Psychology researchers are often interested in mechanisms underlying how randomized interventions affect outcomes such as substance use and mental health. Mediation analysis is a common statistical method for investigating psychological mechanisms that has benefited from exciting new methodological improvements over the last two decades. One of the most important new developments is methodology for estimating causal mediated effects using the potential outcomes framework for causal inference. Potential outcomes-based methods developed in epidemiology and statistics have important implications for understanding psychological mechanisms. We aim to provide a concise introduction to and illustration of these new methods and emphasize the importance of confounder adjustment. First, we review the traditional regression approach for estimating mediated effects. Second, we describe the potential outcome framework. Third, we define what a confounder is and how the presence of a confounder can provide misleading evidence regarding mechanisms of interventions. Fourth, we describe experimental designs that can help rule out confounder bias. Fifth, we describe new statistical approaches to adjust for measured confounders of the mediator – outcome relation and sensitivity analyses to probe effects of unmeasured confounders on the mediated effect. All approaches are illustrated with application to a real counseling intervention dataset. Counseling psychologists interested in understanding the causal mechanisms of their interventions can benefit from incorporating the most up-to-date techniques into their mediation analyses.

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Abstract. Grit, the passion and perseverance for long-term goals, has received attention from personality psychologists because it predicts success and academic achievement. Grit has also been criticized as simply another measure of self-control or conscientiousness. A precise psychometric representation of grit is needed to understand how the construct is unique and how it overlaps with other constructs. Previous research suggests that the Short Grit Scale (Grit-S) has several psychometric limitations, such as uncertain factor structure within and across populations, uncertainty about reporting total or subscale scores, and different assessment precision at low and high levels on the construct. We conducted modern psychometric techniques including parallel analysis, measurement invariance, extrinsic convergent validity, and Item Response Theory models on two American samples. Our results suggest that the Grit-S is essentially unidimensional and that there is construct overlap with the self-control construct. Subscale factors were the result of an item doublet, where two items had highly correlated uniquenesses, showed similar item information, and were more likely to exhibit measurement bias. Findings replicated across samples. Finally, we discuss recommendations for the use of the Grit-S based on the theoretical interpretation of the unidimensional factor and our empirical findings.

Mediation analysis is an important statistical method in prevention research, as it can be used to determine effective intervention components. Traditional mediation analysis defines direct and indirect effects in terms of linear regression coefficients. It is unclear how these traditional effects are estimated in settings with binary variables. An important recent methodological advancement in the mediation analysis literature is the development of the causal mediation analysis framework. Causal mediation analysis defines causal effects as the difference between two potential outcomes. These definitions can be applied to any mediation model to estimate natural direct and indirect effects, including models with binary variables and an exposure–mediator interaction. This paper aims to clarify the similarities and differences between the causal and traditional effect estimates for mediation models with a binary mediator and a binary outcome. Causal and traditional mediation analyses were applied to an empirical example to demonstrate these similarities and differences. Causal and traditional mediation analysis provided similar controlled direct effect estimates, but different estimates of the natural direct effects, natural indirect effects, and total effect. Traditional mediation analysis methods do not generalize well to mediation models with binary variables, while the natural effect definitions can be applied to any mediation model. Causal mediation analysis is therefore the preferred method for the analysis of mediation models with binary variables.

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