For example, much research in the past decade investigates moderation of environment-trait correlations by candidate genes, following one of the most highly cited papers in the behavioral sciences reporting that the influence of life stress on depression depends on DNA variation in a serotonin transporter (Caspi et al. 2003).
Correlation, multi-group moderation analysis, and interaction using structural equation model were used to analyses these data.
That is, if moderation is fully absent, the correlation between M and T equals.39, while genetic and (common) environmental effects explain 40%, 30 % and 30 of the variance in T, respectively.
As a consequence, given that only one of the TEDDICS-CSE correlations showed gender differences, moderation by gender was apparent to a limited extent.
Although these settings may be considered quite special, they conveniently simplify the explanation of the problem of non-zero semi-partial correlations in the univariate moderation model proposed by Purcell, and clarify how this model would need to be extended.
Note that the effect size of the chosen moderation parameters depends on the nature of the correlation between T and M. For example, β a was set at.08.
Further analyses showed moderation effects of age on two TEDDICS-CSE correlations, indicating a stronger relation as teachers' age increases.
We also tested for the statistical significance of the difference between correlations as a means for identifying moderation effects (significant differences are marked with "*").
The unique effect of gender was present for one correlation between CSE and TEDDICS, indicating that moderation by gender was apparent to a limited extent, and related to use of computers for instructional purposes.
We considered both the univariate model and the bivariate model, and fitted the ACE A × E A × C model to it to see whether the moderation effects are detected and how the gene by environment correlation influences the results.
