Reference metadata describe statistical concepts and methodologies used for the collection and generation of data. They provide information on data quality and, since they are strongly content-oriented, assist users in interpreting the data. Reference metadata, unlike structural metadata, can be decoupled from the data.
Central Statistics Office, Skehard Road, Mahon, Cork
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Restricted from publication
2.1. Metadata last certified
8 February 2024
2.2. Metadata last posted
8 February 2024
2.3. Metadata last update
8 February 2024
3.1. Data description
The Adult Education Survey (AES) covers adults’ participation in education and training (formal - FED, non-formal - NFE and informal learning - INF). The 2022 AES focuses on people aged 18-69. The reference period for the participation in education and training is the twelve months prior to the interview.
Information available from the AES is grouped around the following topics:
Participation in formal education, non-formal education and training and informal learning
Volume of instruction hours
Characteristics of the learning activities
Reasons for participating
Obstacles to participation
Access to information on learning possibilities and guidance
The confidentiality of all information provided to the CSO by individual respondents is guaranteed by law under the 1993 Statistics Acts. All CSO office and field personnel become "Officers of Statistics" on appointment and are liable to penalties under this Act if they divulge confidential information to any outside person or body. Extreme precautions are taken to ensure that there are no violations of this principle throughout the AES survey process. The tablets on which the data was collected are encrypted and contain several layers of password protection. Data are only published in aggregate form and care is taken to ensure that the data are aggregated to avoid the indirect identification of respondents.
7.2. Confidentiality - data treatment
Estimates for number of persons where there are less than 30 persons in a cell are too small to be considered reliable. Where there are 30-49 persons in a cell, estimates are considered to have a wider margin of error and should be treated with caution.
8.1. Release calendar
A public release calendar is available on the CSO website.
See https://www.cso.ie/en/aboutus/lgdp/csodatapolicies/.
Every 6 years.
10.1. Dissemination format - News release
No news release for 2022 AES yet.
10.2. Dissemination format - Publications
A national publication planned for April 2024.
10.3. Dissemination format - online database
No online database for 2022 AES.
10.3.1. Data tables - consultations
Not applicable.
10.4. Dissemination format - microdata access
Researchers have access to anonymised sets of microdata based on agreement with Eurostat.
10.5. Dissemination format - other
None.
10.5.1. Metadata - consultations
Not applicable.
10.6. Documentation on methodology
An overview of the methodology will be provided in the background notes in the national release (planned for April 2024).
10.6.1. Metadata completeness - rate
Not applicable.
10.7. Quality management - documentation
This quality report.
11.1. Quality assurance
The quality of the AES is ensured through meeting the requirements of the AES regulation. The Irish AES questionnaire heavily depended on the model questionnaire provided by Eurostat. The questionnaire contained validations inherent in the design and filters to ensure only relevant questions asked. Training was delivered in two separate sessions to improve the delivery of training. Before submission of the data to Eurostat the file had to go through the STRUVAL/CONVAL validation tool.
11.2. Quality management - assessment
This is the only detailed source of information on adult educational and training activities providing an interesting insight into the difficulties which the adult population face in accessing education. Good feedback was received on the series of guidance variables from users.
Problems noted were:
Differentiating between the three concepts of formal/non-formal/informal is difficult for respondents to understand conceptually and for interviewers to probe as well. The CLA 2022 document was useful; several images were extracted as showcards to help interviewers if they had to help a respondent at the door.
Differentiating between the non-formal categories (course/guided on-the-job/workshop/private lesson) also caused some difficulties in the field. When respondents reported an activity as a course it would have to be corrected before the random generator kicked in to select the two non-formal events for further details.
Recall bias cannot be ignored particularly for work-related training 12 months ago; and even more so when these are selected for the detailed questions (hours spent and costs).
Some difficulties with LANGMOTHER and LANGUSED were identified during processing where they were both the same language, particularly when respondent had two mother tongues.
Questions not directly related to adult educational experience caused some difficulty with interviewers and respondents (parental education/household income).
The questions and associated categories could be quite long particularly in the guidance section.
High response burden if respondent has had formal and non-formal activities in the past 12 months.
12.1. Relevance - User Needs
Not available.
12.2. Relevance - User Satisfaction
There is no formal user survey specific to the AES.
12.3. Completeness
The dataset covers all variables as requested in the AES legislation.
12.3.1. Data completeness - rate
Not applicable.
13.1. Accuracy - overall
Good coherence to previous AES.
13.2. Sampling error
The sample is based on the persons. The full sample consisted of 12,000 persons picked using SRS (simple random sample).
13.2.1. Sampling error - indicators
All calculations are carried out in SAS.
Standard Error = s / square root n where s = standard deviation, n = sample size
Coefficient of Variation = (square root of the estimate of the sampling variance) / (estimated value)
See table 13.2.1 “Sampling errors - indicators for 2022 AES key statistics” in annex “IE - QR tables 2022 AES (excel)”.
13.3. Non-sampling error
In addition to known sampling errors, any survey will be subject to other non-sampling errors (for example measurement errors arising from questions not capturing the desired information accurately). Non-sampling error is far more difficult to measure than sampling error.
13.3.1. Coverage error
The entire population at the time of the most recent IPEADS (Irish Population Estimates from Administrative Data Sources) frame in the country represented the full sampling frame for the AES.
13.3.1.1. Over-coverage - rate
See table 13.3.1.1 “Over-coverage - rate” in annex “IE - QR tables 2022 AES (excel)”.
13.3.1.2. Common units - proportion
Not applicable.
13.3.2. Measurement error
No formal evaluation of sources of error is available, although measures are in place to minimise error.
Comprehension errors - An 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 - using two separate training sessions; one session concentrated on the concepts of formal/non-formal/informal education, the second session concentrated on the survey instrument questions.
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.
13.3.3. Non response error
As part of the weighting procedure, design weights were calibrated to population totals in age/sex/education.
13.3.3.1. Unit non-response - rate
See table 13.3.3.1 “Unit non-response - rate” in annex “IE - QR tables 2022 AES (excel)”.
13.3.3.2. Item non-response - rate
See table 13.3.3.2 “Item non-response rate” in annex “IE - QR tables 2022 AES (excel)”.
13.3.4. Processing error
Data capture errors: The majority of questions only allow answers to be entered to a limited set of predefined categories and therefore the number of edits required is limited.
Questionnaire routing is used to ensure questions are only asked to relevant respondents e.g. unemployment questions are only asked to those who are unemployed.
Coding error: Checks are in place to minimise this risk, particularly with respect to industry (NACE) and occupational (ISCO) coding.
In the AES, interviewers collect a detailed description of the enterprise and occupation from respondents.
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.
Field of education data is likewise captured and coded in the field to the relevant classification.
The codes assigned are then subsequently checked for quality purposes.
13.3.5. Model assumption error
Not applicable.
14.1. Timeliness
See below.
14.1.1. Time lag - first result
There was no preliminary data released.
14.1.2. Time lag - final result
Planned for April 2024. T+13 months.
14.2. Punctuality
As per regulation requirements.
See table 14.2 “Project phases - dates” in annex “IE - QR tables 2022 AES (excel)”.
14.2.1. Punctuality - delivery and publication
Not applicable.
15.1. Comparability - geographical
See table 15.1 “Deviations from 2022 AES concepts and definitions” in annex “IE - QR tables 2022 AES (excel)”.
No additional variables related to COVID-19 were collected.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable.
15.2. Comparability - over time
Break in time series between 2011 and 2016.
See table 15.2 “Comparability - over time” in annex “IE - QR tables 2022 AES (excel)”.
15.2.1. Length of comparable time series
Not applicable.
15.3. Coherence - cross domain
See table 15.3 “Coherence - cross-domain” in annex “IE - QR tables 2022 AES (excel)”.
The AES uses a different sample frame and methodology to LFS. To meet the precision requirements, we used SRS (simple random sample) stratified only by age for AES. Thus, we just selected persons. LFS on the other hand selects households so you would gather data on all the household for e.g. HATLEVEL. The overall total figures do not seem to differ to LFS, just around 1-2% difference.
15.3.1. Coherence - sub annual and annual statistics
Not applicable.
15.3.2. Coherence - National Accounts
Not applicable.
15.4. Coherence - internal
AES results for a given data collection round are based on the same microdata and results are calculated using the same estimation methods, therefore the data are internally coherent.
Costs were not derived.
17.1. Data revision - policy
Not applicable.
17.2. Data revision - practice
Not applicable.
17.2.1. Data revision - average size
Not applicable.
18.1. Source data
The sample frame is the IPEADS frame (Irish Population Estimates from Administrative Data Sources). It’s a personal register of persons.
An SRS (simple random sample) of persons was selected stratified by the precision age-groups.
To provide national results, the survey results were weighted to represent the entire population. The process used was as follows:
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.
To obtain the final weights for the results, after the previous steps were carried out, the distribution of persons, NUTS 3 region, highest level of educational attainment, sex and age was calibrated to the population of households in Q2 2022 LFS. The CALMAR2-macro, developed by INSEE, was used for this purpose.
See also table 18.1 “Source data” in annex “IE - QR tables 2022 AES (excel)”.
18.2. Frequency of data collection
Every 6 years.
18.3. Data collection
The data was collected by a team of 100 Field Interviewers and 10 Field coordinators. Interviewers were provided with a map of each of their interview areas as well as a listing of the address of each of the selected persons. Interviewers were trained June 2022 on the main concepts (formal, non-formal and informal education definitions) of the AES. Additionally, the interviewers were experienced, as they were currently working on CSO surveys such as the Survey on Income and Living Conditions and LFS. Interviewers received a manual with information such as detailed explanations about the questionnaire, definitions of the concepts involved and examples.
Most of the survey fieldwork was conducted using a team of face-to-face interviewers using Computer Assisted Personal Interviewing (CAPI). This enabled the use of extensive checks in the BLAISE interviewing software to make sure correct and coherent data was collected. It also ensured that respondents were only asked relevant questions and specific answers were within valid ranges. Information was collected directly from respondents - proxy responses from other members of the household were not accepted. Some interviews were collected via CAWI (computer assisted web-interview). Due to the challenging data collection environment we offered CAWI option to participants who were ‘soft refusals’ that we were unable to convert using CAPI.
See also table 18.1 “Source data” in annex “IE - QR tables 2022 AES (excel)”.
18.4. Data validation
Once the data was back in the CSO it was checked and if necessary queried with the field force. After the data collection phase was complete the field data was aggregated together
Information is collected in the field by a team of interviewers using tablets (CAPI using a Blaise application) and data is then transmitted to the main processing unit in the CSO. The majority of questions only allow answers to be entered to a limited set of predefined categories and therefore the number of entry edits required is limited. Questionnaire routing is used to ensure questions are only asked to relevant respondents e.g. unemployment questions are only asked to those who are unemployed. The fieldwork was carried out by educated interviewers who were entering respondents answers into previously constructed questionnaire on the laptop.
Finally, the data checking tool (STRUVAL/CONVAL) provided by Eurostat has been used for the data set checks just before sending the data.
18.5. Data compilation
See section 18.1.
18.5.1. Imputation - rate
None.
See table 18.5.1 “Imputation - rate” in annex “IE - QR tables 2022 AES (excel)”.
The Adult Education Survey (AES) covers adults’ participation in education and training (formal - FED, non-formal - NFE and informal learning - INF). The 2022 AES focuses on people aged 18-69. The reference period for the participation in education and training is the twelve months prior to the interview.
Information available from the AES is grouped around the following topics:
Participation in formal education, non-formal education and training and informal learning
Volume of instruction hours
Characteristics of the learning activities
Reasons for participating
Obstacles to participation
Access to information on learning possibilities and guidance
Individuals aged 18-69 living in private households.
The Republic of Ireland excluding any offshore islands not connected by road.
The reference period for the AES is the 12 months prior to the interview.
The fieldwork for the 2022 AES took place from 1 July 2022 to 1 March 2023.
Good coherence to previous AES.
Number, EUR.
See section 18.1.
The sample frame is the IPEADS frame (Irish Population Estimates from Administrative Data Sources). It’s a personal register of persons.
An SRS (simple random sample) of persons was selected stratified by the precision age-groups.
To provide national results, the survey results were weighted to represent the entire population. The process used was as follows:
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.
To obtain the final weights for the results, after the previous steps were carried out, the distribution of persons, NUTS 3 region, highest level of educational attainment, sex and age was calibrated to the population of households in Q2 2022 LFS. The CALMAR2-macro, developed by INSEE, was used for this purpose.
See also table 18.1 “Source data” in annex “IE - QR tables 2022 AES (excel)”.
Every 6 years.
See below.
See table 15.1 “Deviations from 2022 AES concepts and definitions” in annex “IE - QR tables 2022 AES (excel)”.
No additional variables related to COVID-19 were collected.
Break in time series between 2011 and 2016.
See table 15.2 “Comparability - over time” in annex “IE - QR tables 2022 AES (excel)”.
