Validation of the indexes
Once the indexes are calculated, they are checked to ensure that they are measuring the desired concept and that the results generally make sense. This validation is important to establish the credibility of the indexes and identify any issues that may have been missed in the construction of the indexes. The methods used to validate SEIFA 2021 include:
- comparison of SEIFA 2021 rankings with 2016 rankings
- identification of the drivers of change from SEIFA 2016 to 2021
- seeking review from internal experts.
Relationships between the indexes
We examined SEIFA for internal consistency by looking at the correlations between the indexes. The table below shows the rank correlation matrix. All correlations are in the expected directions and show significant relationships. The IRSD is very highly correlated with the IRSAD (0.94).
| Dimension | IRSD | IRSAD | IER | IEO |
|---|---|---|---|---|
| IRSD | 1.00 | |||
| IRSAD | 0.94 | 1.00 | ||
| IER | 0.79 | 0.68 | 1.00 | |
| IEO | 0.79 | 0.93 | 0.45 | 1.00 |
The indexes that measure specific dimensions of advantage and disadvantage (IER and the IEO) have a lower correlation with the other indexes with the exception of IEO and IRSAD. The IER includes variables associated with high and low wealth that are not included in the other indexes. The IEO focuses solely on educational qualifications, employment and vocational skills.
The IER and the IEO are positively correlated, but the correlation is much weaker than between the other indexes (0.45). There is a significant difference between the concepts measured by these two indexes, and they do not share any common variables.
Comparing 2016 and 2021 rankings
The SA1 scores from 2021 were checked against comparable areas from 2016, where possible, to identify areas with large changes and determine whether these changes were plausible. Some changes are to be expected, particularly in areas with high population growth and areas that have been affected by economic changes in the region. This process did not identify any results that seemed unrealistic.
Validation of higher-level area indexes
Most of the validation was focused on the SA1 level indexes because SA1s are the primary unit of analysis and indexes for higher level areas (e.g. SA2) are population weighted averages of the SA1 scores. However, we conducted basic validation checks on any higher level area indexes that we produced. This process did not identify any results that seemed unrealistic.