Spring 2021 Methodology Applications Series:

Modern Psychometric Approaches for Diagnostic Assessment
Ray Reichenberg

Ray Reichenberg

The MAP Academy invites you to the first presentation of the Spring 2021 Methodology Applications Series,
featuring Ray Reichenberg, research assistant professor at the Nebraska Academy for Methodology, Analytics and Psychometrics.

Friday, Feb. 5, 2021 12:00-1:30 p.m. Zoom videoconference

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Abstract

This presentation will provide an overview of some modern psychometric methods for modeling diagnostic assessment data and generating attribute profiles. These methods include latent class models, diagnostic classification models and Bayesian networks.

Diagnostic assessment is the evaluation of an individual’s strengths and weaknesses with respect to the components of a particular construct for the purpose of formulating a course of instruction or treatment.

Diagnostic measures are designed to estimate attribute profiles defined by the respondent’s state or ability across a collection of related, latent characteristics. These profiles can guide next steps for the respondent, such as a particular lesson or intervention.

Recent issues, such as modeling attribute hierarchies (e.g., learning progressions) and approaches for capturing growth in an attribute over time, also will be discussed, along with software options for implementing these models.

Details

Date, Time, & Location
Friday, Feb. 5, 2021
12:00-1:30 PM
Zoom videoconference

Presentation: Modern Psychometric Approaches for Diagnostic Assessment

This virtual presentation is free and open to the public.

Join the Zoom videoconference Feb. 5, 2021.

ray-reichenberg

Ray Reichenberg

Research Assistant Professor, Nebraska Academy of Methodology, Analytics and Psychometrics

Ray Reichenberg's research aims to develop innovative approaches to applying advanced quantitative models to problems in education to improve teaching and learning. His current interests lie in psychometric considerations for educational games and simulations. He is particularly interested in the psychometric properties of Bayesian networks.

Reichenberg has extensive experience in conducting applied, interdisciplinary work in collaboration with content-area experts using advanced statistical techniques to answer questions in educational, health and social science contexts. He also conducts methodological work — both developing new methods and testing of existing methods — to facilitate that applied work.

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