Document Type
Article
Original Publication Date
2017
Journal/Book/Conference Title
Cogent Psychology
Volume
2017
Issue
4
DOI of Original Publication
10.1080/23311908.2017.1279435
Date of Submission
February 2017
Abstract
The Expectation-Maximization (EM) algorithm is a method for finding the maximum likelihood estimate of a model in the presence of missing data. Unfortunately, EM does not produce a parameter covariance matrix for standard errors. Both Oakes and Supplemented EM are methods for obtaining the parameter covariance matrix. SEM was discovered in 1991 and is implemented in both opensource and commercial item response model estimation software. Oakes, a more recent method discovered in 1999, had not been implemented in item response model software until now. Convergence properties, accuracy, and elapsed time of Oakes and Supplemental EM family algorithms are compared for a diverse selection IFA models. Oakes exhibits the best accuracy and elapsed time among algorithms compared. We recommend that Oakes be made available in item response model estimation software.
Rights
© 2017 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
Is Part Of
VCU Psychiatry Publications
Comments
Originally published at http://dx.doi.org/10.1080/23311908.2017.1279435
Funded in part by the VCU Libraries Open Access Publishing Fund.