Polychoric correlations for ordered categories using the EM algorithm

Kenpei Shiina, Takashi Ueda, Saori Kubo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

A new method for the estimation of polychoric correlations is proposed in this paper, which uses the Expectation-Maximization (EM) algorithm and the Conditional Covariance Formula. Simulation results show that this method attains the same level of accuracy as other methods, and is robust to deteriorated data quality.

Original languageEnglish
Title of host publicationQuantitative Psychology - The 82nd Annual Meeting of the Psychometric Society, Zurich, Switzerland, 2017
EditorsJorge Gonzalez, Rianne Janssen, Marie Wiberg, Dylan Molenaar, Steven Culpepper
PublisherSpringer New York LLC
Pages247-259
Number of pages13
ISBN (Print)9783319772486
DOIs
Publication statusPublished - 2018
Event82nd Annual meeting of the Psychometric Society, 2017 - Zurich, Switzerland
Duration: 2017 Jul 172017 Jul 21

Publication series

NameSpringer Proceedings in Mathematics and Statistics
Volume233
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017

Other

Other82nd Annual meeting of the Psychometric Society, 2017
CountrySwitzerland
CityZurich
Period17/7/1717/7/21

Keywords

  • Conditional covariance formula
  • EM algorithm
  • Polychoric correlation

ASJC Scopus subject areas

  • Mathematics(all)

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