Labour Force Status of Families methodology

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Reference period
June 2019
Released
3/10/2019

Explanatory notes

Introduction

This publication, Labour Force Status and Other Characteristics of Families (cat. no. 6224.0.55.001), is produced from data collected in the Labour Force Survey (LFS) in June. It includes detailed family data not featured in the monthly Labour Force, Australia (cat. no. 6202.0) or Labour Force Australia, Detailed - Electronic Delivery (cat. no. 6291.0.55.001) publications.

Since these products are all based on data collected in the LFS, the explanatory notes of publication Labour Force, Australia (cat. no. 6202.0) are relevant to all three publications. Additional information is provided in Labour Statistics: Concepts, Sources and Methods (cat. no. 6102.0.55.001).

    Scope

    Family data was collected for persons who were usual residents of private dwellings and whose family relationships could be derived. Children under 15 are included in scope, and their characteristics are used in the classification of parent-child relationships and family type.

    Persons interviewed in the LFS who were classed as visitors to private dwellings, and those living in non-private dwellings (including hotels, motels, hospitals and other institutions) were excluded. After these exclusions are applied, the estimates in this publication for 2019 cover approximately 80% of the survey sample.

    From October 2008, the method of producing family estimates from the LFS was improved to include the following:

    • an expanded scope to include households containing permanent members of the defence forces;
    • an increased range of families in the LFS sample contributing to the family estimates; and
    • improvements to the weighting method by utilising independent population benchmarks (of persons and households), ensuring the estimates more closely reflect the Australian population.


    For more information, see the Information Paper: Improvements to Family Estimates from the Labour Force Survey, 2008 (cat. no. 6224.0.55.002).

    Data interpretability

    Some of the estimates contained in the tables have a relative standard error (RSE) of 50 per cent or greater. These estimates are marked as unreliable for general use. Estimates with an RSE of between 25 and 50 per cent are also marked and should be used with caution.

    The data used to compile families statistics can be based on complicated family relationships and this adds complexity around interpreting the aggregated estimates. The data in these tables are as reported by any responsible adult aged 15 years and over who were usual residents of private dwellings and were selected in the LFS.

    Benchmarking and estimation

    The estimates are calculated in such a way as to sum to independent counts of persons and households (benchmarks). These benchmarks are updated based on Estimated Resident Population (ERP) data. Generally, revisions are made to benchmarks following the final rebasing of population estimates to the latest five-yearly Census of Population and Housing.

    For all three years in this release, estimates have been compiled using benchmarks that have been rebased to the results of the 2016 Census. These benchmarks have been revised to include the ERP data as at June 2019. For more details on population benchmarks, see the Explanatory Notes in Labour Force, Australia (cat. no. 6202.0).

    Comparability with previous estimates

    Care should be taken when comparing the latest estimates from this issue of the publication against earlier estimates published in previous issues. Estimates from previous issues have not been recompiled using the latest population and household benchmarks.

    Products and services

    A number of spreadsheets are available from the Data downloads section of this publication. They present tables of estimates and their corresponding relative standard errors (RSEs).

    For users who wish to undertake more detailed analysis, the underlying microdata is available in ABS TableBuilder. For more details, refer to Microdata, Labour Force Status and Other Characteristics of Families, Australia (cat. no. 6224.0.00.001). For more information see also About TableBuilder.

    Special tabulations are available on request. Subject to confidentiality and sampling variability constraints, tabulations can be produced from the survey incorporating data items, populations and geographic area selections to meet individual requirements. These will be provided in electronic form. All enquiries should be made to the National Information and Referral Service on 1300 135 070.

    Acknowledgement

    ABS surveys draw extensively on information provided freely by individuals, businesses, governments and other organisations. Their continued cooperation is very much appreciated: without it, the wide range of statistics published by the ABS would not be available. Information received by the ABS is treated in strict confidence as required by the Census and Statistics Act, 1905.

    Related publications

    Technical note - data quality

    Reliability of the estimates

    1 Since the estimates in this publication are based on information obtained from occupants of a sample of households, they are subject to sampling variability. That is, they may differ from those estimates that would have been produced if all households had been included in the survey or a different sample was selected.

    Non-sampling error

    2 Non-sampling error may occur in any collection, whether it is based on a sample or a full count such as a census. Sources of non-sampling error include non-response, errors in reporting by respondents or recording of answers by interviewers and errors in coding and processing data. Every effort is made to reduce non-sampling error by careful design and testing of questionnaires, training and supervision of interviewers, and extensive editing and quality control procedures at all stages of data processing

    Sampling error

    3 Sampling error is the difference between the published estimates, derived from a sample of persons, and the value that would have been produced if the total population (as defined by the scope of the survey) had been included in the survey. One measure of the sampling error 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 a sample estimate will differ by less than one SE from the number that would have been obtained if all households had been surveyed, and about 19 chances in 20 (95%) that the difference will be less than two SEs.

    4 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.

    \(\large{RSE\%=(\frac{SE}{estimate})\times100}\)

    5 RSEs for Labour Force Status and Other Characteristics of Families estimates have been calculated using the Jackknife method of variance estimation. This involves the calculation of 30 'replicate' estimates based on 30 different subsamples of the obtained sample. The variability of estimates obtained from these subsamples is used to estimate the sample variability surrounding the main estimate.

    6 The Excel spreadsheets in the Data downloads section contain all the tables produced for this release and the calculated RSEs for each of the estimates.

    7 Only estimates (numbers or percentages) with RSEs less than 25% are considered sufficiently reliable for most analytical purposes. However, estimates with larger RSEs have been included. Estimates with an RSE in the range 25% to 50% should be used with caution while estimates with RSEs greater than 50% are considered too unreliable for general use. All cells in the Excel spreadsheets with RSEs greater than 25% contain a comment indicating the size of the RSE. These cells can be identified by a red indicator in the corner of the cell. The comment appears when the mouse pointer hovers over the cell.

    8 Another measure is the Margin of Error (MOE), which shows the largest possible difference that could be between the estimate due to sampling error and what would have been produced had all persons been included in the survey with a given level of confidence. It is useful for understanding and comparing the accuracy of proportion estimates.

    9 Where provided, MOEs for estimates are calculated at the 95% confidence level. At this level, there are 19 chances in 20 that the estimate will differ from the population value by less than the provided MOE. The 95% MOE is obtained by multiplying the SE by 1.96.

    \(\large{MOE=SE\times1.96}\)

    Calculation of standard error

    10 Standard errors can be calculated using the estimates (counts or percentages) and the corresponding RSEs. See What is a Standard Error and Relative Standard Error, Reliability of estimates for Labour Force data for more details.

    Proportions and percentages

    11 Proportions and percentages formed from the ratio of two estimates are also subject to sampling errors. The size of the error depends on the accuracy of both the numerator and the denominator. A formula to approximate the RSE of a proportion is given below. This formula is only valid when x is a subset of y:

    \(\large{RSE(\frac{x}{y})\approx\sqrt{[RSE(x)]^2-[RSE(y)]^2}}\)

    Differences

    12 The difference between two survey estimates (counts or percentages) can also be calculated from published estimates. Such an estimate is also subject to sampling error. The sampling error of the difference between two estimates depends on their SEs and the relationship (correlation) between them. An approximate SE of the difference between two estimates (x-y) may be calculated by the following formula:

    \(\large {SE(x-y)\approx\sqrt{[SE(x)]^2+[SE(y)]^2}}\)

    13 While this formula will only be exact for differences between separate and uncorrelated characteristics or sub populations, it provides a good approximation for the differences likely to be of interest in this publication.

    Significance testing

    14 A statistical significance test for a comparison between estimates can be performed to determine whether it is likely that there is a difference between the corresponding population characteristics. The SE of the difference between two corresponding estimates (x and y) can be calculated using the formula shown above in the Differences section. This SE is then used to calculate the following test statistic:

    \(\LARGE{(\frac{x-y}{SE(x-y)})}\)

    15 If the value of this test statistic is greater than 1.96 then there is evidence, with a 95% level of confidence, of a statistically significant difference in the two populations with respect to that characteristic. Otherwise, it cannot be stated with confidence that there is a real difference between the populations with respect to that characteristic.

    Glossary

    This publication, Labour Force Status and Other Characteristics of Families (cat. no. 6224.0.55.001) is produced from data collected in the June Labour Force Survey (LFS) for a particular year. It includes detailed family data not featured in the monthly Labour Force, Australia (cat. no. 6202.0) or Labour Force, Australia, Detailed - Electronic Delivery (cat. no. 6291.0.55.001) publications.

    Since these products are all based on data collected in the LFS, the Glossary of publication Labour Force, Australia (cat. no. 6202.0) and information is provided in Labour Statistics: Concepts, Sources and Methods (cat. no. 6102.0.55.001), may be of assistance. Further information is also available in the ABS Family, Household and Income Unit Variables Standard (cat. no. 1286.0)

    The following glossary items are provided as they relate specifically to family characteristics.

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    Institutional environment

    Relevance

    Timeliness

    Accuracy

    Coherence

    Interpretability

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