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The Theory and Practice of Item Response Theory 2nd Edition by RJ De Ayala ISBN 1462547753 9781462547753

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The Theory and Practice of Item Response Theory 2nd Edition R. J. De Ayala Digital Instant Download

Author(s): R. J. de Ayala
ISBN(s): 9781462547753, 1462547753
Edition: 2
File Details: PDF, 22.93 MB
Year: 2022
Language: english
SKU: EB-54633714 Categories: , Tag:

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ISBN 10: 1462547753 
ISBN 13: 9781462547753
Author: RJ De Ayala

Noted for addressing both the “hows” and “whys” of item response theory (IRT), this text has been revised and updated with the latest techniques (multilevel models, mixed models, and more) and software packages. Simple to more complex models are covered in consistently formatted chapters that build sequentially. The book takes the reader from model development through the fit analysis and interpretation phases that would be performed in practice. To facilitate understanding, common data sets are used across chapters, with the examples worked through for increasingly complex models. Exemplary model applications include free (BIGSTEPS, NOHARM, Facets, R packages) and commercial (BILOG-MG, flexMIRT, SAS, WINMIRA, SPSS, SYSTAT) software packages. The companion website provides data files and online-only appendices.    New to This Edition *Chapter on multilevel models. *New material on loglinear models, mixed models, the linear logistic trait model, and fit statistics. *Many additional worked-through examples. *Updated guidance on software; now includes R, SAS, and flexMIRT.  

The Theory and Practice of Item Response Theory 2nd Table of contents:

1. Introduction to Measurement
Measurement
Some Measurement Issues
Item Response Theory
Classical Test Theory
Latent Class Analysis
Summary

2. The One-Parameter Model
Conceptual Development of the Rasch Model
The One-Parameter Model
The One-Parameter Logistic Model and the Rasch Model
Assumptions Underlying the Model
An Empirical Data Set: The Mathematics Data Set
Conceptually Estimating an Individual’s Location
Some Pragmatic Characteristics of Maximum Likelihood Estimates
The Standard Error of Estimate and Information
An Instrument’s Estimation Capacity
Summary

3. Joint Maximum Likelihood Parameter Estimation
Joint Maximum Likelihood Estimation
Indeterminacy of Parameter Estimates
How Large a Calibration Sample?
Example: Application of the Rasch Model to the Mathematics Data, JMLE, BIGSTEPS
Example: Application of the Rasch Model to the Mathematics Data, JMLE, mixRasch
Validity Evidence
Summary of the Application of the Rasch Model
Summary

4. Marginal Maximum Likelihood Parameter Estimation
Marginal Maximum Likelihood Estimation
Estimating an Individual’s Location: Expected A Posteriori
Example: Application of the Rasch Model to the Mathematics Data, MMLE, BILOG-MG
Metric Transformation and the Total Characteristic Function
Example: Application of the Rasch Model to the Mathematics Data, MMLE, mirt
Summary

5. The Two-Parameter Model
Conceptual Development of the Two-Parameter Model
Information for the Two-Parameter Model
Conceptual Parameter Estimation for the 2PL Model
How Large a Calibration Sample?
Metric Transformation, 2PL Model
Example: Application of the 2PL Model to the Mathematics Data, MMLE, BILOG-MG
Fit Assessment: An Alternative Approach for Assessing Invariance
Example: Application of the 2PL Model to the Mathematics Data, MMLE, mirt
Information and Relative Efficiency
Summary

6. The Three-Parameter Model
Conceptual Development of the Three-Parameter Model
Additional Comments about the Pseudo-Guessing Parameter, Xj
Conceptual Parameter Estimation for the 3PL Model
How Large a Calibration Sample?
Assessing Conditional Independence
Example: Application of the 3PL Model to the Mathematics Data, MMLE, BILOG-MG
Fit Assessment: Conditional Independence Assessment
Fit Assessment: Model Comparison
Example: Application of the 3PL Model to the Mathematics Data, MMLE, mirt
Assessing Person Fit: Appropriateness Measurement
Information for the Three-Parameter Model
Metric Transformation, 3PL Model
Handling Missing Responses
Issues to Consider in Selecting among the 1PL, 2PL, and 3PL Models
Summary

7. Rasch Models for Ordered Polytomous Data
Conceptual Development of the Partial Credit Model
Conceptual Parameter Estimation of the PC Model
Example: Application of the PC Model to a Reasoning Ability Instrument, MMLE, flexMIRT
Example: Application of the PC Model to a Reasoning Ability Instrument, MMLE, mirt
The Rating Scale Model
Conceptual Parameter Estimation of the RS Model
Example: Application of the RS Model to an Attitudes Toward Condoms Scale, JMLE, BIGSTEPS
Example: Application of the PC Model to an Attitudes Toward Condoms Scale, JMLE, mixRasch
How Large a Calibration Sample?
Information for the PC and RS Models
Metric Transformation, PC and RS Models
Summary

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Tags: RJ De Ayala, Item Response