4530.0.55.002 - Microdata: Crime Victimisation, Australia, 2010-11 Quality Declaration 
ARCHIVED ISSUE Released at 11:30 AM (CANBERRA TIME) 28/05/2013  First Issue
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For general information relating to TableBuilder or instructions on how to use features of the TableBuilder product, please refer to the User Manual: TableBuilder, 2013 (cat. no. 1406.0.55.005).

More specific information relevant to the Crime Victimisation TableBuilder, which should enable users to understand and interpret the data, is outlined below.


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 person. The weight is the value that indicates how many population units are represented by the sample unit.

Estimates of persons, households and incidents can be obtained when using the Crime Victimisation TableBuilder. It is therefore critical that the correct weight (or 'summation option') is used when specifying tables. The following image shows the available Summation Options.

Picture: screen shot of weight available on the file.

Generally, the 'Socio-demographic and Personal Crime Level' relates to people so a person weight is attached in the 'Summation Options'. The 'Household Crime Level' relates to households so a household weight is used. The 'Social Disorder Level' also relates to people but has a separately labelled person weight in the 'Summation Options' because of the way the file has been structured (the person weight found in 'Socio-demographic and Personal Crime Level' and 'Social Disorder' are the same).

The default weight when producing any table using the Crime Victimisation TableBuilder is the person weight (in bold in the image above). This weight is automatically applied to any table being generated. A weight shown in bold, as in the image above, indicates the weight being used in the table. Weights can be changed through 'Summation Options'. Placing a tick in a 'Sum' tick box and then adding it to a row or column in the table will select a different weight.

While the default is person weight, if it is not shown in bold, it must be specifically selected through 'Summation Options'. To estimate the number of persons with certain characteristics (e.g. 'Number of assaults experienced by a person in the last 12 months') the weight listed under the category heading 'Socio-demographic and Personal Crime Level' must be used. To specifically select a person weight through Summation Options:
1. Click on the blue triangle 'twistie' () next to the 'Summation Options' line
2. Ensure all 'Sum' tick boxes are blank
3. Click on the 'Socio-demographic and Personal Crime Level' 'twistie'
4. Click on the 'Person Weight' 'twistie'
5. Click on the 'Sum' tick box
6. Add the person weights to your table by clicking on 'add to row' or 'add to column'

To estimate the number of households with certain characteristics (e.g. 'Number of break-ins experienced by households in the last 12 months') the weight listed under the category heading 'Household Crime Level' must be used. The same process as above can be followed, ticking the 'Sum' tick box under the Household Crime Level 'twistie' instead. Similarly, for Social Disorder, the directions above should be followed but ticking the 'Sum' tick box under the Social Disorder Level 'twistie'.

The following table summarises the weights recommended for use with each of the levels:

LevelSummation option weightsUnit of measure
Socio-demographic and Personal Crime LevelNumber of Socio-demographic and Personal crime data itemsPerson
Household Crime LevelHousehold Crime Level Data ItemsHousehold
Social Disorder LevelSocial Disorder Level Data ItemsPerson
Household Crime Level x Socio-demographic and Personal Crime Level (except Demographics, Education, Labour Force, Personal Income, Crime Person Level and Fraud Person Level)Household Crime Level Data ItemsHousehold
Social Disorder Level x Socio-demographic and Personal Crime LevelSocial Disorder Level Data ItemsPerson
Household Crime Level x Social Disorder Level Do not cross-tabulate
Household Crime Level x Crime Person Level and Fraud Person Level)Do not cross-tabulate


Apart from the Field Exclusion Rules that are applied in TableBuilder, there are minimal restrictions on the items that can be selected to appear in a table. That is, generally, users are able to cross-tabulate any variable with any other variable on the file. However, often the resulting table is not logical. For example, 'Whether household experienced a break-in in the last 12 months' by 'Place of social disorder incidents' cross-tabulates two different types of crimes which have no meaningful connection.

The following table summarises when variables should and should not be cross-tabulated.

Socio-demographic and Personal Crime
Data Items
Household Crime Level Data Items
Social Disorder Data Items
Socio-demographic and Personal Crime Data Items
Exception: Index of relative socio-economic index disadvantage - Deciles
Household Demographics
Labour Force
Income (see rows below)
Household Income
Personal Income
Crime Person Level
Fraud Person Level
Household Crime Level Data Items
See column 3
Social Disorder Data Items
See column 4


To ensure confidentiality, TableBuilder prevents the cross-tabulation of certain variables which could result in respondents being identified. These are know as field exclusion rules. If field exclusion rules exist for certain variables, users will see the following message: “Maximum number of fields in exclusion group exceeded.”


Crime victim surveys are best suited to measuring crimes against individuals or households with specific victims. Victims need to be aware of and recall what happened to them and how it happened, as well as be willing to relate what they know to interviewers.

Not all types of crime are suitable for measurement by household surveys. No reliable information can be obtained about crimes without specific victims, such as trafficking in narcotics. Crimes of which the victim may not be aware cannot be measured effectively - some crimes involving deception and attempted crimes of many types may fall into this category. It may also be difficult to obtain information about some crimes such as sexual offences and assault committed by other household members due to the sensitivity of the crime and an increased reluctance to disclose. Some of these crimes may not be fully reflected in the data collected. Household survey data excludes crimes against commercial establishments or government agencies.

This survey covered only selected types of personal and household crimes. Personal crimes covered in the survey were physical assault, threatened assault, robbery and sexual assault. Household crimes covered were break-in, attempted break-in, motor vehicle theft, theft from a motor vehicle, malicious property damage and other theft. The survey also covered people's perceptions about social disorder or unruliness in their local area.

For this survey the definition of total victims is restricted to those crimes included in the survey and does not represent all crime in Australia. Information collected in this survey is essentially 'as reported' by respondents and hence may differ from that which might be obtained from other sources or via other methodologies. This factor should be considered when interpreting the estimates and when making comparisons with other data sources.


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 satisfy 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. Whilst the tables may include cells with zero values, the ABS does not publish such zero estimates in Crime Victimisation, Australia, 2010–11 (cat. no. 4530.0) and recommends that TableBuilder clients do not use these data either.


Data was collected using Computer Assisted Interviewing (CAI), whereby responses were recorded directly onto an electronic questionnaire in a notebook computer, usually during a telephone interview. A copy of the household survey questions used to collect the Crime Victimisation data will be sent to TableBuilder users when access to the STB file is given. Response categories in the data collection instrument represent input categories and are not always available as separate output categories on the TableBuilder file as categories are collapsed for output to improve data quality and maintain confidentiality. However, it is important that users consider precise question wording, question order and sequencing and the range of input categories to assist them to interpret the data.


    A number of the survey's data items allow respondents to provide more than one response. These are referred to as 'multi–response data items'. An example of such a data item is shown below. For this data item respondents can report all types of scams to which they have been exposed in the last 12 months.

    When a multiple response data item is tabulated, a person is counted against each response they have provided (e.g. a person who was exposed to an invitation into pyramid selling schemes and also received a request to send bank or financial details to another person will be counted one time in each of these two 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 multiple response data item will be less than or equal to the sum of its components. Multi–response data items can be identified by the initials 'MR' in the data item list, which can be accessed from the Downloads page. In the example below, the sum of the components is 27,798,500 whereas the total population is 17,740,000.

    Picture: an example table that shows types of scams exposed to in last 12 months by number of persons


    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. In the example above, 11,368,700 people had not been a victim of a scam in the 12 months before being interviewed. 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).