Our primary goal was to test a series of alternative models that specified different lifespan trends for the genetic and environmental influences on personality development. To accomplish this goal, we fit random-effects, meta-analytic regression models using the general framework laid out by Cheung (2008). Random-effects models are considered the most conservative and therefore preferred modeling strategy for meta-analytic studies. Random-effects models estimate the error associated with an effect size as well as variation in the across-study true effect size (Hedges & Vevea, 1998). To examine the possibility of continuous growth rather than growth patterns that apply to specific age ranges, we examined exponential models. Exponential models are continuous parametric models, but because they are parametrically nonlinear, it is computationally unfeasible to fit them as random-effects models with currently available software. These models were fit as fixed-effects models. Therefore, we offer these models as a comparison to evaluate whether the preferred linear models approximate a continuous trend or display important deviations from the continuous trend.7
