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; P. P., D'Urso. - (2012). (Intervento presentato al convegno MAF 2012 tenutosi a Dipartiumento di Economia, Università "Cà Foscari" Venezia nel 10-12 aprile 2012).
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
