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Simple components

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  • T. F. Cox
  • D. S. Arnold

Abstract

Interpretation of principal components is difficult due to their weights (loadings, coefficients) being of various sizes. Whereas very small weights or very large weights can give clear indication of the importance of particular variables, weights that are neither large nor small (‘grey area’ weights) are problematical. This is a particular problem in the fast moving goods industries where a lot of multivariate panel data are collected on products. These panel data are subjected to univariate analyses and multivariate analyses where principal components (PCs) are key to the interpretation of the data. Several authors have suggested alternatives to PCs, seeking simplified components such as sparse PCs. Here components, termed simple components (SCs), are sought in conjunction with Thurstonian criteria that a component should have only a few variables highly weighted on it and each variable should be weighted heavily on just a few components. An algorithm is presented that finds SCs efficiently. Simple components are found for panel data consisting of the responses to a questionnaire on efficacy and other features of deodorants. It is shown that five SCs can explain an amount of variation within the data comparable to that explained by the PCs, but with easier interpretation.

Suggested Citation

  • T. F. Cox & D. S. Arnold, 2018. "Simple components," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 83-99, January.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:1:p:83-99
    DOI: 10.1080/02664763.2016.1268104
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    References listed on IDEAS

    as
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