Course in Item Response Theory and Modeling with Stata

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A01=George A. Marcoulides
A01=Tenko Raykov
advanced item response analysis in Stata
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Age Group_Uncategorized
Author_George A. Marcoulides
Author_Tenko Raykov
automatic-update
behavioural science statistics
Category1=Non-Fiction
Category=PBT
classical test theory
COP=United States
Delivery_Delivery within 10-20 working days
educational assessment tools
eq_isMigrated=2
eq_nobargain
Language_English
logistic regression methods
multidimensional scaling
PA=Available
Price_€50 to €100
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psychometric modelling
softlaunch

Product details

  • ISBN 9781597182669
  • Weight: 620g
  • Publication Date: 25 Jan 2018
  • Publisher: Stata Press
  • Publication City/Country: US
  • Product Form: Paperback
  • Language: English
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Over the past several decades, item response theory (IRT) and item response modeling (IRM) have become increasingly popular in the behavioral, educational, social, business, marketing, clinical, and health sciences. In this book, Raykov and Marcoulides begin with a nontraditional approach to IRT and IRM that is based on their connections to classical test theory, (nonlinear) factor analysis, generalized linear modeling, and logistic regression. Application-oriented discussions follow next. These cover the one-, two-, and three-parameter logistic models, polytomous item response models (with nominal or ordinal items), item and test information functions, instrument construction and development, hybrid models, differential item functioning, and an introduction to multidimensional

IRT and IRM. The pertinent analytic and modeling capabilities of Stata are thoroughly discussed, highlighted, and illustrated on empirical examples from behavioral and social research.

Tenko Raykov is Professor of Measurement and Quantitative Methods at Michigan State University.

George A. Marcoulides is Distinguished Professor of Research Methodology in the Department of Education at The Gervitz Graduate School of Education and a member of the Quantitative Methodology in Social Sciences Program at the University of California, Santa Barbara.

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