Treatments of non-metric variables in partial least squares and principal component analysis

ABSTRACT

This paper reviews various treatments of non-metric variables in partial least squares (PLS) and principal component analysis (PCA) algorithms. The performance of different treatments is compared in an extensive simulation study under several typical data generating processes and associated recommendations are made. Moreover, we find that PLS-based methods are to prefer in practice, since, independent of the data generating process, PLS performs either as good as PCA or significantly outperforms it. As an application of PLS and PCA algorithms with non-metric variables we consider construction of a wealth index to predict household expenditures. Consistent with our simulation study, we find that a PLS-based wealth index with dummy coding outperforms PCA-based ones.

KEYWORDS:

• Composite index
• wealth
• principal component analysis
• partial least squares
• non-metric variables

Acknowledgments

We would like to thank the editor and referee for helpful comments on earlier versions of this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Funding from the Deutsche Forschungsgemeinschaft for the support in RTG 1723 is gratefully acknowledged.