In this digital ITEMS module, Dr. Roy Levy describes Bayesian approaches to psychometric modeling. He discusses how Bayesian inference is a mechanism for reasoning in a probability-modeling framework and is well-suited to core problems in educational measurement: reasoning from student performances on an assessment to make inferences about their…

Drawing valid inferences from modern measurement models is contingent upon a good fit of the data to the model. Violations of model-data fit have numerous consequences, limiting the usefulness and applicability of the model. As Bayesian estimation is becoming more common, understanding the Bayesian approaches for evaluating model-data fit models…

Making causal inferences from a quasi-experiment is difficult. Sensitivity analysis approaches to address hidden selection bias thus have gained popularity. This study serves as an introduction to a simple but practical form of sensitivity analysis using Monte Carlo simulation procedures. We examine estimated treatment effects for a school-based…

Reviews two types of multiple scoring practices and discusses how multiple scoring affects inferences. Multiple scoring uses a single observation as evidence for making inferences about an examinee's competence in multiple assessment units. Summarizes key implications of multiple scoring. (SLD)

Practices identified by experts as critical variables in eliciting writing samples were checked against 12 randomly selected studies using holistic ratings to derive descriptions of inferential statistical results for described samples. The studies often lacked precise information about these variables, limiting understanding of writing evaluation…