The Basics of Item Response Theory Using R

  • Frank B. Baker
  • Seock-Ho Kim

Part of the Statistics for Social and Behavioral Sciences book series (SSBS)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Frank B. Baker, Seock-Ho Kim
    Pages 1-15
  3. Frank B. Baker, Seock-Ho Kim
    Pages 17-34
  4. Frank B. Baker, Seock-Ho Kim
    Pages 35-53
  5. Frank B. Baker, Seock-Ho Kim
    Pages 55-67
  6. Frank B. Baker, Seock-Ho Kim
    Pages 69-87
  7. Frank B. Baker, Seock-Ho Kim
    Pages 89-104
  8. Frank B. Baker, Seock-Ho Kim
    Pages 105-125
  9. Frank B. Baker, Seock-Ho Kim
    Pages 127-135
  10. Back Matter
    Pages 137-174

About this book

Introduction

This graduate-level textbook is a tutorial for item response theory that covers both the basics of item response theory and the use of R for preparing graphical presentation in writings about the theory. Item response theory has become one of the most powerful tools used in test construction, yet one of the barriers to learning and applying it is the considerable amount of sophisticated computational effort required to illustrate even the simplest concepts. This text provides the reader access to the basic concepts of item response theory freed of the tedious underlying calculations. It is intended for those who possess limited knowledge of educational measurement and psychometrics.

Rather than presenting the full scope of item response theory, this textbook is concise and practical and presents basic concepts without becoming enmeshed in underlying mathematical and computational complexities. Clearly written text and succinct R code allow anyone familiar with statistical concepts to explore and apply item response theory in a practical way. In addition to students of educational measurement, this text will be valuable to measurement specialists working in testing programs at any level and who need an understanding of item response theory in order to evaluate its potential in their settings.
  • Combines clearly written text and succinct R code 
  • Utilizes a building-block approach from simple to complex, enabling readers to develop a clinical feel for item response theory and how its concepts are interrelated
  • Includes downloadable R functions that implement various facets of item response theory 
Frank B. Baker, Ph.D., is Professor Emeritus of the Department of Educational Psychology at the University of Wisconsin-Madison. He is author of numerous publications dealing with item response theory and statistical methodology. He received his B.S., M.S., and Ph.D. degrees from the University of Minnesota, Minneapolis.

Seock-Ho Kim, Ph.D., is Professor in the Department of Educational Psychology at the University of Georgia. He is author of numerous publications in psychometrics and applied statistics and is a member of the American Educational Research Association, the American Statistical Association, the National Council on Measurement in Education, and the Psychometric Society, among other organizations. He received his B.A. from Korea University and his M.S. and Ph.D. degrees from the University of Wisconsin-Madison.

Keywords

ability parameter binary items classical test theory dichotomously scored difficulty parameter discrimination parameter guessing parameter information function invariance principle item characteristic curve item response theory logistic model maximum likelihood estimation one-parameter model probability of correct response Rasch model test calibration test characteristic curve three-parameter model two-parameter model

Authors and affiliations

  • Frank B. Baker
    • 1
  • Seock-Ho Kim
    • 2
  1. 1.Educational PsychologyUniversity of Wisconsin-MadisonMadisonUSA
  2. 2.Educational PsychologyUniversity of GeorgiaAthensUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-54205-8
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-54204-1
  • Online ISBN 978-3-319-54205-8
  • Series Print ISSN 2199-7357
  • Series Online ISSN 2199-7365
  • About this book