 Mining performance data through nonlinear PCA with optimal scalingPaola Costantini, Marielle Linting and Giovanni C. Porzio Applied Stochastic Models in Business and Industry, 2010, vol. 26, issue 1, 85-101 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. Date: 2010 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:26:y:2010:i:1:p:85-101 Access Statistics for this article More articles in Applied Stochastic Models in Business and Industry from John Wiley & Sons |
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