Classification trees for multivariate ordinal response: an application to Student Evaluation Teaching

Classification trees for multivariate ordinal response: an application to Student Evaluation... Data from multiple items on an ordinal scale are commonly collected when qualitative variables, such as feelings, attitudes and many other behavioral and health-related variables are observed. In this paper we introduce a method to derive a distance-based tree for multivariate ordinal response that allows, when subject-specific characteristics are available, to derive common profiles for respondents giving the same/similar multivariate ratings. Special attention will be paid to the performance comparison in terms of AUC, for three different distances used as splitting criteria. Simulated data an a dataset from a Student Evaluation of Teaching survey will be used as illustrative examples. The latter will be used to show the performance of the procedure in profiling students by identifying which features of their experience are most closely related to their expressed satisfaction. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Classification trees for multivariate ordinal response: an application to Student Evaluation Teaching

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Publisher
Springer Netherlands
Copyright
Copyright © 2016 by Springer Science+Business Media Dordrecht
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-016-0430-2
Publisher site
See Article on Publisher Site

Abstract

Data from multiple items on an ordinal scale are commonly collected when qualitative variables, such as feelings, attitudes and many other behavioral and health-related variables are observed. In this paper we introduce a method to derive a distance-based tree for multivariate ordinal response that allows, when subject-specific characteristics are available, to derive common profiles for respondents giving the same/similar multivariate ratings. Special attention will be paid to the performance comparison in terms of AUC, for three different distances used as splitting criteria. Simulated data an a dataset from a Student Evaluation of Teaching survey will be used as illustrative examples. The latter will be used to show the performance of the procedure in profiling students by identifying which features of their experience are most closely related to their expressed satisfaction.

Journal

Quality & QuantitySpringer Journals

Published: Oct 6, 2016

References

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