Ordinal data are intrinsically imprecise; treating such data as either numerical or categorical might entail inaccuracies or loss of information; in particular using Likert scales the statistical analysis is rather limited and the transition from one category to another is arbitrary. Fuzzy values can provide a easy-to-use representation of such data that is more expressive and accurate than ordinal scales as a fuzzy coding can take into account for vagueness, uncertainty and imprecision of Likert-type scales. In this paper we use ART to detect changes in fuzzified ordinal time series.

Change point analysis of ordinal time series

CAPPELLI, CARMELA;DI IORIO, FRANCESCA;
2012

Abstract

Ordinal data are intrinsically imprecise; treating such data as either numerical or categorical might entail inaccuracies or loss of information; in particular using Likert scales the statistical analysis is rather limited and the transition from one category to another is arbitrary. Fuzzy values can provide a easy-to-use representation of such data that is more expressive and accurate than ordinal scales as a fuzzy coding can take into account for vagueness, uncertainty and imprecision of Likert-type scales. In this paper we use ART to detect changes in fuzzified ordinal time series.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/454124
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