4235.0.55.001 - Microdata: Qualifications and Work, 2015 Quality Declaration 
ARCHIVED ISSUE Released at 11:30 AM (CANBERRA TIME) 27/06/2016   
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For general information relating to the TableBuilder or instructions on how to use features of the TableBuilder product, please refer to the User Manual: TableBuilder (cat. no. 1406.0.55.005).

More specific information applicable to the Qualifications and Work Survey TableBuilder, which should enable users to understand, interpret and tabulate the data, is outlined below.


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:

•perturbation of data
•table suppression

Perturbation Effects

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.

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 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


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 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 'Person weight' by default. This will give you estimates of the number of persons. To produce estimates of the number of qualifications you would have to add 'Qualification level weights' from the Qualification level to your table.

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 weights' must be used. To estimate the number of qualifications (e.g. the number of non–school qualifications completed in 2014 or later) the weight listed under 'Qualification level weights' must be used.

Qualification level data items are weighted according to the characteristics of the person who undertook the qualification, and therefore the weights for each qualification are the same as the weight for the person. For example, if a person in the sample has a weight of 600 and that person has completed three non–school qualifications then the person represents 600 people in the total population and 1,800 qualifications.


The Person Level contains a range of data items detailing the characteristics of the respondent including some education variables. The Qualification Level contains data items about each of the qualifications that a respondent has obtained. The file is hierarchical with each respondent record potentially having multiple qualification records.

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 'Level of most recent non-school qualification' produces a table showing the number of people in each region by the most recent qualification they have obtained.

Cross-tabulating data from the Qualification Level with other data items from the same level will produce data about qualifications when using the Qualification Weight. For example, cross-tabulating 'Level of non-school qualification' by 'Whether completed qualification' in Australia produces a table showing the number of qualifications completed in Australia. If a respondent has several qualifications, each of those qualifications is included in the table. If the same cross-tabulation of 'Level of non-school qualification' by 'Whether completed qualification in Australia' is generated but using the Person weight instead of the Qualification weight, produces a table showing the number of people who completed a non-school qualification in Australia.

When using the Person weight, a respondent with several qualifications may have some qualifications excluded from the table. This occurs because the same combinations of responses can only be counted once in a table when the Person weight is applied. To illustrate, a person has the following five qualifications (each qualification appears as a separate record on the file):

QualificationWhether completed qualification in Australia
1Bachelor degreeCompleted in Australia
2Certificate IIICompleted in Australia
3Bachelor degreeCompleted in Australia
4Post graduate degreeDid not complete in Australia
5Bachelor degreeDid not complete in Australia

In this example, only Qualifications 1, 2, 4 and 5 will be counted in the tabulation. Qualification 3 will be excluded because the qualification and whether it has been completed in Australia is the same as Qualification 1. All the other combinations of qualifications and 'Whether completed qualification in Australia' are unique.

The need for data where the same combinations of responses are only counted once is likely to be limited so as a general rule, Qualification weight should be selected from the 'Summation Options' for cross-tabulations where all variables are from the Qualification Level.

Using Qualification Flags

To assist with analysis, several variables have been created to help isolate specific qualifications. The following shows the available Qualification Flags:

Image: Details of Qualification Flags

By using a Qualification Flag, only one qualification for each respondent is included in a table. Selecting either the Person weight or the Qualification weight when using a Qualification Flag will produce essentially the same result, any difference being the result of perturbation acting slightly differently when using the different weights.

Cross-tabulating Person Level X Qualification Level data items

Cross-tabulating data items from the Person Level with data items from the Qualification Level can produce data about people or qualifications depending on the weight being used. Caution should be used when Cross-tabulating a Qualification Level data item while using a Person weight as a person with multiple qualifications may have the same qualifications counted only once in a table (for more detail see above: Cross-tabulating Qualification Level X Qualification Level data items).

Using a Qualification Flag may be worthwhile when cross-tabulating Person Level with Qualification Level data items as only one selected qualification will be included in the tabulation.

Cross–tabulating qualification level data items by person level data items using the person weight – When using a qualification flag

When using a Qualification Flag (e.g. 'Most recent qualification') in a table that cross–tabulates a qualification level data item by a person level data item, either the Person or the Qualification weight can be used and the same output will be generated, with any difference being due to perturbation (see Perturbation Effects above). Restricting the table to a single qualification for each person in effect turns this into a person level data item, as TableBuilder only needs to read one row of data from the qualification level for each person.

Cross–tabulating qualification level data items by person level data items using the person weight – When NOT using a qualification flag

When a Qualification Flag is not used, TableBuilder will read each row of data from the qualification level for each person. In this case, TableBuilder effectively calculates the tabulation as a 'multi–response' table (i.e. the same person can be counted more than once), but it counts the same categories of information about different qualifications only once. It treats them as 'one or more occurrences' of that category. For example if a respondent completed three qualifications, and the respondent is currently working in the field as one of these qualifications but not the other two then the person would be counted in each column of the data item 'Whether currently working in job in the same field as main field of study'.

Therefore, in these particular types of tabulations, components of the table will not add to the total number of persons (as persons can be counted more than once), but the total will be the correct count of persons as TableBuilder calculates the total in such a way that each person is only counted once. An example table is shown below for Whether currently working in job as same field as main field of study, which shows results for all qualifications for a person so they can appear in both columns:

Image: Example table which shows results for all qualifications for a person so they can appear in both columns

The table below shows the same data item but for the highest qualification only so people only appear once, only in either column and consequently columns add to totals (taking perturbation into account, see Perturbation Effects above).

Image:Example table showing highest qualification only so people only appear once

In summary, qualification level data items can be cross–tabulated with person level data items with or without Qualification Flags. Qualification Flags should be included in tables when a user wants information only about one particular qualification (e.g. the highest qualification or the most recent qualification), but should not be used in tables looking at all qualifications.


The TableBuilder dataset has random adjustment of cell values applied to avoid the release of identifiable data. All cells in a table are adjusted to prevent any identifiable data being exposed. For this dataset 'additivity' has not been applied, that is, when the interior cells are randomly adjusted they have not been set to add up to the totals. 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.


Tables generated from sample surveys will sometimes contain cells with zero values because no respondents that satisfied the parameters of the cell were in the survey. This is despite there being people in the population with those characteristics. That is, the cell may have had a value above zero if all persons in scope of the survey had been enumerated. This is an example of sampling variability which occurs with all sample surveys. Relative Standard Errors cannot be generated for zero cells.


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 of their sources of personal income.

Image:Example of multi–response data item

When a multi–response data item is tabulated, a person is counted against each response they have provided (e.g. a person who responds 'employee income' and 'unincorporated income' and 'government pensions and allowances' 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.


Most data items include a 'Not applicable' category. The 'Not applicable' category comprises those respondents who were not asked a particular question(s) and hence are not applicable to the population to which the data item refers. The classification value of the 'Not applicable' category, where relevant, is shown in the data item list (see the Data Item List in the Downloads tab).


The population relevant to each data item is shown in the data item list and should be considered when extracting and analysing the microdata. The actual population count for each data item is equal to the total cumulative frequency minus the 'Not applicable' category.