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Sample design
To better match donors to recipient records, both sets of records were ordered according to characteristics (such as number of adults and children present) associated with the blocks of variables being imputed. Recipients with missing information were matched with donors who fell into the same classes as themselves. Edits were applied before and after imputation took place, to ensure that errors were not introduced through the addition of donor information. FINAL SAMPLE The sample on which estimates were based, or the final HES sample, is composed of households for which all necessary information is available. The information may have been wholly provided at the interview or may have been completed through imputation for partially responding households. The 1998-99 HES final sample included approximately 600 households which had at least one imputed value. Over 40% of these households had only a single value missing. 2 HES FINAL SAMPLE: NUMBER OF HOUSEHOLDS, 1998-99
WEIGHTING Expansion factors, or weights, are values by which information for sample households is multiplied to produce estimates for the whole population. Initial weights, based on the sample design, are equal to the inverse of the probability of selection. Weights for each member of the household are the same as the weight for the household itself. In previous surveys, these initial weights have been adjusted to account for non-response. For the 1998-99 HES the demographic and geographic information available for non-respondents was analysed to determine whether a strong relationship existed between household non-response and its demographic and geographic characteristics. No strong relationship was detected so no adjustment to the initial weights to account for non-response was required. BENCHMARKING To adjust for underenumeration and to align survey estimates with independent population estimates, the weights were calibrated against person and household benchmarks. Using an iterative procedure, the weights were adjusted so that person and household estimates conformed with external person and household benchmarks. The two person benchmarks which were used in 1998-99 were: state/territory population estimates by eight age categories; and labour force status estimates (from Labour Force Survey data) by capital city/balance of state or territory by sex by five age categories. The two household benchmarks were: nine categories of household composition by capital city/balance of state or territory; and state by capital city/balance of state or territory. See the section on comparability between the 1998-99 HES and the 1993-94 HES in chapter 5 for further details of benchmarks used. The household benchmarks were based on provisional estimates of numbers of households in Australia. The benchmarks were adjusted to include households and persons residing in private dwellings only and therefore do not, and are not intended to, match estimates of the total Australian resident population published in other ABS publications. The benchmarks do not include people living in sparsely settled areas in the Northern Territory. ESTIMATION Estimates produced from the survey are usually in the form of averages (e.g. average weekly household expenditure on clothing and footwear), or counts (e.g. total number of households who own their dwelling). For counts, the estimate is obtained by summing the weights of the responding households in the required group (e.g. those households owning their dwelling). Averages are obtained by adding the weighted household values, and then dividing by the estimated number of households. For example, average weekly expenditure on clothing and footwear by Victorian households is the weighted sum of the average weekly expenditure of each selected household in Victoria who reported such expenditure, divided by the estimated number of households in Victoria. Note that the denominator is the total number of households and not just the number of households which have reported expenditure on the particular item. RELIABILITY OF ESTIMATES The estimates provided in this publication are subject to two types of error. Non-sampling error Non-sampling error can occur whether the estimates are derived from a sample or from a complete collection. Three major sources of non-sampling error are:
Non-sampling errors are difficult to measure in any collection. However, every effort is made to minimise these errors. In particular, the effect of the reporting and processing errors described above is minimised by careful questionnaire design, intensive training and supervision of interviewers, asking respondents to refer to records whenever possible and by extensive editing and quality control checking at all stages of data collection and processing. The error due to non-response is minimised by:
Sampling error The HES estimates are based on a sample of possible observations. Hence, they are subject to sampling variability and estimates may differ from the figures that would have been produced if information had been collected for all households. Further information on sampling error is given in appendix 1.
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