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Gini heterogeneity index for detecting uncertainty in ordinal data surveys

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  • Stefania Capecchi

    (University of Naples Federico II)

  • Maria Iannario

    (University of Naples Federico II)

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.

Suggested Citation

  • Stefania Capecchi & Maria Iannario, 2016. "Gini heterogeneity index for detecting uncertainty in ordinal data surveys," METRON, Springer;Sapienza Università di Roma, vol. 74(2), pages 223-232, August.
    • Handle: RePEc:spr:metron:v:74:y:2016:i:2:d:10.1007_s40300-016-0088-5
    DOI: 10.1007/s40300-016-0088-5
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    References listed on IDEAS

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    1. Lidia Ceriani & Paolo Verme, 2012. "The origins of the Gini index: extracts from Variabilità e Mutabilità (1912) by Corrado Gini," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 10(3), pages 421-443, September.
    2. Maria Iannario, 2008. "Dummy Covariates In Cub Models," Statistica, Department of Statistics, University of Bologna, vol. 68(2), pages 179-200.
    3. D'Elia, Angela & Piccolo, Domenico, 2005. "A mixture model for preferences data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 917-934, June.
    4. Shlomo Yitzhaki, 2003. "Gini’s Mean difference: a superior measure of variability for non-normal distributions," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(2), pages 285-316.
    5. Carina Gerstenberger & Daniel Vogel, 2015. "On the efficiency of Gini’s mean difference," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(4), pages 569-596, November.
    6. Maria Iannario, 2012. "Modelling shelter choices in a class of mixture models for ordinal responses," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 1-22, March.
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    Cited by:

    1. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    2. Raffaele Fiorentino & Sergio Longobardi & Carla Morrone & Alessandro Scaletti, 2024. "Educational heterogeneity of the founding team of innovative start-ups: confirmations and denials," International Entrepreneurship and Management Journal, Springer, vol. 20(3), pages 2347-2367, September.
    3. Stefania Capecchi & Marta Meleddu & Manuela Pulina, 2019. "Quality evaluation and preferences of healthcare services: the case of telemedicine in Sardinia," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2339-2351, September.
    4. Marco Alfò & Antonio Lijoi & Donata Marasini & Giancarlo Ragozini, 2016. "The statistical legacy of Corrado Gini," METRON, Springer;Sapienza Università di Roma, vol. 74(2), pages 141-143, August.

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