Applying the Rasch Model

Fundamental Measurement in the Human Sciences

; Zi Yan ; Moritz Heene

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. Les mer
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Om boka

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.

Fakta

Innholdsfortegnelse

Foreword


Preface


Notes on This Volume


About the Authors












Why Measurement Is Fundamental



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









Important Principles of Measurement Made Explicit



An example: "By How Much?"


Moving From Observations to Measures


Summary









Basic Principles of the Rasch Model



The Pathway Analogy


A Basic Framework for Measurement


The Rasch Model


Summary









Building a Set of Items for Measurement



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









Invariance: A Crucial Property of Scientific Measurement



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









Measurement Using Likert Scales



The Rasch Model for Polytomous Data


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


Summary


Extended Understanding


Summary









The Partial Credit Rasch Model



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









Measuring Facets Beyond Ability and Difficulty



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









Making Measures, Setting Standards, and Rasch Regression



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









The Rasch Model Applied Across the Human Sciences



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









Rasch Modeling Applied: Rating Scale Design



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









Rasch Model Requirements: Model Fit and Unidimensionality



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









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

Om forfatteren

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-Universitat Munchen, Germany.