Family characteristics and transitions methodology

Latest release
Reference period
2012-13 financial year
Released
26/02/2015
Next release Unknown
First release

Explanatory notes

Introduction

1 The statistics in this publication were compiled from data collected in the Multi-Purpose Household Survey (MPHS), which was conducted throughout Australia in the 2012-13 financial year as a supplement to the Monthly Population Survey (MPS). The MPHS is designed to provide statistics annually for labour, social and economic topics.

2 The topics collected in the 2012-13 MPHS, in addition to household and person socio-demographic characteristics, were:

  • Family Characteristics
  • Family Transitions and History
  • Barriers and Incentives to Labour Force Participation
  • Retirement and Retirement Intentions (including Method of Meeting Current Living Costs)
  • Household Use of Information Technology
  • Patient Experience
  • Crime Victimisation
  • Income (Personal, Partner's, Household).
     

3 For all topics, information on labour force characteristics, education, income and other demographics are also available.

4 Data from both Family topics are presented in this publication. Data for other MPHS topics collected in 2012-13 have been released in separate publications.

5 The Family Characteristics topic has been collected before, in 1982, 1992, 1997, 2003, 2006-07 and 2009-10. The Family Transitions topic was collected in 2006-07.

6 The publication Labour Force, Australia (cat. no. 6202.0) contains information about survey and sample design, scope, coverage and population benchmarks relevant to the MPS, and consequently the MPHS. This publication contains definitions of demographic and labour force characteristics, and information about telephone interviewing.

Scope

7 The scope of the 2012-13 Family Characteristics and Transitions Survey (FCTS) included all usual residents in private dwellings, except:

  • diplomatic personnel of overseas governments, and their dependants, excluded from censuses and surveys of Australian residents
  • members of non-Australian defence forces stationed in Australia, and their dependants
  • persons living in non-private dwellings such as hotels, university residences, students at boarding schools, patients in hospitals, residents of homes (e.g. retirement homes, homes for persons with disabilities, women's shelters), and inmates of prisons.
     

8 The survey was conducted in urban, rural, remote and very remote areas in all states and territories. People living in Indigenous Community Frame (ICF) Collection Districts (CDs) were excluded.

Coverage

9 Coverage rules are applied to ensure that each person is associated with only one dwelling and hence has only one chance of selection in the survey. For example, a child with a natural parent living elsewhere is associated with the dwelling in which they usually reside. See Labour Force, Australia (cat. no. 6202.0) for more details.

Data collection

10 ABS interviewers conducted personal interviews by either telephone or at selected dwellings, from July 2012 to June 2013. Each month a sample of dwellings were selected for the MPHS from the responding households in the last rotation group for the MPS. In these dwellings, after the MPS had been fully completed for each person, a usual resident aged 15 years and over was selected at random and asked the additional MPHS questions in a personal interview. Information was collected using Computer Assisted Interviewing (CAI), whereby responses are recorded directly onto an electronic questionnaire in a notebook computer.

11 The Family Characteristics topic collected information from the randomly selected person about the household and about every person in the household, including all children in the household. The Family Transitions and History topic questions were only asked about the randomly selected persons aged 18 years and over, with some sub-topics having additional age restrictions. Therefore, the sample for Family Characteristics is much larger than for Family Transitions and History. There were 36,700 person records for the Family Characteristics topic, and 14,600 person records for the Family Transitions and History topic.

12 Where the randomly selected respondent was aged 15-17 years, and a parent/guardian or other responsible adult aged 18 years and over was resident in the household, permission was sought from the parent or other adult to interview the young person. Regardless of whether permission was granted, details for Family Characteristics and household income (excluding the income of the selected person) were collected from the parent or other adult.

13 The survey collected information about parent-child relationships beyond the usual residence of the child. The survey collected information about resident children aged 0-17 years in the household who had a natural parent living in another household. In addition, the survey identified whether respondents were parents who had natural children aged 0-17 years living elsewhere with the child's other natural parent.

Sample size

14 After taking into account sample loss, the response rate for the Family Characteristics and Transitions survey was 77%. In total, information was collected from 15,104 fully responding households.

Weighting, benchmarking and estimation

Weighting

15 Weighting is the process of adjusting results from a sample survey to infer results for the total in-scope population. To do this, a 'weight' is allocated to each covered sample unit (i.e. a person, a family or a household). The weight is a value which indicates how many population units are represented by the sample unit.

16 The first step in calculating weights for each 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 600, then the person would have an initial weight of 600 (that is, they represent 600 people).

Benchmarking

17 The initial weights were calibrated to align with independent estimates of the population of interest, referred to as 'benchmarks' in designated categories of sex by age by state or territory and part of state or territory. Weights calibrated against population benchmarks ensure that the survey estimates conform to the independently estimated distribution of the population rather than to the distribution within the sample itself. Calibration to population benchmarks helps to compensate for over or under-enumeration of particular groups of persons which may occur due to either the random nature of sampling or non-response.

18 The 2012-13 Family Characteristics and Transitions data were benchmarked to the Estimated Resident Population (ERP) in each state and territory, excluding ICF CDs, at 31st March 2013. The ERP estimates were based on results from the 2006 Census of Population and Housing. Therefore the estimates from this survey do not (and are not intended to) match estimates for the total Australian resident population (which include persons and households living in non-private dwellings, such as hotels and boarding houses) from other ABS sources.

19 The survey estimates conform to person benchmarks by State, part-of-State, age and sex, and to household benchmarks by State, part-of-State and household composition (number of adults and children usually resident in the household). These benchmark variables are the same as those used in the 1997, 2003, 2006-07 and 2009-10 Family Characteristics surveys. The only change has been in relation to age groups for which some collapsing was required for each collection. The impact of this change on estimates not involving age is minimal.

Estimation

20 Survey estimates (e.g. counts of persons, families or households) are obtained by summing the relevant weight (for persons, families or households) with the characteristic of interest.

Confidentiality

21 To minimise the risk of identifying individuals in aggregate statistics, a technique is used to randomly adjust cell values. This technique is called perturbation. Perturbation involves small random adjustment of the statistics and is considered the most satisfactory technique for avoiding the release of identifiable statistics while maximising the range of information that can be released. These adjustments have a negligible impact on the underlying pattern of the statistics. After perturbation, a given published cell value will be consistent across all tables. However, adding up cell values to derive a total will not necessarily give the same result as published totals. The introduction of perturbation in publications ensures that these statistics are consistent with statistics released via services such as TableBuilder.

22 Perturbation has only been applied to 2012–13 data. Data from previous cycles (2006-07 and 2009–10) have not been perturbed.

Reliability of estimates

23 All sample surveys are subject to error which can be broadly categorised as either:

  • sampling error
  • non-sampling error.
     

24 Inaccuracies that arise from selecting a sample rather than conducting a population census are known as sampling errors. Non-sampling error may occur in any collection and include non-response, errors in reporting by respondents or recording of answers by interviewers and errors in coding and processing the data. Every effort is made to reduce the non–sampling error by careful design and testing of the questions, training of interviewers, follow-up of respondents and extensive editing and quality control procedures at all stages of data processing.

25 Sampling and non-sampling error can impact on the reliability of the estimates and are explained further in the Technical Note.

Data comparability with other ABS sources

26 There are reasons why results from the FCTS survey differ from other ABS surveys and Census data. The FCTS is a sample survey and its results are subject to sampling error. Users should take account of the relative standard errors (RSEs) on FCTS estimates and those on other survey estimates where comparisons are made.

27 Differences in FCTS estimates, when compared with the estimates of other surveys, may also result from:

28 Differences in FCTS estimates, when compared with Census data, may also result from:

  • differences in the benchmark base - the FCTS data were benchmarked to the Estimated Resident Population (ERP) as at March 2013, based on results from the 2006 Census. The ABS introduced new methodology after the 2011 Census which enabled a more accurate measure of net undercount, which contributed towards a considerably larger intercensal error for the rebased ERP. See Feature Article 4: Advice on the use of 2011 Preliminary Rebased ERP in Australian Demographic Statistics, Mar 2012 (cat. no. 3101.0) for more details.
  • differences in scope and/or coverage
  • different reference periods.
     
Family Characteristics and Transitions. Australia, 2012-13 ('000)2011 Census ('000)
All Persons22,81921,508
Children 0-17 years5,1834,990
All Families6,7055,684
Couple families5,6914,685
One parent families909902

 

 

Family Characteristics and Transitions. Australia, 2012-13 ('000)Labour Force, Australia: Labour Force Status and Other Characteristics of Families, June 2012 ('000)
Children 0-14 years in families where no-one is employed473528.9
Dependent student aged 15-24 years in families where no-one is employed90109.5

 

 

Historical comparisons

29 Family Surveys were conducted by the ABS in 1982 and 1992, and the Family Characteristics Survey (FCS) was previously conducted in 1997, 2003, 2006-07 and 2009-10. The Family Surveys, and to a lesser extent the 1997 FCS, differed from the 2003, 2006-07 and 2009-10 FCS in some areas. Nevertheless, these differences do not preclude useful comparisons between them for certain data items. Some data from the 2006-07 and 2009-10 surveys have been included in this publication to show changes over time.

30 Changes listed below were made to the content of the FCS between 2006-07, 2009-10 and 2012-13. These changes should be noted when making comparisons over time.

  • Child Support information was collected in 2009-10, but not in 2006-07 or 2012-13.
  • Family Transitions information was collected in 2006-07 and 2012-13, but not in 2009-10.
  • Data on indirect contact (via telephone, letters or email) between children and parents living elsewhere was collected in 2009-10, but not in 2006-07 or 2012-13.
  • Data on direct and indirect contact between children and grandparents was collected in 2009-10, but not in 2006-07 or 2012-13.
  • In 2006-07, 2009-10 and 2012-13 data was collected about whether persons had natural children aged 0 to 17 years living elsewhere with the other natural parent. However, in 2009-10 further information was collected about these children, including the number of children, frequency of visitation, indirect contact (via telephone, letters or email), and direct and indirect contact with grandparents.
  • The ABS recommends that due to changes in the data processing methodology, the following data items are not comparable with equivalent items in the 2006-07 cycle:
    • number of natural children ever born
    • number of male children ever born
    • number of female children ever born
    • age of parent when first natural child born.
       

Family coding practices

31 Data items such as 'family composition' in household surveys are based on initial information gathered about the members of the household and their relationships to each other. Family coding is the process of allocating household members to families, where appropriate, based on their spousal, parent-child, and other familial relationships to other members of the household. All children aged 0-14 years are assigned a parent or nominal parent, for example, a grandchild living with only his/her grandparents will have the grandparents allocated as nominal parents.

32 The family topics in the 2012-13 FCTS and 2009-10 FCS are designed to capture more accurate information about the composition of families than that collected in other ABS surveys. In 2006-07 and 2009-10, as was the case in 2003, a number of populations and data items have been modified to more accurately classify persons and families where there was a parent/guardian and child/ward relationship. Prior to the 2003 FCS, children aged 15-17 years whose relationship fell outside the standard parent-child classifications (e.g. grandchildren living with grandparents, children living with other related or unrelated adults in a guardian-ward relationship) were classified as 'other related individuals' or 'unrelated individuals'.

33 For example, in the 1997 FCS, a 15-17 year old child living with his or her grandparents would have resulted in the grandparents being coded to 'couple family without children' and the child would be an 'other related individual'. For the 2003, 2006-07, 2009-10 and 2012-13 surveys, the family classification allows for inclusion of people with this relationship in the same family. For the example outlined above, the family would be classified as a 'couple family with children'. See Family, Household and Income Unit Variables (cat. no. 1286.0) for further information on family classifications.

Acknowledgement

34 ABS surveys draw extensively on information provided 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.

Products and services

Publication data cubes

35 Data cubes of all tables related to this publication in Excel spreadsheet format can be found on the ABS website (from the Data downloads section of this publication). The spreadsheets present tables of estimates, proportions and the corresponding relative standard errors (RSEs).

Microdata record file

36 In addition to the data available in the Excel spreadsheets, other tables will be able to be produced using TableBuilder. TableBuilder is an online tool for creating tables and graphs from survey data. TableBuilder for the 2012–13 Family Characteristics topic is expected to be available in the first half of 2015. General information about this product, including cost, can be found on the About TableBuilder page.

37 A Confidentialised Unit Record File for the 2012–13 Family Characteristics and Transitions Survey will not be available.

Data available on request

38 A range of additional data not provided in the standard spreadsheets may be able to be provided on a fee-for-service basis. For further information, contact the National Information and Referral Service on 1300 135 070, or email client.services@abs.gov.au. The ABS Privacy Policy outlines how the ABS will handle any personal information that you provide to us.

Data item list

39 A full list of the data items available from the Family Characteristics and Transitions Survey is also available from the ABS website (see the Data downloads section for cat. no. 4442.0).

Technical note - data quality

Reliability of the estimates

1 The estimates in this publication are based on information obtained from a sample survey. Any data collection may encounter factors, known as non-sampling error, which can impact on the reliability of the resulting statistics. In addition, the reliability of estimates based on sample surveys are also subject to sampling variability. That is, the estimates may differ from those that would have been produced had all persons in the population been included in the survey.

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 Differences that arise from selecting a sample rather than conducting a population census are known as sampling errors. One measure of the likely difference 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 persons 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.

\(R S E \%=\left(\frac{S E}{estimate}\right) \times 100\)

5 RSEs for count 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 count 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.

Calculation of standard error

8 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

9 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:

\(R S E\left(\frac{x}{y}\right) \approx \sqrt{[R S E(x)]^{2}-[R S E(y)]^{2}}\)

Differences

10 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:

\(S E(x-y) \approx \sqrt{[S E(x)]^{2}+[S E(y)]^{2}}\)

11 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

12 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 standard error of the difference between two corresponding estimates (x and y) can be calculated using the formula shown above in the Differences section. This standard error is then used to calculate the following test statistic:

\(\large{\frac{|x-y|}{SE(x-y)}}\)

13 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

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Quality declaration - summary

Institutional environment

Relevance

Timeliness

Accuracy

Coherence

Interpretability

Accessibility

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