| Description |
1 online resource (379 p.) |
| Series |
Methodology in the Social Sciences Series |
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Methodology in the Social Sciences Series
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| Contents |
Cover -- Half Title Page -- Series Page -- Title Page -- Copyright -- Series Editor's Note -- Preface -- Brief Contents -- 1. Introduction: The Importance of Multiple Rater Data -- 2. Basic Methodological Concepts -- 3. Basic Models for Structurally Different Raters -- 4. Models with Method Factors for Structurally Different Raters -- 5. Single-Level CFA Models for Interchangeable Raters -- 6. Multilevel CFA Models for Interchangeable Raters -- 7. Models for a Combination of Structurally Different and Interchangeable Raters -- 8. Models for Cross-Classified Multiple Membership Data |
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9. Models for Longitudinal Multirater Data -- 10. Advanced Topics in Multitrait-Multirater Analysis -- 11. Recommendations and Outlook -- References -- Author Index -- Subject Index -- About the Authors -- Contents -- 1. Introduction: The Importance of Multiple Rater Data -- 1.1 Advantages and Limitations of Self-Reports -- 1.2 Advantages and Limitations of Other Reports -- 1.2.1 Models of Accuracy of Other Ratings -- 1.2.2 Sources of Accuracy of Other Ratings -- 1.3 Usefulness of Multiple Rater Studies -- 1.3.1 Analyzing the Validity of Ratings -- 1.3.1.1 Convergent and Discriminant Validity |
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1.3.1.2 Consensus and Accuracy -- 1.3.2 Improving the Validity of Inferences by Multiple Ratings -- 1.4 The Role of Measurement Models -- 1.5 Chapter Summary -- 1.6 Suggested Further Readings -- 2. Basic Methodological Concepts -- 2.1 Design Issues -- 2.1.1 Interchangeable and Structurally Different Raters -- 2.1.2 Measurement Designs -- 2.1.2.1 Each Target is Rated by All Raters -- 2.1.2.2 Each Target is Rated by Partially Different (Overlapping) Raters -- 2.1.2.3 Each Target is Rated by Different (Nonoverlapping) Raters -- 2.1.2.4 Targets and Rater Rate Each Other |
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2.1.2.5 Targets and Raters Partially Rate Each Other -- 2.1.2.6 Extension of Designs -- 2.2 Confirmatory Factor Analysis of Multiple Rater Data -- 2.3 Stochastic Measurement Theory: Basic Ideas -- 2.3.1 Sampling Process for Structurally Different Raters -- 2.3.2 Sampling Process for Interchangeable Raters -- 2.3.3 Differences Between Structurally Different and Interchangeable Raters -- 2.4 Overview of the Present Book -- 2.5 Chapter Summary -- 2.6 Suggested Further Readings -- 3. Basic Models for Structurally Different Raters -- 3.1 Basic Decomposition of Observed Variables |
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3.2 Basic Model with Correlated First-Order Factors -- 3.2.1 Application of the MTMR Model with Correlated First-Order Factors: Loneliness and Flourishing -- 3.3 Model with Indicator-Specific Factors -- 3.3.1 Application of the MTMR Model with Correlated First-Order Factors and Indicator-Specific Factors: Loneliness and Flourishing -- 3.3.2 Recommendations: Model Selection -- 3.4 Basic Model with Measurement Invariance across Raters -- 3.4.1 Statistical Tests for Testing Measurement Invariance |
| Summary |
"The use of multiple raters can improve the validity of conclusions made on self- (and other) reports of emotions, attitudes, goals, and self-perceptions of personality. Yet analyzing these ratings requires special psychometric models that take into account the specific nature of these data. From leading authorities, this book offers the first comprehensive introduction to structural equation modeling (SEM) of multiple rater data. Rather than taking a one-size-fits-all approach, the book shows how the choice of a model should be guided by measurement design and purpose. Practical recommendations are provided for selecting suitable measurement designs, raters, and psychometric models. Models for different combinations of rater types and for cross-sectional as well as longitudinal research designs are described step by step, with a strong emphasis on the substantive meaning of the latent variables in the models. User-friendly features include equation boxes, application boxes, and a companion website with Mplus and R lavaan code for the book's examples"-- Provided by publisher |
|
"The use of multiple raters can improve the validity of self- (and other) reports of emotions, attitudes, goals, and other constructs. Yet analyzing these ratings requires special psychometric models. From leading authorities, this book offers the first comprehensive introduction to structural equation modeling (SEM) of multiple rater data. Rather than taking a one-size-fits-all approach, the book shows how model choice should be guided by measurement design and purpose. Models for different combinations of rater types and for cross-sectional as well as longitudinal research designs are described step by step. The companion website provides Mplus and R lavaan code for the book's examples"-- Provided by publisher |
| Notes |
Description based upon print version of record |
|
3.4.2 Partial Measurement Invariance and Measurement Invariance in Models with Indicator-Specific Factors |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Psychometrics -- Methodology
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Structural equation modeling.
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Self-report inventories -- Evaluation
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SOCIAL SCIENCE / Research.
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PSYCHOLOGY / Personality.
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| Form |
Electronic book
|
| Author |
Geiser, Christian
|
|
Koch, Tobias
|
| ISBN |
146255573X |
|
9781462555734 |
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