1. Contact |
1.1 Contact organisation |
Istat - Italian National Statistical Institute |
1.2 Contact organisation unit |
SSE - Division for integrated system for labour, education and
training |
1.3 Contact name |
1.4 Contact person function |
1.5 Contact mail address |
Via Cesare Balbo, 16
00184 Rome Italy |
1.6 Contact email address |
1.7 Contact phone number |
1.8 Contact fax number |
2. Statistical presentation |
2.1 Data description |
The Adult Education Survey (AES) covers adults’ participation in education and training (formal - FED, non-formal - NFE and informal learning - INF). The 2016 AES focuses on people aged 25-64. The reference period for the participation in education and training is the twelve months prior to the interview.
For further information see the 2016 AES legislation (http://ec.europa.eu/eurostat/web/education-and-training/legislation) and the 2016 AES implementation manual (http://ec.europa.eu/eurostat/web/education-and-training/methodology). |
2.2 Classification system |
|
2.3 Coverage - sector |
AES covers all economic sectors. |
2.4 Statistical concepts and definitions |
Definitions as well as the list of variables covered are available in the 2016 AES implementation manual (http://ec.europa.eu/eurostat/web/education-and-training/methodology). |
2.5 Statistical unit |
Statistical units: Persons aged 18-24: 1687 units; Persons aged 25-64: 10536; persons aged 65-74: 3789 |
2.6 Statistical population |
Reporting units: Individuals The 2016 AES covers the resident population living in private households. |
2.7 Reference area |
Italy - The whole national territory |
2.8 Coverage - Time |
Fieldwork period Wave 1 - 1 september 2012 to 15 december 2012 Fieldwork period Wave 2 - 1 may 2017 to 4 september 2017 |
2.9 Base period |
Not applicable |
3. Statistical processing |
3.1 Source data |
Sampling design of the AES survey is a one stage stratified random sampling, with the strata defined by the combination of the modalities of the following planned domains: territorial unit (the North-West, the North–East, the Centre and the South with the Islands), municipality typology, gender, and age class. A fixed number of individuals is selected in each stratum without replacement and with equal probabilities. The optimal number of units to be selected in each stratum is defined as a solution of a linear integer problem (Bethel, 1989). In particular, the minimum sample size in each stratum is determined in order to ensure that the variance of sampling estimates of the target variable in each domain of interest does not exceed a given threshold, in terms of coefficient of variation. The target variables used for optimising the sample allocation are ‘not in education or training, ‘in formal education’, ‘in non-formal education and training’, whose mean and variance are estimated in each stratum by data collected from the previous survey. The optimal allocation has been obtained by constraining the precision of the estimates of these target variables to a maximum CV of 9 % per estimation domain for the first and the second variables, and a maximum CV of 15 % for the third one respectively. In this way, an overall sample size of 18 500 individuals has been the result of Bethel’s algorithm. The corresponding optimal sample size at the level of each territorial unit has been then distributed into the 22 Italian administrative regions (representing hence unplanned domains of estimation), according to a proportional criterion. The sample weights that refer to the entire reference population have been calibrated with respect to the total Italian population (national and regional) of the second semester of 2017 (deduced from the LFS).
The distance function used for calibration is the truncated logarithmic. See also table 3.1 in annex 'IT - ESQRS tables 2016 AES (excel)'. |
3.2 Frequency of data collection |
Frequency of data collection is five-yearly |
3.3 Data collection |
See table 3.3 in annex 'IT - ESQRS tables 2016 AES (excel)'. See also the national questionnaire below. |
3.4 Data validation |
CATI technique was used for data collection during the interview (an outsourcing of data entry). Coherence and compatibility rules were incorporated into the data acquisition software. Any discrepancy or partial non-response has been solved during the interview and accordingly there was no process of data check and correction in the post-collection phase. Regarding the final validation, coherence control with data from other surveys (LFS) or sources was done, as well as checks against previous AES data. |
3.5 Data compilation |
Not applicable |
3.6 Adjustment |
Not applicable |
4. Quality management |
4.1 Quality assurance |
Since the 90s Istat adopted a systematic approach to ensure quality in both statistical information and service to the community. For this purpose the Institute has defined a quality policy providing itself with appropriate tools as well as management changes to carry it out. Istat quality policy is coherent with the European framework developed by Eurostat, taking up its main principles and definitions. In 2005, the European Statistics Code of Practice (revised in 2011) established the principles to follow in order to ensure and strengthen both accountability and governance of the European Statistical System and the National Statistical Systems inside it. Essential points of Istat quality policy are: Process quality: consisting in the production of accurate statistical information efficiently and effectively; Product quality: consisting in the dissemination of high-quality timely statistical data which are relevant for the users, also the potential ones; Documentation: consisting in the storage and availability of information necessary not only for a proper use of data but also to ensure transparency in all the production activities of statistical data; Respect for respondents: consisting in the reduction of response burden and in the respect of respondent's privacy; Strengthening of statistical literacy: consisting in promoting a proper use of statistical information in policy-making to better support decisions and policies; Users' orientation: consisting in making statistical information easily accessible and understandable and in satisfying user needs as much as possible.
For details:
http://www.istat.it/en/about-istat/quality |
4.2 Quality management - assessment |
The process was submitted to Quality statistical auditing for the 2012 edition Increase in the number of individuals in the sample to arrive at providing representative estimates at the regional level. Estimates at regional level are important to allow policies on the territory to increase the participation of individuals in training. In addition, regional estimates affect several academic users. |
5. Relevance |
5.1 Relevance - User Needs |
See table 5.1 in annex 'IT - ESQRS tables 2016 AES (excel)'. |
5.2 Relevance - User Satisfaction |
ISTAT is constantly interested in understanding who the users of the statistics it produces are, what the information needs are and whether they match production, if the statistics produced satisfy users. To this end, direct consultation tools have been developed, such as the online survey of Customer satisfaction and the collection of feedback, and indirect tools such as the analysis of access to the site and the search methods. To complete the Istat user engagement strategy, the Statistical information Users Committee (Cuis) was set up in 2011, with the aim of assisting the Institute in the recognition of the demand for statistical information expressed by the institutions. public and private sectors and society as a whole. The Commission currently consists of about 50 members, represented by associations, bodies and institutions that use official statistical information. It is responsible for assessing the compliance of official data with users' needs and for reporting any information gaps, proposing solutions to fill them. |
5.3 Completeness |
Our dataset covers all variables as requested in the 2016 AES legislation |
5.3.1 Data completeness - rate |
Not applicable |
6. Accuracy and reliability |
6.1 Accuracy - overall |
The 2016 AES can be assessed as being successful regarding the overall accuracy due to using the appropriate statistical methods for minimizing the errors (e.g. measurement errors, coverage errors, processing errors, calibration) |
6.2 Sampling error |
A computer procedure developed by the Institute is currently used to calculate the sampling errors. See table 6.2a in annex 'IT - ESQRS tables 2016 AES (excel)'. |
6.2.1 Sampling error - indicators |
See table 6.2.1 in annex 'IT - ESQRS tables 2016 AES (excel)'. |
6.3 Non-sampling error |
See items 6.3.1 - 6.3.4 below. |
6.3.1 Coverage error |
Public registers on households - Households are selected from the municipalities’ registers; they cover the whole reference population. The data might contain wrong information like addresses (due for instance to recent household mobility), wrong inclusions (recent emigration) and missed inclusions (recent immigration). For the CATI sample, we used the oversampling. Time lag between last update of the sampling frame and the moment of the actual sampling = 9 months; geographical coverage = national (except municipalities affected by earthquakes); coverage of different subpopulation = no. |
6.3.1.1 Over-coverage - rate |
See table 6.3.1.1 in annex 'IT - ESQRS tables 2016 AES (excel)'. |
6.3.1.2 Common units - proportion |
Not applicable |
6.3.2 Measurement error |
The quality actions to control the sources of measurement error (questionnaire, interviewers, respondents) are: Training course for interviewers Supervision of interviewers by observing interviews Monitoring response rates per interviewer during data collection Interviewer-questionnaire matching by identification codes Collection of information on interviewer characteristics Debriefing with interviewers on data collection problems Ex post evaluation of interviewers performance based on indicators Availability of a database on interviewers Drafting an interviewer instruction manual |
6.3.3 Non response error |
The quality actions to control unit nonresponse are: Description of survey objectives by interviewers Survey presentation letter signed by Istat President Telephone contacts to make an appointment for the interview Establishing a toll free line or telephone number for further explanations Guarantees on statistical confidentiality Control on interview outcome codes (completed, refused, noncontacted, duplicated, etc.) |
6.3.3.1 Unit non-response - rate |
See table 6.3.3.1 in annex 'IT - ESQRS tables 2016 AES (excel)'. |
6.3.3.2 Item non-response - rate |
There aren't 2016 AES variables with item non-response rates 10% and higher |
6.3.4 Processing error |
Data entry was realized by CATI techinque. The control process and editing systems applied to the data were part of the CATI software. |
6.3.4.1 Imputation - rate |
The imputation of data was very limited. We use only deterministic methods with auxiliary information for very few cases. |
6.3.5 Model assumption error |
Not applicable. |
6.4 Seasonal adjustment |
Not applicable. |
6.5 Data revision - policy |
Not applicable. |
6.6 Data revision - practice |
Not applicable. |
6.6.1 Data revision - average size |
Not applicable. |
7. Timeliness and punctuality |
7.1 Timeliness |
The reference period for the 2016 AES are the 12 months prior to the interview. |
7.1.1 Time lag - first result |
9 months |
7.1.2 Time lag - final result |
Not applicable. |
7.2 Punctuality |
See table 7.2 in annex 'IT - ESQRS tables 2016 AES (excel)'. |
7.2.1 Punctuality - delivery and publication |
Not applicable. |
8. Coherence and comparability |
8.1 Comparability - geographical |
National comparability: The sample design allows the comparison of results by italian regions. International comparability: Commission Regulation (EU) No 1175/2014 establishes the obligation of the survey in all countries of the European Union, thus ensuring the comparability of information on adult education. See also table 8.1 in annex 'IT - ESQRS tables 2016 AES (excel)'. |
8.1.1 Asymmetry for mirror flow statistics - coefficient |
Not applicable. |
8.2 Comparability - over time |
See table 8.2 in annex 'IT - ESQRS tables 2016 AES (excel)'. |
8.2.1 Length of comparable time series |
Not applicable. |
8.3 Coherence - cross domain |
See table 8.3 in annex 'IT - ESQRS tables 2016 AES (excel)'. |
8.4 Coherence - sub annual and annual statistics |
Not applicable. |
8.5 Coherence - National Accounts |
Not applicable. |
8.6 Coherence - internal |
Not applicable. |
9. Accessibility and clarity |
9.1 Dissemination format - News release |
A press release linked to the publication of the data is planned. |
9.2 Dissemination format - Publications |
National report on Istat website |
9.3 Dissemination format - online database |
No online database. |
9.3.1 Data tables - consultations |
Not applicable. |
9.4 Dissemination format - microdata access |
Access to the micro data will be possible on Istat web site. |
9.5 Dissemination format - other |
No other dissemination format. |
9.6 Documentation on methodology |
Metadata and methodology notes will be published together the national report. |
9.7 Quality management - documentation |
Metadata and methodology notes will be published together the national report. |
9.7.1 Metadata completeness - rate |
Not applicable. |
9.7.2 Metadata - consultations |
Not applicable. |
10. Cost and Burden |
Data collection staff: 40 interviewers full-time for data collection via the CATI survey and one supervisor. The average time for answering the questionnaire was 10 minutes. |
11. Confidentiality |
11.1 Confidentiality - policy |
Several national legal acts guarantee the confidentiality of data requested for statistical purposes.
According to art. 9, paragraph 1 of the Legislative Decree n. 322 of 1989, personal data cannot be disseminated but in aggregated form, in order to make it impossible to make any reference to identifiable individuals. They can only be used for statistical purposes.
Legislative Decree n. 322 of 1989, art. 6 bis and Legislative Decree n. 196 of 2003 Annex A3 (Code of conduct and professional practice applying to the processing of personal data for statistical and scientific research purposes within the framework of the national statistical system), art. 8, provide that the exchange of personal data within the National Statistical System (Sistan) is possible if it is necessary to fulfil requirements provided by the National Statistical Programme or to allow the pursuit of institutional purposes. The supply of the identification data of statistical units is allowed within the framework of entities included in the National Statistical System if the requesting party declares that no identical statistical result can be obtained otherwise .
Regarding subjects who do not belong to Sistan, Article. 7 of the Code of conduct (Decree n. 196/2003, Annex A3) states that it is possible to transmit individual data files without direct identifiers within the framework of specific laboratories set up by entities included in the National Statistical System, under certain conditions and only if that the data are protected by the application of different statistical methods that make it highly unlikely the identification of statistical units. |
11.2 Confidentiality - data treatment |
the 3-unit threshold rule is applied to published aggregated data. |
12. Comment |
No further comments. |