This is the second edition of an introductory text that describes the principles of invariant measurement; how invariant measurement can be achieved using Rasch measurement theory; and how to use invariant measurement to solve a variety of measurement problems in the social, behavioral, and health sciences.

Contenu

Preface
Acknowledgments
About the authors

Part I: Introduction

Introduction and Overview
Variable maps
What are logits?
The dichotomous Rasch model
Five requirements of invariant measurement
Method and meaning of Rasch measurement
Illustrative data set: Measuring the home environment
Discussion and summary

Part II: Conceptual and Theoretical Issues

Invariant Measurement
What is measurement?
What is invariant measurement?
Ideal-type scales and the structure of measurement data
What are Rasch Models?
Item-invariant person measurement
Person-invariant item calibration
Discussion and Summary
Rasch Models
Operating characteristic functions
Dichotomous Rasch model
Polytomous Rasch Models
Partial Credit model
Rating Scale model
Many Facet Model
Discussion and Summary
Researcher-Constructed Measures
Building Blocks for Researcher-Constructed Measures
Latent variable: What is the latent variable being measured?
Observational Design: What is the plan for collecting structured observations or responses from persons in order to define the latent variable?
Scoring rules: How do we categorize the systematic observations, and then assign scores to the categories to be used as indicators of the latent variable?
Rasch Measurement model: How are person and item responses or observations mapped onto the latent variable?
Applications
Learning stimulation in the home environments of preschool children
Assessment in the health sciences: The five rights of safe administration of medications
Discussion and summary
An Historical and Comparative Perspective on Research Traditions in Measurement
What are measurement theories?
What are research traditions?
What are the three major research traditions in measurement?
Test-Score Tradition
The founding of classical test theory: Spearman
Generalizability Theory: Cronbach and his colleagues
Scaling Tradition
Psychophysics and the beginning of the scaling tradition: Thorndike
Absolute scaling and psychophysics: Thurstone
Item response theory: Birnbaum and Rasch
Non-Parametric item response theory: Guttman, Lazarsfeld, and Mokken
Structural Tradition
Factor analysis: Spearman and Thurstone
Path analysis: Wright
Structural equation modeling: Joreskog
Explanatory Item Response Models: De Boeck & Wilson
Discussion and summary
The Quest for Invariant Measurement within the Scaling Tradition
General issues guiding the comparisons among the scaling theories
Item-invariant person measurement
Parametric models: Thorndike, Thurstone, Birnbaum and Rasch
Non-parametric models: Guttman, Lazarsfeld and Mokken
Person-invariant item calibration
Parametric models: Thorndike, Thurstone, Birnbaum and Rasch
Non-parametric models: Guttman, Lazarsfeld and Mokken
Operating characteristic functions
Item response functions
Person response functions
Variable maps
Discussion and summary

Part III: Technical Issues

Methods of Estimation for the Dichotomous Rasch Model
Dichotomous Model for Rasch Measurement
Methods of Estimation
Non-iterative Estimation Methods
LOG Method
PAIR Method
PROX Method
Iterative Estimation Methods
Joint Maximum Likelihood Estimation Method
Marginal Maximum Likelihood Method
Conditional Maximum Likelihood Method
Bayesian Estimation Method
Item calibration: Comparison of non-iterative, MLE, and Bayesian methods
Person measurement: Illustrative data analysis of JMLE Method
Discussion and Summary
Model-Data Fit for the Dichotomous Rasch Model
Brief history of model-data fit for categorical data
Conceptual framework for model-data fit based on residual analyses
Guttman's Perspective on Model-Data Fit
Model-data fit statistics for dichotomous Rasch Model
Additional issues related to model-data fit
Discussion and Summary
Rasch Measurement Theory and Generalized Linear Mixed Models
What are generalized linear mixed models?
Specifying Explanatory Rasch Models with Generalized Linear Mixed Models
Dichotomous Model with no covariates
Linear Logistic Rasch Model with item covariates
Latent Regression Rasch Model with person covariates
Combined Covariates Rasch Model with item and person covariates
Illustrations of Explanatory Rasch Models with the Learning Stimulation Scale
Dichotomous Model with no covariates
LLRM with items classified as child or adult activities
LRRM with homes categorized by education level of mother
CCRM with both item classification and home categorization
Model Comparisons
Discussion and Summary

Part IV: Assessments with raters: Rater-invariant measurement

Rater-mediated assessments: A Conceptual framework
Rater-mediated assessments
Brief description of measurement models for raters
Rater-invariant measurement
Extending the requirements of invariant measurement
Criteria for developing and evaluating rater-mediated assessments
Guidelines for evaluating functioning of rating categories
The Many Facet Rasch Model
Using variable maps with rater-mediated assessments
Discussion and summary
Evaluating the quality of rater-mediated assessments I:
Indices of rater errors and systematic biases
Rater Errors and Systematic Biases
Illustrative data analyses
Rater Facet
Domain Facet
Person Facet
Rater Invariant Measurement
Discussion and Summary
Evaluating the quality of rater-mediated assessments II:
Direct Indices of rater accuracy
What is rater accuracy?
Rater accuracy as the underlying construct
Indices of rating accuracy
Illustrative data analyses
Relationship between rater error and accuracy
Discussion and Summary

Part V: Final Word

Invariant measurement: Discussion and summary
Perennial issues in assessment from the perspective of invariant measurement
Measurement Models
Assessment Development
Administration of assessments
Use of assessments
Evaluation of assessments
Final word

References
Glossary (definitions of terms)
Author Index
Subject Index