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This document was added 14/08/2015.
USING THE TABLEBUILDER
...and the Summation Options.
The following table shows the responses for 'Systolic Blood Pressure' by 'Sex of person' using the version in the Person folder. The continuous values of the data item are contained in the 'A valid response was recorded' row. If the actual continuous values are to be displayed then it is necessary to use the Summation Option version and create a range for them.
Here is the same table with a range applied for the continuous values of 'Systolic Blood Pressure (mmHG)' (called 'Systolic ranged'). Note that the numbers of respondents for the other responses 'Not applicable', 'Valid reading not obtained' and 'Not measured' no longer contribute to the table.
Any special codes for continuous data items are listed in the Data Item List.
Continuous items can be used to create custom categories in 'My Custom Data' by first ranging the item. For example, to create five year age groupings for 'Age first told had diabetes or high sugar levels' this can be done by ranging the item with a five year increments from Summation Options. However to deviate from groupings of equal increments, this must be done in 'My Custom Data'. As age is a continuous item, for deviations from equal increments, it must first be ranged (for example in one year increments) and then this ranged item can be grouped under the 'My Custom Data' tab to form unique age categories. For more information see the 'My Custom Data' section of the User Manual: TableBuilder, Jun 2013 (cat. no. 1406.0.55.005).
CONFIDENTIALITY FEATURES IN 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.
Processes used in TableBuilder to confidentialise records include the following:
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 called 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 Aboriginal and Torres Strait Islander Health Survey there is one field exclusion group. This consists of the 2011 geographical and Socio-Economic Indexes for Areas (SEIFA) data items (see below for items).
Only one data item from this group may be used in a single table.
The geographic exception to this is the State or Territory item, which can be used in addition to one item from this group.
Items included in the field exclusion group are:
2011 Geographic Items
2011 SEIFA Items
There are two weight variables visible on the TableBuilder file:
TableBuilder will apply 'Persons (Benchmarked weight)' by default.
The NATSIHS is a sample survey. To produce estimates for the in-scope population you must use weight fields in your tables. The weight fields are listed below. 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 use '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 'Household (Benchmarked weight)' from the Household level to your table.
The Household Weight is stored on the Household Level while the Person Weight is stored on the Person level. 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.
MEANS AND MEDIANS
Means, medians and sums of continuous data items are calculated at the level of the continuous data item. A weight is automatically applied from a weight allocated behind the scenes to the level of the variable. For all levels, other than Household and Persons in household level, 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.
Due to current functionality of the software, a weight from another level cannot be brought into such calculations. The "subject" of means, medians and sums 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 "Total volume of pure alcohol" data item at the Alcohol Day level would give the mean total volume of pure alcohol per alcohol day while the mean of the "Total volume of pure alcohol" data item at the Alcohol Type level would be the mean total volume of pure alcohol per alcohol type.
The weights used for these calculations on levels are not visible, other than the Household and Persons levels, but are referenced in the 'Weighted by' statement with continuous variables, as per:
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 LEVEL VERSUS PERSON LEVEL 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 (see the File Structure page in this product or Structure of the TableBuilder section above for further information on structure). 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). When summing the Person 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 and Marital status at the Person level will produce estimates of the type "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 and Marital status at the Persons in Household level, and using the Persons (Benchmarked weight) from the Person level, will produce estimates of the type "Number of persons 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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