Home » Handbook of Item Response Theory
Product Image

Handbook of Item Response Theory

€80.99
Quantity:
Ships in 10-20 days
Delivery/Collection within 10-20 working days
Shipping & Delivery
2PL IRT Model
3PL Model
advanced statistics textbook
Bayesian approaches
Category=JMB
Category=JMBT
Cml Estimation
Complete Data Log Likelihood
Conditional Expectation
Conditional Posterior Distribution
Data Set
educational measurement
Em Algorithm
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Exponential Family Model
Fisher Information Matrix
Generalized Partial Credit Model
IRT
IRT Model
IRT Model Fit
Item Parameters
Item Response Models
latent trait analysis
Loglinear Models
Markov chain Monte Carlo
MCMC Algorithm
methods for dealing with missing data
model fit and comparison
model identification
Multilevel IRT Model
Newton Raphson Algorithm
Number Correct Score
Observed Score Distribution
parameter estimation
parameter estimation techniques in psychometrics
Person Parameters
Posterior Distribution
probability distributions
psychometrics
Rasch Model
Response modeling
statistical inference
statistical tools in IRT
test theory

Product details

  • ISBN 9780367221041
  • Weight: 880g
  • Dimensions: 178 x 254mm
  • Publication Date: 15 Apr 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
Secure checkout Fast Shipping Easy returns
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.
About

Wim J. van der Linden is a distinguished scientist and director of research innovation at Pacific Metrics Corporation. He is also a professor emeritus of measurement and data analysis at the University of Twente. He is a past president of the Psychometric Society and National Council on Measurement in Education (NCME) and a recipient of career achievement awards from NCME, Association of Test Publishers (ATP), and American Educational Research Association (AERA). His research interests include test theory, computerized adaptive testing, optimal test assembly, parameter linking, test equating, and response-time modeling as well as decision theory and its application to problems of educational decision making. Dr. van der Linden earned a PhD in psychometrics from the University of Amsterdam.