The statistical literature is replete with calls to report standardized measures of effect size alongside traditional p-values and null hypothesis tests. While effect-size such as Cohen’s d Hedges’s g are straightforward calculate for t tests, this not the case parameters in more complex linear models, where η 2 ω face limitations. After a review sizes their implementation Stata, I introduce co...

Modern data-analysis methods are typically applicable to a single dataset. In particularly, they cannot integratively analyze datasets containing different, but overlapping, sets of variables. We show that by employing causal models instead of models based on the concept of association alone, it is possible to make additional interesting inferences by integrative analysis than by independent an...

Abstract Bayesian causal inference in randomized experiments usually imposes model-based structure on potential outcomes. Yet inferences from are especially credible because they depend a known assignment process, not probability model of In this article, I derive randomization-based procedure for effects finite population setting. formally show that satisfies analogues unbiasedness and consist...

In linear mixed models, the Akaike information criterion (AIC) is often used to decide on the inclusion of a random effect. An important special case is the choice between linear and nonparametric regression models estimated using mixed model penalized splines. We investigate the behavior of two commonly used versions of the AIC, derived either from the implied marginal model or the conditional...