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For any question on data and metadata, please contact: Eurostat user support |
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1.1. Contact organisation | Statistics Austria |
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1.2. Contact organisation unit | Directorate Social Statistics - Science, Technology, Education |
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1.5. Contact mail address |
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2.1. Metadata last certified | 16/01/2024 | ||
2.2. Metadata last posted | 16/01/2024 | ||
2.3. Metadata last update | 16/01/2024 |
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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:
For further information see the 2022 AES legislation (http://ec.europa.eu/eurostat/web/education-and-training/legislation) and the 2022 AES implementation manual (http://ec.europa.eu/eurostat/web/education-and-training/methodology). |
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3.2. Classification system | |||
- Classification of Learning Activities (CLA, 2016 edition) |
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3.3. Coverage - sector | |||
AES covers all economic sectors. |
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3.4. Statistical concepts and definitions | |||
Definitions as well as the list of variables covered are available in the 2022 AES implementation manual (http://ec.europa.eu/eurostat/web/education-and-training/methodology). |
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3.5. Statistical unit | |||
Individuals, non-formal learning activities. |
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3.6. Statistical population | |||
Population stock - third quarter of 2022 - aged 18 to 69: 6 135 974 persons. Sample drawn from Central Register of Registration (ZMR): 18 751 persons. Respondents in final data set transmitted to Eurostat: 7 826. |
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3.7. Reference area | |||
Austria. Persons living in collective households are excluded. |
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3.8. Coverage - Time | |||
Fieldwork period: 2007 AES pilot: 16/04/2007-15/11/2007 2011/12 AES: 02/10/2011-30/05/2011 2016/17 AES: 01/10/2016-31/03/2017 2022/23 AES: 01/10/2022-31/03/2023 |
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3.9. Base period | |||
Not applicable. |
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Number, EUR. |
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The fieldwork period took place between 01.10.2022 and 31.03.2023. The reference period are the 12 months prior to the interview. |
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6.1. Institutional Mandate - legal acts and other agreements | |||
At European level: Basic legal act: Regulation (EU) 2019/1700 Implementing act: Commission Implementing Regulation (EU) 2021/861 At national level: Austrian Federal Statistic Act 2000 |
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6.2. Institutional Mandate - data sharing | |||
Not applicable. |
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7.1. Confidentiality - policy | |||
The strict confidentiality provisions of the Austrian Federal Statistic Act 2000 regulate the handling of sensitive data relating to individuals and organisations. |
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7.2. Confidentiality - data treatment | |||
The Statistics Act contains measures for the protection of the right to confidentiality of individuals and organisations as well as measures for ensuring the confidentiality of micro data. These include the deletion of names and addresses at the earliest possible moment and the obligation of secrecy imposed on persons entrusted with tasks of official statistics. Compliance with confidentiality provisions is monitored by a data protection agent who acts as an internal controlling body. |
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8.1. Release calendar | |||
Press release is planned for the 09.01.2024. A release calendar is available on the website of Statistics Austria. |
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8.2. Release calendar access | |||
8.3. Release policy - user access | |||
Tables with main results will be made available on the homepage of Statistics Austria in the 1st quarter of 2024. Microdata for scientific use will be provided through the Austrian Micro Data Center (AMDC) in the 1st quarter of 2024. A comprehensive print publication will be available in the 4th quarter of 2024. |
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Every 6 years. |
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10.1. Dissemination format - News release | |||
Press release is planned for 09.01.2024. |
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10.2. Dissemination format - Publications | |||
Tables with main results are planned to be made available on the homepage of Statistics Austria on 09.01.2024. Moreover, a research report with all main results will be published on the homepage of Statistics Austria in the 4th quarter of 2024 that can be downloaded for free. |
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10.3. Dissemination format - online database | |||
Excluding EUROSTAT online database, no other online database will be available for Austrian AES results. |
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10.3.1. Data tables - consultations | |||
Not applicable. |
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10.4. Dissemination format - microdata access | |||
Anonymized microdata for scientific use will be provided through the Austrian Micro Data Center (AMDC) in the first quarter of 2024. |
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10.5. Dissemination format - other | |||
None. |
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10.5.1. Metadata - consultations | |||
Not applicable. |
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10.6. Documentation on methodology | |||
National standard documentation about AES 2022/23 will be published in 2024. |
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10.6.1. Metadata completeness - rate | |||
Not applicable. |
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10.7. Quality management - documentation | |||
National standard documentation about AES 2022/23 will be published in 2024. |
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11.1. Quality assurance | |||
Preparation:
Fieldwork:
Data processing:
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11.2. Quality management - assessment | |||
Strengths:
Weaknesses:
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12.1. Relevance - User Needs | |||
The main user groups for Austrian AES data are:
AES results are an important source of information to policy makers at the national level. Results serve as a benchmark for adult learning in the population and give direction and incentive to further improve participation in adult education. |
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12.2. Relevance - User Satisfaction | |||
There are no measures available at national level to analyse user satisfaction on 2022 AES. |
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12.3. Completeness | |||
The dataset covers all variables as requested by the legislation. |
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12.3.1. Data completeness - rate | |||
Not applicable. |
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13.1. Accuracy - overall | |||
Sample size was calculated in order to fulfil pre-defined criteria according to sample and precision requirements defined in the EU regulation. The survey faced relative high unit- and item- non-response rates, but complex methods for weighting and imputation (see table 18.5.1 “Imputation - rate” in annex “AT - QR tables 2022 AES (excel)” were employed to prevent any systematic bias. We assume a high accuracy of our estimates. |
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13.2. Sampling error | |||
The requirement for the sampling error in the regulation was met by the sample, see table 13.2.1 “Sampling errors - indicators for 2022 AES key statistics” in annex “AT - QR tables 2022 AES (excel)”. |
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13.2.1. Sampling error - indicators | |||
See table 13.2.1 “Sampling errors - indicators for 2022 AES key statistics” in annex “AT - QR tables 2022 AES (excel)”. For the calculation of variation measures bootstrap weights were generated with respect to the survey design. Implementation took place with the surveyed package in R. |
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13.3. Non-sampling error | |||
Non-sampling errors are covered by items 13.3.1 - 13.3.5 below. |
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13.3.1. Coverage error | |||
390 persons were identified as over-coverage which is 2.1 percent of the sample. |
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13.3.1.1. Over-coverage - rate | |||
See table 13.3.1.1 “Over-coverage - rate” in annex “AT - QR tables 2022 AES (excel)”. |
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13.3.1.2. Common units - proportion | |||
Not applicable. |
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13.3.2. Measurement error | |||
In order to reduce measurement errors Pre-Testing (CAPI and CAWI) was undertaken and the interviewers took part in trainings at Statistics Austria (before the fieldwork started). Additionally, a system of checks and warnings was implemented into the E-Questionnaire according to the validation rules. Systematic errors are not known. |
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13.3.3. Non response error | |||
Overall, the response rate surpassed the anticipated target. However, consistent with the typical pattern in AES, response rates were higher among younger and more educated participants (especially in CAWI-Mode). To mitigate bias, using the experience from previous AES surveys efforts were made to ensure that groups with traditionally low response rates were adequately represented in the sample. Furthermore, the following measures were implemented to address non-response bias: Unit Non-Response:
Item Non-Response: Considerable item non-response was mainly observed for variables concerning monetary values. In such cases, the K-nearest neighbour method was employed for imputation. |
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13.3.3.1. Unit non-response - rate | |||
See table 13.3.3.1 “Unit non-response - rate” in annex “AT - QR tables 2022 AES (excel)”. |
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13.3.3.2. Item non-response - rate | |||
See table 13.3.3.2 “Item non-response rate” in annex “AT - QR tables 2022 AES (excel)”. |
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13.3.4. Processing error | |||
The questionnaire provided fixed response options for most questions, minimizing processing errors. However, certain variables (HATFIELD, JOBISCO, LOCNACE, FEDFIELD, NFEFIELD1, and NFEFIELD2) allowed for open-ended responses, which were coded by Statistics Austria's coding experts post-interview. |
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13.3.5. Model assumption error | |||
No model calculated. |
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14.1. Timeliness | |||
The reference period for the 2022 AES were the 12 months prior to the interview. |
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14.1.1. Time lag - first result | |||
1st quarter of 2024. |
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14.1.2. Time lag - final result | |||
Not applicable. |
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14.2. Punctuality | |||
No deviations. See table 14.2 “Project phases - dates” in annex “AT - QR tables 2022 AES (excel)”. |
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14.2.1. Punctuality - delivery and publication | |||
Not applicable. |
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15.1. Comparability - geographical | |||
See table 15.1 “Deviations from 2022 AES concepts and definitions” in annex “AT - QR tables 2022 AES (excel)”. No additional variables related to COVID-19 were collected. |
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15.1.1. Asymmetry for mirror flow statistics - coefficient | |||
Not applicable. |
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15.2. Comparability - over time | |||
Despite changing modes (from mainly CAPI to mainly CAWI) results between AES 2022/23 and AES 2016/17 and between both modes are coherent. A pilot study was conducted in March of 2022 and logistical regression did not reveal any significant effect of modes on respondent answer behaviour. A detailed report of the pilot can be found in the annex below. See table 15.2 “Comparability - over time” in annex “AT - QR tables 2022 AES (excel)”. Annexes: Pretest Report |
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15.2.1. Length of comparable time series | |||
Not applicable. |
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15.3. Coherence - cross domain | |||
See table 15.3 “Coherence - cross-domain” in annex “AT - QR tables 2022 AES (excel)”. |
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15.3.1. Coherence - sub annual and annual statistics | |||
Not applicable. |
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15.3.2. Coherence - National Accounts | |||
Not applicable. |
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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. |
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The average time for answering the questionnaire CAPI/CAWI was 21 minutes. |
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17.1. Data revision - policy | |||
Not applicable. |
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17.2. Data revision - practice | |||
Not applicable. |
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17.2.1. Data revision - average size | |||
Not applicable. |
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18.1. Source data | |||
1) Sampling frame is the Central Register of Registration (ZMR) and the sampling unit is a single person; data from the Central Register of Registrations (ZMR) concerning valid main residence registrations has been forwarded quarterly to Statistics Austria since 2002. Processing of the databases is based on a uniform concept for classifying registration results for the purposes of demographic analyses. The sample was drawn in two parts with the reporting dates 30.06.2022 and 30.09.2022, respectively. The delayed drawing of the second sample made it possible to adjust sampling for low response groups in the first half of the fieldwork period. 2) The survey is based upon a stratified random sample. The total sample size was determined by precision requirements as defined in the regulation. Thus the net sample was fixed with a size around 5000 persons for the population between 25 and 69 years and 2000 persons for the population between 18 and 24 years. The strata are defined by interviewer region and 3 groups with different NFE participation probabilities. These 3 groups use the variables age, gender, nationality and education. See also table 18.1 “Source data” in annex “AT - QR tables 2022 AES (excel)”. |
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18.2. Frequency of data collection | |||
Every 6 years. |
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18.3. Data collection | |||
See attached files and also table 18.1 “Source data” in annex “AT - QR tables 2022 AES (excel)”. Annexes: AES Questionnaire 2022 in German AES 2022 Interviewer Guidelines in German |
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18.4. Data validation | |||
During the survey: A system of checks and warnings was implemented into the E-questionnaire. Pop-ups for invalid or implausible values directly informed the respondent or interviewer to recheck or correct their answers. National testing: The national version of the AES questionnaire data set underwent transformation into the default EU code book structure using statistical software (R). Testing procedures adhered to pre-established checking rules outlined in the AES manual (Eurostat), supplemented by additional plausibility checks (e.g., highest level of education, field of education, HATYEAR, JOBTIME, etc.) to ensure the absence of data or coding errors. Additionally, data were compared to LFS and previous AES data to ensure plausibility. Eurostat testing: The final data set underwent validation through the online checking tools STRUVAL and CONVAL, and thus only contain verified and valid values. |
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18.5. Data compilation | |||
Weighting: Weighting was performed by iterative proportional fitting according to the distribution of the number of persons in AgeGroups(4) X Education(5) X Gender, AgeGroups(10) X Gender, Gender X Occupational Status and NUTS2. Different non-response models were tested, however since the models performed poorly it was decided to skip the step. Imputation: Imputation was performed using the k-nearest neighbour method for the variables (HHINCOME, NFEPAIDVAL1, NFEPAIDVAL2 and LANGMOTH1). |
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18.5.1. Imputation - rate | |||
See table 18.5.1 “Imputation - rate” in annex “AT - QR tables 2022 AES (excel)”. |
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18.6. Adjustment | |||
Not applicable. |
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18.6.1. Seasonal adjustment | |||
Not applicable. |
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None. |
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AT - QR tables 2022 AES (excel) |