Diagnostic Measurement ：Theory, Methods, and Applications ( Methodology in the Social Sciences )

Publication subTitle ：Theory, Methods, and Applications

Publication series ：Methodology in the Social Sciences

Author： Rupp> André A.; Templin3> Jonathan

Publisher： Guilford Publications Inc‎

Publication year： 2010

E-ISBN: 9781606235430

P-ISBN(Paperback): 9781606235287

Subject： B841.7 心理测验

Keyword：心理学,护理学,临床医学,预防医学、卫生学,教育学,教育,商品销售,贸易经济

Language： ENG

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Description

This book provides a comprehensive introduction to the theory and practice of diagnostic classification models (DCMs), which are useful for statistically driven diagnostic decision making. DCMs can be employed in a wide range of disciplines, including educational assessment and clinical psychology. For the first time in a single volume, the authors present the key conceptual underpinnings and methodological foundations for applying these models in practice. Specifically, they discuss a unified approach to DCMs, the mathematical structure of DCMs and their relationship to other latent variable models, and the implementation and estimation of DCMs using Mplus. The book's highly accessible language, real-world applications, numerous examples, and clearly annotated equations will encourage professionals and students to explore the utility and statistical properties of DCMs in their own projects. The companion website (www.guilford.com/rupp-materials) features chapter exercises with answers, data sets, Mplus syntax code, and output.

Winner--Award for Significant Contribution to Educational Measurement and Research Methodology, AERA Division D

Chapter

Chapter 1. Introduction

1.1 Objectives of Diagnostic Measurement

1.2 Diagnostic Measurement with DCMs

1.3 Selecting versus Constructing DCMs

1.4 The Role of DCMs in Diagnostic Assessment Design and Implementation

Part I. THEORY: PRINCIPLES OF DIAGNOSTIC MEASUREMENT WITH DCMs

Chapter 2. Implementation, Design, and Validation of Diagnostic Assessments

2.1 Diagnostic Assessment in Education

2.2 The Process Character of Diagnostic Assessment Implementations

2.3 Frameworks for Principled Design of Diagnostic Assessments

2.4 Frameworks for Validity Studies of Diagnostic Assessments

2.5 Chapter Conclusion

Chapter 3. Diagnostic Decision Making with DCMs

3.1 Diagnostic Characteristics of DCMs

3.2 Developing Diagnostic Rules

3.3 Contexts for Using DCMs

3.4 Reduction of Classification Error with DCMs

3.5 Chapter Conclusion

Chapter 4. Attribute Specification for DCMs

4.1 The Nature of Attributes

4.2 Attribute Hierarchies

4.3 Reporting Attribute Profiles

4.4 Developing Cognitive Processing Models

4.5 Chapter Conclusion

Part II. METHODS: PSYCHOMETRIC FOUNDATIONS OF DCMs

Chapter 5. The Statistical Nature of DCMs

5.1 DCMs and Other Latent-Variable Models

5.2 A Taxonomy of Core DCMs

5.3 Related Classification Approaches

5.4 Bayesian Inference Networks

5.5 Chapter Conclusion

Chapter 6. The Statistical Structure of Core DCMs

6.1 General Mathematical Structure of DCMs

6.2 Noncompensatory DCMs

6.3 Compensatory DCMs

6.4 Chapter Conclusion

Chapter 7. The LCDM Framework

7.1 A Brief Introduction to Log-Linear Models

7.2 Log-Linear Models with Latent Classes

7.3 Representing DCMs as Log-Linear Models with Latent Classes

7.4 The LCDM as a General DCM for Diagnostic Measurement

7.5 Representing Core DCMs with the LCDM

7.6 Chapter Conclusion

Chapter 8. Modeling the Attribute Space in DCMs

8.1 Structural Models in DCMs

8.2 Unstructured Structural Models

8.3 Log-Linear Structural Models

8.4 Unstructured Tetrachoric Models

8.5 Structured Tetrachoric Models

8.6 Summary of Parameter Complexity in Structural Models

8.7 Special Topics for Structural Models

8.8 Chapter Conclusion

Part III. APPLICATIONS: UTILIZING DCMs IN PRACTICE

Chapter 9. Estimating DCMs Using Mplus

9.1 Sample Data Set

9.2 Basic Concepts for Estimating DCMs in Mplus

9.3 Preliminary Command Syntax

9.4 Parameterizing Latent Class Models for Categorical Data in Mplus

9.5 Syntax for Estimating the LCDM in Mplus

9.6 Syntax for Specifying Output Information

9.7 Running Mplus and Interpreting Output

9.8 Estimation of Core DCMs in Mplus

9.9 Advanced Topics for Mplus Estimation

9.10 Chapter Conclusion

Chapter 10. Respondent Parameter Estimation in DCMs

10.1 Principles of Estimation Relevant for Attribute Profile Estimation

10.2 Estimating Attribute Profiles

10.3 Extended Examples of Attribute Profile Estimation

10.4 EAP versus MAP Estimation

10.5 The Impact of Prior Attribute Distributions on Posterior Attribute Profile Estimation

10.6 Standard Errors for Attribute Profile Estimates

10.7 Attribute Profile Estimation in Mplus

10.8 Chapter Conclusion

Chapter 11. Item Parameter Estimation in DCMs

11.1 Conceptual Underpinnings for Estimating Item Parameters

11.2 Estimation of Item Parameters Using the E-M Algorithm

11.3 Estimation of Item Parameter Using MCMC

11.4 Chapter Conclusion

Chapter 12. Evaluating the Model Fit of DCMs

12.1 Basic Statistical Principles for Constructing Fit Statistics

12.2 Problems with General Goodness-of-Fit Statistics for DCMs

12.3 Practical Approaches for Determining Goodness-of-Fit for DCMs

12.4 An Example of Goodness-of-Fit Evaluation

12.5 Evaluating Relative Model Fit

12.6 Evaluating Person Fit

12.7 Chapter Conclusion

Chapter 13. Item Discrimination Indices for DCMs

13.1 Basic Concepts for Item Discrimination Indices

13.2 Item Discrimination Indices for DCMs

13.3 Information-Based Item Discrimination Indices for DCMs

13.4 Chapter Conclusion

Chapter 14. Accommodating Complex Sampling Designs in DCMs

14.1 Defining Complex Sampling Designs

14.2 Accommodating Complex Sampling Designs of Respondents in DCMs

14.3 Accommodating Complex Sampling Designs of Items in DCMs

14.4 Accommodating Additional Sources of Heterogeneity in DCMs

14.5 Chapter Conclusion

Glossary

References

Author Index

Subject Index

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