^b	38614.08 0.0498 (Model #14)^b
	AE	38612.21 0.1268 (Model #7)^b	38611.91 0.1478 (Model #15)^b
	CE	38615.26 0.0277 (Model #8)^b	38616.38 0.0158 (Model #16)
	A	38612.46 0.1120 (Model #9)^b	38612.1 0.1339 (Model #17)^b
	C	38619.31 0.0036 (Model #10)	38619.78 0.0029 (Model #18)
	E	38621.47 0.0012 (Model #11)	38620.83 0.0017 (Model #19)
	None	38628.91 3.00 × 10⁻⁵ (Model #12)	38627.9 4.99 × 10⁻⁵ (Model #4)^a

Model #4 is part of Block #1 (see Table II).

Model is in the 95% confidence set for best-approximating model (see Appendix).

Table notes: AICcs are underlined; Akaike weights are proportions. Smaller AICcs and greater Akaike weights both correspond to a more-preferable model. The overall preferred model, #15, is bolded. A model’s Akaike weight is interpretable as the posterior probability that the model is the best at approximating full reality in the population, given the size of the sample and the set of models under consideration (see Appendix). “Age Moderation Effects” are those latent biometric factors the loadings of which were allowed to be moderated by age; ”none” indicates that no age-moderation effects were included, whereas “AC” indicates that both A × Age and C × Age effects were included. “SES Moderation Effects” are those latent biometric factors the loadings of which were allowed to be moderated by SES. For example, the models in the row marked “CE” included C × SES and E × SES effects.