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E-grāmata: Applying the Rasch Model: Fundamental Measurement in the Human Sciences 4th edition [Taylor & Francis e-book]

, (The Education University of Hong Kong, HK),
  • Formāts: 376 pages, 100 Tables, black and white; 74 Line drawings, black and white; 174 Illustrations, black and white
  • Izdošanas datums: 20-Jul-2020
  • Izdevniecība: Routledge
  • ISBN-13: 9780429030499
  • Taylor & Francis e-book
  • Cena: 128,96 €*
  • * this price gives unlimited concurrent access for unlimited time
  • Standarta cena: 184,22 €
  • Ietaupiet 30%
  • Formāts: 376 pages, 100 Tables, black and white; 74 Line drawings, black and white; 174 Illustrations, black and white
  • Izdošanas datums: 20-Jul-2020
  • Izdevniecība: Routledge
  • ISBN-13: 9780429030499
"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 meansthat 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 (chpt 4). -A new four-option data set from the IASQ (Instrumental Attitude towards Self-assessment Questionnaire) for the Rating Scale Model (RSM) analysis exemplar (chpt 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 (chpt 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"--

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.

    List of Figures
    xii
    List of Tables
    xv
    About the Authors viii
    Foreword xx
    Preface xxii
    Notes on This Volume xxv
    Acknowledgments xxviii
    1 Why Measurement Is Fundamental
    1(17)
    Children Can Construct Measures
    3(2)
    Interval Scales v. Ratio Scales: A Conceptual Explanation
    5(1)
    Statistics and/or Measurement
    6(1)
    Why Fundamental Measurement?
    7(1)
    Derived Measures
    7(2)
    Conjoint Measurement
    9(2)
    The Rasch Model for Measurement
    11(1)
    A More Suitable Analogy for Measurement in the Human Sciences
    12(2)
    In Conclusion
    14(1)
    Summary
    15(3)
    2 Important Principles of Measurement Made Explicit
    18(13)
    An Example: "By How Much?"
    21(5)
    Moving from Observations to Measures
    26(2)
    Summary
    28(3)
    3 Basic Principles of the Rasch Model
    31(19)
    The Pathway Analogy
    31(10)
    A Basic Framework for Measurement
    41(2)
    The Rasch Model
    43(4)
    Summary
    47(3)
    4 Building a Set of Items for Measurement
    50(19)
    The Nature of the Data
    50(1)
    Analyzing Dichotomous Data: The BLOT
    51(1)
    A Simple Rasch Summary: The Item Pathway
    52(2)
    Item Statistics
    54(1)
    Item Fit
    54(2)
    The Wright Map
    56(2)
    Targeting
    58(1)
    Comparing Persons and Items
    59(1)
    Summary
    60(2)
    Extended Understanding
    62(1)
    The Problem of Guessing
    63(1)
    Difficulty, Ability, and Fit
    64(2)
    The Theory--Practice Dialog
    66(1)
    Summary
    67(2)
    5 Invariance: A Crucial Property of Scientific Measurement
    69(27)
    Person and Item Invariance
    72(1)
    Common-Item Linking
    72(2)
    Please Keep in Mind
    74(1)
    Anchoring Item Values
    74(3)
    Vertical Scaling
    77(1)
    Common-Person Linking
    78(2)
    Invariance of Person Estimates across Tests: Concurrent Validity
    80(1)
    The PRTIII-Pendulum
    81(1)
    Common-Person Linking: BLOT & PRTIII
    82(5)
    The Theory-Practice Dialog
    87(1)
    Measurement Invariance: Where It Really Matters
    88(1)
    Failures of Invariance: DIF
    89(2)
    Differential Rater Functioning
    91(1)
    DIF: Not Just a Problem, but an Opportunity
    92(1)
    Summary
    92(4)
    6 Measurement Using Likert Scales
    96(28)
    The Rasch Model for Polytomous Data
    97(3)
    Analyzing Rating Scale Data: The Instrumental Attitude toward Self-Assessment
    Questionnaire
    100(5)
    Summary
    105(2)
    Extended Understanding
    107(13)
    Summary
    120(4)
    7 The Partial Credit Rasch Model
    124(21)
    Clinical Interview Analysis: A Rasch-Inspired Breakthrough
    128(1)
    Scoring Interview Transcripts
    129(3)
    Partial Credit Model Results
    132(2)
    Interpretation
    134(3)
    The Theory-Practice Dialog
    137(1)
    Unidimensionality
    137(1)
    Summary
    138(1)
    Extended Understanding
    139(2)
    Point--Measure Correlations
    141(1)
    Fit Statistics
    142(1)
    Dimensionality: Primary Components Factor Analysis of the Rasch Residuals
    142(1)
    Summary
    142(3)
    8 Measuring Facets Beyond Ability and Difficulty
    145(18)
    A Basic Introduction to the Many-Facets Rasch Model
    146(1)
    Why Not Use Interrater Reliability?
    147(1)
    Relations among the Rasch Family of Models
    148(1)
    Data Specifications of the Many-Facets Rasch Model
    149(1)
    Rating Creativity of junior Scientists
    150(2)
    Many-Facets Analysis of Eighth-Grade Writing
    152(6)
    Summary
    158(1)
    Extended Understanding
    158(1)
    Rasch Measurement of Facets Beyond Rater Effects
    159(1)
    Summary
    160(3)
    9 Making Measures, Setting Standards, and Rasch Regression
    163(30)
    Creating a Measure from Existing Data: The RMPFS (ZiYan, EdUHK)
    163(1)
    Method: Data
    163(1)
    Physical Fitness Indicators
    164(1)
    Data A nalysis
    164(1)
    Seven Criteria to Investigate the Quality of Physical Fitness Indicators
    165(1)
    Results and Discussion
    165(2)
    Optimising Response Categories
    167(1)
    Influence of Underfitting Persons on the RMPFS
    167(1)
    Properties of the RMPFS with Subsamples
    168(1)
    Age Dependent or Age Related?
    168(1)
    The Final Version of RMPFS
    168(3)
    Objective Standard Setting: The OSS Model (Gregory Stone, UToledo)
    171(3)
    Early Definitions
    174(1)
    The Objective Standard Setting Models
    175(1)
    Objective Standard Setting for Dichotomous Examinations
    175(4)
    Objective Standard Setting for Judge-Mediated Examinations
    179(2)
    Fair Standards, Not Absolute Values
    181(1)
    Rasch Regression (Svetlana Beltyukova, UToledo)
    182(1)
    Predicting Physician Assistant Faculty Intention to Leave Academia
    182(1)
    Rasch Regression Using the Anchored Formulation
    183(5)
    Rasch Regression: Alternative Approaches
    188(1)
    Discussion
    189(1)
    Summary
    190(3)
    10 The Rasch Model Applied across the Human Sciences
    193(29)
    Rasch Measurement in Health Sciences
    193(3)
    Optimising an Existing Instrument: The NIHSS and a Central Role for PC A
    196(1)
    Creating a Short Form of an Existing Instrument: The FSQ
    197(1)
    FSQ-SF
    198(1)
    Theory Guides Assessment Revisions: The PEP-S8
    198(1)
    Applications in Education and Psychology
    199(2)
    Rasch Measures as Grist for the Analytical Mill
    201(1)
    Rasch Gain Calculations: Racking and Stacking
    202(1)
    Rasch Learning Gain Calculations: The CCI
    203(1)
    Racking and Stacking
    203(1)
    Stacking Can Be Enough: UPAM
    204(1)
    Sub-Test Structure Informs Scoring Models
    205(1)
    Applications to Classroom Testing
    206(6)
    Can Rasch Measurement Help S.S. Stevens?
    212(1)
    Using Rasch Measures with Path Analysis (SEM Framework)
    212(1)
    Rasch Person Measures Used in a Partial Least Squares (PLS) Framework
    213(2)
    And Those Rasch Measurement SEs?
    215(1)
    Can We Really Combine SEM and Rasch Models?
    216(1)
    Conclusion
    217(1)
    Summary
    218(4)
    11 Rasch Modeling Applied: Rating Scale Design
    222(16)
    Rating Scale Design
    222(2)
    Category Frequencies and Average Measures
    224(1)
    Thresholds and Category Fit
    225(3)
    Revising a Rating Scale
    228(1)
    An Example
    228(1)
    Guidelines for Collapsing Categories
    229(3)
    Problems with Negatively Worded Items
    232(2)
    The Invariance of the Measures across Groups
    234(1)
    Summary
    235(3)
    12 Rasch Model Requirements: Model Fit and Unidimensionality
    238(30)
    Model Fit and Unidimensionality
    238(1)
    The Data, the Model, and the Residuals
    239(1)
    Residuals
    240(1)
    Fit Statistics
    241(1)
    Expectations of Variation
    241(4)
    Fit, Misfit, and Interpretation
    245(6)
    Fit: Issues for Resolution
    251(1)
    Misfit: A Fundamental Issue
    252(3)
    Principal Components Analysis of Rasch Residuals: The BLOT as an Exemplar
    255(4)
    One Dimension, Two Dimensions, Three Dimensions, More?
    259(2)
    Extended Understanding
    261(1)
    A Further Investigation: BLOT and PRTIII
    262(2)
    Summary
    264(4)
    13 A Synthetic Overview
    268(25)
    Additive Conjoint Measurement (ACM)
    269(4)
    True Score Theory, Latent Traits, and Item Response Theory
    273(4)
    WouldYou Like an Interval Scale with That?
    277(2)
    Model Assumptions and Measurement Requirements
    279(2)
    Construct Validity
    281(3)
    The Rasch Model and Progress of Science
    284(1)
    Back to the Beginning and Back to the End
    285(3)
    Summary
    288(5)
    Appendix A Getting Started 293(15)
    Appendix B Technical Aspects of the Rasch Model 308(10)
    Appendix C Going All the Way 318(13)
    Glossary 331(10)
    Author Index 341(4)
    Subject Index 345
    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.