Australian Bureau of Statistics
4402.0.55.001 - Microdata: Childhood Education and Care, Australia , June 2011
Previous ISSUE Released at 11:30 AM (CANBERRA TIME) 25/10/2012
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The survey was conducted in both urban and rural areas in all states and territories, but excluded persons living in very remote parts of Australia who would otherwise have been within the scope of the survey. The exclusion of these persons will have only a minor impact on any aggregate estimates that are produced for individual states and territories, except in the Northern Territory where such persons account for around 23% of the population.
The estimates in this publication relate to persons covered by the survey in June 2011. In the LFS, coverage rules are applied that aim to ensure that each person is associated with only one dwelling, and hence has only one chance of selection. The chance of a person being enumerated at two separate dwellings in the one survey is considered to be negligible.
Persons who are away from their usual residence for six weeks or less at the time of the interview are enumerated at their usual residence (relevant information may be obtained from other usual residents present at the time of the survey). The LFS is described more fully in Labour Force, Australia (cat. no. 6202.0).
DATA COLLECTION METHODOLOGY
Information was collected between 5 and 18 June 2011, through interviews conducted with parents of children aged 0-12 years. Interviews were conducted either face-to-face or over the telephone, using computer assisted interviewing (CAI). Information about usual child care arrangements and usual preschool attendance is affected by the specific timing of data collection, the age of the child at that time, and state policies on age eligibility for enrolment at school and preschool which affect the likelihood of a child being enrolled at the time of the survey.
For interviews conducted between 5 and 11 June 2011, the reference week was 29 May to 4 June. For interviews conducted between 12 and 18 June, the reference week was 5 to 11 June, with the exception of those in Tasmania where the reference week was 29 May to 4 June to avoid Tasmanian school holidays.
In each selected household, detailed information about child care arrangements and early childhood education was collected for a maximum of two children aged 0-12 years. Information was obtained from an adult who permanently resided in the selected household and was either the child's parent or guardian. In households with more than two children aged 0-12 years, two children were randomly selected from within the same family and the complete set of information was collected for these children. Summary information was collected for other children in the family including the number attending child care and/or preschool and the cost of this care (including any rebates such as the Child Care Benefit). In households with multiple families information was collected for children from only one family.
This sampling methodology is similar to that used in 2005 and 2008. However, in 2005, in selected households with more than two children aged 0–12 years, two children were randomly selected from across all families in the household i.e. children could have been selected from two different families within a multi-family household.
More survey-specific information, including changes in the survey since 2008, the CCB and Child Care Rebate (CCR) or any other issues, can be found in the Explanatory Notes of Childhood Education and Care, Australia, June 2011 (cat. no. 4402.0).
WEIGHTING, BENCHMARKING AND ESTIMATION
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 sample unit which for CEaCS can be either person or a household. The weight is a value which indicates how many population units are represented by the sample unit.
The first step in calculating weights for each sample unit is to assign an initial weight, which is the inverse of the probability of being selected in the survey. For example, if the probability of a person being selected in the survey was 1 in 300, then the person would have an initial weight of 300 (that is, they represent 300 people).
The initial weights were calibrated to align with independent estimates of the population of interest, referred to as 'benchmarks'. Weights calibrated against population benchmarks ensure that the survey estimates conform to the independently estimated distribution of the population rather than the distribution within the sample itself. Calibration to population benchmarks helps to compensate for over or under-enumeration of particular categories of persons/households which may occur due to either the random nature of sampling or survey non-response.
For person estimates, CEaCS was benchmarked to the Estimated Resident Population (ERP) as at 30 June 2011 in each state and territory, excluding the ERP living in very remote areas of Australia. For household estimates, CEaCS was benchmarked to independently calculate estimates of the total number of households in Australia with children aged under 13 years. CEaCS estimates do not (and are not intended to) match estimates for the total Australian person/household population obtained from other sources (which may include persons living in very remote parts of Australia).
Survey estimates of counts of persons or households are obtained by summing the relevant weight for persons or households with the characteristic of interest.
RELIABILITY OF ESTIMATES
All sample surveys are subject to error which can be broadly categorised as either sampling error or non-sampling error.
Sampling error occurs because only a small proportion of the total population is used to produce estimates that represent the whole population. Sampling error can be reliably measured as it is calculated based on the scientific methods used to design surveys. Non-sampling error can occur at any stage throughout the survey process. For example, persons selected for the survey may not respond (non-response); survey questions may not be clearly understood by the respondent; responses may be incorrectly recorded by interviewers; or there may be errors when coding or processing the survey data.
One measure of the likely difference between an estimate derived from a sample of persons and the value that would have been produced if all persons in scope of the survey had been included, is given by the Standard Error (SE) which indicates the extent to which an estimate might have varied by chance because only a sample of persons was included. There are about two chances in three (67%) that the sample estimate will differ by less than one SE from the number that would have been obtained if all persons had been surveyed and about 19 chances in 20 (95%) that the difference will be less than two SEs.
Another measure of the likely difference is the Relative Standard Error (RSE), which is obtained by expressing the SE as a percentage of the estimate. Generally, only estimates (numbers, percentages, means and medians) with RSEs less than 25% are considered sufficiently reliable for most purposes. In ABS publications, estimates with an RSEs between 25% and 50% have been included with a cell comment to indicate they are subject to high sample variability and should be used with caution. Estimates with RSEs greater than 50% are included with a cell comment to indicate that they are considered unreliable for general use. The formula for calculating the RSE of an estimate (y) is:
RSE(y) = SE(y)/y x 100%
where y = the estimate of interest
In addition to the main weight (as outlined earlier), each record on the CURF also contains 60 'replicate weights'. The purpose of these replicate weights is to enable the calculation of the standard error on each estimate produced. This method is known as the 60 group Jackknife variance estimator.
The basic concept behind this replication approach is to select different sub-samples repeatedly (60 times) from the whole sample. For each of these sub-samples the statistic of interest is calculated. The variance of the full sample statistics is then estimated using the variability among the replicate statistics calculated from these sub-samples. As well as enabling variances of estimates to be calculated relatively simply, replicate weights also enable unit record analyses such as chi-square and logistic regression to be conducted which take into account the sample design.
Further information about RSEs and how they are calculated can be referenced in the 'Technical Note' section of the following publication relevant to this microdata: Childhood Education and Care, Australia, June 2011 (cat. no. 4402.0). RSEs for estimates in the tables published in this publication are available in spreadsheet format, as attachments to this publication.
Non-sampling error may occur in any collection, whether it is based on a sample or a full count such as a census.
One of the main sources of non-sampling error is non-response by persons selected in the survey. Non-response occurs when persons cannot or will not cooperate, or cannot be contacted. Non-response can affect the reliability of results and can introduce a bias. The magnitude of any bias depends upon the rate of non-response and the extent of the difference between the characteristics of those persons who responded to the survey and those that did not.
Every effort was made to reduce non-response and other non-sampling errors by careful design and testing of the questionnaire, training and supervision of interviewers, and undertaking extensive editing and quality control procedures at all stages of data processing.
One advantage of the CAI technology used to conduct interviews is that it potentially reduces non-sampling error by enabling edits to be applied as the data are being collected. The interviewer is alerted immediately if information entered into the computer is either outside the permitted range for a particular question, or contradictory to information previously recorded during the interview. These edits allow the interviewer to query respondents and resolve issues during the interview. CAI sequencing of questions is also automated so that respondents are only asked relevant questions and in the appropriate order, thereby eliminating interviewer sequencing errors.
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This page last updated 3 July 2015