PROC CORRESP: Different Perspectives for Nominal Analysis
نویسنده
چکیده
Correspondence Analysis as a fundamental approach to the analysis data having any scale of measurement fails to achieve wide acceptance in the United States. As a descriptive and inferential statistical technique, it should be riding the crest of currently popular categorical modeling procedures for a number of reasons: 1) it allows one to plot, in metric space the relationships among nominal categories of variables, to 2) quantify the size and number of dimensions comprising these relationships, and, 3) generate 'optimal scores' that reflect the interval scaled metric values underlying each variable. This paper compares this technique to more traditional categorical modeling procedures and emphasizes its relative advantages for business and behavioral science applications.
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تاریخ انتشار 2012