Microdata and TableBuilder: Disability, Ageing and Carers, Australia

The Survey of Disability, Ageing and Carers (SDAC) is collected every 3 to 4 years and provides information on:

• people with disability
• people aged 65 years and over
• primary carers of people with disability. 

Key topics include: 

• long-term health conditions
• need for and receipt of assistance for persons with disability or aged 65 years and over
• the use of aids to help manage a person's disability
• experiences of violence, abuse and neglect of people with disability aged 18 years and over and all people aged 65 years and over
• accessibility issues and discrimination experienced by people with disability
• primary carers' need for and access to support in their caring role
• for primary carers, the impact of their caring role on their health, wellbeing and employment
• access to health services and satisfaction with level of social participation for all target populations.

This information can be cross classified by selected demographic and socioeconomic characteristics. 

Data is collected from people living in:

• private dwellings such as houses, flats, apartments, townhouses and self-care components of retirement villages, and
• cared-accommodation such as hospitals, nursing homes and hostels.

This product provides information about the microdata releases for the 2022 and 2018 SDAC collections, including details about the data files and how to use the different microdata products. Data Item Lists, information about the survey methodology and links to microdata for SDAC releases prior to 2018 are also provided.

Most data in the 2022 SDAC are considered to be comparable with the 2018 SDAC and previous Surveys of Disability, Ageing and Carers. However, comparisons should be made with caution. 

The 2022 and 2018 SDAC Data Item Lists for Detailed Microdata and TableBuilder provide more information about comparability of specific data items. 

For more detail about the 2022 SDAC, see 2022 Methodology information. For more detail about the 2018 SDAC, see 2018 Methodology information.

The following microdata products are available from this survey:

• Detailed microdata - approved users can access DataLab for in-depth and interactive data analysis using a range of statistical software packages. This product is available for SDAC cycles 2015 to 2022.
• TableBuilder - an online tool for creating tables and graphs. This product is available for SDAC cycles 2003 to 2022.
• Basic microdata – approved users can download and analyse unit record data in their own environment. This product is available for SDAC cycles from 1993 to 2018. This product is not available for the 2022 SDAC.

To apply for access, see Microdata Entry Page.

Before you apply for access, read Responsible Use of ABS Microdata, User Guide.

Datasets from the Survey of Disability, Ageing and Carers are hierarchical in nature. A hierarchical data file is an efficient means of storing and retrieving information which describes one to many, or many to many, relationships. For example, a person may need assistance with multiple activities and have multiple assistance providers for each of those activities.

The following table shows the levels available in the microdata products and the information contained on those levels:

The following table shows the hierarchical file structure and the relationship between each level:

For SDAC 2022, data are available from 43,026 people. This includes: 

• 33,764 people from households
• 9,262 people from cared-accommodation.

The Person level contains the item 'Living Arrangements' (POPESTAB) which indicates whether the data were collected in the household or cared-accommodation component.

For SDAC 2018, data are available from 65,805 people: 

• 54,142 from households
• 11,663 from cared-accommodation.

For SDAC, there are two weight variables on the file:

• Household Weight (FINWTH) - Household level – Benchmarked
• Person Weight (FINWTP) - Person level - Benchmarked to the total population for the household component and to the estimated number of people living in long-term cared-accommodation for the cared-accommodation component.

The SDAC is a sample survey, so to produce estimates for the in-scope population you must use weight fields in your calculations. When analysing a Household, Family, or Income Unit level item, you will need to use the household weight. When analysing a Person or Sub-Person level item, you will need to use the person weight.

Caution should be used when applying the ‘Household’ weight to items from other levels. For example, if the household weight is applied to a person level demographic item, such as ‘Sex’, your table will show the number of households with one or more persons of that sex.

Data items for 2022 SDAC include:

• demographics including age, sex at birth, gender and sexual orientation, country of birth, main language spoken and marital status
• household details including household composition, tenure type, landlord type, number of bedrooms and household income
• socio-economic characteristics of people including labour force status, educational attainment, personal income, geography and SEIFA
• long-term health conditions
• need for and receipt of assistance for persons with disability or aged 65 years and over
• the use of aids to help manage a person's disability
• experiences of violence, abuse and neglect of people with disability aged 18 years and over and all people aged 65 years and over
• accessibility issues and discrimination experienced by people with disability
• primary carers' need for and access to support in their caring role
• for primary carers, the impact of their caring role on their health, wellbeing and employment
• access to health services and satisfaction with level of social participation for all target populations.

The Data Item Lists section is the definitive source of available data items and categories.

Data items may differ between 2022 and 2018 iterations. For information, see the Summary of content changes.

Every record on each level of the file is uniquely identified. See Data Item Lists for details on which ID equates to which level.

Each household has a unique random identifier, ABSHID. This identifier appears on the Household level and is repeated on each level on each record pertaining to that household. A combination of identifiers for a particular level and all levels above in the hierarchical structure uniquely identifies a record at a particular level. For example, each record on the conditions level is uniquely identified by a combination of the Household, Person, and Conditions level identifiers.

The Household record identifier, ABSHID, assists with linking people from the same household, and with household characteristics such as household composition, to the Person records. When merging data with a level above, only those identifiers relevant to the level above are required.

Several questions in the survey allowed respondents to provide one or more responses. Each response category for these multi-response data items is treated as a separate data item. In the detailed microdata, these data items share the same identifier (SAS name) prefix but are each separately suffixed with a letter - A for the first response, B for the second response, C for the third response and so on.

For example, the multi-response data item 'Disability type(s)’ has nineteen response categories. There are nineteen data items named RESTIMPA, RESTIMPB, RESTIMPC.... RESTIMPS. Each data item in the series will have either a positive response code or a null response code. The data item list identifies all multi-response items and lists the corresponding codes with the corresponding response categories.

Note that the sum of individual multi-response categories will be greater than the population applicable to a particular data item as respondents can select more than one response.

Some continuous data items are allocated special codes for certain responses (e.g. 9999 = 'Not applicable'). When creating ranges for such continuous items for use in the TableBuilder, these special codes will NOT be included in these ranges. Any special codes for continuous (summation) data items are listed in the Data Item List (DIL) and will be found in the categorical version of the continuous item. 

As the survey was conducted on a sample of private households in Australia, it is important to take account of the method of sample selection when deriving estimates from the detailed microdata. This is important because a person's chance of selection in the survey varied depending on the state or territory in which the person lived. This also applies to the cared-accommodation component, where a person’s chance of selection varied based on the state or territory in which the cared-accommodation is located and the number of long-term cared occupants living in the establishment. If these chances of selection are not accounted for by use of appropriate weights, the results could be biased.

For the household component, each person or household record has a main weight (FINWTP or FINWTH). For the cared-accommodation component, each occupant has a main person weight (FINTWP). This weight indicates how many population units are represented by the sample unit. When producing estimates of sub-populations from the detailed microdata, it is essential that they are calculated by adding the weights of persons or households in each category and not just by counting the sample number in each category. If each person or household’s weight were to be ignored when analysing the data to draw inferences about the population, then no account would be taken of a person or household's chance of selection or of different response rates across population groups, with the result that the estimates produced could be biased. The application of weights ensures that estimates will conform to an independently estimated distribution of the population by age, by sex, etc. rather than to the distributions within the sample itself.

It is also important to calculate a measure of sampling error for each estimate.  Sampling error occurs because only part of the population is surveyed to represent the whole population.  Sampling error should be considered when interpreting estimates as this gives an indication of accuracy and reflects the importance that can be placed on interpretations using the estimate. Measures of sampling error include standard error (SE), relative standard error (RSE) and margin of errors (MoE).  These measures of sampling error can be estimated using the replicate weights. The replicate weight variables provided on the microdata are labelled WPMXX (person) and WHMXX (household), where XX represents the number of the given replicate group. The exact number of replicates will vary depending on the survey but will generally be 30, 60 or 200 replicate groups. As an example, for survey microdata with 60 replicate groups, you will find 60 person replicate weight variables labelled WPM01 to WPM60.

Overview of replication methods

ABS household surveys employ complex sample designs and weighting which require special methods for estimating the variance of survey statistics.  Variance estimators for a simple random sample are not appropriate for this survey microdata.

A class of techniques called 'replication methods' provide a general process for estimating variance for the types of complex sample designs and weighting procedures employed in ABS household surveys. The ABS uses a method called the Group Jackknife Replication Method. 

A basic idea behind the replication approach is to split the sample into G replicate groups. One replicate group is then dropped from the file and a new set of weights is produced for the remaining sample. This is repeated for all G replicate groups to provide G sets of replicate weights. For each set of replicate weights, the statistic of interest is recalculated and the variance of the full sample statistic is estimated using the variability among the replicate statistics.

The statistics calculated from these replicates are called replicate estimates. Replicate weights provided on the microdata file enable variance of survey statistics, such as means and medians, to be calculated relatively simply (Further technical explanation can be found in Section 4 of Research Paper: Weighting and Standard Error Estimation for ABS Household Surveys (Methodology Advisory Committee).

How to use replicate weights

To calculate the standard error of any statistic derived from the survey data, the method is as follows:

1. Calculate the estimate of the statistic of interest using the main weight.
2. Repeat the calculation above for each replicate weight, substituting the replicate weight for the main weight and creating G replicate estimates.  In the example where there are 60 replicate weights, you will have 60 replicate estimates. 
3. Use the outputs from step 1 and 2 as inputs to the formula below to calculate the estimate of the Standard Error (SE) for the statistic of interest.

[Equation 2]

[Equation 3]

To calculate the RSE you divide the SE by the estimate of y ($500) and multiply by 100 to get a %

To calculate the margin of error you multiply the SE by 1.96

See Disability, Ageing and Carers, Australia: Summary of Findings, 2022 and Disability, Ageing and Carers, Australia: Summary of Findings methodology  for further information about the 2022 Survey of Disability, Ageing and Carers collection.

See Disability, Ageing and Carers, Australia: Summary of Findings and Disability, Ageing and Carers, Australia: Summary of Findings methodology for further information about the 2018 Survey of Disability, Ageing and Carers collection.