Polychoric correlations for ordered categories using the EM algorithm

Kempei 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
    PublisherSpringer New York LLC
    Pages247-259
    Number of pages13
    Volume233
    ISBN (Print)9783319772486
    DOIs
    Publication statusPublished - 2018 Jan 1
    Event82nd Annual meeting of the Psychometric Society, 2017 - Zurich, Switzerland
    Duration: 2017 Jul 172017 Jul 21

    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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  • Cite this

    Shiina, K., Ueda, T., & Kubo, S. (2018). Polychoric correlations for ordered categories using the EM algorithm. In Quantitative Psychology - The 82nd Annual Meeting of the Psychometric Society, Zurich, Switzerland, 2017 (Vol. 233, pp. 247-259). Springer New York LLC. https://doi.org/10.1007/978-3-319-77249-3_21