Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 7-Day Trial for You or Your Team.

Learn More →

Kernel smoothing approaches to nonparametric item characteristic curve estimation

Kernel smoothing approaches to nonparametric item characteristic curve estimation Abstract The option characteristic curve, the relation between ability and probability of choosing a particular option for a test item, can be estimated by nonparametric smoothing techniques. What is smoothed is the relation between some function of estimated examinee ability rankings and the binary variable indicating whether or not the option was chosen. This paper explores the use of kernel smoothing, which is particularly well suited to this application. Examples show that, with some help from the fast Fourier transform, estimates can be computed about 500 times as rapidly as when using commonly used parametric approaches such as maximum marginal likelihood estimation using the three-parameter logistic distribution. Simulations suggest that there is no loss of efficiency even when the population curves are three-parameter logistic. The approach lends itself to several interesting extensions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Psychometrika Cambridge University Press

Kernel smoothing approaches to nonparametric item characteristic curve estimation

Ramsay, J. O.
Psychometrika , Volume 56 (4): 20 – Dec 1, 1991
Download PDF
20 pages

Loading next page...
 
/lp/cambridge-university-press/kernel-smoothing-approaches-to-nonparametric-item-characteristic-curve-0DtqcecMRQ

References (46)

Publisher
Cambridge University Press
Copyright
1991 The Psychometric Society
ISSN
0033-3123
eISSN
1860-0980
DOI
10.1007/bf02294494
Publisher site
See Article on Publisher Site

Abstract

Abstract The option characteristic curve, the relation between ability and probability of choosing a particular option for a test item, can be estimated by nonparametric smoothing techniques. What is smoothed is the relation between some function of estimated examinee ability rankings and the binary variable indicating whether or not the option was chosen. This paper explores the use of kernel smoothing, which is particularly well suited to this application. Examples show that, with some help from the fast Fourier transform, estimates can be computed about 500 times as rapidly as when using commonly used parametric approaches such as maximum marginal likelihood estimation using the three-parameter logistic distribution. Simulations suggest that there is no loss of efficiency even when the population curves are three-parameter logistic. The approach lends itself to several interesting extensions.

Journal

Psychometrika – Cambridge University Press

Published: Dec 1, 1991

There are no references for this article.