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Jiangang Hao; Alina A. von Davier; Victoria Yaneva; Susan Lottridge; Matthias von Davier; Deborah J. Harris – Educational Measurement: Issues and Practice, 2024
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,…
Descriptors: Evaluation Methods, Artificial Intelligence, Educational Change, Computer Software
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Wang, Jue; Engelhard, George, Jr. – Educational Measurement: Issues and Practice, 2019
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…
Descriptors: Item Response Theory, Evaluation Methods, Measurement, Goodness of Fit
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Ames, Allison J.; Penfield, Randall D. – Educational Measurement: Issues and Practice, 2015
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…
Descriptors: Item Response Theory, Goodness of Fit, Models, Evaluation Methods
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Lei, Pui-Wa; Wu, Qiong – Educational Measurement: Issues and Practice, 2007
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…
Descriptors: Structural Equation Models, Guidelines, Definitions, Computer Software
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Kim, Jee-Seon; Bolt, Daniel M. – Educational Measurement: Issues and Practice, 2007
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…
Descriptors: Placement, Monte Carlo Methods, Markov Processes, Measurement
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Skaggs, Gary – Educational Measurement: Issues and Practice, 2004
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…
Descriptors: Psychometrics, Computer Software, Computer Assisted Testing, Evaluation Methods
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Switzer, Deborah M.; Connell, Michael L. – Educational Measurement: Issues and Practice, 1990
Two easy-to-use microcomputer programs, the Student Problem Package and the Test Analysis Package, both by D. L. Harnisch et al. (1985), are described. These programs efficiently analyze test data for teachers. (SLD)
Descriptors: Classroom Techniques, Computer Assisted Testing, Computer Software, Data Analysis