Household use of information technology methodology

This release has ceased
Reference period
2016-17 financial year
Next release Ceased
First release

Explanatory notes


1 The statistics presented in this release were compiled from data collected in the 2016-17 Multipurpose Household Survey (MPHS). The MPHS is conducted each financial year throughout Australia from July to June as a supplement to the ABS' monthly Labour Force Survey (LFS) and is designed to provide annual statistics for a number of small, self-contained topics.

2 For all topics, general demographic information such as age, sex, labour force characteristics and education are also available.

3 This publication covers the HUIT topic and presents details about household and personal internet use providing statistics on:

  • households with internet access
  • characteristics of persons accessing the internet
  • reasons for accessing the internet
  • types of goods and services purchased over the internet
  • whether the internet was accessed for home based work
  • type and mean number of devices used to access the internet at home
  • cyber security indicators (new data item for 2016-17), and
  • child protection online indicators (new data item for 2016-17).

4 The HUIT topic has been included in MPHS every two years, however, this is the final iteration of the HUIT survey.

Scope and coverage

5 The scope of the LFS is restricted to persons aged 15 years and over and excludes the following:

  • members of the Australian permanent defence forces
  • certain diplomatic personnel of overseas governments, usually excluded from census and estimated resident population counts
  • overseas residents in Australia, and
  • members of non-Australian defence forces (and their dependants).

6 In addition, the 2016-17 MPHS excluded the following from scope:

  • people living in households in the Indigenous Community Strata (ICS)
  • people living in non-private dwellings (e.g. hotels, university residences, students at boarding schools, patients in hospitals, inmates of prisons and residents of other institutions (e.g. retirement homes, homes for persons with disabilities)), and
  • visitors to private dwellings.

7 In the LFS, rules are applied which aim to ensure each person in coverage is associated with only one dwelling, and hence has only one chance of selection in the survey. See the Labour Force, Australia (cat. no. 6202.0) for more details.

Data collection

8 The publication Labour Force, Australia (cat. no. 6202.0) contains information about survey design, sample redesign, scope, coverage and population benchmarks relevant to the monthly LFS, which also applies to supplementary surveys. It also contains definitions of demographic and labour force characteristics, and information about telephone interviewing relevant to both the monthly LFS and supplementary surveys.

9 ABS interviewers conducted personal interviews during the 2016-17 financial year for the monthly LFS. Each month, one-eighth of the dwellings in the LFS sample were rotated out of the survey. The dwellings that were rotated out of the survey were selected for the MPHS and 50% of these dwellings were selected to complete the HUIT topic.

10 A usual resident aged 15 years or over was selected at random (based on a computer algorithm) and asked the additional questions in a personal interview. If the randomly selected person was aged 15 to 17 years, permission was sought from a parent or guardian before conducting the interview. If permission was not given, the parent or guardian was asked the questions on behalf of the 15 to 17 year old (proxy interview).

11 Data were collected using Computer Assisted Interviewing (CAI), whereby responses were recorded directly onto an electronic questionnaire in a notebook computer, with interviews conducted either face-to-face or over the telephone. Most interviews were conducted over the telephone.

Sample size

12 The initial sample for the 2016-17 HUIT topic was 23,694 private dwellings, from which one person was randomly selected. Of the 19,632 private dwellings that remained in the survey after sample loss (for example, dwellings selected in the survey which had no residents in scope for the LFS, vacant or derelict dwellings and dwellings under construction), 14,035 private dwellings (72%) fully responded to the questions on the household use of information technology.

Weighting, benchmarking and estimation


13 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 which can be either a person or a household. The weight is a value which indicates how many population units are represented by the sample unit.

14 The first step in calculating weights for each unit was 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 (i.e. they represent 600 people).


15 The initial weights were then calibrated to align with independent estimates of the population of interest, referred to as 'benchmarks', in designated categories of age by sex by area of usual residence. 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 non-response.

16 For household estimates, the MPHS was benchmarked to independently calculated estimates of the total number of households in Australia. The MPHS estimates do not (and are not intended to) match estimates for the total Australian person/household populations obtained from other sources.

17 For person estimates, the survey was benchmarked to the Estimated Resident Population (ERP) in each state or territory at December 2016.


18 Survey estimates of counts of persons or households are obtained by summing the weights of persons or households with the characteristic of interest.


19 To minimise the risk of identifying individuals in aggregate statistics, a technique called perturbation is used to randomly adjust cell values. 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.

Reliability of estimates

20 All sample surveys are subject to error which can be broadly categorised as either sampling error or non-sampling error.

Sampling error

21 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. For more information refer to the Technical Note.

Non-sampling error

22 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.

Data comparability

23 Household data in the 2016-17 HUIT are generally comparable with previous surveys (where those household data items have been collected before).

24 Persons data are not directly comparable between HUIT iterations. The definition of an internet user and the reference period for reasons for accessing the internet has changed in different iterations of the survey.

Comparability of geographic areas

25 HUIT survey data for 'Capital City' and 'Balance of State' areas in the 2007-08, 2008-09, 2010-11 and 2012-13 publications were based on Area of Usual Residence boundaries contained in the Australian Statistical Geography Classification (ASGC). The Australian Standard Geographical Classification (ASGS), introduced in 2011, contained new boundaries for Greater Capital City Statistical Areas (GCCSA) and these were used for the first time in HUIT for 2014-15. The new definitions of Greater Capital City and Rest of State are not comparable with the ASGC boundaries. A suite of geographical correspondences are available to assist users make comparisons and maintain time series between the ASGC and the ASGS, see Australian Statistical Geography Standard (ASGS): Correspondences, July 2011(cat. no. 1270.0.55.006).

Comparability of international frameworks

26 There are established international frameworks and reporting models for the collection of HUIT statistics (e.g. the OECD model questionnaire of ICT access and use by households and individuals). Suggested question wording from these frameworks have been used as a starting point for HUIT questionnaire design and, where applicable, used in the HUIT survey.

Comparability of state and territory data

27 Due to the age structure of the populations of Australia's states and territories caution should be used when making comparisons. For example, the level of internet use may be a reflection of a younger age profile, rather than general levels of access to the internet.

Comparability with monthly LFS statistics

28 Due to differences in the scope and sample size of the MPHS and that of the LFS, the estimation procedure may lead to some small variations between labour force estimates from this survey and those from the LFS.

Future surveys

29 This is the final issue of HUIT.


30 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.

Technical note

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 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 sub samples of the obtained sample. The variability of estimates obtained from these sub samples 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:

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


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:

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

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:

\(Test \ statistic =\left(\frac{x-y}{S E(x-y)}\right)\)

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.


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