Australian Bureau of Statistics
4430.0.30.002 - Microdata: Disability, Ageing and Carers, Australia, 2009 Quality Declaration
Previous ISSUE Released at 11:30 AM (CANBERRA TIME) 09/03/2012 Reissue
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7. Specific activities
8. All recipients
9. Broad activities
Levels 5 to 9 relate to the characteristics of conditions, restrictions and activities, with each being a sub-level of level 4 (Person). That is, a person can have multiple conditions and restrictions, as well as require assistance with one or more activities. Level 10 is a sub-level of level 9 (Broad activities), as it relates to characteristics of the assistance provided for activities identified in level 9. An activity can be undertaken with the assistance of one or more providers.
There are ‘dummy’ or ‘Not Applicable’ records at each of the sub-person levels 5 to 9, which allow for those instances where a person does not contribute a record to a particular level. For example, a person with no conditions will not contribute a record to the 'All conditions' level. This allows data items on sub-person levels to be used for calculating the total of ‘all persons’.
Additionally, ‘Not Applicable’ records exist at the Assistance Providers level for those people who experience difficulty with a broad activity (i.e. a record exists on level 9), but do not have a provider of assistance for that activity (i.e. no record exists on level 10).
Households may comprise:
Families may comprise one or more income units.
Data items include:
An income unit is one person or a group of related persons within a household, whose command over income is assumed to be shared. Income sharing is assumed to take place within married (registered or de facto) couples, and between parents and dependent children.
Data items include:
This level contains all of the standard demographic characteristics of each person such as age, sex, country of birth, education and labour force status. The level also contains person characteristic data items relevant to the survey, such as conditions, restrictions and carer status.
Other data items include:
This level contains records for each health condition reported by respondents, including an indicator of which condition is the main condition reported.
Data items include:
A person has a restriction if he/she has difficulty participating in life situations, needs assistance from another person or uses an aid. This level may include a person multiple times depending on the number of restrictions (or disabilities) reported.
Data items include:
This level contains information about each of the specific activities for which assistance or supervision is needed or difficulty is experienced.
Data items include:
This is a repeating dataset where there is a separate record for each recipient/carer relationship. Individual's IDs could appear multiple times as a recipient and/or a carer as they may receive care from multiple people and/or may provide care to multiple people.
Data items include:
This level outlines the broad areas of activity where a person experiences difficulty, such as mobility and/or communication. This is a repeating dataset where person records may appear a number of times but once for each broad activity reported.
Data items include:
This level outputs information about the persons or organisations that provide assistance to respondents who require assistance or supervision with a broad area of activity. It looks at what formal services provide assistance to the respondent (nurse, doctor, lawyer, etc.) or who provides them with informal assistance (parent, sibling, partner, etc.). This is a repeating dataset where broad activities and person records may appear a number of times but once for each provider.
Data items include:
RECORD COUNTS - CURF
RECORD COUNTS - STB
The 'one to many' relationships described by levels 5 to 10 are known as repeating datasets i.e. sets of data with a counting unit which may be repeated for a person. For example, a repeating dataset for conditions will have one record per condition reported because condition is the counting unit. Repeating datasets are only useful when common information is collected for each instance of a counting unit. For example, each condition reported has the data item 'Whether reported condition is main condition' associated with it. This data item corresponds to each condition reported. Note that only one of the conditions reported for a particular person has a 1 (Yes) for 'Whether reported condition is main condition'. This enables a table to be run on 'All conditions' by 'Whether reported condition is the main condition' to ascertain which conditions cause the most problems.
Note that although the output above only relates to a single person, the totals are a count of all conditions for that person. As with the person level file, some data items in a repeating dataset are only applicable to a particular sub-population of the dataset. For instance, the item 'Whether assistance is always or sometimes required with each activity' from the specific activities level is only applicable for activities where the respondent needs assistance. Records outside the sub-population will appear as 'Not applicable'.
Counting units and weights
The counting unit for level one is the household, for level two the family, for level three the income unit, for level four the person, for level five all conditions, for level six all restrictions, for level seven all specific activities, for level eight recipients of care, for level nine all broad activities and for level ten all assistance providers. There is a weight attached to each level, to estimate the total population of the respective counting unit. The weight on levels one to three is the household weight and the weight on levels four to ten is the person weight.
What you count depends on the level from which you select the weight. A household level weight estimates the number of households with a particular characteristic. Likewise, the weight included in the family level estimates the number of families, and the weight included in the income unit level estimates the number of income units, with the selected characteristic. Only private dwellings are included at the household, family and income unit levels.
A person weight stored on the person level, or below, provides an estimate of the number of persons with the selected characteristic. When the weights from levels five to ten are used, the population is restricted to persons who have a record on the particular level but will be repeated for each instance of the counting unit. Replicate weights have also been included and these can be used to calculate the standard error. For more information, refer to the 'Standard Errors' section below.
Combining carer and recipient data
Combining carer and recipient data can sometimes be confusing, both in selecting an appropriate item and in understanding the counting unit. For example, if your interest is in people with a disability, and you want to analyse them by their main condition, such as dementia or arthritis, etc., using the item 'Main disabling condition of main care recipient' and the person weight from the person level identifies a population of primary carers whose main recipient co-resides with them and has dementia, or arthritis, or some other condition as a main condition. Using an item such as 'Disability status' with this item provides information on the disability status of persons who are primary carers of people with the specified condition. It is, therefore, important when using items relating to carers and care recipients to pay particular attention to the populations for the items used.Using disability populations
Persons were identified as having a disability if they had one or more of the following
limitations, restrictions or impairments which had lasted, or was likely to last, for a
period of six months or more and restricted everyday activities. This includes:
WEIGHTS AND ESTIMATION
As the survey was conducted on a sample of households in Australia, it is important to take account of the method of sample selection when deriving estimates. This is particularly important as a person's chance of selection in the survey varied depending on the state or territory in which they lived. Survey 'weights' are values which indicate how many population units are represented by the sample unit.
There are two weights provided: a person weight (PERS_WT) and a household weight (HHWT). These should be used when analysing data at the person and household level respectively. The household weight should also be used for the family level and income unit level and the person weight for all other levels.
Where estimates are derived, it is essential that they are calculated by adding the weights of persons or households, as appropriate, in each category, and not just by counting the number of records falling into each category. If each person's or household's 'weight' were to be ignored, then no account would be taken of a person's or household's chance of selection in the survey or of different response rates across population groups, with the result that counts produced could be seriously biased. The application of weights ensures that:
Each record on the household level and person level also contains 60 replicate weights and, by using these weights, it is possible to calculate standard errors for weighted estimates produced from the microdata. This method is known as the 60 group Jack-knife variance estimator.
Under the Jackknife method of replicate weighting, weights were derived as follows:
This process was repeated for each replicate group (i.e. a total of 60 times). Ultimately each record had 60 replicate weights attached to it with one of these being the zero weight.
Replicate weights enable variances of estimates to be calculated relatively simply. They also enable unit records analyses such as chi-square and logistic regression to be conducted which take into account the sample design. Replicate weights for any variable of interest can be calculated from the 60 replicate groups, giving 60 replicate estimates. The distribution of this set of replicate estimates, in conjunction with the full sample estimate (based on the general weight) is then used to approximate the variance of the full sample.
To obtain the standard error of a weighted estimate y, the same estimate is calculated using each of the 60 replicate weights. The variability between these replicate estimates (denoting y(g) for group number g) is used to measure the standard error of the original weighted estimate y using the formula:
g = the replicate group number
y(g) = the weighted estimate, having applied the weights for replicate group g
y = the weighted estimate from the sample.
The 60 group Jack-knife method can be applied not just to estimates of the population total, but also where the estimate y is a function of estimates of the population total, such as a proportion, difference or ratio. For more information on the 60 group Jack-knife method of SE estimation, see Research Paper: Weighting and Standard Error Estimation for ABS Household Surveys (Methodology Advisory Committee), July 1999 (cat. no. 1352.0.55.029).
Use of the 60 group Jack-knife method for complex estimates, such as regression parameters from a statistical model, is not straightforward and may not be appropriate. The method as described does not apply to investigations where survey weights are not used, such as in unweighted statistical modelling.
Most data items included in the microdata include a 'Not applicable' category. The classification value of the 'Not applicable' category, where relevant, are shown in the data item lists (see the Data Item List in the Downloads tab).
A number of questions included in the survey allowed respondents to provide one or more responses. Each response category for one of these 'multi-response questions' (or data items) is basically treated as a separate data item. These data items have the same general data item identifier (SASName) but are each suffixed with a letter – A for the first response, B for the second response, C for the third response, D for the fourth response and so on.
For example, the multi-response data item 'Purpose for computer use at home in the last 12 months' (with a general SASName of COMPRCM – see data item list), has six response categories. Consequently, six data items have been produced - COMPRCMA, COMPRCMB, COMPRCMC, COMPRCMD, COMPRCME and COMPRCMF.
Each data item in the series (i.e. COMPRCMA -- COMPRCMF) will have two response codes: A 'Yes' response (for the first in the series (code 1), for the second in the series (code 2) etc.) and a 'Null' response (code 0) indicating that the response was not relevant for the respondent. The last data item in the series will represent a 'Not Applicable' response (i.e. value of last character in series) which comprises the respondents not asked the questions (e.g. COMPRCMF with values of 0 or 6).
It should be noted that the sum of individual multi-response categories will be greater than the population or number of people applicable to the particular data item as respondents are able to select more than one response. Multi-response data items can be identified in the data item list as SASNames followed by a range of letters in brackets; for example, COMPRCM(A-F).
Disability is a difficult concept to measure because it depends on a respondent's perception of their ability to perform a range of activities associated with daily life. Factors discussed below should also be considered when interpreting the data.
Information in the survey was based, wherever possible, on the personal response given by the respondent. However, in cases where information was provided by another person, some answers may differ from those the selected person would have provided. In particular, interpretation of the concepts of 'need' and 'difficulty' may be affected by the proxy-interview method.
A number of people may not have reported certain conditions because of:
Excel spreadsheets which provide concordances between the ABS condition codes and the ICD–10 codes, and the ABS condition codes and the CURF codes can be found on the Downloads tab.
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This page last updated 7 July 2014