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ERIC Number: EJ815003
Record Type: Journal
Publication Date: 2007-Dec
Pages: 15
Abstractor: As Provided
Reference Count: 30
ISSN: ISSN-0027-3171
Discriminant Analysis Using Mixed Continuous, Dichotomous, and Ordered Categorical Variables
Lee, Sik-Yum; Song, Xin-Yuan; Lu, Bin
Multivariate Behavioral Research, v42 n4 p631-645 Dec 2007
This article proposes an intuitive approach for predictive discriminant analysis with mixed continuous, dichotomous, and ordered categorical variables that are defined via an underlying multivariate normal distribution with a threshold specification. The classification rule is based on the comparison of the observed data logarithm probability density functions. To reduce the computational burden, the analysis is conducted in the context of a confirmatory factor analysis model with independent error measurements. Identification of the dichotomous and ordered categorical variables is discussed. Results are obtained by implementations of a Monte Carlo expectation maximization (MCEM) algorithm and a path sampling procedure. Probabilities of misclassification are estimated via the idea of the "jackknife" method. A real example is given to illustrate the proposed method. (Contains 2 figures and 2 tables.)
Psychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site:
Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A