The remarkable strides in artificial intelligence (AI), exemplified by ChatGPT, have unveiled a wealth of opportunities and challenges in assessment. Applying cutting-edge large language models (LLMs) and generative AI to assessment holds great promise in boosting efficiency, mitigating bias, and facilitating customized evaluations. Conversely,…

In this digital ITEMS module, Dr. Michael Bunch provides an in-depth, step-by-step look at how standard setting is done. It does not focus on any specific procedure or methodology (e.g., modified Angoff, bookmark, and body of work) but on the practical tasks that must be completed for any standard setting activity. Dr. Bunch carries the…

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…

In this ITEMS module, we frame the topic of scale reliability within a "confirmatory factor analysis" and "structural equation modeling" (SEM) context and address some of the limitations of Cronbach's a. This modeling approach has two major advantages: (1) it allows researchers to make explicit the relation between their items…

In this digital ITEMS module, Nikole Gregg and Dr. Brian Leventhal discuss strategies to ensure data visualizations achieve graphical excellence. Data visualizations are commonly used by measurement professionals to communicate results to examinees, the public, educators, and other stakeholders. To do so effectively, it is important that these…

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. From a theoretical perspective, they discuss the historical and philosophical perspectives on measurement with a focus on Rasch's concept of specific objectivity and…

Committing errors is expected in the development of language acquisition and learning; however, there is limited research that contributes to the literature on the effect of gender of English as a foreign language (EFL) writing. This study explored the gender differences in EFL students' writing using two approaches: human evaluation and…

Drawing valid inferences from item response theory (IRT) 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. This instructional module provides an overview of methods used for evaluating the fit of IRT models. Upon completing…

Diagnostic classification models (aka cognitive or skills diagnosis models) have shown great promise for evaluating mastery on a multidimensional profile of skills as assessed through examinee responses, but continued development and application of these models has been hindered by a lack of readily available software. In this article we…

Structural equation modeling (SEM) is a versatile statistical modeling tool. Its estimation techniques, modeling capacities, and breadth of applications are expanding rapidly. This module introduces some common terminologies. General steps of SEM are discussed along with important considerations in each step. Simple examples are provided to…

MicroCAT version 3.0--an integrated test development, administration, and analysis system--is reviewed in this first article of a series on testing software. A framework for comparing testing software is presented. The strength of this package lies in the development, banking, and administration of items composed of text and graphics. (SLD)

How computers are used to analyze item data is reviewed, and the information that existing item-analysis programs provide is described. Summaries of studies comparing the performance of some of these packages reveal some of their current limitations. Emphasis is on the usefulness to educational practice of these packages. (SLD)

The purpose of this ITEMS module is to provide an introduction to Markov chain Monte Carlo (MCMC) estimation for item response models. A brief description of Bayesian inference is followed by an overview of the various facets of MCMC algorithms, including discussion of prior specification, sampling procedures, and methods for evaluating chain…

FINDIT Version 2.20--a database-retrieval software application to assist researchers/users in locating references in the "Journal of Educational Measurement" and "Educational Measurement: Issues and Practice" from 1975 to 1988--is reviewed. The system includes a cumulative index of citations and search/retrieval software.…

Research on psychometric methods is heavily dependent on software. The quality, availability, and documentation of such software are critical to the advancement of the field. In 2000, an ad hoc committee of NCME recommended that NCME adopt policies that promote greater availability and better documentation of software. This article follows the ad…