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Clustering of Stratified Aggregated Data Using the Aggregate Association Index: Analysis of New Zealand Voter Turnout (1893–1919)

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Analysis and Modeling of Complex Data in Behavioral and Social Sciences

Abstract

Recently, the Aggregate Association Index (AAI) was proposed to identify the likely association structure between two dichotomous variables of a 2×2 contingency table when only aggregate, or equivalently the marginal, data are available. In this paper we shall explore the utility of the AAI and its features for analysing gendered New Zealand voting data in 11 national elections held between 1893 and 1919. We shall demonstrate that, by using these features, one can identify clusters of homogeneous electorates that share similar voting behaviour between the male and female voters. We shall also use these features to compare the association between gender and voting behaviour across each of the 11 elections.

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Author information

Authors and Affiliations

  1. School of Mathematical and Physical Sciences, University of Newcastle, Callaghan, 2308, NSW, Australia

    Eric J. Beh, Duy Tran & Irene L. Hudson

  2. Statistics New Zealand, Wellington, New Zealand

    Linda Moore

Authors
  1. Eric J. Beh
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  2. Duy Tran
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  3. Irene L. Hudson
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  4. Linda Moore
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Corresponding author

Correspondence to Eric J. Beh .

Editor information

Editors and Affiliations

  1. Department of Statistical Science, University of Rome "La Sapienza", Rome, Italy

    Donatella Vicari

  2. and Information Sciences, Tama University Graduate School of Management, Tokyo, Japan

    Akinori Okada

  3. Department of Political Science, University of Naples "Federico II", Naples, Italy

    Giancarlo Ragozini

  4. Fakultät Statistik, Technische Universität Dortmund, Dortmund, Germany

    Claus Weihs

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Beh, E.J., Tran, D., Hudson, I.L., Moore, L. (2014). Clustering of Stratified Aggregated Data Using the Aggregate Association Index: Analysis of New Zealand Voter Turnout (1893–1919). In: Vicari, D., Okada, A., Ragozini, G., Weihs, C. (eds) Analysis and Modeling of Complex Data in Behavioral and Social Sciences. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-06692-9_3

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