https://ogma.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:32246 Wed 04 Sep 2019 12:18:04 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:32245 Wed 04 Sep 2019 12:18:03 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:34232 Wed 04 Sep 2019 12:17:21 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:22977 Sat 24 Mar 2018 07:11:38 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:22979 aggregate association index (or the AAI), developed by Beh (2008 and 2010) which enumerates the overall extent of association about individuals that may exist at the aggregate level when individual level data is not available. The applicability of the technique is demonstrated by using leukaemia relapse data of Cave et al. (1998). This data is presented in the form of a contingency table that cross-classifies the follow up status of leukaemia relapse by whether cancer traces were found (or not) on the basis of polymerase child reaction (PCR) – a modern method used to detect cancerous cells in the body assumed superior than conventional for that period, microscopic identification. Assuming that the joint cell frequencies of this table are not available, and that the only available information is contained in the aggregate data, we first quantify the extent of association that exists between both variables by calculating the AAI. This index shows that the likelihood of association is high. As the AAI has been developed by exploiting Pearson’s chi-squared statistics, the AAI inherently suffers from the well-known large sample size effect that can overshadow the true nature of the association shown in the aggregate data of a given table. However, in this paper we show that the impact of sample size can be isolated by generating a pseudo population of 2x2 tables under the given sample size. Therefore, the focus of this paper is to present an approach to help answer the question “is this high AAI value statistically significant or not?” by using aggregate data only. The answer to this question lies we believe, in the calculation of the p-value of the nominated index. We shall present a new method of numerically quantifying the p-value of the AAI thereby gaining new insights into the statistical significance of the association between two dichotomous variables when only aggregate level information is available. The pseudo p-value approach suggested in this paper enhances the applicability of the AAI and thus can be considered a valuable addition to the literature of aggregate data analysis.]]> Sat 24 Mar 2018 07:11:37 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:32415 Mon 23 Sep 2019 14:00:18 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:32388 Mon 23 Sep 2019 13:21:58 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:32449 Mon 23 Sep 2019 13:08:19 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:32456 Mon 23 Sep 2019 12:46:33 AEST ]]>