Individual differences or different individuals? That is the question

Test of Nyborg's General Trait Covariance (GTC) model for hormonally guided development by means of structural equation modeling

Nyborg's General Trait Covariance (GTC) model for hormonally guided development investigates the influence of gonadal hormones and fluid intelligence on body build, achievement, and socioeconomic variables. According to the model, testosterone should be negatively related to height, fat/muscle ratio, intelligence, income, and education. It is conceived that this influence should be determined to a great extent by mutual relationships between these variables. The model was tested by means of structural equation modeling (SEM) in a sample of 4375 males who had served in the United States Armed Forces. The results largely confirm Nyborg's androtype model but in addition reflect the relationships between the variables included in a quantitative causal manner. It could be shown that testosterone has a negative influence on crystallized intelligence and that this effect is mainly mediated by the negative influence of testosterone on education. An additional multiple group analysis testing for structural invariance across age groups revealed that the mediating role of education is more pronounced in old veterans. Copyright © 2002 John Wiley & Sons, Ltd.

Insensitivity of the analysis of variance to heredity-environment interaction

It makes sense to attribute a definite percentage of variation in some measure of behavior to variation in heredity only if the effects of heredity and environment are truly additive. Additivity is often tested by examining the interaction effect in a two-way analysis of variance (ANOVA) or its equivalent multiple regression model. If this effect is not statistically significant at the α = 0.05 level, it is common practice in certain fields (e.g., human behavior genetics) to conclude that the two factors really are additive and then to use linear models, which assume additivity. Comparing several simple models of nonadditive, interactive relationships between heredity and environment, however, reveals that ANOVA often fails to detect nonadditivity because it has much less power in tests of interaction than in tests of main effects. Likewise, the sample sizes needed to detect real interactions are substantially greater than those needed to detect main effects. Data transformations that reduce interaction effects also change drastically the properties ofthe causal model and may conceal theoretically interesting and practically useful relationships. If the goal ofpartitioning variance among mutually exclusive causes and calculating “heritability” coefficients is abandoned, interactive relationships can be examined more seriously and can enhance our understanding of the ways living things develop.