Using SEIFA for Research and Data Analysis

The indexes may be useful for modelling or explaining behaviour in other variables. In some studies it is desirable to determine if socio-economic factors are associated with a variable of interest. The researcher may also be interested in reducing the number of variables in the analysis. In such cases, one or more of the indexes can be used as a summary of a range of socio-economic variables.

Example:
A health researcher may be investigating the relationship between fertility rates and the relative socio-economic standing in different areas across Australia. One way to do this is to use the fertility rate for each Statistical Local Area (SLA) across Australia (available from the ABS 'Births' publication [Cat. 3301.0]) and compare it with the SEIFA index scores for SLAs.

In this example, the Index of Relative Socio-Economic Advantage/Disadvantage (IRSAD) scores for each SLA have been plotted against the fertility rate, to see if the fertility rate is lower in advantaged areas. The result is shown in the graph below.




The graph shows that on average more highly advantaged areas have lower fertility rates than more relatively disadvantaged areas. This relationship seems more pronounced from 2011 SLA level IRSD scores of 900 upwards. To further understand the relationship, a researcher may wish to look more closely at the disadvantaged SLAs with scores below 900 and determine if there are any particular characteristics that could further explain the fertility rates. For example, SLAs could be divided into those with index values above 900 and those with index values below 900 and separate graphs plotted, or a regression analysis done, to understand more fully relationships between socio-economic advantage/disadvantage and the fertility rate.



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