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Mining performance data through nonlinear PCA with optimal scaling

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  • Paola Costantini
  • Marielle Linting
  • Giovanni C. Porzio

Abstract

Performance data are usually collected in order to build well‐defined performance indicators. Since such data may conceal additional information, which can be revealed by secondary analysis, we believe that mining of performance data may be fruitful. We also note that performance databases usually contain both qualitative and quantitative variables for which it may be inappropriate to assume some specific (multivariate) underlying distribution. Thus, a suitable technique to deal with these issues should be adopted. In this work, we consider nonlinear principal component analysis (PCA) with optimal scaling, a method developed to incorporate all types of variables, and to discover and handle nonlinear relationships. The reader is offered a case study in which a student opinion database is mined. Though generally gathered to provide evidence of teaching ability, they are exploited here to provide a more general performance evaluation tool for those in charge of managing universities. We show how nonlinear PCA with optimal scaling applied to student opinion data enables users to point out some strengths and weaknesses of educational programs and services within a university. Copyright © 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • Paola Costantini & Marielle Linting & Giovanni C. Porzio, 2010. "Mining performance data through nonlinear PCA with optimal scaling," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(1), pages 85-101, January.
    • Handle: RePEc:wly:apsmbi:v:26:y:2010:i:1:p:85-101
    DOI: 10.1002/asmb.771
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    2. Qirui Li & T. S. Amjath-Babu & Peter Zander & Zhen Liu & Klaus Müller, 2016. "Sustainability of Smallholder Agriculture in Semi-Arid Areas under Land Set-aside Programs: A Case Study from China’s Loess Plateau," Sustainability, MDPI, vol. 8(4), pages 1-17, April.
    3. M. Browne & G.F. Ortmann & S.L. Hendriks, 2014. "Household food security monitoring and evaluation using a resilience indicator: an application of categorical principal component analysis and simple sum of assets in five African countries," Agrekon, Taylor & Francis Journals, vol. 53(2), pages 25-46, June.
    4. Netshipale, A.J. & Raidimi, E.N. & Mashiloane, M.L. & de Boer, I.J.M. & Oosting, S.J., 2022. "Farming system diversity and its drivers in land reform farms of the Waterberg District, South Africa," Land Use Policy, Elsevier, vol. 117(C).
    5. Ming Li & Yukuan Wang & Congshan Tian & Liang Emlyn Yang & Md. Sarwar Hossain, 2022. "Defining Household Typologies Based on Cropland Use Behaviors for Rural Human-Environment Systems Simulation Research: A Case Study in Southwest China," IJERPH, MDPI, vol. 19(10), pages 1-15, May.

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