Gini heterogeneity index for detecting uncertainty in ordinal data surveys

Abstract In sample surveys where people are asked to give responses to items by means of ordinal scales it is common to register an inherent indecision generated by both objective and subjective causes. Thus, an effective statistical analysis should take this component into account to avoid bias in...
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Autor*in:	Capecchi, Stefania [verfasserIn] Iannario, Maria

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Heterogeneity indexes Uncertainty CUSH models Subjects’ covariates

Anmerkung:	© Sapienza Università di Roma 2016

Übergeordnetes Werk:	Enthalten in: Metron - [S.l.] : Springer, 2010, 74(2016), 2 vom: 20. Mai, Seite 223-232
Übergeordnetes Werk:	volume:74 ; year:2016 ; number:2 ; day:20 ; month:05 ; pages:223-232

Links:	Volltext

DOI / URN:	10.1007/s40300-016-0088-5

Katalog-ID:	SPR036534722

Internformat


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520			\|a Abstract In sample surveys where people are asked to give responses to items by means of ordinal scales it is common to register an inherent indecision generated by both objective and subjective causes. Thus, an effective statistical analysis should take this component into account to avoid bias in estimation, interpretation and prediction. In this paper, we show that the heterogeneity index proposed by Gini and its variants are effective measures to detect such an uncertainty. The relationships of Gini heterogeneity index with the parameter of a mixture model are discussed and exploited as a tool for the exploratory selection of covariates. Then, a real case study is considered to check for the effectiveness of these proposals. Some concluding remarks end the paper.
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allfields	10.1007/s40300-016-0088-5 doi (DE-627)SPR036534722 (SPR)s40300-016-0088-5-e DE-627 ger DE-627 rakwb eng Capecchi, Stefania verfasserin aut Gini heterogeneity index for detecting uncertainty in ordinal data surveys 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sapienza Università di Roma 2016 Abstract In sample surveys where people are asked to give responses to items by means of ordinal scales it is common to register an inherent indecision generated by both objective and subjective causes. Thus, an effective statistical analysis should take this component into account to avoid bias in estimation, interpretation and prediction. In this paper, we show that the heterogeneity index proposed by Gini and its variants are effective measures to detect such an uncertainty. The relationships of Gini heterogeneity index with the parameter of a mixture model are discussed and exploited as a tool for the exploratory selection of covariates. Then, a real case study is considered to check for the effectiveness of these proposals. Some concluding remarks end the paper. Heterogeneity indexes (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 CUSH models (dpeaa)DE-He213 Subjects’ covariates (dpeaa)DE-He213 Iannario, Maria aut Enthalten in Metron [S.l.] : Springer, 2010 74(2016), 2 vom: 20. Mai, Seite 223-232 (DE-627)735688761 (DE-600)2701242-6 2281-695X nnns volume:74 year:2016 number:2 day:20 month:05 pages:223-232 https://dx.doi.org/10.1007/s40300-016-0088-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 74 2016 2 20 05 223-232
spelling	10.1007/s40300-016-0088-5 doi (DE-627)SPR036534722 (SPR)s40300-016-0088-5-e DE-627 ger DE-627 rakwb eng Capecchi, Stefania verfasserin aut Gini heterogeneity index for detecting uncertainty in ordinal data surveys 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sapienza Università di Roma 2016 Abstract In sample surveys where people are asked to give responses to items by means of ordinal scales it is common to register an inherent indecision generated by both objective and subjective causes. Thus, an effective statistical analysis should take this component into account to avoid bias in estimation, interpretation and prediction. In this paper, we show that the heterogeneity index proposed by Gini and its variants are effective measures to detect such an uncertainty. The relationships of Gini heterogeneity index with the parameter of a mixture model are discussed and exploited as a tool for the exploratory selection of covariates. Then, a real case study is considered to check for the effectiveness of these proposals. Some concluding remarks end the paper. Heterogeneity indexes (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 CUSH models (dpeaa)DE-He213 Subjects’ covariates (dpeaa)DE-He213 Iannario, Maria aut Enthalten in Metron [S.l.] : Springer, 2010 74(2016), 2 vom: 20. Mai, Seite 223-232 (DE-627)735688761 (DE-600)2701242-6 2281-695X nnns volume:74 year:2016 number:2 day:20 month:05 pages:223-232 https://dx.doi.org/10.1007/s40300-016-0088-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 74 2016 2 20 05 223-232
allfields_unstemmed	10.1007/s40300-016-0088-5 doi (DE-627)SPR036534722 (SPR)s40300-016-0088-5-e DE-627 ger DE-627 rakwb eng Capecchi, Stefania verfasserin aut Gini heterogeneity index for detecting uncertainty in ordinal data surveys 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sapienza Università di Roma 2016 Abstract In sample surveys where people are asked to give responses to items by means of ordinal scales it is common to register an inherent indecision generated by both objective and subjective causes. Thus, an effective statistical analysis should take this component into account to avoid bias in estimation, interpretation and prediction. In this paper, we show that the heterogeneity index proposed by Gini and its variants are effective measures to detect such an uncertainty. The relationships of Gini heterogeneity index with the parameter of a mixture model are discussed and exploited as a tool for the exploratory selection of covariates. Then, a real case study is considered to check for the effectiveness of these proposals. Some concluding remarks end the paper. Heterogeneity indexes (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 CUSH models (dpeaa)DE-He213 Subjects’ covariates (dpeaa)DE-He213 Iannario, Maria aut Enthalten in Metron [S.l.] : Springer, 2010 74(2016), 2 vom: 20. Mai, Seite 223-232 (DE-627)735688761 (DE-600)2701242-6 2281-695X nnns volume:74 year:2016 number:2 day:20 month:05 pages:223-232 https://dx.doi.org/10.1007/s40300-016-0088-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 74 2016 2 20 05 223-232
allfieldsGer	10.1007/s40300-016-0088-5 doi (DE-627)SPR036534722 (SPR)s40300-016-0088-5-e DE-627 ger DE-627 rakwb eng Capecchi, Stefania verfasserin aut Gini heterogeneity index for detecting uncertainty in ordinal data surveys 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sapienza Università di Roma 2016 Abstract In sample surveys where people are asked to give responses to items by means of ordinal scales it is common to register an inherent indecision generated by both objective and subjective causes. Thus, an effective statistical analysis should take this component into account to avoid bias in estimation, interpretation and prediction. In this paper, we show that the heterogeneity index proposed by Gini and its variants are effective measures to detect such an uncertainty. The relationships of Gini heterogeneity index with the parameter of a mixture model are discussed and exploited as a tool for the exploratory selection of covariates. Then, a real case study is considered to check for the effectiveness of these proposals. Some concluding remarks end the paper. Heterogeneity indexes (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 CUSH models (dpeaa)DE-He213 Subjects’ covariates (dpeaa)DE-He213 Iannario, Maria aut Enthalten in Metron [S.l.] : Springer, 2010 74(2016), 2 vom: 20. Mai, Seite 223-232 (DE-627)735688761 (DE-600)2701242-6 2281-695X nnns volume:74 year:2016 number:2 day:20 month:05 pages:223-232 https://dx.doi.org/10.1007/s40300-016-0088-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 74 2016 2 20 05 223-232
allfieldsSound	10.1007/s40300-016-0088-5 doi (DE-627)SPR036534722 (SPR)s40300-016-0088-5-e DE-627 ger DE-627 rakwb eng Capecchi, Stefania verfasserin aut Gini heterogeneity index for detecting uncertainty in ordinal data surveys 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Sapienza Università di Roma 2016 Abstract In sample surveys where people are asked to give responses to items by means of ordinal scales it is common to register an inherent indecision generated by both objective and subjective causes. Thus, an effective statistical analysis should take this component into account to avoid bias in estimation, interpretation and prediction. In this paper, we show that the heterogeneity index proposed by Gini and its variants are effective measures to detect such an uncertainty. The relationships of Gini heterogeneity index with the parameter of a mixture model are discussed and exploited as a tool for the exploratory selection of covariates. Then, a real case study is considered to check for the effectiveness of these proposals. Some concluding remarks end the paper. Heterogeneity indexes (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 CUSH models (dpeaa)DE-He213 Subjects’ covariates (dpeaa)DE-He213 Iannario, Maria aut Enthalten in Metron [S.l.] : Springer, 2010 74(2016), 2 vom: 20. Mai, Seite 223-232 (DE-627)735688761 (DE-600)2701242-6 2281-695X nnns volume:74 year:2016 number:2 day:20 month:05 pages:223-232 https://dx.doi.org/10.1007/s40300-016-0088-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 74 2016 2 20 05 223-232
language	English
source	Enthalten in Metron 74(2016), 2 vom: 20. Mai, Seite 223-232 volume:74 year:2016 number:2 day:20 month:05 pages:223-232
sourceStr	Enthalten in Metron 74(2016), 2 vom: 20. Mai, Seite 223-232 volume:74 year:2016 number:2 day:20 month:05 pages:223-232
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Heterogeneity indexes Uncertainty CUSH models Subjects’ covariates
isfreeaccess_bool	false
container_title	Metron
authorswithroles_txt_mv	Capecchi, Stefania @@aut@@ Iannario, Maria @@aut@@
publishDateDaySort_date	2016-05-20T00:00:00Z
hierarchy_top_id	735688761
id	SPR036534722
language_de	englisch
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author	Capecchi, Stefania
spellingShingle	Capecchi, Stefania misc Heterogeneity indexes misc Uncertainty misc CUSH models misc Subjects’ covariates Gini heterogeneity index for detecting uncertainty in ordinal data surveys
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topic_title	Gini heterogeneity index for detecting uncertainty in ordinal data surveys Heterogeneity indexes (dpeaa)DE-He213 Uncertainty (dpeaa)DE-He213 CUSH models (dpeaa)DE-He213 Subjects’ covariates (dpeaa)DE-He213
topic	misc Heterogeneity indexes misc Uncertainty misc CUSH models misc Subjects’ covariates
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title	Gini heterogeneity index for detecting uncertainty in ordinal data surveys
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title_full	Gini heterogeneity index for detecting uncertainty in ordinal data surveys
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author_browse	Capecchi, Stefania Iannario, Maria
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author-letter	Capecchi, Stefania
doi_str_mv	10.1007/s40300-016-0088-5
title_sort	gini heterogeneity index for detecting uncertainty in ordinal data surveys
title_auth	Gini heterogeneity index for detecting uncertainty in ordinal data surveys
abstract	Abstract In sample surveys where people are asked to give responses to items by means of ordinal scales it is common to register an inherent indecision generated by both objective and subjective causes. Thus, an effective statistical analysis should take this component into account to avoid bias in estimation, interpretation and prediction. In this paper, we show that the heterogeneity index proposed by Gini and its variants are effective measures to detect such an uncertainty. The relationships of Gini heterogeneity index with the parameter of a mixture model are discussed and exploited as a tool for the exploratory selection of covariates. Then, a real case study is considered to check for the effectiveness of these proposals. Some concluding remarks end the paper. © Sapienza Università di Roma 2016
abstractGer	Abstract In sample surveys where people are asked to give responses to items by means of ordinal scales it is common to register an inherent indecision generated by both objective and subjective causes. Thus, an effective statistical analysis should take this component into account to avoid bias in estimation, interpretation and prediction. In this paper, we show that the heterogeneity index proposed by Gini and its variants are effective measures to detect such an uncertainty. The relationships of Gini heterogeneity index with the parameter of a mixture model are discussed and exploited as a tool for the exploratory selection of covariates. Then, a real case study is considered to check for the effectiveness of these proposals. Some concluding remarks end the paper. © Sapienza Università di Roma 2016
abstract_unstemmed	Abstract In sample surveys where people are asked to give responses to items by means of ordinal scales it is common to register an inherent indecision generated by both objective and subjective causes. Thus, an effective statistical analysis should take this component into account to avoid bias in estimation, interpretation and prediction. In this paper, we show that the heterogeneity index proposed by Gini and its variants are effective measures to detect such an uncertainty. The relationships of Gini heterogeneity index with the parameter of a mixture model are discussed and exploited as a tool for the exploratory selection of covariates. Then, a real case study is considered to check for the effectiveness of these proposals. Some concluding remarks end the paper. © Sapienza Università di Roma 2016
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title_short	Gini heterogeneity index for detecting uncertainty in ordinal data surveys
url	https://dx.doi.org/10.1007/s40300-016-0088-5
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up_date	2024-07-03T18:12:44.631Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR036534722</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230328165813.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2016 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s40300-016-0088-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR036534722</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s40300-016-0088-5-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Capecchi, Stefania</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Gini heterogeneity index for detecting uncertainty in ordinal data surveys</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Sapienza Università di Roma 2016</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In sample surveys where people are asked to give responses to items by means of ordinal scales it is common to register an inherent indecision generated by both objective and subjective causes. 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Gini heterogeneity index for detecting uncertainty in ordinal data surveys

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