Mediation has played a critical role in developmental theory and research. Yet, developmentalists rarely discuss the methodological challenges of establishing causality in mediation analysis or potential strategies to improve the identification of causal mediation effects. In this article, we discuss the potential outcomes framework from…

Practitioners are interested in not only the average causal effect of a treatment on the outcome but also the underlying causal mechanism in the presence of an intermediate variable between the treatment and outcome. However, in many cases we cannot randomize the intermediate variable, resulting in sample selection problems even in randomized…

Drawing causal inference with observational studies is the central pillar of many disciplines. One sufficient condition for identifying the causal effect is that the treatment-outcome relationship is unconfounded conditional on the observed covariates. It is often believed that the more covariates we condition on, the more plausible this…

One interesting idea in social network analysis is the directionality test that utilizes the directions of social ties to help identify peer effects. The null hypothesis of the test is that if contextual factors are the only force that affects peer outcomes, the estimated peer effects should not differ, if the directions of social ties are…

Recent methods to improve generalizations from nonrandom samples typically invoke assumptions such as the strong ignorability of sample selection, which is challenging to meet in practice. Although researchers acknowledge the difficulty in meeting this assumption, point estimates are still provided and used without considering alternative…

Three experiments examined children's and adults' abilities to use statistical and temporal information to distinguish between common cause and causal chain structures. In Experiment 1, participants were provided with conditional probability information and/or temporal information and asked to infer the causal structure of a 3-variable mechanical…

Purpose: The purpose of this paper is to conduct a critical review of the mediation studies published in the field of Human Resource Development (HRD) to discern if the study designs, the nature of data collection and the choice of statistical methods justify the causal claims made in those studies. Design/methodology/approach: This paper conducts…

We contribute to debate about causal inferences in educational research in two ways. First, we quantify how much bias there must be in an estimate to invalidate an inference. Second, we utilize Rubin's causal model to interpret the bias necessary to invalidate an inference in terms of sample replacement. We apply our analysis to an inference…

Individuals have difficulty changing their causal beliefs in light of contradictory evidence. We hypothesized that this difficulty arises because people facing implausible causes give greater consideration to causal alternatives, which, because of their use of a positive test strategy, leads to differential weighting of contingency evidence.…

In Shadish (2010) and West and Thoemmes (2010), the authors contrasted 2 approaches to causality. The first originated in the psychology literature and is associated with work by Campbell (e.g., Shadish, Cook, & Campbell, 2002), and the second has its roots in the statistics literature and is associated with work by Rubin (e.g., Rubin, 2006). In…

This article reports on a delicious finding from a recent study claiming a causal link between dark chocolate consumption and blood pressure reductions. In the article, I provide ideas for using this study to whet student appetites for a discussion of statistical ideas, including experimental design, measurement error and inference methods.

Two chapters of this issue consider critical multiplism as a research strategy with links to meta analysis and generalizability theory. The unifying perspective it can provide for quantitative and qualitative evaluation is discussed. The third chapter explores meta analysis as a way to improve causal inferences in nonexperimental data. (SLD)