9781466514324-1466514329-Handbook of Item Response Theory: Volume 2: Statistical Tools (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Handbook of Item Response Theory: Volume 2: Statistical Tools (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

ISBN-13: 9781466514324
ISBN-10: 1466514329
Edition: 1
Author: Wim J. van der Linden
Publication date: 2016
Publisher: Chapman and Hall/CRC
Format: Hardcover 454 pages
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Book details

ISBN-13: 9781466514324
ISBN-10: 1466514329
Edition: 1
Author: Wim J. van der Linden
Publication date: 2016
Publisher: Chapman and Hall/CRC
Format: Hardcover 454 pages

Summary

Handbook of Item Response Theory: Volume 2: Statistical Tools (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences) (ISBN-13: 9781466514324 and ISBN-10: 1466514329), written by authors Wim J. van der Linden, was published by Chapman and Hall/CRC in 2016. With an overall rating of 4.5 stars, it's a notable title among other Psychology & Counseling books. You can easily purchase or rent Handbook of Item Response Theory: Volume 2: Statistical Tools (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences) (Hardcover) from BooksRun, along with many other new and used Psychology & Counseling books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

Drawing on the work of internationally acclaimed experts in the field, Handbook of Item Response Theory, Volume Two: Statistical Tools presents classical and modern statistical tools used in item response theory (IRT). While IRT heavily depends on the use of statistical tools for handling its models and applications, systematic introductions and reviews that emphasize their relevance to IRT are hardly found in the statistical literature. This second volume in a three-volume set fills this void.

Volume Two covers common probability distributions, the issue of models with both intentional and nuisance parameters, the use of information criteria, methods for dealing with missing data, and model identification issues. It also addresses recent developments in parameter estimation and model fit and comparison, such as Bayesian approaches, specifically Markov chain Monte Carlo (MCMC) methods.

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