Journal article
What You See May Not Be What You Think You Get: Discriminant Analysis in Statistical Packages
Educational and psychological measurement, v 41(2), pp 267-282
Jul 1981
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Discriminant-classification analysis is a multivariate statistical technique which will increasingly be used by psychologists as research situations become more varied. Use of discriminant analysis will be facilitated by statistical packages such as SPSS (Nie et al., 1975) and BMDP (Dixon and Brown, 1977). This paper discusses considerations in the unwary use of packaged discriminant analysis procedures including: the differences between the "group classification function" and the textbook classification function in both form and use, classification table confusions and their alleviation, and the hazards of stepping procedures. Recommendations concerning how to conduct an exploratory discriminant analysis are made and an example is presented.
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Details
- Title
- What You See May Not Be What You Think You Get: Discriminant Analysis in Statistical Packages
- Creators
- Paul C. Gondek - University of Connecticut
- Publication Details
- Educational and psychological measurement, v 41(2), pp 267-282
- Publisher
- Sage
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:A1981LU72400004
- Scopus ID
- 2-s2.0-84973817937
- Other Identifier
- 991021229992204721
UN Sustainable Development Goals (SDGs)
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Source: SDGs in the Output
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Mathematics, Interdisciplinary Applications
- Psychology, Educational
- Psychology, Mathematical