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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. 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 (in red) 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
COUNTING UNITS AND WEIGHTS
Weighting is the process of adjusting results from a sample survey to infer results for the total population. To do this, a 'weight' is allocated to each record. The weight is the value that indicates how many population units are represented by each sample unit.
To produce estimates for the in-scope population you must use a weight field 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 'Person weight' by default. This will give you estimates of the number of persons.
If you are estimating the number of persons with certain characteristics (e.g. 'Number of non–school qualifications completed') the weight listed under the category heading 'Person level weighting' must be used.
When creating a table a default Summation Item will need to be the Reference year as this item will provide data for the relevant year. This item will then be used for time-series purposes as future data becomes available.
SELECTING DATA ITEMS FOR CROSS–TABULATION
The Person level contains a range of data items detailing the characteristics of the respondent including PJSM, demographic, education, labour force and population variables.
Populations and Data items
When adding a data item to a table, an associated population should also be used to ensure correct data is retrieved from TableBuilder. For example, the data item All reasons for looking for other work while still employed is only applicable to population group 11 Persons employed more than a year in their current job who looked for work in the previous 12 months (as per the image below), so when using this item in a table only that population should be used.
Similarly, if users want to add multiple data items to a table they should ensure that these data items are applicable to the same population group.
For more information about data items and applicable populations, users should refer to the PJSM TableBuilder Data Items List available from the downloads tab.
Cross-tabulating Data items on the same level
Cross-tabulating data from the Person Level with other data items from the same level will produce data about people. For example, cross-tabulating the geographic variable 'State or territory of usual residence' by the 'Hours usually worked in main job' produces a table showing the number of people in each region by the hours that they usually work each week in their main job.
MULTI–RESPONSE DATA ITEMS
A number of the survey's data items allow respondents to report more than one response. These are referred to as 'multi–response data items'. An example of such a data item is pictured below. For this data item respondents can report all the difficulties they had in finding work.
When a multi–response data item is tabulated, a person is counted against each response they have provided (e.g. a person who responds 'Studying or returning to studies' and 'Caring for ill or elderly person/family member' and 'Problems with access to transport' will be counted once in each of these three categories).
As a result, each person in the appropriate population is counted at least once, and some persons are counted multiple times. Therefore, the total for a multi–response data item will be less than or equal to the sum of its components.
For more information on definitions and concepts that apply to the data items in this file, please refer to Participation, Job Search and Mobility, Australia (cat. no. 6226.0) and Labour Force, Australia (cat. no. 6202.0).
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