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This document was added 04/10/2014.
USING THE TABLEBUILDER
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.
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 result in tables not adding up. While some datasets apply a technique call additivity to give internally consistent results, additivity has not been implemented on this TableBuilder. As a result, 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.
Please be aware that the effects of perturbing the data may result in components being larger than their totals. This includes determining proportions.
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 below is displayed at the bottom of the table when table suppression has occurred.
ERROR: The table has been suppressed as it is too sparse
ERROR: table cell values have been suppressed
Field exclusion rules
Certain groups of similar variables are restricted from being used together in a table. These restrictions are referred to as field exclusion rules, and are in place in order to protect confidentiality. The collections of similar variables restricted in this way are called field exclusion groups.
For the Australian Health Survey, there is one field exclusion group. This consists of the 2006 and 2011 geographical and Socio-Economic Indexes for Areas (SEIFA) data items (see below for items).
2011 Geographic Items
2006 SEIFA Items
2011 SEIFA Items
There are three weight variables visible on the file:
The NHS is a sample survey. To produce estimates for the in-scope population you must use weight fields in your tables. In TableBuilder they can be found under the Summation Options category in the left hand pane under the applicable level. If you do not select a weight field, TableBuilder will apply 'Persons (Benchmarked weight)' by default. This will give you estimates of the number of persons. To produce estimates of the number of households you would have to add Households (Benchmarked weight) from the Household level to your table. To produce estimates for NHS selected persons who participated in the National Health Measures Survey (NHMS), the Biomedical persons (Benchmarked weight) located on the Biomedical level must be used.
Note that when dealing with the functions of means, medians, and sums, a weight is automatically applied from a weight allocated behind the scenes to the level of the variable (for all levels, other than Persons in household, a person weight has been applied to each record (e.g. alcohol record) on the level based on the weight allocated to selected persons on the Person level. For Persons in household, the household weight has been applied to each person in the household). These weights are not visible but are referenced in the 'Weighted by' statement with continuous variables, as per:
For more details, see Means and Medians below.
When using a Weight/Summation from a level that is different to that of the variables in the table please be careful in interpreting the results.
Note that the Biomedical level also contains non-biomedical participant records, however their biomedical weight is set to 0 so they will not contribute to estimates when the Biomedical persons (Benchmarked weight) is used. However, if the Persons (Benchmarked weight) is used with biomedical data items then these non-participants will contribute to estimates. When using biomedical variables in conjunction with other variables on the biomedical level or with variables from other levels, the Biomedical persons (Benchmarked weight) should be used.
For example, a table of reported fasting status using the 'Persons (Benchmarked weight)' will show the fasting status for the entire NHS survey. Note that the 'Not applicable' persons include those people who did not participate in the NHMS and those persons aged 5 to 11 years who participated in the NHMS but were only required to provide a urine sample. The population for this table represents the entire Australian population.
The same table using the 'Biomedical persons (Benchmarked weight)' will show the fasting status for only persons who participated in the NHMS. Note that in this case, 'Not applicable' persons are those people aged 5 to 11 years who participated in the NHMS but were only required to provide a urine sample. People who did not participate in the biomedical component do not have a biomedical person weight and therefore do not contribute to the table when this weight is used. The biomedical population now presents weighted estimates for the Australian population aged 5 years and over.
MEANS AND MEDIANS
Means and medians of continuous data items are calculated at the level of the continuous data item and therefore use the weight at that level. Due to current functionality of the software, a weight from another level cannot be brought into such calculations. The "subject" of the means and medians calculated in TableBuilder is therefore the statistical unit associated with the level of the database on which the continuous data item is stored. For example, the mean of the "Mls of pure alcohol consumed by day" data item at the the Alcohol Day level would give the mean total volume of pure alcohol per day while the mean of "Mls of pure alcohol consumed by type of drink" data item at the Alcohol Type level would be the mean total mls of pure alcohol per alcohol type (or per alcohol type by day if desired).
ITEMS LOCATED ON MULTIPLE LEVELS
Where items are available on more than one level, an additional number is added to the label to indicate the level version. For example a (1) indicates it's a Household level version, a (2) indicates a Persons in household level version, a (3) indicates a Person level version, and so on. These are identified in the Data item list labelling as well as the item in TableBuilder. The numbering is based on the ordering of levels found in the File Structure page of this product.
Care should be used to ensure the correct version of the item is used, particularly with regards to demographic items located on both the Persons in household and Person levels. See below for more details.
PERSONS IN HOUSEHOLD VS PERSON VARIABLES
The Persons in Household level contains data for every person in the household while the Persons level only contains data for the selected persons. Both levels are children of the Household level - that is, they are siblings and are not linked by person but by household. This means that there is a many-to-many link between records at these levels (persons on the Person level are linked to all the people in their household on the Persons in household level). The Persons in Household level is available in order to produce compositional information about the household (e.g. Whether there are persons in the household aged 4-14 years) which can then either be used with the household weight to represent for example the the number of households which contain persons aged 4-14 years, or with the person weight to represent the number of people living in household that contain persons aged 4-14 years.
When summing the Persons (Benchmarked weight) (which is stored at the Person level) the meaning of the estimates produced when disaggregating by another data item at the Person level will not be the same as the meaning of the estimates produced when disaggregating by a data item at the Persons in Household level. For example, disaggregating by Sex of person (3) and Registered marital status (3) from the Person level will produce estimates of the "Number of persons who are Male and Married". These estimates will be additive (aside from the effects of perturbation) as shown below.
On the other hand, disaggregating by Sex of person (2) and Registered marital status (2) from the Persons in Household level, and using the Persons (Benchmarked weight) from the Person level, will produce estimates of the type "Number of persons living in households containing one or more persons who are Male and Married". These estimates will usually not be additive as shown below.
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