Kernel-Based Discriminant Techniques for Educational Placement

Miao-hsiang Lin, Su-yun Huang and Yuan-chin Chang

Journal of Educational and Behavioral Statistics, 2004, vol. 29, issue 2, 219-240

Abstract: This article considers the problem of educational placement. Several discriminant techniques are applied to a data set from a survey project of science ability. A profile vector for each student consists of five science-educational indictors. The students are intended to be placed into three reference groups: advanced, regular, and remedial. Various discriminant techniques, including Fisher’s discriminant analysis and kernel-based nonparametric discriminant analysis, are compared. The evaluation work is based on the leaving-one-out misclassification score. Results from the five school data sets and 500 bootstrap samples reveal that the kernel-based nonparametric approach with bandwidth selected by cross validation performs reasonably well. The authors regard kernel-based nonparametric procedures as desirable competitors to Fisher’s discriminant rule for handling problems of educational placement.

Keywords: classification; data-driven bandwidth selection approaches; educational placement; Fisher’s discriminant analysis; generalized kth-nearest-neighbor method; science-education indicators (search for similar items in EconPapers)
Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:29:y:2004:i:2:p:219-240

DOI: 10.3102/10769986029002219

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