Bayesian Psychometric Modeling (Hardcover)

A Single Cohesive Framework of Tools and Procedures for Psychometrics and Assessment Bayesian Psychometric Modeling presents a unified Bayesian approach across traditionally separate families of psychometric models. It shows that Bayesian techniques, as alternatives to conventional approaches, offer distinct and profound advantages in achieving many goals of psychometrics. Adopting a Bayesian approach can aid in unifying seemingly disparate-and sometimes conflicting-ideas and activities in psychometrics. This book explains both how to perform psychometrics using Bayesian methods and why many of the activities in psychometrics align with Bayesian thinking. The first part of the book introduces foundational principles and statistical models, including conceptual issues, normal distribution models, Markov chain Monte Carlo estimation, and regression. Focusing more directly on psychometrics, the second part covers popular psychometric models, including classical test theory, factor analysis, item response theory, latent class analysis, and Bayesian networks. Throughout the book, procedures are illustrated using examples primarily from educational assessments. A supplementary website provides the datasets, WinBUGS code, R code, and Netica files used in the examples.

General

Imprint:	Taylor & Francis
Country of origin:	United States
Series:	Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
Release date:	May 2016
First published:	2016
Authors:	Roy Levy • Robert J. Mislevy
Dimensions:	254 x 178 x 33mm (L x W x T)
Format:	Hardcover
Pages:	490
ISBN-13:	978-1-4398-8467-6
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Social sciences > Sociology, social studies > Social research & statistics > General Books > Social sciences > Psychology > Psychological methodology > Psychological testing & measurement Books > Science & Mathematics > Mathematics > Applied mathematics > General
LSN:	1-4398-8467-6
Barcode:	9781439884676

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