Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Exploratory Visual Inspection of Category Associations and Correlation Estimation in Multidimensional Subspaces

Exploratory Visual Inspection of Category Associations and Correlation Estimation in... In this paper, we aimed to estimate associations among categories in a multi-way contingency table. To simplify estimation and interpretation of results, we stacked multiple variables to form a two-way stacked table and analyzed it using the biplot in correspondence analysis (CA) paradigm. The correspondence analysis biplot allowed visual inspection of category associations in a twodimensional plane, and the CA solution numerically estimated the category relationships. We utilized parallel analysis and identified two statistically meaningful dimensions with which a plane was constructed. In the plane, we examined metric space mapping, which was converted into correlations, between school districts and categories of school-relevant variables. The results showed differential correlation patterns among school districts and this correlational information may be useful for stake holders or policy makers to pinpoint possible causes of low school performance and school-relevant behaviors. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Classification Springer Journals

Exploratory Visual Inspection of Category Associations and Correlation Estimation in Multidimensional Subspaces

Journal of Classification , Volume 36 (2) – Nov 16, 2018

Loading next page...
 
/lp/springer-journals/exploratory-visual-inspection-of-category-associations-and-correlation-YuE1UTnd3j

References (36)

Publisher
Springer Journals
Copyright
Copyright © 2018 by Classification Society of North America
Subject
Statistics; Statistical Theory and Methods; Pattern Recognition; Bioinformatics; Signal,Image and Speech Processing; Psychometrics; Marketing
ISSN
0176-4268
eISSN
1432-1343
DOI
10.1007/s00357-018-9277-7
Publisher site
See Article on Publisher Site

Abstract

In this paper, we aimed to estimate associations among categories in a multi-way contingency table. To simplify estimation and interpretation of results, we stacked multiple variables to form a two-way stacked table and analyzed it using the biplot in correspondence analysis (CA) paradigm. The correspondence analysis biplot allowed visual inspection of category associations in a twodimensional plane, and the CA solution numerically estimated the category relationships. We utilized parallel analysis and identified two statistically meaningful dimensions with which a plane was constructed. In the plane, we examined metric space mapping, which was converted into correlations, between school districts and categories of school-relevant variables. The results showed differential correlation patterns among school districts and this correlational information may be useful for stake holders or policy makers to pinpoint possible causes of low school performance and school-relevant behaviors.

Journal

Journal of ClassificationSpringer Journals

Published: Nov 16, 2018

There are no references for this article.