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Ordinal Data Models for No-Opinion Responses in Attitude Survey

Author

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  • Maria Iannario
  • Marica Manisera
  • Domenico Piccolo
  • Paola Zuccolotto

Abstract

In analyzing data from attitude surveys, it is common to consider the “don’t know†responses as missing values. In this article, we present a statistical model commonly used for the analysis of responses/evaluations expressed on Likert scales and extended to take into account the presence of don’t know responses. The main objective is to offer an alternative to the usual custom to treat them as missing values by considering them as a source of uncertainty. The original proposal in this article is the introduction of the relevant covariates in order to discriminate subpopulations that can show different behaviors in choosing between a substantive response and the don’t know option.

Suggested Citation

  • Maria Iannario & Marica Manisera & Domenico Piccolo & Paola Zuccolotto, 2020. "Ordinal Data Models for No-Opinion Responses in Attitude Survey," Sociological Methods & Research, , vol. 49(1), pages 250-276, February.
    • Handle: RePEc:sae:somere:v:49:y:2020:i:1:p:250-276
    DOI: 10.1177/0049124118769081
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    References listed on IDEAS

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    1. Leonardo Grilli & Maria Iannario & Domenico Piccolo & Carla Rampichini, 2014. "Latent class CUB models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(1), pages 105-119, March.
    2. Maria Iannario & Marica Manisera & Domenico Piccolo & Paola Zuccolotto, 2012. "Sensory analysis in the food industry as a tool for marketing decisions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(4), pages 303-321, December.
    3. Maria Iannario, 2015. "Detecting latent components in ordinal data with overdispersion by means of a mixture distribution," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 977-987, May.
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    Cited by:

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    2. Manisera, Marica & Zuccolotto, Paola, 2022. "A mixture model for ordinal variables measured on semantic differential scales," Econometrics and Statistics, Elsevier, vol. 22(C), pages 98-123.
    3. Heng Xu & Nan Zhang, 2022. "From Contextualizing to Context Theorizing: Assessing Context Effects in Privacy Research," Management Science, INFORMS, vol. 68(10), pages 7383-7401, October.

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