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Comparing COSTATIS and Generalized Procrustes Analysis with Multi-Way Public Education Expenditure Data

Author

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  • María Concepción Vega-Hernández

    (Department of Statistics, University of Salamanca, 37007 Salamanca, Spain)

  • Carmen Patino-Alonso

    (IGA Research Group, Department of Statistics, University of Salamanca, 37007 Salamanca, Spain)

Abstract

Governments serve a variety of purposes, and where governments spend their money has always been of concern to society. In particular, spending on public education is of great interest. However, the volume of this information can be difficult to manage. Therefore, the purpose of this work is to compare the COSTATIS method and generalized Procrustes analysis (GPA) when working with multi-way data. Despite the particular characteristics of each of them, they present similarities and differences that, when analyzed together, can provide complementary results to researchers. The COSTATIS consists of a co-inertia analysis of the compromise of two k-table analyses. The GPA method provides an optimal superimposed representation of individual configurations, and a common consensus configuration is constructed as the mean of all transformed configurations. In addition, the GPA method includes the translation, rotation and scaling of coordinates. In this study, both methods were applied, and the advantages and disadvantages of each are presented. The treated data are a sequence of tables from various countries where different public expenditures on education have been measured over time.

Suggested Citation

  • María Concepción Vega-Hernández & Carmen Patino-Alonso, 2021. "Comparing COSTATIS and Generalized Procrustes Analysis with Multi-Way Public Education Expenditure Data," Mathematics, MDPI, vol. 9(15), pages 1-13, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:15:p:1816-:d:605836
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    References listed on IDEAS

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    4. Dray, Stéphane & Dufour, Anne-Béatrice, 2007. "The ade4 Package: Implementing the Duality Diagram for Ecologists," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 22(i04).
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