In Bayesian statistics, the most widely used criteria of Bayesian model assessment and comparison are Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC). A multilevel mediation model is used as an illustrative example to compare different types of DIC and WAIC. More specifically, the study compares the…

Although the null hypothesis significance testing procedure is problematic, many still favor the use of "p"-values as indicating the state of evidence against the model used to generate the "p"-value. From this perspective, "p"-values benefit science; or would benefit science if used correctly. In contrast, the novel…

This report aims to showcase the value of implementing a Bayesian framework to analyse and report results from international large-scale surveys and provide guidance to users who want to analyse the data using this approach. The motivation for this report stems from the recognition that Bayesian statistical inference is fast becoming a popular…

This study shows that residual variation can cause problems related to scaling in exponential random graph models (ERGM). Residual variation is likely to exist when there are unmeasured variables in a model--even those uncorrelated with other predictors--or when the logistic form of the model is inappropriate. As a consequence, coefficients cannot…

In this paper, we investigate two purported problems with Bayesian Knowledge Tracing (BKT), a popular statistical model of student learning: "identifiability" and "semantic model degeneracy." In 2007, Beck and Chang stated that BKT is susceptible to an "identifiability problem"--various models with different…

In this paper, we investigate two purported problems with Bayesian Knowledge Tracing (BKT), a popular statistical model of student learning: "identifiability" and "semantic model degeneracy." In 2007, Beck and Chang stated that BKT is susceptible to an "identifiability problem"--various models with different…

We extend a unified and easy-to-use approach to measurement error and missing data. In our companion article, Blackwell, Honaker, and King give an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details, more sophisticated measurement error model…

This article explores the potential of Bayesian inference as a theoretical framework for describing how people evaluate hypotheses. First, it identifies a set of logically possible forms of non-Bayesian behavior. Second, it reviews existing research in a variety of areas to see whether these possibilities are ever realized. (Author/BW)

Discusses problems with the use of stochastic (probabilistic) models for the analysis of juvenile offense data. Analyzes longitudinal data for White and non-White delinquents which show significant evidence of offense specialization and a random distribution of offenses if no specialization occurs. (Author/GC)

This paper identifies typical sampling problems, including improper application of the Central Limit Theorem, that are associated with the probability-based sampling procedures currently used in clinical psychology research. It then presents two alternative research designs, the theory validation model and the extended case study model, which…

The effect of certain methodological changes on the fit of the Wyer subjective probability model and the effect of belief change on cognitive structure were investigated. Using syllogistically-related proposition sets of the form "A,""If A then B,""If not A then B,""B," it was found that certain…

Policy analysis, or policy study, is defined as the study of the nature, causes, and effects of alternative public policies; sometimes policy analysis refers to specific methods used. Two new developments associated with policy analysis are discussed. One is the concern for deducing the effects of alternative policies before they are adopted, as…