R. J. de Ayala, Bruno D. Zumbo, David J. Weiss

The Theory and Practice of Item Response Theory, Second Edition

• Gebundenes Buch

Produktbeschreibung

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. The book takes the reader from model development through the fit analysis and interpretation phases that would be performed in practice, using common datasets across chapters. 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.

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Produktdetails

• Verlag: Guilford Publications
• Seitenzahl: 643
• Erscheinungstermin: 27. Mai 2022
• Englisch
• Abmessung: 186mm x 260mm x 37mm
• Gewicht: 1334g
• ISBN-13: 9781462547753
• ISBN-10: 1462547753
• Artikelnr.: 62635051

Autorenporträt

R. J. de Ayala, PhD, is Professor of Quantitative, Qualitative, and Psychometric Methods and Director of the Institutional Research Master's Program in the College of Educational and Human Sciences at the University of Nebraska-Lincoln (UNL). His research interests include psychometrics, item response theory, computerized adaptive testing, applied statistics, and multilevel models. His work has appeared in Applied Psychological Measurement, Applied Measurement in Education, the British Journal of Mathematical and Statistical Psychology, Educational and Psychological Measurement, the Journal of Applied Measurement, and the Journal of Educational Measurement. He is a Fellow of the American Psychological Association's Division 5: Evaluation, Measurement, and Statistics and of the American Educational Research Association. He is a recipient of a Big 12 Faculty Fellowship and holds a Gallup Research Professorship at UNL.

Inhaltsangabe

Symbols and Acronyms
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
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, X¿
- 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 Towards Condoms
Scale, JMLE, BIGSTEPS
- Example: Application of the PC Model to an Attitudes Towards Condoms
Scale, JMLE, mixRasch
- How Large a Calibration Sample?
- Information for the PC and RS Models
- Metric Transformation, PC and RS Models
- Summary
8. Non-Rasch Models for Ordered Polytomous Data
- The Generalized Partial Credit Model
- Example: Application of the GPC Model to a Reasoning Ability Instrument,
MMLE, flexMIRT
- Example: Application of the GPC Model to a Reasoning Ability Instrument,
MMLE, mirt
- Conceptual Development of the Graded Response Model
- How Large a Calibration Sample?
- Information for Graded Data
- Metric Transformation, GPC and GR Models
- Example: Application of the GR Model to an Attitudes Towards Condoms
Scale, MMLE, flexMIRT
- Example: Application of the GR Model to an Attitudes Towards Condoms
Scale, MMLE, mirt
- Conceptual Development of the Continuous Response Model
- Summary
9. Models for Nominal Polytomous Data
- Conceptual Development of the Nominal Response Model
- Information for the NR Model
- Metric Transformation, NR Model
- Conceptual Development of the Multiple-Choice Model
- How Large a Calibration Sample?
- Example: Application of the NR Model to a General Science Test, MMLE,
mirt
- Summary
10. Models for Multidimensional Data
- Conceptual Development of a Multidimensional IRT Model
- Multidimensional Item Location and Discrimination
- Item Vectors and Vector Graphs
- The Multidimensional Three-Parameter Logistic Model
- Assumptions of the MIRT Model
- Estimation of the M2PL Model
- Information for the M2PL Model
- Indeterminacy in MIRT
- Metric Transformation, M2PL Model
- Example: Calibration of interpersonal engagement instrument, M2PL Model,
sirt.noharam
- Obtaining Person Location Estimates
- Example: Calibration of interpersonal engagement instrument, M2PL Model,
mirt
- Example: Calibration of interpersonal engagement instrument, M2PL Model,
flexMIRT
- Summary
11. Linking and Equating
- Equating Defined
- Equating: Data Collection Phase
- Equating: Transformation Phase
- Example: Application of the Total Characteristic Function Equating
Method, EQUATE
- Example: Application of the Total Characteristic Function Equating
Method, SNSequate
- Example: Fixed-item and Concurrent Calibration Equating
- Summary
12. Differential Item Functioning
- Differential Item Functioning and Item Bias
- Mantel-Haenszel Chi-Square
- The TSW Likelihood Ratio Test
- Logistic Regression
- Example: DIF Analysis of vocabulary test, SAS CMH
- Example: DIF Analysis of vocabulary test, mantelhaen.test and difR
- Example: DIF Analysis of vocabulary test, SAS proc logistic
- Example: DIF Analysis of vocabulary test, glm and difR
- Summary
13. Multilevel IRT Models
- Multilevel IRT-Two Levels
- Example: Equivalence of the Rasch model and its Multilevel Model
Parameterization, proc glimmix
- Example: Rasch model estimation, lme4
- Person-Level Predictors for Items
- Example: Person-Level Predictors for Items-DIF Analysis, proc glimmix
- Example: Person-Level Predictors for Items-DIF Analysis, lme4
- Person-Level Predictors for Respondents
- Example: Person-Level Predictors for Respondents-Nutrition Literacy, proc
glimmix
- Example: Person-Level Predictors for Respondents, lme4
- Item-Level Predictors for Items
- Example: Item-Level Predictors for Items - Nutrition Literacy, proc
glimmix
- Example: Item-Level Predictors