The posterior predictive model checking method is a flexible Bayesian model‐checking tool and has recently been used to assess fit of dichotomous IRT models. This paper extended previous research to polytomous IRT models. A simulation study was conducted to explore the performance of posterior predictive model checking in evaluating different aspects of fit for unidimensional graded response models. A variety of discrepancy measures (test‐level, item‐level, and pair‐wise measures) that reflected different threats to applications of graded IRT models to performance assessments were considered. Results showed that posterior predictive model checking exhibited adequate power in detecting different aspects of misfit for graded IRT models when appropriate discrepancy measures were used. Pair‐wise measures were found more powerful in detecting violations of the unidimensionality and local independence assumptions.

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The study explored how traditional and social media use produced various cognitive responses toward COVID-19, including perceived severity, susceptibility, and efficacy, and direct and indirect facilitation of COVID-19 preventive behaviors. We tested the hypotheses on data collected from 433 university students in Wuhan, China, using structural equation modeling. We found that traditional media enhanced engagement for preventive behaviors both directly and indirectly by enhancing perceived severity and efficacy, whereas social media showed no impact on preventive behaviors, either directly or indirectly. Furthermore, the direct effect of traditional media on preventive behaviors was markedly stronger than the indirect effect through perceptions.

This study examined the relative effectiveness of Bayesian model comparison methods in selecting an appropriate graded response (GR) model for performance assessment applications. Three popular methods were considered: deviance information criterion (DIC), conditional predictive ordinate (CPO), and posterior predictive model checking (PPMC). Using these methods, several alternative GR models were compared with Samejima’s unidimensional GR model, including the one-parameter GR model, the rating scale model, simple- and complex-structure two-dimensional GR models, and the GR model for testlets. Results from a simulation study indicated that these methods appeared to be equally accurate in selecting the preferred model. However, CPO and PPMC can be used to compare models at the item level, and PPMC can also be used to compare both the relative and absolute fit of different models.

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