- We will send in 10–14 business days.
/images/books/3333657/9780367221041.jpg)
- Publisher: CRC Press
- ISBN-10: 0367221047
- ISBN-13: 9780367221041
- Format: 17.8 x 25.4 x 2.3 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
Handbook of Item Response Theory (e-book) (used book) | bookbook.eu
Reviews
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.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 19d.18:20:26
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Publisher: CRC Press
- ISBN-10: 0367221047
- ISBN-13: 9780367221041
- Format: 17.8 x 25.4 x 2.3 cm, softcover
- Language: English English
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.
Reviews