2085.0 - Microdata: Australian Census Longitudinal Dataset with Social Security and Related Information, experimental statistics, 2006-2011 Quality Declaration 
Latest ISSUE Released at 11:30 AM (CANBERRA TIME) 23/08/2017  First Issue
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The TableBuilder, User Guide (cat. no. 1406.0.55.005) is a comprehensive reference guide for the web interface of TableBuilder. It includes information on Getting started, Customised data and Interpreting results from ABS data.


Weighting is the process of adjusting results from a sample to infer results for the total population. To do this, a weight is allocated to each person. The weight is the value that indicates how many population units are represented by the sample unit.

Both the sample and weighted count options have been made available for the ACLD-SSRI. It is therefore critical that weighted or unweighted counts are selected as appropriate when specifying tables. The following image shows the available Summation Options.

The default option used for the ACLD-SSRI is weighted count. Weights should be used when making inferences about the longitudinal Australian population and will be the basis for most analyses. Uses for unweighted counts are generally limited to research into unlinked records and more sophisticated analysis for those seeking to understand the weighting methodology better or wishing to apply their own weighting methods.


While weighted counts are available in the ACLD-SSRI TableBuilder, the Relative Standard Error will not be calculated for these counts due to the confounding effects of linking error present in the sample, which were not able to be quantified.


In accordance with the Census and Statistics Act 1905, all the data in TableBuilder are subjected to a confidentiality process before release. This confidentiality process is undertaken to avoid releasing information that may allow the identification of particular individuals, families, households, dwellings or businesses.

Processes used in the ACLD-SSRI in TableBuilder to confidentialise records include the following:

    • perturbation of data
    • table suppression

Perturbation of data

To minimise the risk of identifying individuals in aggregate statistics, a technique is used to randomly adjust cell values. This technique is called perturbation. Perturbation involves small random adjustments of the statistics and is considered the most satisfactory technique for avoiding the release of identifiable statistics while maximising the range of information that can be released. These adjustments have a negligible impact on the underlying pattern of the statistics.

The introduction of these random adjustments results in tables not adding up. Randomly adjusted individual cells will be consistent across tables, but the totals in any table will not be the sum of the individual cell values. The size of the difference between summed cells and the relevant total will generally be very small, as demonstrated below.

(Sum of cells = 390,467 + 410,293 = 800,760. Difference of 1 relative to displayed total.)

Table suppression

Some tables generated within TableBuilder may contain a substantial proportion of very low counts within cells (excluding cells that have counts of zero). When this occurs, all values within the table are suppressed in order to preserve confidentiality. The following error message displayed at the bottom of the table indicates when table suppression has occurred.

'ERROR: The table has been suppressed as it is too sparse'.