Theory and Practice of Item Response Theory

ISBN-10: 1593858698

ISBN-13: 9781593858698

Edition: 2009

Authors: R. J. de Ayala
List price: $70.00
30 day, 100% satisfaction guarantee

If an item you ordered from TextbookRush does not meet your expectations due to an error on our part, simply fill out a return request and then return it by mail within 30 days of ordering it for a full refund of item cost.

Learn more about our returns policy

Description:

what's this?
Rush Rewards U
Members Receive:
coins
coins
You have reached 400 XP and carrot coins. That is the daily max!

Customers also bought

Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading

Book details

List price: $70.00
Copyright year: 2009
Publisher: Guilford Publications
Publication date: 12/30/2008
Binding: Hardcover
Pages: 448
Size: 7.25" wide x 10.25" long x 1.00" tall
Weight: 2.442
Language: English

Symbols and Acronyms
Introduction to Measurement
Measurement
Some Measurement Issues
Item Response Theory
Classical Test Theory
Latent Class Analysis
Summary
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
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
Summary
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
Metric Transformation and the Total Characteristic Function
Summary
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
Fit Assessment: An Alternative Approach for Assessing Invariance
Information and Relative Efficiency
Summary
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
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
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
The Rating Scale Model
Conceptual Estimation of the RS Model
Example: Application of the RS Model to an Attitudes Towards Condoms Scale, JMLE
How Large a Calibration Sample?
Information for the PC and RS Models
Metric Transformation, PC and RS Models
Summary
Non-Rasch Models for Ordered Polytomous Data
The Generalized Partial Credit Model
Example: Application of the GPC Model to a Reasoning Ability Instrument, MMLE
Conceptual Development of the Graded Response Model
How Large a Calibration Sample?
Example: Application of the GR Model to an Attitudes Towards Condoms Scale, MMLE
Information for Graded Data
Metric Transformation, GPC and GR Models
Summary
Model for Nominal Polytomous Data
Conceptual Development of the Nominal Response Model
How Large a Calibration Sample?
Example: Application of the NR Model to a Science Test, MMLE
Example: Mixed Model Calibration of the Science Test-NR and PC Models, MMLE
Example: NR and PC Mixed Model Calibration of the Science Test, Collapsed Options, MMLE
Information for the NR Model
Metric Transformation, NR Model
Conceptual Development of the Multiple-Choice Model
Example: Application of the MC Model to a Science Test, MMLE
Example: Application of the BS Model to a Science Test, MMLE
Summary
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: Application of the M2PL Model, Normal-Ogive Harmonic Analysis Robust Method
Obtaining Person Location Estimates
Summary
Linking and Equating
Equating Defined
Equating: Data Collection Phase
Equating: Transformation Phase
Example: Application of the Total Characteristic Function Equating Method
Summary
Differential Item Functioning
Differential Item Functioning and Item Bias
Mantel-Haenszel Chi-Sqyare
The TSW Likelihood Ratio Test
Logistic Regression
Example: DIF Analysis
Summary
Maximum Likelihood Estimation of Person Locations
Estimating and Individual's Location: Empirical Maximum Likelihood Estimation
Estimating and Individual's Location: Newton's Method for MLE
Revisiting Zero Variance Binary Response Patterns
Maximum Likelihood Estimation of Item Locations
The Normal Ogive Models
Conceptual Development of the Normal Ogive Model
The Relationship between IRT Statistics and Traditional Item Analysis Indices
Relationship of the Two-Parameter Normal Ogive and Logistic Model
Extending the Two-Parameter Normal Ogive Model to a Multidimensional Space
Computerized Adaptive Testing
A Brief History
Fixed-Branching Techniques
Variable-Branching Techniques
Advantages of Variable-Branching over Fixed-Branching Methods
IRT-Based Variable-Branching Adaptive Testing Algorithm
Miscellanea
Linear Logistic Test Model (LLTM)
Using Principal Axis for Estimating Item Discrimination
Infinite Item Discrimination parameter Estimates
Example: NOHARM Unidimensional Calibration
An Approximate Chi-Square Statistic for NOHARM
Mixture Models
Relative Efficiency, Monotonicity, and Information
FORTRAN Formats
Example: Mixed Model Calibration of the Science Test-NR and 2PL Models, MMLE
Example: Mixed Model Calibration of the Science Test-NR and GR Models, MMLE
Odds, Odds Ratios, and Logits
The Person Response Function
Linking: A Temperature Analogy Example
Should DIF Analyses Be Based on Latent Classes?
The Separation and Reliability Indices
Dependency in Traditional Item Statistics and Observed Scores
References
Author Index
Subject Index
About the Author
×
Free shipping on orders over $35*

*A minimum purchase of $35 is required. Shipping is provided via FedEx SmartPost® and FedEx Express Saver®. Average delivery time is 1 – 5 business days, but is not guaranteed in that timeframe. Also allow 1 - 2 days for processing. Free shipping is eligible only in the continental United States and excludes Hawaii, Alaska and Puerto Rico. FedEx service marks used by permission."Marketplace" orders are not eligible for free or discounted shipping.

Learn more about the TextbookRush Marketplace.

×