4th Edition

Applying the Rasch Model Fundamental Measurement in the Human Sciences

By Trevor Bond, Zi Yan, Moritz Heene Copyright 2021
    376 Pages 174 B/W Illustrations
    by Routledge

    376 Pages 174 B/W Illustrations
    by Routledge

    Recognised as the most influential publication in the field, ARM facilitates deep understanding of the Rasch model and its practical applications. The authors review the crucial properties of the model and demonstrate its use with examples across the human sciences. Readers will be able to understand and critically evaluate Rasch measurement research, perform their own Rasch analyses and interpret their results. The glossary and illustrations support that understanding, and the accessible approach means that it is ideal for readers without a mathematical background.

    Highlights of the new edition include:

    • More learning tools to strengthen readers’ understanding including chapter introductions, boldfaced key terms, chapter summaries, activities and suggested readings.
    • Greater emphasis on the use of R packages; readers can download the R code from the Routledge website.
    • Explores the distinction between numerical values, quantity and units, to understand the measurement and the role of the Rasch logit scale (Chapter 4).
    • A new four-option data set from the IASQ (Instrumental Attitude towards Self-assessment Questionnaire) for the Rating Scale Model (RSM) analysis exemplar (Chapter 6).
    • Clarifies the relationship between Rasch measurement, path analysis and SEM, with a host of new examples of Rasch measurement applied across health sciences, education and psychology (Chapter 10).

    Intended as a text for graduate courses in measurement, item response theory, (advanced) research methods or quantitative analysis taught in psychology, education, human development, business, and other social and health sciences. Professionals in these areas will also appreciate the book’s accessible introduction.

    Foreword

    Preface

    Notes on This Volume

    About the Authors

    1. Why Measurement Is Fundamental
    2. Children Can Construct Measures

      Interval Scales v. Ratio Scales: A Conceptual Explanation

      Statistics and/or Measurement

      Why Fundamental Measurement?

      Derived Measures

      Conjoint Measurement

       The Rasch Model for Measurement

       A More Suitable Analogy for Measurement in the Human Sciences

       In Conclusion

       Summary

    3. Important Principles of Measurement Made Explicit
    4. An example: "By How Much?"

      Moving From Observations to Measures

      Summary

    5. Basic Principles of the Rasch Model
    6. The Pathway Analogy

      A Basic Framework for Measurement

      The Rasch Model

      Summary

    7. Building a Set of Items for Measurement
    8. The Nature of the Data

      Analyzing Dichotomous Data: The BLOT

      A Simple Rasch Summary: The Item Pathway

      Item Statistics

      Item Fit

      The Wright Map

      Targeting

      Comparing Persons and Items

      Summary

      Extended Understanding

      The Problem of Guessing

      Difficulty, Ability, and Fit

      The Theory–Practice Dialog

      Summary

    9. Invariance: A Crucial Property of Scientific Measurement
    10. Person and Item Invariance

      Common-Item Linking

      Please Keep in Mind

      Anchoring Item Values

      Vertical Scaling

      Common-Person Linking

      Invariance of Person Estimates across Tests: Concurrent Validity

      The PRTIII-Pendulum

      Common-Person Linking: BLOT & PRTIII

      The Theory–Practice Dialog

      Measurement Invariance: Where It Really Matters

      Failures of Invariance: DIF

      Differential Rater Functioning

      DIF: Not Just a Problem, but an Opportunity

      Summary

    11. Measurement Using Likert Scales
    12. The Rasch Model for Polytomous Data

      Analyzing Rating Scale Data: The Instrumental Attitude towards Self-Assessment Questionnaire

      Summary

      Extended Understanding

      Summary

    13. The Partial Credit Rasch Model
    14. Clinical Interview Analysis: A Rasch-Inspired Breakthrough

      Scoring Interview Transcripts

      Partial Credit Model Results

      Interpretation

      The Theory–Practice Dialog

      Summary

      Extended Understanding

      Point–Measure Correlations

      Fit Statistics

      Dimensionality: Primary Components Factor Analysis

      Summary

    15. Measuring Facets Beyond Ability and Difficulty
    16. A Basic Introduction to the Many-Facets Rasch Model

      Why Not Use Interrater Reliability?

      Relations Among the Rasch Family of Models

      Data Specifications of the Many-Facets Rasch Model

      Rating Creativity of Junior Scientists

      8.6 Many-Facets Analysis of Eighth-Grade Writing

      Summary

      Extended Understanding

      Rasch Measurement of Facets Beyond Rater Effects

      Summary

    17. Making Measures, Setting Standards, and Rasch Regression
    18. Creating a Measure from Existing Data: The RMPFS (Zi Yan, EdUHK)

      Method: Data 

      Physical Fitness Indicators

      Data Analysis

      Seven Criteria to Investigate the Quality of Physical Fitness Indicators

      Results and Discussion

      Optimising Response Categories

      Influence of Underfitting Persons on the RMPFS

      Properties of the RMPFS With Subsamples

      Age Dependent or Age Related?

      The Final Version of RMPFS

      Objective Standard Setting: The OSS Model (Gregory Stone, U Toledo)

      Early Definitions

      The Objective Standard Setting Models

      Objective Standard Setting for Dichotomous Examinations

      Objective Standard Setting for Judge-Mediated Examinations

      Fair Standards, Not Absolute Values

      Rasch Regression (Svetlana Beltyukova, U Toledo)

      Predicting Physician Assistant Faculty Intention to Leave Academia

      Rasch Regression Using the Anchored Formulation

      Rasch Regression: Alternative Approaches

      Discussion

      Summary

    19. The Rasch Model Applied Across the Human Sciences
    20. Rasch Measurement in Health Sciences

      Optimising an Existing Instrument: The NIHSS and a Central Role for PCA

      Creating a Short Form of an Existing Instrument: The FSQ

      FSQ-SF

      Theory Guides Assessment Revisions: The PEP–S8

      Applications in Education and Psychology

      Rasch Measures as Grist for the Analytical Mill

      Rasch Gain Calculations: Racking and Stacking

      Rasch Learning Gain Calculations: The CCI

      Racking and Stacking

      Stacking Can Be Enough: UPAM

      Sub- Test Structure Informs Scoring Models

      Applications to Classroom Testing

      Can Rasch Measurement Help S.S. Stevens?

      Using Rasch Measures with Path Analysis (SEM Framework)

      Rasch Person Measures Used in a Partial Least Squares (PLS) Framework

      And Those Rasch Measurement SEs?

      Can We Really Combine SEM and Rasch Models?

      Conclusion

      Summary  

    21. Rasch Modeling Applied: Rating Scale Design
    22. Rating Scale Design

      Category Frequencies and Average Measures

      Thresholds and Category Fit

      Revising a Rating Scale

      An Example

      Guidelines for Collapsing Categories

      Problems With Negatively Worded Items

      The Invariance of the Measures across Groups

      Summary

    23. Rasch Model Requirements: Model Fit and Unidimensionality
    24. The Data, the Model, and the Residuals

      Residuals

      Fit Statistics

      Expectations of Variation

      Fit, Misfit, and Interpretation

      Fit: Issues for Resolution

      Principal Components Analysis of Rasch Residuals: The BLOT as an Exemplar

      One Dimension, Two Dimensions, Three Dimensions, More?

      Extended Understanding

      A Further Investigation: BLOT and PRTIII

      Summary

    25. A Synthetic Overview

              Additive Conjoint Measurement (ACM)

              True Score Theory, Latent Traits, and Item Response Theory

              Would You Like an Interval Scale With That?

              Model Assumptions and Measurement Requirements

              Construct Validity

              The Rasch Model and Progress of Science

              Back to the Beginning and Back to the End

              Summary

    Appendix A: Getting Started

    Appendix B: Technical Aspects of the Rasch Model

    Appendix C: Going All the Way

    Glossary

    Author Index

    Subject Index

    Biography

    Trevor G. Bond is currently Adjunct Professor at the College of Arts, Society and Education at James Cook University, Australia.

    Zi Yan is Associate Professor in the Department of Curriculum and Instruction at the Education University of Hong Kong.

    Moritz Heene is Full Professor of Learning Sciences Research Methodologies (i.e., Quantitative Methods) at the Ludwig-Maximilians-Universität München, Germany.

    From a previous edition:

    "The tiresome debate about Rasch vs. IRT is over  if you want to construct valid measurements from uncertain observations you need to understand and learn how to use Rasch measurement. Bond and Fox is your huckleberry read it and get to work!" – Robert W. Massof, Johns Hopkins University School of Medicine, USA

    "Bond & Fox's book is a must read for anyone interested in measurement. This book is my go-to for introducing graduate students to the Rasch model." Kelly D. Bradley, University of Kentucky, USA

    "The authors have successfully made sophisticated measurement theory into feasible practice for practitioners by providing clear and intuitive explanations, numerous examples, and nice computer outputs. It is a textbook that I have used and will continue to use in the future." Wen Chung Wang, Hong Kong Institute of Education, Hong Kong

    "The Rasch model represents modern measurement theory at its best … Rasch models are used around the world to create psychometrically defensible scales and tests. Bond and Fox provide an accessible introduction to the Rasch model that describes the logic and essential importance of fundamental measurement in the human sciences." George Engelhard, Jr., The University of Georgia, USA