@article{ref1,
title="Grade correspondence analysis applied to contingency tables and questionnaire data",
journal="Intelligent Data Analysis",
year="2002",
author="Szczesny, W.",
volume="6",
number="1",
pages="17-51",
abstract="An alternative approach methodology for Correspondence Analysis is presented. This approach, called Grade Correspondence Analysis (GCA), utilizes Spearman's rho to detect underlying associations and trends. Two examples are presented using: (1) a contingency table (Heuer's suicide data) with cause of death, gender, and age; and (2) a survey questionnaire (data matrix) concerning employment, personal economics, computer skills, and disability level of handicapped computer specialists in Poland. GCA uses a search strategy (multi-starts/random starts) to detect trends (not forced to be orthogonal) among rows and columns. (A similar strategy permits the determination of significance levels.) Results are discussed using measures of the &quot;representativness&quot; of the trends, as well as measures of their &quot;regularity&quot;. Visualization of trends (as well as outlier trend detection) is via the concept of &quot;overrepresentation&quot; maps. Survey data may be measured on any non-negative scale. Meaningful disjoint aggregation (or division) of sub-populations and variables are possible. This paper is written for the practitioner and includes a &quot;grade&quot; concepts example in an appendix. There is also, however, an appendix with GCA theory relating to: grade distributions; local maxima of Spearman's rho and their representativness, regularity and regions of attraction; total positivity of order 2 (TP2); similarity measures; suitable &quot;random references&quot; for the determination of significance levels; and the application of GCA to non-negative data matrices. © 2002-IOS Press. All rights reserved.<p /><p>Language: en</p>",
language="en",
issn="1088-467X",
doi="10.3233/ida-2002-6103",
url="http://dx.doi.org/10.3233/ida-2002-6103"
}