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

Comments

Originally published at http://dx.doi.org/10.1080/23311908.2017.1279435

Funded in part by the VCU Libraries Open Access Publishing Fund.

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

Share

COinS