Household use of information technology methodology

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Reference period
2016-17 financial year
Released
28/03/2018
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Explanatory notes

Introduction

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

Weighting

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).

Benchmarking

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.

Estimation

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

Confidentiality

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.

Acknowledgement

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:

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

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:

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

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.

Glossary

Show all

Age

The age of a person on their last birthday.

Australian Standard Classification of Education (ASCED)

The ASCED is a national standard classification which includes all sectors of the Australian education system: that is, schools, vocational education and training, and higher education. The ASCED comprises two classifications: Level of education and Field of education. See Australian Standard Classification of Education (ASCED), 2001 (cat. no. 1272.0).

Australian Statistical Geography Standard (ASGS)

Effective from July 2011, the Australian Statistical Geography Standard (ASGS) developed by the ABS provides the framework for the collection and dissemination of statistics. See Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2016 (cat. no. 1270.0.55.001), Australian Statistical Geography Standard (ASGS): Volume 4 - Significant Urban Areas, Urban Centres and Localities, Section of State, July 2016 (cat. no. 1270.0.55.004) and Australian Statistical Geography Standard (ASGS): Volume 5 - Remoteness Structure, July 2016 (cat. no. 1270.0.55.005).

Country of birth

Country of birth is classified according to the Standard Australian Classification of Countries (SACC), 2016 (cat. no. 1269.0).

Cyberbullying

The use of technology to bully an individual or a group with the intent to cause harm. The intended harm may be social, psychological, or physical.

Cyber security

Cyber security comprises technologies, processes and controls that are designed to protect systems, networks and data from cyber attacks.

Employed

All persons aged 15 years and over who met one of the following criteria during the reference week:

  • worked for one hour or more for pay, profit, commission or payment in kind, in a job or business or on a farm (employees and owner managers of incorporated or unincorporated enterprises)
  • worked for one hour or more without pay in a family business or on a farm (contributing family workers)
  • were owner managers who had a job, business or farm, but were not at work, or
  • were employees who had a job but were not at work and were:
    • away from work for less than four weeks up to the end of the reference week
    • away from work for more than four weeks up to the end of the reference week and received pay for some or all of the four week period to the end of the reference week
    • away from work as a standard work or shift arrangement
    • on strike or locked out, or
    • on workers' compensation and expected to return to their job.
       

Equivalised weekly household income qualities

Equivalised household income can be viewed as an indicator of the economic resources available to each member of the household. Using equivalised household income enables the direct comparison of the relative incomes of households of different sizes and composition. 'Equivalised household income' is 'Total income' at the household level adjusted using an equivalence scale. 'Total income', also referred to as gross income, is the sum of income received from all sources before any deductions such as income tax, Medicare Levy and Medicare Levy Surcharge or salary sacrificed amounts are taken out.

Equivalised weekly household income quintiles are derived by ranking households in ascending order according to their total equivalised weekly household income from all sources and dividing the ranked population into five equally sized groups, each comprising 20% of the population. Equivalised household income quintiles for 2016-17 HUIT have been calculated on the full Multipurpose Household Survey sample (of which HUIT is a 50% sample). Quintiles based on the full sample, rather than the smaller sample used to enumerate the HUIT topic, will provide a more accurate estimate of the likely income distribution of the whole population.

While equivalised income generally provides a useful indicator of economic wellbeing, there are some circumstances which present particular difficulties. Some households report extremely low and even negative income, which places them well below the safety net of income support provided by government pensions and allowances. Households may under report their incomes in the survey at all income levels, including low income households. However, households can correctly report low levels of income if they incur losses in their unincorporated business or have negative returns from their other investments. Studies of income and expenditure from the Household Expenditure Survey, Australia (cat. no. 6530.0) have shown that such households in the bottom income decile and with negative gross incomes tend to have expenditure levels that are comparable to those of households with higher income levels. This suggests that these households have access to economic resources such as wealth, or that the instance of low or negative income is temporary, perhaps reflecting business or investment start up.

Exposure to inappropriate material

Internet users exposed to concepts and materials that they are not ready to comprehend or which may be illegal.

Greater Capital City Statistical Area (GCCSA)

Represent the socioeconomic area of each of the eight state and territory capital cities as defined in Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2016 (cat. no. 1270.0.55.001). These boundaries are built from aggregations of whole Statistical Areas Level 4. GCCSA boundaries represent a broad socioeconomic definition of each capital city, they contain not only the urban area of the capital city, but also surrounding and non-urban areas where much of the population has strong links to the capital city, through for example, commuting to work. The whole of the ACT is included in the Greater capital city area.

Household

A group of two or more related or unrelated people who usually reside in the same dwelling, who regard themselves as a household, and who make common provision for food or other essentials for living; or a person living in a dwelling who makes provision for his/her own food and other essentials for living, without combining with any other person.

Household internet access

A household connected to the internet via a computer, mobile phone or other device.

Internet user

An internet user is a person aged 15 years or over who accessed the internet for personal use in the last three months.

Level of highest educational attainment

Level of highest educational attainment identifies the highest achievement a person has attained in any area of study. It is not a measurement of the relative importance of different fields of study but a ranking of qualifications and other educational attainments regardless of the particular area of study or the type of institution in which the study was undertaken. Level not determined includes inadequately described responses or where no responses were given. For more information regarding how 'Level of highest educational attainment' is derived, see the coding rules described in Education Variables, June 2014 (cat. no. 1246.0) - The Standard for Highest educational attainment variables, Version 1.6, Collection methods. Level of highest educational attainment is based on the Australian Standard Classification of Education (ASCED), 2001 (cat. no. 1272.0).

Main English-speaking countries

Refers to the main countries from which Australia receives, or has received, significant numbers of overseas settlers who are likely to speak English. Comprises the United Kingdom, Ireland, South Africa, Canada, the United States of America and New Zealand. Classified according to the Standard Australian Classification of Countries (SACC), 2016 (cat. no. 1269.0).

Mean number of devices

The total number of devices used to access the internet at home by a group of households (e.g. households with children under 15 years), divided by the number of households in that group.

Not employed

Refers to a combination of those people Not in the labour force and Unemployed. Not in the labour force describes persons who, during the reference week, were neither employed nor unemployed, as defined. Unemployed persons are those aged 15 years and over who were not employed during the reference week and had actively looked for full-time or part-time work at any time in four weeks up to the end of the reference week and were available for work in the reference week; or were waiting to start a new job within four weeks from the end of the reference week and could have started in the reference week if the job had been available then.

Occupation

Occupation is classified according to the ANZSCO - Australian and New Zealand Standard Classification of Occupations, 2013, Version 1.2 (cat. no. 1220.0).

Other countries

The group comprises all countries except Australia and the Main English-speaking countries (the United Kingdom, Ireland, South Africa, Canada, the United States of America and New Zealand). Classified according to the Standard Australian Classification of Countries (SACC), 2016 (cat. no. 1269.0).

Personal income

Indicates the total income, from all sources, that the person usually receives each year before tax.

Purchasing goods or services online

Refers to persons who purchased or ordered goods or services over the internet in the last 3 months.

Remoteness area

The ABS has defined Remoteness within the Australian Statistical Geography Standard (ASGS): Volume 5 - Remoteness Structure, July 2016 (cat. no. 1270.0.55.005). The structure defines six Remoteness Areas (RA): Major Cities of Australia; Inner Regional Australia; Outer Regional Australia; Remote Australia; Very Remote Australia; and Migratory. It divides each state and territory into several regions on the basis of their relative access to services. The Remoteness Structure is categorised into Remoteness Areas (RAs). RAs aggregate to states and territories and cover the whole of Australia without gaps or overlaps.

The delimitation criteria for RAs are based on the Accessibility/Remoteness Index of Australia (ARIA), which measures the remoteness of a point based on the physical road distance to the nearest Urban Centre in each of five size classes. The ASGS SA1 boundaries are overlayed onto the ARIA+ grid and an average score is calculated based upon the grid points that are contained within each SA1. The resulting average score determines which remoteness category is allocated to each SA1. The RA categories are:

  • Major Cities of Australia - SA1 average ARIA+ value of 0 to 0.2
  • Inner Regional Australia - SA1 average ARIA+ value of greater than 0.2 and less than or equal to 2.4
  • Outer Regional Australia - SA1 average ARIA+ value of greater than 2.4 and less than or equal to 5.92
  • Remote Australia - SA1 average ARIA+ value of greater than 5.92 and less than or equal to 10.53, and
  • Very Remote Australia - SA1 average ARIA+ value of greater than 10.53.
     

For 2016-17 HUIT the categories of Remote and Very Remote have been combined into one category.

Section of State (SOS)

The ABS has defined SOS within the Australian Statistical Geography Standard (ASGS): Volume 4 - Significant Urban Areas, Urban Centres and Localities, Section of State, July 2016 (cat. no. 1270.0.55.004). The structure represents areas of concentrated urban development. It consists of Statistical Areas Level 1 (SA1s) aggregated together to form regions defined according to population density and other criteria. Urban centre and localities (UCLs) can cross state or territory boundaries so the structure therefore does not aggregate to state and territories. The UCL/SOS structure covers the whole of Australia without gaps or overlaps:

  • Major Urban - a combination of all Urban centres with a population of 100,000 or more
  • Other Urban - a combination of all Urban centres with a population between 1,000 to 99,999
  • Bounded Locality - a combination of all bounded localities, and
  • Rural Balance - represents the remainder of state/territory.
     

For 2016-17 HUIT the categories of Major Urban and Other Urban have been combined into one category called Urban, and the categories of Bounded Locality and Rural Balance have been combined into one category called Rural.

Quality declaration

Institutional environment

For information on the institutional environment of the Australian Bureau of Statistics (ABS), including the legislative obligations of the ABS, financing and governance arrangements, and mechanisms for scrutiny of ABS operations, please see ABS Institutional Environment.

Relevance

The Multipurpose Household Survey (MPHS) is collected as a supplement to the monthly Labour Force Survey (LFS) and is designed to collect statistics on a number of small self-contained topics. The Household Use of Information Technology (HUIT) topic collects a range of information on household access to and persons use of the internet in Australia. These data are presented by various demographic and geographic characteristics.

Timeliness

The MPHS is collected annually with enumeration undertaken in each month over the financial year period from July to June. Generally, data from the MPHS are released approximately 6–8 months after enumeration. The HUIT topic has been collected biennially via the MPHS, however the 2016-17 HUIT is the last iteration of the survey.

Accuracy

The HUIT topic comprised a sample of 14,035 fully responding households, which represented a response rate of 72% (after taking sample loss into account). The LFS, and consequently the MPHS, is primarily designed to provide estimates for the whole of Australia and, secondly, for each state and territory.

Estimates are subject to sampling and non-sampling error. All aggregate statistics presented in tables have been randomly adjusted to avoid the release of any data that may inadvertently identify an individual. The technique to adjust the data is called perturbation. These adjustments have a negligible impact on the underlying pattern of the data. For further information, please refer to the Explanatory Notes.

Coherence

Person level data from 2005-06 MPHS onwards (the scope of which is 15 years and over) are not directly comparable with data from previous years, which was limited to persons aged 18 years and over.

Person level data may not be directly comparable with data from previous years due to changes in the definition of an internet user, question structure and response categories. Household level data are comparable.

While the ABS seeks to maximise consistency and comparability over time by minimising changes to the survey, ongoing survey review has adjusted to the changing needs of users of information and communication technology statistics.

Interpretability

To aid in the interpretation of HUIT data, detailed information on the terminology, classifications and other technical aspects associated with the survey can be found on the relevant web pages included with this release.

Accessibility

All tables and associated RSEs are available in Excel spreadsheets and can be accessed from the Data downloads section.

Additional tables can also be produced on request. Note that detailed data can be subject to high RSEs and, in some cases, may result in data being confidentialised or not being available.

For further information about these or related statistics, contact the National Information and Referral Service.