Abstract

The analysis of aggregate, or marginal, data for contingency tables is an increasingly important area of statistics, especially in political science and epidemiology. Aggregation often exists due to confidentiality issues or by source of the data itself. Aggregate data alone makes drawing conclusions about the true association between categorical variables difficult, especially in dealing with the aggregate analysis of single or stratified 2x2 contingency tables. These tables are the most fundamental of data structures when dealing with cross-classifying categorical variables hence it is not surprising that the analysis of this type of data has received an enormous amount of attention in the statistical, and related, literature. However, the information, from which the aggregate data can provide for the inference of association between the variables, is still a long standing issue. In order to analyse the association that exists between the variables of a 2x2 table, or stratified 2x2 tables, based only on the aggregate data, numerous approaches that lie within the area of Ecological Inference (EI) have been proposed. As an application of this new development, we shall analyse a unique record of New Zealand gendered election data from 1893 when it was the first selfgo verning country in the world allowing women to vote, this trend quickly spread across the globe

Highlights

  • In statistics, aggregate data has always been an interesting topic which attracts a lot of attention, especially for aggregate categorical data

  • Given only the aggregate data, or marginal information, of a 2x2 contingency table, this paper shows that the Association Index (AAI) can provide the same result for testing the statistical significant association between the two dichotomous categorical variables irrespective of the association measure considered

  • Moore (2005) confirmed that gender was a significant factor at New Zealand elections from 1893

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Summary

INTRODUCTION

Aggregate data has always been an interesting topic which attracts a lot of attention, especially for aggregate categorical data. Due to confidentiality and availability of the data, aggregate categorical data can become very difficult to analyse In contingency tables, it means that only the aggregate, or marginal, totals are available. An approach that does not require ensuring the integrity of the untestable EI assumptions given only aggregate data is the Aggregate Association Index (AAI). This technique was proposed by Beh (2008, 2010) and it is currently applied for the case of a single, or stratified 2x2 tables. The main purpose of this paper is to generalise the current AAI and explore its connection with one such classic association measure - the standardised residual. We shall demonstrate the properties of this generalised AAI, and the residual (as a special case), by analysing the early New Zealand voting data of 1893

New Zealand Voting Data
Notations
THE TRANSFORMATION OF p11 IN THE AAI
STANDARDISED RESIDUAL AND THE AAI
THE AAI AND THE NZ ELECTION DATA
Aggregate data analysis
Findings
DISCUSSION
Full Text
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