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Handbook of item response theory modeling : applications to typical performance assessment

Author: Steven Paul Reise; Dennis A Revicki
Publisher: New York : Routledge, Taylor & Francis Group, [2015] ©2015
Series: Multivariate applications book series.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
Item response theory (IRT) has moved beyond the confines of educational measurement into assessment domains such as personality, psychopathology, and patient-reported outcomes. Classic and emerging IRT methods and applications that are revolutionizing psychological measurement, particularly for health assessments used to demonstrate treatment effectiveness, are reviewed in this new volume. World renowned  Read more...
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Document Type: Book
All Authors / Contributors: Steven Paul Reise; Dennis A Revicki
ISBN: 9781848729728 1848729723 9781138787858 113878785X 131756569X 9781317565697
OCLC Number: 883648907
Description: xvi, 465 pages : illustrations ; 26 cm.
Contents: 1. Introduction: Age-Old Problems And Modern Solutions --
2. Evaluating The Impact Of Multidimensionality On Unidimensional Item Response Theory Model Parameters / Steven P. Reise / Dennis A. Revicki --
3. Modern Approaches To Parameter Estimation In Item Response Theory / Tyler M. Moore / Steven P. Reise / Karon F. Cook --
4. Estimating The Latent Density In Unidimensional IRT To Permit Non-Normality / David Thissen / Li Cai --
5. The Use Of Nonparametric Item Response Theory To Explore Data Quality / Carol M. Woods --
6. Evaluating The Fit Of IRT Models / Rob B.K. Wanders / Rob R. Meijer / Jorge N. Tendeiro --
7. Assessing Person Fit In Typical-Response Measures / Alberto Maydeu-Olivares --
8. Three (Or Four) Factors, Four (Or Three) Models / Pere J. Ferrando --
9. Using Hierarchical IRT Models To Create Unidimensional Measures From Multidimensional Data / Carrie R. Houts / Andrew J. Bodine / R.J. Wirth / Michael C. Edwards --
10. An Illustration Of The Two-Tier Item Factor Analysis Model / Maria Orlando Edelen / Brian D. Stucky --
11. Using Projected Locally Dependent Unidimensional Models To Measure Multidimensional Response Data / Wes E. Bonifay --
12. Multidimensional Explanatory Item Response Modeling / Edward H. Ip / Shyh-Huei Chen --
13. Unipolar Item Response Models / Paul De Boeck / Mark Wilson --
14. Selecting Among Polytomous IRT Models / Joseph F. Lucke --
15. Scoring And Estimating Score Precision Using Multidimensional IRT Models / Michael Nering / Matthew Finkelman / Remo Ostini --
16. Developing Item Banks For Patient-Reported Health Outcomes / Tim J. Croudace / Anna Brown --
17. Using Item Response Theory To Evaluate Measurement Invariance In Health-Related Measures / Wen-Hung Chen / Dennis A. Revicki / Carole Tucker --
18. Detecting Faulty Within-Item Category Functioning With The Nominal Response Model / Roger E. Millsap / Heather Gunn / Alex Zautra / Howard T. Everson --
19. Multidimensional Test Linking / Steven P. Reise / Kathleen S.J. Preston --
20. IRT For Growth And Change / Jonathan P. Weeks --
21. Summary: New IRT Problems And Future Directions / Kevin T. Petway / John J. Mcardle / Earl S. Hishinuma.
Series Title: Multivariate applications book series.
Responsibility: edited by Steven P. Reise and Dennis A. Revicki.

Abstract:

Item response theory (IRT) has moved beyond the confines of educational measurement into assessment domains such as personality, psychopathology, and patient-reported outcomes. Classic and emerging IRT methods and applications that are revolutionizing psychological measurement, particularly for health assessments used to demonstrate treatment effectiveness, are reviewed in this new volume. World renowned contributors present the latest research and methodologies about these models along with their applications and related challenges. Examples using real data, some from NIH-PROMIS, show how to.

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"The discussion of several crucial IRT concepts and method with a focus on TPAs, which offers multiple perspectives on a broad topic, would be immensely helpful to the intended audience. Most Read more...

 
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