ISSN:
1860-0980
Schlagwort(e):
ordinal categorical data
;
entropy
;
minimum discrimination information
;
scaling
;
convex programming
Quelle:
Springer Online Journal Archives 1860-2000
Thema:
Psychologie
Notizen:
Abstract This paper studies the problem of scaling ordinal categorical data observed over two or more sets of categories measuring a single characteristic. Scaling is obtained by solving a constrained entropy model which finds the most probable values of the scales given the data. A Kullback-Leibler statistic is generated which operationalizes a measure for the strength of consistency among the sets of categories. A variety of data of two and three sets of categories are analyzed using the entropy approach.
Materialart:
Digitale Medien
URL:
http://dx.doi.org/10.1007/BF02294515
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