Structural Equation Modeling of Multiple Rater Data - Christian Geiser

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Résumé :

1. Introduction: The Importance of Multiple Rater Data
1.1 Advantages and Limitations of Self-Reports
1.2 Advantages and Limitations of Other Reports
1.21 Models of accuracy of other ratings
1.22 Sources of accuracy of other ratings
1.3 Usefulness of Multiple Rater Studies
1.31 Analyzing the validity of ratings
1.32 Improving the validity of inferences by multiple ratings
1.4 The Role of Measurement Models
1.5 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
2.2 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 Summary
2.6 Suggested Further Readings
3. Basic Models for Structurally Different Raters
3.1 Basic Decomposition of Observed Variables
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
3.4.2 Partial measurement invariance and measurement invariance in models with indicator-specific factors
3.4.3 How important is measurement invariance in multirater studies?
3.5 Application of the Models to Other Measurement Designs
3.6 Chapter Summary
3.7 Suggested Further Readings
4. Models with Method Factors for Structurally Different Raters
4.1 Basic CTC(M-1) Model
4.1.1 Choice of a reference rater group
4.1.2 Application of the CTC(M-1) model: Loneliness and flourishing
4.1.3 Comparing the CTC(M-1) model with the model with correlated first-order factors
4.2 Restricted CTC(M-1) Model as Reformulation of the Model with Correlated First-Order Factors
4.3 Restricted CTC(M-1) Model with Measurement Invariance Across Raters
4.4 CTC(M-1) Models with Indicator-Specific Effects
4.4.1 CTC(M-1) models with indicator-specific factors
4.4.2 CTC(M-1) model with indicator-specific traits
4.4.3. Choosing a CTC(M-1) model with indicator-specific effects
4.5 Alternative Models
4.5.1 Latent difference model
4.5.2 Latent means model
4.5.3 Reference rating as outcome model
4.6 Models with Covariates
4.7 Application of the Models to Other Measurement Designs
4.7.1 Analyzing round-robin data for structurally different raters
4.8 Chapter Summary
4.9 Suggested Further Readings
5. Single-Level CFA Models for Interchangeable Raters
5.1 The Interchangeable-Saturated (I-SAT) Model
5.2 Basic Decomposition
5.3 Basic Models with Correlated First-Order Factors
5.3.1 Adjustment of fit statistics with interchangeable models
5.3.2 Application of the basic model with correlated first-order factors with and without indicator-specific factors: Loneliness and flourishing
5.4 Models with Method Factors
5.4.1 Basic correlated traits-interchangeable methods (CTIM) model
5.4.2 Application of the basic CTIM model: Loneliness and flourishing
5.4.3 Restricted CTIM model
5.4.4 Application of the restricted CTIM model: Loneliness and flourishin...

Biographie:

Michael Eid, PhD, is Professor of Methods and Evaluation at the Free University of Berlin in Germany. His research focuses on measurement theory, in particular on the development of psychometric models for longitudinal and multimethod research. Since the early 2000s, he has been contributing to the development of structural equation models for analyzing multirater data for different types of raters and research designs. His more applied research contributions are in the areas of subjective well-being, mood regulation, and health psychology.

Christian Geiser, PhD, is a former professor of quantitative psychology. He currently works as an instructor and statistical consultant with QuantFish. His areas of expertise are in structural equation modeling, measurement, longitudinal data analysis, latent class modeling, and multitrait-multimethod analysis.

Tobias Koch, PhD, is Professor of Psychological Methods at the Friedrich-Schiller-Universit?t Jena in Germany. His research focuses on measurement theory and psychometrics, structural equation modeling, longitudinal data analysis, multilevel analysis, Bayesian analysis, and multitrait-multimethod analysis.