Constructing bootstrap confidence intervals for principal component loadings in the presence of missing data: A multiple-imputation approach

Authors

  • Joost R. van Ginkel,

    Corresponding author
    1. Leiden University, The Netherlands
      Joost R. van Ginkel, Leiden University, Faculty of Social and Behavioural Sciences, PO Box 9555, 2300 RB Leiden, The Netherlands (e-mail: jginkel@fsw.leidenuniv.nl).
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  • Henk A. L. Kiers

    1. University of Groningen, The Netherlands
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Joost R. van Ginkel, Leiden University, Faculty of Social and Behavioural Sciences, PO Box 9555, 2300 RB Leiden, The Netherlands (e-mail: jginkel@fsw.leidenuniv.nl).

Abstract

Earlier research has shown that bootstrap confidence intervals from principal component loadings give a good coverage of the population loadings. However, this only applies to complete data. When data are incomplete, missing data have to be handled before analysing the data. Multiple imputation may be used for this purpose. The question is how bootstrap confidence intervals for principal component loadings should be corrected for multiply imputed data. In this paper, several solutions are proposed. Simulations show that the proposed corrections for multiply imputed data give a good coverage of the population loadings in various situations.

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