In this presentation, we outline a new methodology for assessing multidimensional material deprivation, based on partial order theory. The methodology is designed to deal with binary variables; it avoids any form of aggregation between non numerical variables and allows for taking into account, in purely ordinal terms, exogenous judgments about the relative relevance of deprivation dimensions. The methodology, thus, overcomes many of the epistemological, conceptual and technical drawbacks affecting “classical” assessment procedures that, implicitly or not, rely on aggregative and compensative approaches.

Evaluation is not aggregation: assessing material deprivation through partial order theory / F.Maggino; M.Fattore. - ELETTRONICO. - (2011), pp. 1-1. (Intervento presentato al convegno Paper presented at the Workshop organized by AIQUAV on “Exploring and exploiting quality of life complexity (QoLexity): epistemological, methodological and statistical issues”. Session “QoLexity in managing measures and analyses: statistical perspectives” tenutosi a Firenze nel 9-10 Settembre 2011).

Evaluation is not aggregation: assessing material deprivation through partial order theory

MAGGINO, FILOMENA;
2011

Abstract

In this presentation, we outline a new methodology for assessing multidimensional material deprivation, based on partial order theory. The methodology is designed to deal with binary variables; it avoids any form of aggregation between non numerical variables and allows for taking into account, in purely ordinal terms, exogenous judgments about the relative relevance of deprivation dimensions. The methodology, thus, overcomes many of the epistemological, conceptual and technical drawbacks affecting “classical” assessment procedures that, implicitly or not, rely on aggregative and compensative approaches.
2011
Paper presented at the Workshop organized by AIQUAV on “Exploring and exploiting quality of life complexity (QoLexity): epistemological, methodological and statistical issues”. Session “QoLexity in managing measures and analyses: statistical perspectives”
Firenze
F.Maggino; M.Fattore
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/503472
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