Rasch Measurement Theory Analysis in R provides researchers and practitioners with a step-by-step guide for conducting Rasch measurement theory analyses using R. It includes theoretical introductions to major Rasch measurement principles and techniques, demonstrations of analyses using several R packages that contain Rasch measurement functions, and sample interpretations of results.

Features:

Accessible to users with relatively little experience with R programming
Reproducible data analysis examples that can be modified to accommodate users’ own data
Accompanying e-book website with links to additional resources and R code updates as needed
Features dichotomous and polytomous (rating scale) Rasch models that can be applied to data from a wide range of disciplines

This book is designed for graduate students, researchers, and practitioners across the social, health, and behavioral sciences who have a basic familiarity with Rasch measurement theory and with R. Readers will learn how to use existing R packages to conduct a variety of analyses related to Rasch measurement theory, including evaluating data for adherence to measurement requirements, applying the dichotomous, Rating Scale, Partial Credit, and Many-Facet Rasch models, examining data for evidence of differential item functioning, and considering potential interpretations of results from such analyses.

About the Author

Stefanie A. Wind is an Associate Professor of Educational Measurement at the University of Alabama. Her primary research interests include the exploration of methodological issues in the field of educational measurement, with emphases on methods related to rater-mediated assessments, rating scales, Rasch models, item response theory models, and nonparametric item response theory, as well as applications of these methods to substantive areas related to education.

Cheng Hua is a Ph.D. candidate in Educational Measurement program at the University of Alabama. His primary research interests include Rasch Measurement theory, advanced regression models, Bayesian statistics, and visual learning tools (such as Mind Maps and Concept Maps). He enjoys applying his psychometric and statistical skills to address real-world research questions through interdisciplinary collaborations.

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