|Page tools: Print Page Print All|
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:
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:
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).
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.
ADJUSTMENT OF CELL VALUES
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.
ZERO VALUE CELLS
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.
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 of their sources of personal income.
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.
NOT APPLICABLE CATEGORIES
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.
These documents will be presented in a new window.