Mx, a structural-equation modeling program (Neale et al. 2003), was used to fit models to the transformed raw data using full-information maximum-likelihood techniques. When fitting models to raw data, variances, covariances and means are first freely estimated (minus twice the log-likelihood; −2lnL). Model fit for the more restrictive biometric G × E models was then evaluated using four information-theoretic indices that balance overall fit (via –2lnL) with model parsimony: the Akaike’s Information Criterion (AIC; Akaike, 1987), the Bayesian Information Criterion (BIC; Raftery, 1995), the sample size-adjusted BIC (SABIC; Sclove, 1987) and the deviance information criterion (DIC; Spiegelhalter et al. 2002). The lowest or most negative AIC, BIC, SABIC and DIC among a series of nested models is considered best. Because fit indices do not always agree (they place different values on parsimony, among other things), we reasoned that the best-fitting model should yield lower or more negative values for at least three of the four fit indices (Hicks et al. 2009).
