Central Statistics Office Ireland (CSO)

Social Analysis and Modules Division

Central Statistics Office Ireland

Central Statistics Office

Skehard Road

Mahon

Cork

Ireland T12X00E

The EU survey on the use of ICT in households and by individuals is an annual survey conducted since 2002. In Ireland, it has been conducted since 2004.

In 2022 the survey collects data on the access to information and communication technologies (ICT), on the use of the internet, use of e-commerce, use of e-government, as well as Internet of Things and Green ICT.

National language:

ICT Household Survey 2022

English:

Questionnaire(s) in national language(s) and the translation in English are available in the annex.

The following common concepts and definitions apply under the Integrated European Social Statistics (IESS):

• the International Standard Classification of Education (ISCED) 2011 published in the following breakdowns: low (ISCED levels 0-2: no formal education, primary education or lower secondary education), medium (ISCED levels 3-4: upper secondary or post-secondary non-tertiary education) and high (ISCED levels 5-6: tertiary programmes which normally need a successful completion of ISCED 3 or 4, or second-stage tertiary education leading to an advanced research qualification);
• the International Standard Classification for Occupation ISCO-08 at the 2-digit level;
• the Classification of Economic Activities (NACE Rev.2-2008), at section level;
• the Common classification of territorial units for statistics (NUTS 1) – finer granularity of NUTS 2 is provided on optional basis by some Member states;
• the SCL - Geographical code list;
• information about household income is provided at lower level of detail. 

Additional classifications used in the national questionnaire:

The Pobal Haase-Pratschke Deprivation Index is used to create the underlying sample and is used to analyse the data. The Index uses Census data to measure levels of disadvantage or affluence in a geographical area.

Common classification of territorial units for statistics (NUTS 3)

The ICT survey in households and by individuals covers those households having at least one member in the age group 16 to 74 years old. Internet access of households refers to the percentage of households that have an internet access, so that anyone in the household could use the internet.

The national survey also covers persons aged between 75 and 89 years.

The survey is collecting data of internet users, individuals who have used the internet in the three months prior to the survey. Regular internet users are individuals who used the internet, on average, at least once a week in the three months prior to the survey.

This annual survey is used to benchmark ICT-driven developments, both by following developments for core variables over time and by looking in greater depth at other aspects at a specific point in time. While the survey initially concentrated on access and connectivity issues, its scope has subsequently been extended to cover a variety of subjects (for example, the use of e-government and e-commerce) and socio-economic analysis (such as regional diversity, gender specificity, differences in age, education and the employment situation). The scope of the survey with respect to different technologies is also adapted so as to cover new product groups and means of delivering communication technologies to end-users.

For more details on the methodology applicable in each survey year, please consult the Methodological Manual for the respective year on CIRCABC - Methodological Manual - Information society statistics (europa.eu).

Deviations from standard ICT concepts: None

Households and Individuals

In the ICT usage survey, the target population for the different statistical units is:

- individuals: all individuals aged 16 to 74;

- households: all (private) households with at least one member aged 16 to 74. 

Target population composed of households and/or individuals:

• Number of households:  1,710,480
• Number of individuals:  3,942,675

Non-compulsory age groups also included in the target population:

The survey covers all private households in the State (Republic of Ireland)

Year 2022

Not applicable

Percentages of ‘Households’ and Percentages of ‘Individuals’

For all questions except for questions on the use of e-Government, the reference period if three months prior to the interview.

Quarters 1 and 2 2022

The legal basis for the 2022 EU survey on the use of ICT in households and by individuals is the Regulation (EU) 2019/1700 of the European Parliament and of the Council of 10 October 2019 establishing a common framework for European statistics relating to persons and households, based on data at individual level collected from samples (OJ  L 261 I, 14.10.2019, p. 1), as implemented by the Commission Implementing Regulation (EU) 2021/1223 of 27 July 2021 specifying the technical items of the data set, establishing the technical formats for transmission of information and specifying the detailed arrangements and content of the quality reports on the organisation of a sample survey in the use of information and communication technologies domain for reference year 2022 pursuant to Regulation (EU) 2019/1700 of the European Parliament and of the Council (OJ L 2269, 27.07.2021, pp. 1-45).

Complementary national legislation constituting the legal basis for the survey on the use of ICT in households and by individuals:

Irish statistics were compiled under the provisions of the Statistics Acts, 1926 and 1946 up until 1 November 1994 when the Statistics Act, 1993 came into operation.

The Statistics Act, 1993 provides a modern legislative basis for the compilation and dissemination of official statistics. The Act came into operation on 1 November 1994. It incorporated, inter alia, the following provisions:

• the establishment on a statutory basis of the CSO as an independent Office under the aegis of the Taoiseach;
• the functions of the CSO, including in addition to its basic mandate the obligation to co-ordinate official statistics compiled by public authorities, the right to assess the statistical potential of the records maintained by public authorities and to ensure that this potential is realised in so far as resources permit;
• the appointment on a formal basis of the Director-General of the CSO who, in addition to being responsible for the management of the Office, shall also be independent on statistical matters (i.e. sole responsibility for the statistical methodologies and professional standards to be followed, the content of statistical releases and publications, and the timing and methods of dissemination of the statistics compiled);
• the establishment of a National Statistics Board to guide, with the agreement of the Taoiseach, the strategic direction of the CSO;
• the right of access, subject to some limitations and conditions, of the CSO to administrative records held by public authorities for statistical purposes; and
• the obligation on the CSO to treat all individual information relating to persons or concerns as strictly confidential and to use such information solely for statistical purposes.

The Act fully reflects the Fundamental Principles of Official Statistics adopted by the UN Economic Commission for Europe in 1992.

In accordance with Section 8(c) of the Data Protection Act, 1988 any restrictions in that Act on the disclosure of personal data do not apply in the case of the CSO if it requests such disclosure as a requirement (other than for the Courts, Garda Síochána, Prison Administration, Ombudsman and Medical Records) under Section 30 of the Statistics Act, 1993.

Not applicable. Based on survey results only.

The Government of Ireland adopted the Commitment on Confidence in Statistics in May 2017, thus fulfilling obligations set out in Regulation (EU) 2015/759 of the European Parliament and of the Council amending Regulation (EC) No 223/2009 on European statistics.

The Commitment on Confidence approved by the Government is a further declaration of support for the existing laws and for those policies and practices instigated by the Central Statistics Office (CSO) to meet its obligations under the European Statistics Code of Practice.

The Statistics Act 1993 and EU Regulation No. 223/2009 on European Statistics are the primary legal instruments which allow the Director General of the Central Statistics Office to carry out their duties.  In particular, the Statistics Act 1993 makes the CSO independent of Government and responsible for statistical methods and policies.  The CSO’s independence and compliance with the Code of Practice provides an assurance to the citizen that these duties are being carried out to the highest standards. 

• Commitment on Confidence in Statistics (PDF 82KB)
• Commitment on Confidence in Statistics - Citizen’s Summary (PDF 179KB) 

Link to CSO's Code of Practice on Statistical Confidentiality

Link to CSO Data Protocol 

Information for data providers on CSO statistical confidentiality (this information is available on the CSO website)

The CSO guarantees the anonymity of every respondent and confidentiality of survey answers.

The CSO takes every precaution to ensure the security of the information collected. CSO makes sure that the results of each survey are published in aggregate format only. Personal data at household/individual level is never published which means it is impossible for individuals or households to be identified. This confidentiality is guaranteed by law under the Statistics Act, 1993. Information supplied to the CSO is treated as strictly confidential. The confidentiality of data is provided for under sections 32, 33, and 34 of the Statistics Act, 1993, which sets stringent confidentiality standards. These include the use of data for statistical purposes only, and the non-disclosure of data in an identifiable form.

The personal data collected to inform this survey will be stored for a maximum of four months. In addition, the response to the questionnaire will immediately be separated from the personal identifiers, providing further anonymity to responses. The CSO has developed a CSO Code of Practice on Statistical Confidentiality 

The CSO release calendar is publicly available on the CSO website. 

The CSO release calendar is publicly available on the CSO website.

The CSO release calendar is publicly available on the CSO website, and the publication, press statement, etc. is available on the CSO website at 11am on the publication date as scheduled in the release calendar.

Annual

In 2022, the survey results were published in a series of four publications as follows. A Press Statement accompanied each one and was published on the CSO website, alongside the publication, on the day of release.

Household Digital Consumer Behaviour 2022 (published 12 December 2022)

Smart Technology 2022 (published 16 December 2022)

Sustainability pf Personal ICT Devices 2022 (published 19 December 2022)

Internet Coverage and Usage in Ireland 2022 (published 21 December 2022)

In 2022, the survey results were published in a series of four publications as follows. A Press Statement accompanied each one and was published on the CSO website, alongside the publication, on the day of release.

Household Digital Consumer Behaviour 2022 (published 12 December 2022)

Smart Technology 2022 (published 16 December 2022)

Sustainability pf Personal ICT Devices 2022 (published 19 December 2022)

Internet Coverage and Usage in Ireland 2022 (published 21 December 2022)

The aggregated data is available on PXStat, the CSO main online database. The following tables are available on the PX Stat Data dissemination Service:

Information Society Statistics Digital Consumer Behaviour Tables:

• Individuals aged 16 and over who used the internet in the last 3 months table ICA95 - ICA100
• Purchases made online ICA116 - ICA117, ICA84 - ICA87
• Individuals aged 16 and over who bought or ordered goods and/or services online in the last 3 months tables ICA120 - ICA129
• Individuals aged 16 and over who bought or subscribed to streaming apps tables ICA130 - ICA134
• Individuals aged 16 and over who used the internet to purchase other services online ICA135 - ICA139
• Individuals aged 16 and over who used the internet in the last 3 months ICA140 - ICA144
• Individuals aged 16 and over who conducted online learning activities for educational, professional or private purposes in the last 3 months ICA145 - ICA147, ICA167
• Individuals aged 16 and over who made contact/official requests or claims with public authorities or services online ICA148 - ICA152
• Individuals aged 16 and over who submitted/did not submit completed official forms online by their reasons for not doing so ICA153 - ICA156
• Individuals aged 16 and over who accessed the services of a public authority or service online in the last 12 months by problems encountered ICT157 - ICA161, ICA168
• Individuals aged 16 and over who arranged accommodation or transport services online for private use in the last 3 months ICA162 - ICA166

Frequency of internet use ICA177 - ICA179, ICA182 - ICA183, ICA76, ICA78

• Individuals aged 16 and over who use the internet every day ICA180
• Individuals aged 16 and over who use the internet daily: frequency of daily use ICA109 - ICA112
• Percentage of households with internet access ICA172 - ICA176
• Percentage of households without internet access by their reasons for not having internet access ICA169 - ICA171

Individuals use of devices to access the internet ICA 101 - ICA106

Smart Technology:

• Individuals Use of the Internet of Things Tables SMRT01 - SMRT19

Green ICT:

• Sustainability of Use of Personal ICT Devices SUST01 - SUST10

2,320 users since data was published in December.

The CSO also provides access to two types of microdata files:

1. Anonymised Microdata Files (AMFs)

Anonymised microdata files contain microdata that are provided for statistical/research purposes only in such a form that the information related to an identifiable entity/person cannot be directly (so, no direct identifiers) or indirectly (in many cases having undergone additional anonymisation procedures such as "top-coding", e.g., specific age re-coded to an age class) identified.

 Further information on AMFs is available here.

2. Research Microdata Files (RMFs)

RMFs are unit record files that do not contain direct identifiers but where the risk of disclosure through indirect identification is considered to be significant. RMFs are not statistical products, as CSO's products relate to aggregated statistical analysis, but are research files that are made available to persons authorised to access such files under the Statistics Act, 1993 subject to strict criteria.

Approval for access to microdata

Access to RMFs is strictly controlled and can only be granted within the framework of the Statistics Act, 1993. In order for RMF access to be granted, approval must be granted by a Statistician, a Senior Statistician, a Director/Assistant Director General and by the Director General of the CSO. Approval will be considered for RMF access only where a researcher meets the following requirements:       

• The researcher is employed by or is formally related to a registered Research Organisation
• The researcher is a CSO registered researcher
• The researcher has completed CSO researcher training which reinforces the terms and conditions of the RMF Standard Agreement
• The researcher agrees to abide by the terms and conditions of the RMF Standard Agreement
• The researcher signs a Declaration of Secrecy and is appointed an Officers of Statistics

More detailed information on the process is available here.

Ad-hoc special analyses tables in response to data requests.

Metadata information collected in the statistical documentation for 2022 was published in December 2022. 

All methodology documents are available on the Methods Page of the CSO website.

100% as per CSO Ireland's requirements.

CSO Quality Management Framework

To support the culture of continuous quality improvement, the CSO has invested significant resources to advance the quality agenda both within the Office and in the broader Irish statistical system. The statistical quality and integrity of CSO's outputs is part of CSO's DNA and this focus on quality is reflected in CSO's corporate documentation, business plans and the Role Profile Forms of individual staff.

While each statistical area is responsible for managing the quality of their statistical processes and outputs, they are supported by staff from the CSO’s Quality Management, Support and Assurance (QMSA) division who are responsible for the development and implementation of the CSO’s Quality Management Framework (QMF). The QMF is an extensive and long-term programme of activities, which will ensure that the statistical production standards applied in the CSO continue to meet the highest standards as regards quality and efficiency. This is especially relevant in the current context of increasing and more formal scrutiny of official statistics at an EU and wider international level.

The overall goal of the QMF is meeting the required standard as set out in the European Statistical System Code of Practice (ESCOP) and the QMF foundations are based on establishing the UNECE’s Generic Statistical Business Process Model (GSBPM) as the operating statistical production model in the CSO.

The QMSA team have been working on the implementation phase of the QMF since mid- 2016 where they have systematically rolled out the new policies and standards in the form of the quality projects detailed below:

The establishment of the GSBPM as the business process model for the Office. This model is an UNECE standard for statistical production and allows the CSO to advance a more standardised, horizontal approach to quality management. The GSBPM is the central plank on which each of the QMF projects are built.

Survey documentation – This project focused on improving the level of quality and standardisation of survey documentation across the Office. One of the main goals of the project was to reduce operational ambiguity so that the documents act as the store of collective organisational knowledge regarding the processes. Survey Documentation also acts as training material to help new staff move up the learning curve faster. All survey documentation is categorised by phase of the GSBPM and made available centrally to all staff via an internal Quality Information System so that similar processes can be compared.

Process Mapping project – Process mapping is the visual display of steps involved in a business process. It draws a concise picture of the sequence of tasks needed to bring a product or service from start to completion. The main purpose behind business process mapping is to provide clarity on exactly how the process happens, not how it is supposed to happen. All statistical products in the CSO have been now been process mapped and are available on the Quality Information System so that similar processes can be compared.

Process maintenance project – In order to keep the maps up to date, the process map maintenance policy has been developed which requires business areas to certify that their maps are valid and up to date once a year. In addition, the policy requires that a process walkthrough take place every two year with QMSA and the business areas assuring that everything is still valid. The walkthrough also links to the survey documentation to ensure that documentation is also up to date and covers all statistical processes and activity reflected on the maps.

Process Metrics and Indicators – In order for staff to make an assessment on how their processes are performing and to better manage the phases of the statistical lifecycle (collection, processing, analysing and the ultimate dissemination of statistical data) appropriate metrics are identified and collected at each phase of the statistical process. These metrics and indicators include response rates, timeliness, edit and imputation rates, precision rates and the degree of revisions.

The QMF metadata project designed to establish the standards, based on international best practice, for all relevant parts of the survey life cycle. The focus to date has been on disseminate metadata but will continue to develop over time to include meeting the Eurostat standard for metadata (SIMS).

Quality Review System – This is a self-assessment tool which allows survey owners to review the quality of their statistical processes against the principles of the ESCOP for each phase of the GSBPM they are using. It allows them to rate their survey performance, highlight good practice and set out improvement actions for areas which need improvement. The outputs from the Quality review can be used as an input for the Quality and Methodology divisions to provide relevant support where required.

A range of data and process governance initiatives are also underway including: the File Structure Model, the Directory of Product and Services and the development of the Data Inventory so that are data assets are stored and managed in a consistent, standardised manner.

Data Management and governance support tools – These include data owners attesting to which data they own and are responsible for, where this data is located and who can access this data. This is further supported by data governance tools to take account of data retention and archiving policies.

While the main focus has been on QMF implementation, there has also been a large element of process and output improvement during the roll-out of the QMF across the Office as any quality or methodology improvement requirements which were encountered by the Quality staff when engaging with the business areas have been acted on by means of direct support from the Quality and Methodology divisions.

In fulfilling its mandate the CSO applies the best statistical standards and methodology, and adheres to the highest professional standards of impartiality, integrity and independence. The Office fully subscribes to the UN Fundamental Principles of Official Statistics.

The CSO operates under a strict legal regime, supported by a robust quality framework, the backbone of which is the European Statistics Code of Practice (ESCOP). This Code of Practice is made up of 16 principles covering the institutional environment, the statistical production process and the output of statistics. The Central Statistics Office (CSO) as a member of the European Statistical system is duty-bound and committed to following the Code. Each of the 16 principles has a number of specific indicator measures which are enacted through the policies, standards and practices of the CSO.

In accordance with the ESCOP quality standards, the quality requirements of CSO’s statistical outputs are:

• to be relevant with regard to meeting users information needs
• to be accurate so that estimates or indicators accurately and reliably portray reality
• to be timely so that statistics are made available to users in a timely and punctual manner
• to be accessible so that statistics are presented to users in a clear, understandable form, released in a suitable and convenient manner, available and accessible on an impartial basis with supporting metadata
• to be comparable and coherent to enable comparison internally, over time or among related sources

The Quality Policy for the Office is set out in “Quality in Statistics - A Handbook of Quality Standards and Guidelines”. This provides information and recommendations on best practice and contains clear guidelines and standards to ensure that the quality of CSO's processes and outputs are of the highest standard.

The CSO’s commitment to the quality of the statistics produced and disseminated is set out in its quality statement (see Central Statistics Office Quality Statement (PDF 101KB) ). This is further supported by the Government of Ireland adopted “Commitment on Confidence in Statistics” which declares support for the existing laws and for those policies and practices instigated by the Central Statistics Office (CSO) to meet its obligations under the European Statistics Code of Practice.

Standard reports on methods and quality for each statistical output are available on the Methods page of the CSO website. These reports provide information to users on the methodology in place in the particular survey area together with an assessment of the quality of the resulting survey output in terms of the quality dimensions set out in the European Statistics Code of Practice.

Central Statistics Office Quality Statement

While each statistical area is responsible for managing the quality of their statistical processes and outputs, they are supported by staff from the CSO’s Quality Management, Support and Assurance (QMSA) division who are responsible for the development and implementation of the CSO’s Quality Management Framework (QMF). The QMF is an extensive and long-term programme of activities, which will ensure that the statistical production standards applied in the CSO continue to meet the highest standards as regards quality and efficiency. This is especially relevant in the current context of increasing and more formal scrutiny of official statistics at an EU and wider international level.

The overall goal of the QMF is meeting the required standard as set out in the European Statistical System Code of Practice (ESCOP) and the QMF foundations are based on establishing the UNECE’s Generic Statistical Business Process Model (GSBPM) as the operating statistical production model in the CSO.

Standard reports on methods and quality for each statistical output are available on the Methods page of the CSO website. These reports provide information to users on the methodology in place in the particular survey area together with an assessment of the quality of the resulting survey output in terms of the quality dimensions set out in the European Statistics Code of Practice.

The Office of the Government Chief Information Officer (OGCIO) which has the leadership role for the digital agenda across Government in Ireland.

Government Departments, including the Department of Energy, Climate and Communications who are responsible for the National Broadband Plan (Government initiative to deliver high speed broadband services to all premises in Ireland) and rollout of the Digital Agenda in Ireland. ComReg (Commission for Communications Regulation).

Media

Researchers/Academia

Each year, a consultation process is held with all main stakeholders. Such stakeholders would have also been consulted for their views when the Model Questionnaire was being developed.

All required variables, except optional variables, have been included.

100%

The headline indicator questions of the ICT Household Survey on household internet connectivity and frequency of individual usage of the internet were asked of all waves of the LFS in Quarters 1 and 2, and all other questions on the ICT Household Survey on detailed ICT usage were asked in a follow-on CAWI questionnaire, of wave 5 respondents only in Quarter 1 and waves 4 and 5 respondents in Quarter 2. This change has affected sample size, and also item response for some variables.

1) Estimation method for the random variation of an estimator due to sampling:
Taylor linearisation analytic method, Jacknife, Bookstrap and CALMAR2-macro, developed by INSEE
2) The basic formula using unweighted figures is:
Standard error = SQRT ((proportion * (1 - proportion) / total)) * design effect
3) Tools used to estimate sampling errors: CLAN and GENESEES
4) Methods used to assess the standard errors take into account the following specific effects:
• Unit non-response: Response Homogeneity Groups Model plus further correction of sampling weights (calibration) to compensate for bias due to coverage problems.
• Imputation:
Deductive imputation for item non-response. For example for income class.
Deterministic imputation - mean/median by class.
Hot deck random imputation.
• Calibration: CALMAR2-macro, developed by INSEE.
5) Output results are aggregated to produce the various totals published. These aggregations are usually produced using key variables such as sex, age region, ILO status etc. In general, all aggregations produced are done by way of various SAS procedures.
The aggregate results produced for any given set of classifications will be the sum of the individual grossing factors of the valid responses which belong to that set of classifications and no estimates are made unless the data itself has been captured within the survey.

Precision estimates for the question "Individuals having ordered goods or services for private use over the internet in the last 12 months" (individuals who ticked 'Within the last 3 months' or 'Between 3 months and a year ago' in question D1 of the 2022 model questionnaire):

Number of respondents (absolute value for ‘Yes’ answers):  930

Estimated proportion (in %):   72.5%

Standard error (in percentage points):  0.75

Calculation methods for variance estimation include:

• Taylor linearisation analytic method
• Jacknife
• Bookstrap

Details of the breakdown are available in document INFOSOC_HHNSI_A_2022_IE in the Annexes below.

See more details on non-sampling error below.

The following measures are in place to minimise error:

• Comprehension errors – A substantial effort is made to ensure that the terms used in the survey are clear and readily understood. The more complicated question topics had help text displayed on the online questionnaire.
• Clear training - Members of the staff are fully trained on the questionnaire. 
• Governance of staff - Information on the interviews is collected and analysed to help minimise non-sampling effects (including, for example, when interviews were conducted and their duration). 

Data capture errors: These errors are minimised by logic checks and limits on values that can be keyed for each question in the electronic questionnaire at the data collection point. In certain cases where text strings (used as an “other” category for some questions) were re-coded to the proper category while further validation checks were done.

Coding error: Checks are in place to minimise this risk, particularly with respect to Industry and occupational coding. The coding is conducted in-house at the CSO using an automated coding facility which is reviewed by a small team of coding experts. This approach reduces subjectivity and coding error. Overall it increases the quality and standard of coding of these key variables.

Persons aged 75 to 89 years: Approximately 8.1%

Not requested in the ICT survey.

1)       Measurement errors:  

• Comprehension errors - a substantial effort is made to ensure that the terms used in the survey are clear and readily understood. The more complicated question topics had help text displayed on the laptop screen in CAPI interview.
• Data capture errors: These errors are minimised by logic checks and limits on values that can be keyed for each question in the electronic questionnaire at the data collection point. In certain cases where text strings (used as an “other” category for some questions) were re-coded to the proper category while further validation checks were done.
• Coding error: Checks are in place to minimise this risk, particularly with respect to Industry and occupational coding. The coding is conducted in-house at the CSO using an automated coding facility which is reviewed by a small team of coding experts. This approach reduces subjectivity and coding error. Overall it increases the quality and standard of coding of these key variables.

2)       Questionnaire design and testing: 

The Statistician responsible for the survey works hand-in-hand with the Questionnaire Design unit (QDU) in the CSO. The questionnaire specification is provided to QDU June of the previous year and enters into a T+19 development cycle including 3-4 rounds of iterative testing. When developing the questionnaire for CAWI and CTI modes, cognitive and field testing was also carried out.

3)       Interviewer training:  

• Clear training - Members of the field staff are fully trained on the questionnaire. Field staff (interviewers and field coordinators) are trained and provided with visual aids (show cards especially for questions with long answer options) to boost response.
• Governance of field staff - Information on the interviews is collected and analysed to help minimise non-sampling effects (including, for example, when interviews were conducted and their duration). This information is compared across the interview team to ensure no unusual variation in interviewer performance exists. Co-ordinators, as an additional check on the quality of the interviewer's work, call back to around 2% of households to check the quality of the data collected.

4)       Proxy interview rates:  0% Direct interviews only

Information about non-respondents:  

An adjustment for non-response was introduced into the weighting procedure from 2018. The adjustment applies extra weight to the groups who tend to be less likely to respond to the survey to make the results from the achieved sample more representative of the target sample and target population.

The unit response rate is the ratio of the number of in-scope respondents (= the number of achieved interviews or the net sample size to the number of eligible elements selected from the sampling frame).

Unit non-response rate for

• Households:   43.7%
  
• Individuals (aged 16-74):   43.7%

Comments, if any:

The data collection mode has changed to CAWI/CATI, largely CAWI. With this change in data collection mode, response was impacted, also post COVID impact on response rates. To this end, four reminders (instead of three form 2021) were issued to non respondents.

1)       Methods used for dealing with unit non-response:

Following the first email with link to CAWI questionnaire, four reminders (an additional reminder was ent in 2022 - three reminders were sent the previous year) follow for non-respondents. For those respondents who have opted to be contacted by phone, the call centre will make three attempts to make contact, and having made contact, they will reschedule the interview if it does not suit the householder at that time. Also any non respondents who did not complete online and for whom CSO had phone contact details, were also contacted by phone.

2)       Methods used for minimizing unit non-response: 

Non-response adjustment included in the weighting procedure. The adjustment applies extra weight to the groups who tend to be less likely to respond to the survey to make the results from the achieved sample more representative of the target sample and target population.

3)       Substitution permitted: No  

4)       Substitution rate (in %): 0%

Items with low response rates (observed rates in %):

Overall item response was very good. The only variable with low response rate was the

Variable HH_IQ5 74.8%

This is information that is very personal to the respondent: the respondent does not know this information for the household. CSO placed this question towards tthe end of the questionnaire so as not to put off respondents from completing the rest of the questionnaire (hence reducing partial response returns).

The following measures are in place to minimise error:

• Comprehension errors – A substantial effort is made to ensure that the terms used in the survey are clear and readily understood.
• Clear training - Members of the field staff are fully trained on the questionnaire. Field staff (interviewers and field coordinators) are trained and provided with visual aids (show cards especially for questions with long answer options) to boost response.
• Governance of field staff - Information on the interviews is collected and analysed to help minimise non-sampling effects (including, for example, when interviews were conducted and their duration). This information is compared across the interview team to ensure no unusual variation in interviewer performance exists. Co-ordinators, as an additional check on the quality of the interviewer's work, call back to around 2% of households to check the quality of the data collected.

Data capture errors: These errors are minimised by logic checks and limits on values that can be keyed for each question in the electronic questionnaire at the data collection point. In certain cases where text strings (used as an “other” category for some questions) were re-coded to the proper category while further validation checks were done.

Coding error: Checks are in place to minimise this risk, particularly with respect to Industry and occupational coding. The coding is conducted in-house at the CSO using an automated coding facility which is reviewed by a small team of coding experts. This approach reduces subjectivity and coding error. Overall it increases the quality and standard of coding of these key variables.

Not requested for ICT Survey

The annual publication has grown in recent years, and to make it more easily accessible to users, the survey results in 2022 were published as a series of four publications as follows:

Household Digital Consumer Behaviour 2022 (published 12 December 2022)

Smart Technology 2022 (published 16 December 2022)

Sustainability pf Personal ICT Devices 2022 (published 19 December 2022)

Internet Coverage and Usage in Ireland 2022 (published 21 December 2022)

and were published within 16 weeks of the end of the data collection period.

This was the second time that the national publication was published as a series of publications covering different topics (also in 2021).

In previous years, the national publication on Information society Statistics Households was published in the third week of December, so all of the above publications were published ahead of this target date, so 100% of the release was delivered on time.

Length of time between end of data collection end July and publication of results is approximately 12 weeks. No delays compared to the announced time of publication as published in the CSO Release Calendar.

Household Digital Consumer Behaviour 2022 (published 12 December 2022) with press release

Smart Technology 2022 (published 16 December 2022) with press release

Sustainability pf Personal ICT Devices 2022 (published 19 December 2022) with press release

Internet Coverage and Usage in Ireland 2022 (published 21 December 2022) with press release

The ICT Household Survey 2022 was designed in line with the European Community Survey on ICT Usage in Households and By Individuals 2022 Model Questionnaire. Data given in this domain are collected annually by the National Statistical Institutes (NSIs) and are based on Eurostat's annual Model Questionnaire on ICT usage in households and by individuals. The collection of the data under the aforementioned European Regulation implies that harmonised data can be obtained across the European continent. The regional classifications in this release is based on the NUTS (Nomenclature of Territorial Units) classification used by Eurostat. Until Q4 2017, the NUTS3 regions corresponded to the eight Regional Authorities established under the Local Government Act, 1991 (Regional Authorities) (Establishment) Order, 1993, which came into operation on 1 January 1994 while the NUTS2 regions, which were proposed by Government and agreed by Eurostat in 1999, were groupings of those historic NUTS3 regions. There were no problems with comparability between regions as CSO used the same questionnaire, and modes of data collections in all of the NUTS3 regions, and the sample was representative in all regions.

The 2022 ICT household questionnaire is available at: Household Survey Questionnaire 2022.

Not relevant

Possible limitations in the use of data for comparisons over time:

In 2021, the mode of data collection for the ICT Household survey changed to dual CAWI/CATI data collection. This dual mode data collection continued in 2022, although largely CAWI. For CAWI respondents in younger age groups, in particular 16-24, response was low, especially for males in this age cohort. Response post COVID remained a challenge. CSO sent out an extra reminder (four in total) to non respondents. Also any non respondents who did not complete online and for whom CSO had phone contact details, were also contacted by phone.

It should be noted that prior to 2021, data collection was CAPI/CAWI but largely CAWI/CATI due to COVID-19 pandemic. It was not possible to carry out face-to-face interviews in the field, so CAPI interviews were moved to CATI mode, and carried out by interviewers in a home-CATI environment. Also the impact of COVID on response and completeness of data collected impacts on time series.

The length of comparable time series depends on the module and variable considered within each of the modules of the survey.

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The CSO Digital Economy Coherence Group is made up of representatives from ICT Households and Enterprises Surveys, Retail Sales, Trade, National Accounts, Prices, and looks at data coherence between difference digital economy data flows. There is a new Digital Economy theme page on the CSO website https://www.cso.ie/en/statistics/digitaleconomy/

MANDATORY questions in the Eurostat model questionnaire 2022:

Table 15.4.1. of document INFOSOC_HHNSI_A_2022_IE in the Annexes lists the questions that do not reflect the coverage of subjects and characteristics of Annex 2 of the Commission Delegated Regulation (EU) 2021/1898 of the 20 July 2021.

Adoption of OPTIONAL questions and items in the Eurostat model questionnaire 2022:

Table 15.4.1. of document INFOSOC_HHNSI_A_2022_IE lists the optional questions from the annual Eurostat model questionnaire 2022 included in the national questionnaire and their coverage for age groups beyond the standard scope.

Additional questions introduced in the national questionnaire:

No additional questions were asked in the national questionnaire.

Effects of deviations from the routing used in the Eurostat model questionnaire:

The routing recommended in the Model Questionnaire was used throughout the national questionnaire, but for Question B1 where the following questions were asked to ascertain frequency of internet usage:

Question A1_V2

Do you use the internet every day or almost every day? Yes/No

If one answers 'Yes' they are asked only answer this question

• If A1_V2 contains any 'Yes'
• A2_V2 On average, how often do you use the internet?
• Briefly, or 1 or 2 times a day
• Several times a day
• Nearly all the time every day
• All the time every day

But if one answers 'No' to question

• A1_V2 they are asked All the time every day
• Only answer this question if A1_V2 contains any  'No'
• A3_V2 On average how often do you use the internet?
• At least once a week but not every day
• Less than once a week but within the last 3 months
• Between 3 months and a year ago
• More than a year ago
• Never used it

CSO’s General Revisions Policy

Domain specific revisions practices must be documented by each area in their survey background notes or methodological documents which will contain more detailed information taking account of this policy together with the requirement of any domain specific revision requirements.

• Notification of Revisions to Published Statistical Series
• Statistical results that are subject to revisions must be indicated at dissemination.
• Statistical results that have been revised must be indicated in the release and/or on PxStat. (See Section 5 for PxStat revision codes and labels.)
• Advance notice must be given of future revision plans arising from major changes to statistical methodology and data sources.
• Where possible advance notice must be given to users of discontinuities / series breaks.
• Substantial revisions must be accompanied by a detailed explanation of their nature and extent.
• Users who detect errors will be contacted directly and informed of revised data.

Revisions Calendar

The General Revisions Policy is supplemented by a Revisions Calendar, a copy of which will be made available on the CSO website. This calendar provides an overview of which sets of statistics are subject to revision and describes the respective revisions cycle.

Consultation with Users

Consultation, where appropriate, with key users on revision practices will be documented by each business area.

Revisions to Research Micro-Data

All users of research micro-data will be informed of revisions to the micro-data file.

Revision Practices

Detailed revision practices must be documented by statistical areas in their survey background notes, quality reports and methodological documents and are also kept up to date on their Methods page on the CSO website.

Discontinuities in Statistical Series

Discontinuities in statistical series will be documented in detail when they occur, and users will be informed of the reasons and potential usage of the discontinued series, where applicable.

Timeliness of Release

Timeliness of releases will be balanced with the need to avoid frequent revisions.

Data storage and keeping records of Revisions

All data prior and post revision (i.e. provisional, final and other revised published data) must be stored in the Disseminate location based on the agreed corporate data standard. This data should be clearly named and labelled so that each dataset associated with revised data is easily identifiable and should be marked as having a permanent retention period.

Quality of Provisional data

Revision analyses must be carried out, where appropriate, and the results will be used in the assessment of data quality and to continually monitor and improve statistical processes. The scale of revisions must be published for certain statistical series where it is the norm to issue at least one set of estimates prior to final figures. Users will thus be informed of the reliability and the utility of preliminary to penultimate estimates of the data.

The publication of provisional data in many cases is a statutory requirement. In this context it is important that the CSO evaluate, on an ongoing basis, the quality of provisional data being produced and where appropriate provide the relevant information necessary to ensure that users are made aware of the limitations of such data. However there are some statistical series (e.g. National Accounts) where it is recognised internationally that significant revisions to early estimates may be necessary and thus form an integral part of the dissemination process.

Official Series

When a statistical series is revised retrospectively (for example, due to rebasing or a change in methodology or seasonal adjustment), there is more than one published statistical value at each time point over the period of revision. Users will be informed which values for previous periods represent the official series.

Analysis of Revisions

Revisions analyses should be carried out to ensure and enhance data quality, revisions procedures and methods.

Revisions analyses study the impact of revisions by comparing provisional and final results (or preliminary and later estimates). The difference between provisional and final results (or preliminary and later estimates) indicates the scope of the revision. Systematic trends may also become apparent from revisions analyses, for instance, if preliminary values are usually revised upwards or downwards. In cases where significant systematic trends are identified, future preliminary estimates could be adjusted by the average bias established.

The results of revisions analysis should be presented in the background notes of releases and in the domain specific quality report.

Typically, there are three types of revisions:

• Planned Routine revisions

Routine revisions are changes in published data which are related to the regular data production process. The majority of revisions are planned, and include regular updates where:

It is the practice to publish preliminary estimates in advance of final estimates primarily due to the receipt of late responses, the production cycle or the availability of administrative sources.

• Seasonal factors are updated.
• Planned Major revisions

Major revisions are changes in published data, often substantial, due to one of the following reasons:

• Changes to definitions, classifications and methodology are incorporated.
• Updating / rebasing of the series takes place to reflect both the changes in the relative importance of sub-components and the insertion of new items into the statistical series (e.g. updating the basket of goods and services for the Consumer Price Index every 5 years).
• Updating of key statistical series (e.g. series based on Census of Population) is necessary.
• New data sources become available.
• Cessation of existing data sources occurs.

Data producers often take the opportunity of a forthcoming major revision to introduce methodological improvements. Therefore it is common that major revisions are not determined by one single cause, but a combination of them.

Unplanned revisions generally occur when:

• Published statistics, that are considered final, need to be revised due to the receipt of updated data that impacts significantly on the figures.
• Errors are detected.
• Improved estimated data become available from other sources.

Not relevant

The source of the raw data is described with more details in the paragraphs below.

In 2022, the headline indicator questions of the ICT Household Survey on household internet connectivity and frequency of individual usage of the internet, were asked of all respondents of the Labour Force Survey (LFS), using both Computer Assisted Personal Interviewing (CAPI) and Computer Assisted Telephone Interviewing (CATI).

The entire stock of private households at the time of the most recent Census of Population in the country represents the full sampling frame for the LFS. The sample based on the 2011 Census was first introduced on a wave by wave basis in Q1 2016 and was fully in effect as of Q1 2017. Effective from Q2 2019, a new sample based on the 2016 Census of Population was introduced incrementally on a quarterly basis and fully operational from Quarter 2 2020.

For the purposes of achieving sufficient sample size and Eurostat reporting requirements, the ICT Household Survey headline indicator questions were included in the LFS survey for two quarters in 2022, Quarters 1 and 2. The LFS data collection is carried out in 5 waves over 5 quarters. These ICT Household Survey questions on household internet access and individual frequency of internet usage were asked of all waves of the LFS in Quarters 1 and 2. Wave 5 respondents in Quarter 1 and Waves 4 and 5 respondents in Quarter 2 were asked if they would be willing to participate in a survey on ICT usage where all other questions on the ICT Household Survey on detailed ICT usage were covered. Respondents who were willing to partake in this ICT survey could complete the survey online (CAWI mode) or they could choose to do so by telephone interview (CATI). This change in mode of data collection has affected sample size. For the purposes of this publication and Eurostat reporting requirements, data from two quarters, Quarter 1 and 2, was used.

For the purposes of conducting the 2022 ICT Household Survey, respondents to the LFS in wave 5 in Quarter 1 and waves 4 and 5 of the LFS in Quarter 2, were asked if they would be willing to participate in the ICT Household Survey. Contact details (email and phone) were collected and the respondent indicated their preference to be contacted by email (with link to CAWI questionnaire) or by phone from call centre. The reference population is all individuals living in private households in Ireland. It therefore excludes persons with no usual address or those with a usual residence in a public institution, such as hospitals, nursing homes etc. All usual residents in each household are included. The sampling frame for the LFS is all private households in Ireland. Beginning in Q1 2019 a new sample for the LFS based on the 2016 Census of Population was introduced incrementally and this sample will be fully operational by Q2 2020. A new sample based on the 2016 Census of Population was introduced on a phased basis (over five quarters) from Q2 2019. This sample is stratified using administrative county and the Pobal HP (Hasse and Pratschke) Deprivation Index.

There was no longitudinal component to the survey.

Only one person per household was interviewed.

The survey results are weighted to agree with population estimates broken down by age, sex and region and are also calibrated to nationality control totals.

Annual

1) Methods used to gather data:

In 2022, the headline indicator questions of the ICT Household Survey on household internet connectivity and frequency of individual usage of the internet, were asked of all respondents of the Labour Force Survey (LFS), using both Computer Assisted Personal Interviewing (CAPI) and Computer Assisted Telephone Interviewing (CATI). For the purposes of achieving sufficient sample size and Eurostat reporting requirements, the ICT Household Survey headline indicator questions were included in the LFS survey for two quarters in 2022, Quarters 1 and 2.

The LFS data collection is carried out in 5 waves over 5 quarters. These ICT Household Survey questions on household internet access and individual frequency of internet usage were asked of all waves of the LFS in Quarters 1 and 2. Wave 5 respondents in Quarter 1 and Wave 4 and 5 respondents in Quarter 2 were asked if they would be willing to participate in a survey on ICT usage where all other questions on the ICT Household Survey on detailed ICT usage were covered.

Respondents who were willing to partake in this ICT survey could complete the survey online (CAWI mode) or they could choose to do so by telephone interview.

In 2022, the detailed questions on individual ICT usage were included in the LFS data collection. The change in mode of data collection has affected sample size. For the purposes of this publication and Eurostat reporting requirements, data from two quarters, Quarter 1 and 2, was used.

The method of contact for those LFS respondents who had opted to take part in the ICT Household Survey provided their contact details (email and telephone) for further follow up for participation in the ICT Household Survey. Some opted to be contacted by email (with link to CAWI questionnaire) and some opted to be contacted by phone. However, some email addresses and phone numbers given were incorrect. Some could be corrected on scrutiny for example typos or obvious errors in email address or phone number. CSO corrected where possible and made every effort to make contact and completion of questionnaire where possible. However, in 2022, there was immediate follow-up with respondents willing to take part in the ICT detailed individual questionnaire, once they had completed their LFS interview.

2) Short description of the survey method:

For the purposes of conducting the 2022 ICT Household Survey, respondents to the LFS in wave 5 in Quarter 1 and waves 4 and 5 of the LFS in Quarter 2, were asked if they would be willing to participate in the ICT Household Survey. Contact details (email and phone) were collected and the respondent indicated their preference to be contacted by email (with link to CAWI questionnaire) or by phone from call centre.

The reference population is all individuals living in private households in Ireland. It therefore excludes persons with no usual address or those with a usual residence in a public institution, such as hospitals, nursing homes etc. All usual residents in each household are included. The sampling frame for the LFS is all private households in Ireland. Beginning in Q1 2019 a new sample for the LFS based on the 2016 Census of Population was introduced incrementally and this sample will be fully operational by Q2 2020. A new sample based on the 2016 Census of Population was introduced on a phased basis (over five quarters) from Q2 2019. This sample is stratified using administrative county and the Pobal HP (Hasse and Pratschke) Deprivation Index.

The survey results are weighted to agree with population estimates broken down by age, sex and region and are also calibrated to nationality control totals.

3) Variables completed from an external source:

None

The following measures are in place to minimise error:

• Questionnaire design - quality and consistency checks integrated into the survey instrument.
• Comprehension errors – A substantial effort is made to ensure that the terms used in the survey are clear and readily understood. The more complicated question topics had help text displayed on the screen in CAWI interview.
• Clear training - Members of the staff are fully trained on the questionnaire for follow up calls to respondents, dealing with queries, etc.

Data capture errors: These errors are minimised by logic checks and limits on values that can be keyed for each question in the electronic questionnaire at the data collection point. In certain cases where text strings (used as an “other” category for some questions) were re-coded to the proper category while further validation checks were done.

Coding error: Checks are in place to minimise this risk, particularly with respect to Industry and occupational coding. The coding is conducted in-house at the CSO using an automated coding facility which is reviewed by a small team of coding experts. This approach reduces subjectivity and coding error. Overall it increases the quality and standard of coding of these key variables.

The reference population is all individuals living in private households in Ireland. It therefore excludes persons with no usual address or those with a usual residence in a public institution, such as hospitals, nursing homes etc. All usual residents in each household are included. The sampling frame is all private households in Ireland. Beginning in Q1 2019 a new sample for the LFS based on the 2016 Census of Population was introduced incrementally and this sample will be fully operational by Q2 2020.

A new sample based on the 2016 Census of Population was introduced on a phased basis (over five quarters) from Q2 2019 and is fully operational in Q2 2020 which is currently still in the field. As with the expiring sample below, the new sample is stratified using administrative county and the Pobal HP (Hasse and Pratschke) Deprivation Index and consists of 32,500 households per quarter.

The previous sample (used for Quarter 1 2021) was based on the 2011 Census of Population and was introduced incrementally from Q1 2016 and expired in Q1 2019. The sample was stratified using administrative county and the Pobal HP (Haase and Pratschke) Deprivation Index. A two-stage sample design was used. In the first stage 1,300 blocks were selected using Probability Proportional to Size (PPS) sampling. In the second stage households were selected using Simple Random Sampling (SRS). This ensured each household in the sample frame had an equal probability of selection.

Households are asked to take part in the survey for five consecutive quarters and are then replaced by other households in the same block. Thus, one fifth of the households in the survey are replaced each quarter and the LFS sample involves an overlap of 80% between consecutive quarters and 20% between the same quarter in consecutive years.

The ICT headline indicators on household internet connectivity and individual frequency of internet usage were asked of all waves in Quarters 1 and 2. The remaining questions on individual ICT usage were asked in a follow-on CAWI questionnaire where respondents from wave 5 in Quarter 1 and waves 4 and 5 in Quarter 2 were asked if they would be willing to participate in the follow-up CAWI IVCT Household Survey. Achieved response rate for this part of the data collection process was approximately 37%.

The survey results are weighted to agree with population estimates broken down by age, sex and region and are also calibrated to nationality control totals. To provide national results, the survey results were weighted to represent the entire population. The process used was as follows:

• Firstly, design weights were calculated for all units selected in the initial sample and are computed as the inverse of the selection probability of the unit. The purpose of design weights is to eliminate the bias induced by unequal selection probabilities.
• Next, these design weights were then adjusted for non-response. This eliminated the bias induced by discrepancies caused by non-response between the initial sample and the achieved sample, particularly critical when the non-responding households are different from the responding ones in respect to some survey variables as this may create substantial bias in the estimates. Design weights were adjusted for non-response by dividing the design weights of each responding unit in the final/achieved sample by the (weighted) response probability of the corresponding group or strata.

To obtain the final weights for the results, after the previous steps were carried out, the distribution of households by deprivation, NUTS3 region, sex and age was calibrated to the population of households. The CALMAR2-macro, developed by INSEE, was used for this purpose.

For the target indicator "Individuals having ordered goods or services for private use over the internet in the last 12 months" (individuals who ticked 'Within the last 3 months' or 'Between 3 months and a year ago' in question D1 of the 2022 model questionnaire):

Imputation rate (% of observations): 5.6%

Imputation rate (share of estimate): 5.2%

Methods used to impute item non-response: 

Response Homogeneity Groups Model plus further correction of sampling weights (calibration) to compensate for bias due to coverage problems.

Imputation methods:

• Deductive imputation
• Deterministic imputation mean/median by class
• Hot deck imputation
• Nearest neighbour imputation

Grossing up procedures have been applied to: 

Results are grossed up separately for individuals and households so each observation has an individual grossing factor and household grossing factor

Description of the weighting procedures:

The survey results are weighted to agree with population estimates broken down by age, sex and region and are also calibrated to nationality control totals. To provide national results, the survey results were weighted to represent the entire population. The process used was as follows:

• Firstly, design weights were calculated for all units selected in the initial sample and are computed as the inverse of the selection probability of the unit. The purpose of design weights is to eliminate the bias induced by unequal selection probabilities.

• Next, these design weights were then adjusted for non-response. This eliminated the bias induced by discrepancies caused by non-response between the initial sample and the achieved sample, particularly critical when the non-responding households are different from the responding ones in respect to some survey variables as this may create substantial bias in the estimates. Design weights were adjusted for non-response by dividing the design weights of each responding unit in the final/achieved sample by the (weighted) response probability of the corresponding group or strata.

To obtain the final weights for the results, after the previous steps were carried out, the distribution of households by deprivation, NUTS3 region, sex and age was calibrated to the population of households. The CALMAR2-macro, developed by INSEE, was used for this purpose.

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