Causal inference requires contrasting counterfactual states under specified interventions. Obtaining these contrasts from data depends on explicit assumptions and careful, multi-step workflows. Causal diagrams are crucial for clarifying the identifiability of counterfactual contrasts from data. Here, I explain how to use causal directed acyclic graphs (DAGs) to determine if and how causal effects can be identified from non-experimental observational data, offering practical reporting tips and suggestions to avoid common pitfalls.

Researchers often aim to assess whether repeated measures of an exposure are associated with repeated measures of an outcome. A question of particular interest is how associations between exposures and outcomes may differ over time. In other words, researchers may seek the best form of a temporal model. While several models are possible, researchers often consider a few key models. For example, researchers may hypothesize that an exposure measured during a sensitive period may be associated with repeated measures of the outcome over time. Alternatively, they may hypothesize that the exposure measured immediately before the current time period may be most strongly associated with the outcome at the current time. Finally, they may hypothesize that all prior exposures are important. Many analytic methods cannot compare and evaluate these alternative temporal models, perhaps because they make the restrictive assumption that the associations between exposures and outcomes remains constant over time. Instead, we provide a tutorial describing four temporal models that allow the associations between repeated measures of exposures and outcomes to vary, and showing how to test which temporal model is best supported by the data. By finding the best temporal model, developmental psychopathology researchers can find optimal windows for intervention.

Fuzzy set qualitative comparative analysis (fsQCA) is a method for assessing the effects of configurations of variables leading to an outcome. The recent growth of interest in this technique in organizational psychology is proving this method to be an important tool for addressing new and decisive research hypotheses. However, the effectiveness of fsQCA is dictated not only by its general principles, but also by how well these are understood and applied in the research community. Consequently, a guide that covers the fundamental ideas and tenets of the approach is required to aid the research community in its comprehension and practical application. The current study seeks to offer an understanding of FsQCA by providing: (a) A complete description of the method highlighting some of the most important theoretical-methodological aspects; (b) a perspective on the most used guidelines and recommendations, and (c) step-by-step instructions on how to carry out FsQCA in R using the QCA package. Data from 120 employees and supervisors derived from a company based in central Italy were used o best to illustrate how to carry out fsQCA. Codes for conducting the analyses from the QCA package for R accompany the tutorial and can be adapted to a new dataset.

In this manuscript we use realistic data to conduct a network meta-analysis using a Bayesian approach to analysis. The purpose of this manuscript is to explain, in lay terms, how to interpret the output of such an analysis. Many readers are familiar with the forest plot as an approach to presenting the results of a pairwise meta-analysis. However when presented with the results of network meta-analysis, which often does not include the forest plot, the output and results can be difficult to understand. Further, one of the advantages of Bayesian network meta-analyses is in the novel outputs such as treatment rankings and the probability distributions are more commonly presented for network meta-analysis. Our goal here is to provide a tutorial for how to read the outcome of network meta-analysis rather than how to conduct or assess the risk of bias in a network meta-analysis.

Psychology and neighboring disciplines are currently consumed with a replication crisis. Recent work has shown that replication can have the unintended consequence of perpetuating unwarranted conclusions when repeating an incorrect line of scientific reasoning from one study to another. This tutorial shows how decision researchers can derive logically coherent predictions from their theory by keeping track of the heterogeneity of preference the theory permits, rather than dismissing such heterogeneity as a nuisance. As an illustration, we reanalyze data of Barron and Ursino (2013). By keeping track of the heterogeneity of preferences permitted by Cumulative Prospect Theory, we show how the analysis and conclusions of Barron and Ursino (2013) change. This tutorial is intended as a blue-print for graduate student projects that dig deeply into the merits of prior studies and/or that supplement replication studies with a quality check.

Fetal sensitivity to radiation-induced health effects is related to gestational age, and it is highly dependent on fetal dose. Typical fetal doses from diagnostic radiology are usually below any level of concern. Although rare, significant fetal radiation doses can result from interventional medical exposures (fluoroscopically guided techniques), radiation therapy, or radiological or nuclear incidents, including terrorism. The potential health effects from these large radiation doses (possibly large enough to result in acute radiation syndrome in the expectant mother) include growth retardation, malformations, impaired brain function, and neoplasia. If exposure occurs during blastogenesis (and the embryo survives), there is a low risk for congenital abnormalities. (In all stages of gestation, radiation-induced noncancer health effects have not been reported for fetal doses below about 0.05 Gy [5 rad].) The additional risk for childhood cancer from prenatal radiation exposure is about 12% per Gy (0.12%/rad) above the background incidence.

(Disaster Med Public Health Preparedness. 2011;5:62-68)

Bayesian statistics provide effective techniques for analyzing data and translating the results to inform decision making. This paper provides an elementary tutorial overview of the WinBUGS software for performing Bayesian statistical analysis. Background information on the computational methods used by the software is provided. Two examples drawn from the field of medical decision making are presented to illustrate the features and functionality of the software.