Applying the Rasch model: fundamental measurement in the human sciences
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| Main Authors: | , , |
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| Format: | Book |
| Language: | English |
| Published: |
New York ; London
Routledge, Taylor & Francis Group
2021
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| Edition: | Fourth edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | xxviii, 348 Seiten Diagramme |
| ISBN: | 9780367141417 9780367141424 |
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| adam_text | Contents List of Figures List ofTables About the Authors Foreword Preface Notes on This Volume Acknowledgments 1 Why Measurement Is Fundamental xii XV xviii xx xxii xxv xxviii 1 Children Can Construct Measures 3 Interval Scales v. Ratio Scales: A Conceptual Explanation 5 Statistics and/or Measurement 6 Why Fundamental Measurement? 7 Derived Measures 7 Conjoint Measurement 9 The Rasch Modelfor Measurement 1 i A More Suitable Analogy for Measurement in the Human Sciences 12 In Conclusion 14 Summary 15 2 Important Principles of Measurement Made Explicit An Example: “By How Much? 21 Moving from Observations to Measures 26 Summary 28 18 3 Basic Principles of the Rasch Model The Pathway Analogy 31 A Basic Framework for Measurement 41 The Rasch Model 43 Summary 47 31 4 Building a Set of Items for Measurement 50 The Nature of the Data 50 Analyzing Dichotomous Data: The BLOT 51
viii Contents A Simple Rasch Summary: The Item Pathway 52 Item Statistics 54 Item Fit 54 The Wright Map 56 Targeting 58 Comparing Persons and Items 59 Summary 60 Extended Understanding 62 The Problem of Guessing 63 Difficulty, Ability, and Fit 64 The Theory-Practice Dialog 66 Summary 67 5 Invariance: A Crucial Property of Scientific Measurement Person and Item Invariance 72 Common-Item Linking 72 Please Keep in Mind 74 Anchoring Item Values 74 Vertical Scaling 77 Common-Person Linking 78 Invariance of Person Estimates across Tests: Concurrent Validity 80 The PRTHI-Pendulum 81 Common-Person Linking: BLOT PRTIH 82 The Theory-Practice Dialog 87 Measurement Invariance: Where It Really Matters 88 Failures of Invariance: DIF 89 Differential Rater Functioning 91 DIF: Not Just a Problem, but an Opportunity 92 Summary 92 6 Measurement Using Likert Scales The Rasch Model for Polytomous Data 97 Analyzing Rating Scale Data: The Instrumental Attitude toward Self-Assessment Questionnaire 100 Summary 105 Extended Understanding 107 Summary 120 7 The Partia! Credit Rasch Model Clinical Interview Analysis: A Rasch-Inspired Breakthrough 128 Scoring Interview Transcripts 129 Partial Credit Model Results 132 Interpretation 134 The Theory-Practice Dialog 137 Unidimensionality 137 Summary 138 Extended Understanding 139 Point—Measure Correlations 141 Fit Statistics 142 69 124
Contents ix Dimensionality: Primary Components Factor Analysis of the Rasch Residuals 142 Summary 142 8 Measuring Facets Beyond Ability and Difficulty 145 A Basic Introduction to the Many-Facets Rasch Model 146 Why Not Use Interrater Reliability? 147 Relations among the Rasch Family of Models 148 Data Specifications of the Many-Facets Rasch Model 149 Rating Creativity ofJunior Scientists 150 Many-Facets Analysis of Eighth- Grade Writing 152 Summary 158 Extended Understanding 158 Rasch Measurement of Facets Beyond Rater Effects 159 Summary 160 9 Making Measures, Setting Standards, and Rasch Regression Creating a Measure from Existing Data: The RMPFS (Zi Yan, EđUHK) 163 Method: Data 163 Physical Fitness Indicators 164 Data Analysis 164 Seven Criteria to Investigate the Quality of Physical Fitness Indicators 165 Results and Discussion 165 Optimising Response Categories 167 Influence of Underfitting Persons on the RMPFS 167 Properties of the RMPFS with Subsamples 168 Age Dependent or Age Related? 168 The Final Version of RMPFS 168 Objective Standard Setting: The OSS Model (Gregory Stone, U Toledo) 171 Early Definitions 174 The Objective Standard Setting Models 175 Objective Standard Setting for Dichotomous Examinations 175 Objective Standard Setting forJudge-Mediated Examinations 179 Fair Standards, Not Absolute Values 181 Rasch Regression (Svetlana Beltyukova, U Toledo) 182 Predicting Physician Assistant Faculty Intention to Leave Academia 182 Rasch Regression Using the Anchored Formulation 183 Rasch Regression: Alternative Approaches 188 Discussion 189 Summary 190 10 The
Rasch Model Applied across the Human Sciences Rasch Measurement in Health Sciences 193 Optimising an Existing Instrument: The NIHSS and a Central Role for PCA 196 Creating a Short Form of an Existing Instrument: The FSQ 197 FSQ-SF 198 Theory Guides Assessment Revisions: The PEP-S8 198 Applications in Education and Psychology 199 Rasch Measures as Gristfor the Analytical Mill 201 Rasch Gain Calculations: Racking and Stacking 202 163 193
x Contents Rasch Learning Gain Calculations: The CCI 203 Racking and Stacking 203 Stacking Can Be Enough: UPAM 204 Sub-Test Structure Informs Scoring Models 205 Applications to Classroom Testing 206 Can Rasch Measurement Help S.S. Stevens? 212 Using Rasch Measures with Path Analysis (SEM Framework) 212 Rasch Person Measures Used in a Partial Least Squares (PLS) Framework 213 And Those Rasch Measurement SEs? 215 Can We Really Combine SEM and Rasch Models? 216 Conclusion 217 Summary 218 11 Rasch Modeling Applied: Rating Scale Design Rating Scale Design 222 Category Frequencies and Average Measures 224 Thresholds and Category Fit 225 Revising a Rating Scale 228 An Example 228 Guidelines for Collapsing Categories 229 Problems with Negatively Worded Items 232 The Invariance of the Measures across Groups 23 4 Summary 235 222 12 Rasch Model Requirements: Model Fit and Unidimensionality Model Fit and Unidimensionality 238 The Data, the Model, and the Residuals 239 Residuals 240 Fit Statistics 241 Expectations of Variation 241 Fit, Misfit, and Interpretation 245 Fit: Issues for Resolution 251 Misfit: A Fundamental Issue 252 Principal Components Analysis of Rasch Residuals: The BLOT as an Exemplar 255 One Dimension, Two Dimensions, Three Dimensions, More? 259 Extended Understanding 261 A Further Investigation: BLOT and PRTIH 262 Summary 264 238 13 A Synthetic Overview Additive Conjoint Measurement (ACM) 269 True Score Theory, Latent Traits, and Item Response Theory 273 Would You Like an Interval Scale with That? 277 Model Assumptions and Measurement Requirements 279 Construct
Validity 281 The Rasch Model and Progress of Science 284 Back to the Beginning and Back to the End 285 Summary 288 268
Contents Appendix A: Getting Started Appendix B: Technical Aspects of the Rasch Model Appendix C: Going All the Way Glossary Author Index Subject Index xi 293 308 318 331 341 345
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| adam_txt |
Contents List of Figures List ofTables About the Authors Foreword Preface Notes on This Volume Acknowledgments 1 Why Measurement Is Fundamental xii XV xviii xx xxii xxv xxviii 1 Children Can Construct Measures 3 Interval Scales v. Ratio Scales: A Conceptual Explanation 5 Statistics and/or Measurement 6 Why Fundamental Measurement? 7 Derived Measures 7 Conjoint Measurement 9 The Rasch Modelfor Measurement 1 i A More Suitable Analogy for Measurement in the Human Sciences 12 In Conclusion 14 Summary 15 2 Important Principles of Measurement Made Explicit An Example: “By How Much?" 21 Moving from Observations to Measures 26 Summary 28 18 3 Basic Principles of the Rasch Model The Pathway Analogy 31 A Basic Framework for Measurement 41 The Rasch Model 43 Summary 47 31 4 Building a Set of Items for Measurement 50 The Nature of the Data 50 Analyzing Dichotomous Data: The BLOT 51
viii Contents A Simple Rasch Summary: The Item Pathway 52 Item Statistics 54 Item Fit 54 The Wright Map 56 Targeting 58 Comparing Persons and Items 59 Summary 60 Extended Understanding 62 The Problem of Guessing 63 Difficulty, Ability, and Fit 64 The Theory-Practice Dialog 66 Summary 67 5 Invariance: A Crucial Property of Scientific Measurement Person and Item Invariance 72 Common-Item Linking 72 Please Keep in Mind 74 Anchoring Item Values 74 Vertical Scaling 77 Common-Person Linking 78 Invariance of Person Estimates across Tests: Concurrent Validity 80 The PRTHI-Pendulum 81 Common-Person Linking: BLOT PRTIH 82 The Theory-Practice Dialog 87 Measurement Invariance: Where It Really Matters 88 Failures of Invariance: DIF 89 Differential Rater Functioning 91 DIF: Not Just a Problem, but an Opportunity 92 Summary 92 6 Measurement Using Likert Scales The Rasch Model for Polytomous Data 97 Analyzing Rating Scale Data: The Instrumental Attitude toward Self-Assessment Questionnaire 100 Summary 105 Extended Understanding 107 Summary 120 7 The Partia! Credit Rasch Model Clinical Interview Analysis: A Rasch-Inspired Breakthrough 128 Scoring Interview Transcripts 129 Partial Credit Model Results 132 Interpretation 134 The Theory-Practice Dialog 137 Unidimensionality 137 Summary 138 Extended Understanding 139 Point—Measure Correlations 141 Fit Statistics 142 69 124
Contents ix Dimensionality: Primary Components Factor Analysis of the Rasch Residuals 142 Summary 142 8 Measuring Facets Beyond Ability and Difficulty 145 A Basic Introduction to the Many-Facets Rasch Model 146 Why Not Use Interrater Reliability? 147 Relations among the Rasch Family of Models 148 Data Specifications of the Many-Facets Rasch Model 149 Rating Creativity ofJunior Scientists 150 Many-Facets Analysis of Eighth- Grade Writing 152 Summary 158 Extended Understanding 158 Rasch Measurement of Facets Beyond Rater Effects 159 Summary 160 9 Making Measures, Setting Standards, and Rasch Regression Creating a Measure from Existing Data: The RMPFS (Zi Yan, EđUHK) 163 Method: Data 163 Physical Fitness Indicators 164 Data Analysis 164 Seven Criteria to Investigate the Quality of Physical Fitness Indicators 165 Results and Discussion 165 Optimising Response Categories 167 Influence of Underfitting Persons on the RMPFS 167 Properties of the RMPFS with Subsamples 168 Age Dependent or Age Related? 168 The Final Version of RMPFS 168 Objective Standard Setting: The OSS Model (Gregory Stone, U Toledo) 171 Early Definitions 174 The Objective Standard Setting Models 175 Objective Standard Setting for Dichotomous Examinations 175 Objective Standard Setting forJudge-Mediated Examinations 179 Fair Standards, Not Absolute Values 181 Rasch Regression (Svetlana Beltyukova, U Toledo) 182 Predicting Physician Assistant Faculty Intention to Leave Academia 182 Rasch Regression Using the Anchored Formulation 183 Rasch Regression: Alternative Approaches 188 Discussion 189 Summary 190 10 The
Rasch Model Applied across the Human Sciences Rasch Measurement in Health Sciences 193 Optimising an Existing Instrument: The NIHSS and a Central Role for PCA 196 Creating a Short Form of an Existing Instrument: The FSQ 197 FSQ-SF 198 Theory Guides Assessment Revisions: The PEP-S8 198 Applications in Education and Psychology 199 Rasch Measures as Gristfor the Analytical Mill 201 Rasch Gain Calculations: Racking and Stacking 202 163 193
x Contents Rasch Learning Gain Calculations: The CCI 203 Racking and Stacking 203 Stacking Can Be Enough: UPAM 204 Sub-Test Structure Informs Scoring Models 205 Applications to Classroom Testing 206 Can Rasch Measurement Help S.S. Stevens? 212 Using Rasch Measures with Path Analysis (SEM Framework) 212 Rasch Person Measures Used in a Partial Least Squares (PLS) Framework 213 And Those Rasch Measurement SEs? 215 Can We Really Combine SEM and Rasch Models? 216 Conclusion 217 Summary 218 11 Rasch Modeling Applied: Rating Scale Design Rating Scale Design 222 Category Frequencies and Average Measures 224 Thresholds and Category Fit 225 Revising a Rating Scale 228 An Example 228 Guidelines for Collapsing Categories 229 Problems with Negatively Worded Items 232 The Invariance of the Measures across Groups 23 4 Summary 235 222 12 Rasch Model Requirements: Model Fit and Unidimensionality Model Fit and Unidimensionality 238 The Data, the Model, and the Residuals 239 Residuals 240 Fit Statistics 241 Expectations of Variation 241 Fit, Misfit, and Interpretation 245 Fit: Issues for Resolution 251 Misfit: A Fundamental Issue 252 Principal Components Analysis of Rasch Residuals: The BLOT as an Exemplar 255 One Dimension, Two Dimensions, Three Dimensions, More? 259 Extended Understanding 261 A Further Investigation: BLOT and PRTIH 262 Summary 264 238 13 A Synthetic Overview Additive Conjoint Measurement (ACM) 269 True Score Theory, Latent Traits, and Item Response Theory 273 Would You Like an Interval Scale with That? 277 Model Assumptions and Measurement Requirements 279 Construct
Validity 281 The Rasch Model and Progress of Science 284 Back to the Beginning and Back to the End 285 Summary 288 268
Contents Appendix A: Getting Started Appendix B: Technical Aspects of the Rasch Model Appendix C: Going All the Way Glossary Author Index Subject Index xi 293 308 318 331 341 345 |
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| spelling | Bond, Trevor G. Verfasser aut Applying the Rasch model fundamental measurement in the human sciences Trevor G. Bond, Zi Yan and Moritz Heene Fourth edition New York ; London Routledge, Taylor & Francis Group 2021 xxviii, 348 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Psychometrie (DE-588)4176236-8 gnd rswk-swf Rasch-Modell (DE-588)4176964-8 gnd rswk-swf Psychometrie (DE-588)4176236-8 s Rasch-Modell (DE-588)4176964-8 s DE-604 Yan, Zi Verfasser (DE-588)1208770381 aut Heene, Moritz Verfasser (DE-588)13360540X aut Erscheint auch als Online-Ausgabe 978-0-429-03049-9 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032259177&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bond, Trevor G. Yan, Zi Heene, Moritz Applying the Rasch model fundamental measurement in the human sciences Psychometrie (DE-588)4176236-8 gnd Rasch-Modell (DE-588)4176964-8 gnd |
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| title | Applying the Rasch model fundamental measurement in the human sciences |
| title_auth | Applying the Rasch model fundamental measurement in the human sciences |
| title_exact_search | Applying the Rasch model fundamental measurement in the human sciences |
| title_exact_search_txtP | Applying the Rasch model fundamental measurement in the human sciences |
| title_full | Applying the Rasch model fundamental measurement in the human sciences Trevor G. Bond, Zi Yan and Moritz Heene |
| title_fullStr | Applying the Rasch model fundamental measurement in the human sciences Trevor G. Bond, Zi Yan and Moritz Heene |
| title_full_unstemmed | Applying the Rasch model fundamental measurement in the human sciences Trevor G. Bond, Zi Yan and Moritz Heene |
| title_short | Applying the Rasch model |
| title_sort | applying the rasch model fundamental measurement in the human sciences |
| title_sub | fundamental measurement in the human sciences |
| topic | Psychometrie (DE-588)4176236-8 gnd Rasch-Modell (DE-588)4176964-8 gnd |
| topic_facet | Psychometrie Rasch-Modell |
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