_In this digital ITEMS module, Dr. Allison Ames and Aaron Myers _discuss the most common Bayesian approach to model-data fit evaluation, which is called Posterior Predictive Model Checking (PPMC), for simple linear regression and item response theory models.
In this digital ITEMS module, Dr. Sandip Sinharay reviews the status quo on the reporting of subscores, which includes how they are used in operational reporting, what kinds of professional standards they need to meet, and how their psychometric properties can be evaluated.
In this digital ITEMS module, Dr. Hanwook Yoo and Dr. Ronald K. Hambleton provide an accessible overview of operational item analysis approaches for dichotomously scored items within the frameworks of classical test theory and item response theory.
In this digital ITEMS module, Dr. Robert Mislevy and Dr. Maria Elena Oliveri introduce and illustrate a sociocognitive perspective on educational measurement, which focuses on a variety of design and implementation considerations for creating fair and valid assessments for learners from diverse populations with diverse sociocultural experiences.
In this digital ITEMS module, Dr. Jue Wang and Dr. George Engelhard Jr. describe the Rasch measurement framework for the construction and evaluation of new measures and scales and demonstrate the estimation of core models with the Shiny_ERMA and Winsteps programs.
In this digital ITEMS module, Dr. Roy Levy discusses how Bayesian inference is a mechanism for reasoning in probability-modeling framework, describes how this plays out in a normal distribution model and unidimensional item response theory (IRT) models, and illustrates these steps using the JAGS software and R.
In this digital ITEMS module Dr. Stefanie Wind introduces the framework of nonparametric item response theory (IRT), in particular Mokken scaling, which can be used to evaluate fundamental measurement properties with less strict assumptions than parametric IRT models.
_In this digital ITEMS module, Dr. Natacha Carragher, Dr. Jonathan Templin, and colleagues provide a didactic overview of the specification, estimation, evaluation, and interpretation steps for diagnostic measurement / classification models (DCMs) centered around checklists for practitioners. A library of macros and supporting files for Excel, SAS, and Mplus is provided along with video tutorials for key practices.
In this digital ITEMS module, Dr. Wenchao Ma and Dr. Jimmy de la Torre introduce the G-DINA model, which is a general framework for specifying, estimating, and evaluating a wide variety of cognitive diagnosis models for the purpose of diagnostic measurement.
In this digital ITEMS module, Dr. Greg Hancock and Dr. Ji An provide an overview of scale reliability from the perspective of structural equation modeling (SEM) and address some of the limitations of Cronbach’s α.