The <<interpretive plane>> in the interpretation of the factorial plane with supplementary variables: Application to Financial Data Analytics

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Published: Apr 22, 2024
Keywords:
Data analytics Machine learning Accounting - Finance Exploratory analysis Multivariate visualization
Stratos Moschidis
1.Hellenic Statistical Authority 2. Department of Accounting and Finance University of Macedonia
Athanasios C. Thanopoulos
Hellenic Statistical Authority
Abstract

Multiple Correspondence Analysis (MCA) is a method of unsupervised machine learning applied to categorical data in order to investigate the associations between variable categories, as well as the directions (trends) of their dispersion. The so-called supplementary variables, also used in the context of ΜCA, do not participate in the construction of the factorial axes, are simply projected through the transition formulas together with the other points (categories of variables) on the factorial diagrams, and can further contribute to the interpretation of the phenomenon under study. This paper examines the way in which graphical information can be integrated from the "Interpretive Plane" into the factorial planes that include supplementary points. Through the Interpretive Plane, the important interpretive points of the factorial plane are identified, and then those that are  "close" to supplementary points. Thus, practitioners who wish to include supplementary variables in their analyses can achieve better and safer interpretative results-conclusions. The proposed approach was applied to the investigation of the rewards system, the performance, development and effectiveness of businesses. The business sector variables and the hierarchical position of employees in the enterprise were considered as supplementary.

Article Details
  • Section
  • Empirical studies
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References
Abdi, H., & Valentin, D. (2007). Multiple correspondence analysis. Encyclopedia of Measurement and Statistics, 2(4), 651–657.
Fithian, W., & Josse, J. (2017). Multiple correspondence analysis and the multilogit bilinear model. Journal of Multivariate Analysis, 157, 87–102. https://doi.org/10.1016/j.jmva.2017.02.009
Gabriel, K. (1995). Biplot display of multivariate categorical data, with comments on multiple correspondence analysis. Recent Advances in Descriptive Multivariate Analysis, 190, 226.
Graffelman, J., & Aluja-Banet, T. (2003). Optimal Representation of Supplementary Variables in Biplots from Principal Component Analysis and Correspondence Analysis. Biometrical Journal, 45(4), 491–509. https://doi.org/10.1002/bimj.200390027
Greenacre, M., & Blasius, J. (Eds.). (2006). Multiple Correspondence Analysis and Related Methods (0 ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781420011319
Greenacre, M. J. (1991). Interpreting multiple correspondence analysis. Applied Stochastic Models and Data Analysis, 7(2), 195–210. https://doi.org/10.1002/asm.3150070208
Greenacre, M. J. (1993). Biplots in correspondence analysis. Journal of Applied Statistics, 20(2), 251–269.
Hoffman, D. L., & De Leeuw, J. (1992). Interpreting multiple correspondence analysis as a multidimensional scaling method. Marketing Letters, 3(3), 259–272.
Lê, S., Josse, J., & Husson, F. (2008). FactoMineR: An R Package for Multivariate Analysis. Journal of Statistical Software, 25(1). https://doi.org/10.18637/jss.v025.i01
Moschidis, S., Markos, A., & Thanopoulos, A. C. (2022). “Automatic” interpretation of multiple correspondence analysis (MCA) results for nonexpert users, using R programming. Applied Computing and Informatics. https://doi.org/10.1108/ACI-07-2022-0191
Torres, A., & van de Velden, M. (2007). Perceptual mapping of multiple variable batteries by plotting supplementary variables in correspondence analysis of rating data. Food Quality and Preference, 18(1), 121–129. https://doi.org/10.1016/j.foodqual.2005.09.005
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T., Miller, E., Bache, S., Müller, K., Ooms, J., Robinson, D., Seidel, D., Spinu, V., … Yutani, H. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686
Wickham, H., Chang, W., & Wickham, M. H. (2016). Package ‘ggplot2.’ Create Elegant Data Visualisations Using the Grammar of Graphics. Version, 2(1), 1–189.