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Dual Pls Analysis

Author

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  • STAN LIPOVETSKY

    (GfK Custom Research North America, 8401 Golden Valley Road, Minneapolis, MN 55427, USA)

Abstract

A new multivariate statistical technique is obtained for comparing and combining two or more data sets each of which has a different number of respondents but the same variables. This approach can be considered as dual to such techniques as partial least squares, also known as inter-battery factor analysis and robust canonical correlation analysis for two data sets. It is shown that the problem can be reduced to the eigenproblem of the product of correlation matrices of each data set. The technique is generalized to three or more data sets in an eigenproblem of block-matrices of the correlations within each data set. This type of multivariate analysis can serve various practical problems of integration of data obtained from heterogeneous sources, particularly, for data merging in constructing data warehouses.

Suggested Citation

  • Stan Lipovetsky, 2012. "Dual Pls Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 11(05), pages 879-891.
    • Handle: RePEc:wsi:ijitdm:v:11:y:2012:i:05:n:s0219622012500241
    DOI: 10.1142/S0219622012500241
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    Cited by:

    1. Stan Lipovetsky & Michael Conklin, 2018. "Decreasing Respondent Heterogeneity by Likert Scales Adjustment via Multipoles," Stats, MDPI, vol. 1(1), pages 1-7, November.
    2. Stan Lipovetsky, 2022. "Canonical Concordance Correlation Analysis," Mathematics, MDPI, vol. 11(1), pages 1-12, December.

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