National Documentation Centre (EKT)

RDI Metrics and Services Department

Zefirou 56, P Faliro, 17564, Greece

Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.

The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics(EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.

Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.

• The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
• The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
• The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).

See below.

According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).

See below.

See below.

Reporting units implemented the calculation of the FTEs following the questionnaire guidelines that have been drafted in line with FM recommendations (§ 333). Information about how calculations were performed has been provided by respondents in the metadata chapter of the questionnaire. Note that since 2017, FTEs less than 10% are not reported as R&D activities.

For the HES sector, R&D coefficients were applied for the calculation of the R&D share (and FTE) of the typical activities of the permanent personnel in Universities (GUF component of the HES funds). The coefficients have been derived from an extensive time use survey which was carried out by EKT in 2020 and included all categories of permanent personnel within Universities (professors, technical and administrative staff). Different coefficients were applied for the three personnel categories. FTE of the R&D personnel other than the permanent as well as some FTE of the permanent personnel regarding R&D activities that are additional to their regular activities, were calculated by the responding units and filled in the respective questionnaires. For that, the most prominent approach was the use of time sheets or the calculation of FTE based on the actual contractual cost of persons employed.

The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.

The statistical unit is the legal entity: University, University Hospital, HE Research Institute etc.

See below.

The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population of institutional units.

The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the HES Sector should consist of all R&D performing institutional units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level. 

Not requested. R&D statistics cover national and regional data.

Not requested. 

Not requested. 

- Personnel figures: PS (HC in older DSD versions), FT (FTE in older DSD versions)
- Expenditure figures: XDC (MIO_NAC - Millions of National Currency in older DSD versions)
- Percentage: PC

Reference year 2021.

See below.

- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development

- European Business Statistics Methodological Manual on R&D Statistics

Not requested.

Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes.

A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.

a)       Confidentiality protection required by law:

Confidentiality issues are clearly defined in the provisions on statistical confidentiality of the Greek statistical law (Law 3832/2010, as amended and in force), and are further specified in the Regulation on the Statistical Obligations of the Agencies of the Hellenic Statistical System (ELSS).

As a National Authority Agency of the ELSS, EKT fully implements the above law and regulation as well as the European Statistics Code of Practice (principle 5 and relevant indicators). To this end, EKT has developed and published its Statistical Confidentiality Policy (https://metrics.ekt.gr/sites/metrics-ekt/files/pages-pdf/EKT_Policy_Statistical_Confidentiality_1.1_en.pdf ).

b)       Confidentiality commitments of survey staff:

The internal personnel employed in the RDI statistics unit at EKT, the external statistical correspondents used for the collection and checking of primary data of its statistical surveys, as well as the external experts providing EKT with technical support or being assigned to carry out statistical works on account of EKT, commit themselves to the observance of statistical confidentiality of the data to which they have access or which they handle and sign a statistical confidentiality declaration.

Concerning the Procedures to identify confidential cells in data delivered to Eurostat, no confidential suppression/protection was applied on HES data. 

Before the beginning of each calendar year, EKT compiles and publishes on its website its Statistical Work Programme, which includes the planned statistical survey/work for the following year (https://metrics.ekt.gr/en/annual-program ). More specifically, EKT’s Statistical Work Programme presents the list of European and national statistics produced by EKT, refers to the key statistical legislation and sets out EKT’s annual objectives.

Data releases are also preannounced on the dedicated website of EKT for RDI indicators in the form of a “Data Release Calendar”, which specifies the scheduled month for each statistical data release.

The release calendar is available online (https://metrics.ekt.gr/statistics-announcements).

The calendar is accessible by all users at the following link: https://metrics.ekt.gr/en/statistics-announcements

EKT provides equal and simultaneous access to its statistical products to all users, as mentioned in the Dissemination Policy it applies (https://metrics.ekt.gr/sites/metrics-ekt/files/pages-pdf/EKT_Policy_Dissemination_1.1_en.pdf ). EKT is fully complying with the relevant principles and regulations of the Statistical Confidentiality Policy.

Annually.      

See below.

1) Y - Yes, N – No

See below.

1) Y – Yes, N - No 

Data tables (https://metrics.ekt.gr/research-development/datatables (available only in Greek language).

Not requested.

See below.

See below.

Not requested.

1) Y – Yes, N - No 

The production of R&D statistics follows the Frascati Manual 2015 concepts, definitions and methodology as well as Eurostat "FM2015 Implementation, Harmonisation EU Guidelines" as updated.

Detailed handbooks on R&D collection processes have been developed (internal) for all sectors and are continuously enriched and improved.

National metadata (in Greek) are made available to all users in the dedicated EKT website:

https://metrics.ekt.gr/sites/metrics-ekt/files/pages-pdf/EKT_SIMS_RDstatistics_el.pdf

Not requested.

See below.

EKT is an Agency of the Hellenic Statistical System (ELSS) and a National Authority, and as such it fully complies to the European and international standards concerning statistical methodologies, organizational procedures and IT infrastructure. EKT also complies strictly with the national and European legislative framework about statistics. EKT's quality policy is publicly available at https://metrics.ekt.gr/policies.

EKT follows the GSBPM model (Generic Statistical Business Process Model) for the production of RDI statistics. Accordingly, the workflow of a typical R&D collection follows all level 1 phases of the GSBPM model and level 2 sub processes, modified to meet the specific sector and data collection requirements. A detailed handbook on the production of R&D statistics, coupled with methodological annexes, has been developed and is continuously enriched and improved.

The quality of the data that EKT collects is controlled through a carefully implemented procedure that guarantees the production of meaningful statistics. The statistical process follows all level 1 phases of the GSBPM model and level 2 sub processes, modified to meet the specific sector and data collection requirements.  In particular, the following practices are in place to enhance data quality:

Designing of the statistical process: Before the collection begins, a thorough investigation of the actions needed to ensure the quality of the data is conducted. This includes a) (electronical ) registry updates (e.g., addition of new firms/organisations, updates with respect to the characteristics of each entry, such as Nace, Size, contact details, etc.), b) questionnaire updates (e.g., inclusion or modification of questions in line with the most recent EU methodological guidelines), c) preparation of the relevant infrastructure (see below), d) preparation of a calendar program (e.g., periodic reminders on specific dates), and e) the employment of external statistical  correspondents to assist the collection (including their training).

Data collection – start of the collection period: At the beginning of the collection period, a request to complete the questionnaire is forwarded electronically to all respondents, through an online questionnaire completion tool (LimeSurvey). The request is accompanied by an official letter signed by EKT’s Director, detailed instructions on how to complete the questionnaire, as well as instructions on how to request guidance regarding the completion process. For this purpose, EKT operates an electronic Help Desk which provides definitions, glossaries, and completion instructions with representative examples for each questionnaire. In addition, respondents can electronically submit questions and comments in the system which are, in turn, monitored by EKT members who are responsible for providing the relevant feedback. It is important to note that LimeSurvey provides statistics that assist the monitoring of the collection process (e.g., response rates, number of completed questionnaires, etc.). For administrative data, that mainly concern the GOV and HES R&D Sectors, separate requests are sent via emails to the corresponding government entities.

Data collection - During the collection period: During the collection period, external statistical correspondents as well as EKT members closely monitor survey populations to assist the questionnaire completion process. In a weekly basis, a thorough report is generated with the use of Python libraries, including basic quality and validation tests (e.g., time series consistency, outliers, etc.). Based on these reports, follow-up phone calls are conducted to elaborate on the improvement of the submitted answers. Important R&D performers are, in certain cases, addressed by means of on-site working meetings arranged to further explain RDI concepts and definitions and to provide further instructions for filling in the questionnaires. Importantly, EKT’s questionnaires at LimeSurvey have been internally developed and customised and have been equipped with real time (e.g., during the questionnaire completion) automated filters that test the validity of the data (for example, the total R&D expenditure is equal to the total R&D funding) according to the validation rules set in the surveys. Thus, a respondent cannot continue with the completion if the entry data is not valid. Cross validation rules are also available in real time (for example, statistical units reporting internal permanent R&D personnel in table B1.1 must also repot personnel costs in the relevant line of table C1). Through this effective filtering process, data quality is readily guaranteed at the collection level, up to a significant degree.

Data Processing: After the end of the collection period, the micro-data are passed through several, and more sophisticated, validation layers. For the analysis process for R&D statistics, a Data Management System (DMS) is in place, along with peripheral analytics tools such as Python and R libraries. The validation process includes tests with respect to: a) logical rules not provided in the online questionnaire (e.g. check if an increase of R&D personnel is accompanied by an increase of R&D labour costs, or check if enterprises that declare cooperation with others, for R&D/innovation activities, are a sub-group of innovation active firms, etc.), b) the time-series component (e.g., comparison with historical data), c) ratios (e.g., expenditure over the number of FTEs, etc.), d) cross-testing with data reported from other countries (e.g., Eurostat and OECD databases), cross-testing with administrative data from external (to EKT) sources, and e) statistical tests (e.g., identification of outliers).

The indicators production is automatically implemented via a combination of the DMS (R&D statistics) and R/Python libraries. Indicators are monitored for their validity through a second layer of tests based on the aggregated data. The validation process includes basic logical tests (e.g., the sum of R&D expenditure components is equal to the total expenditure), time series tests (e.g., consistency with historical indicators) as well as distribution tests (e.g., R&D and Innovation activities by region). Further, depending on their economic content, statistical outputs are additionally evaluated by EKT members with expertise on the field from other departments (e.g., policy analysts). The final SDMX file is tested and automatically corrected for rounding errors, through specific Python libraries.

The overall quality of the R&D statistical outputs is very good. The methodology has been designed in line with the FM recommendations, the relevant Commission Regulation and Eurostat guidelines. The continuous improvement is a key goal set by EKT and is implemented alongside the phases of GSBPM model. Firstly, requirements of national users were met (such as the Hellenic Statistical Authority, the central /regional Monitoring Committees of the national development projects (ESPA projects) etc.).

The overall quality of the HES R&D statistical outputs is very good. In 2015, EKT realized detailed case studies in 17 HES institutions coming from all HES R&D Directory categories: Special Account of Research Funds operating at Universities and TEIs, University Hospitals, EPIs, private colleges. The case studies were performed through on-site visits and interviews with respondents. The following topics were investigated: the data collection methodology and the systems used by the institutions, the problems faced as well as probable measurement errors, best practices in data collection as well as motives for respondents to participate in the survey. Moreover, any comments made by the respondents in the relevant section of the R&D questionnaire, regarding the questionnaire’s structure or the clarity of the guidelines provided, were considered. Overall, the respondents in HES declared a satisfaction rate above 95%. Based on the results of the case studies as well as the respondents’ input, the structure of the online questionnaires was improved and the guidelines available to respondents through the RDI e-helpdesk operating at EKT, were enriched.

For the RD survey round with reference year 2017, it is to be noted that the new methodological guidelines regarding the implementation of the FM 2015 were fully incorporated.

EKT’s R&D Information System is based on relevant international standards, such as CERIF and SDMX, robust technologies and best practices. The R&D Statistics Information System serves the objectives of:

a) R&D micro-data collection

b) Workflow-based statistical analysis

c) Validation of data

d) R&D indicators production

e) Benchmarking analysis with third party datasets

f) Dissemination of R&D statistics.

The desired functionality is achieved by four subsystems, namely the Organisation Registry (OR), the Online Data Collection System (ODCS), the Data Management System (DMS) and the SDMX Reference Implementation.

The Organisation Registry stores information about the businesses, institutions (e.g., universities and research Institutes) and organisations (e.g., government organisations) which are surveyed for collecting the R&D micro-data (e.g., EKT’s unique identification database key - UUID, VAT number, Nace, Size, address, contact person, etc). In addition, it is used for managing access control and permissions for all services, including access to the online R&D surveys.

The Online Data Collection System (LimeSurvey) is where the R&D questionnaires reside and where the organisations are invited to login in order to participate in the survey. As a modern online survey tool, it covers a multitude of important requirements for conducting a survey, such as real time data validation, respondents’ management and the management of participants’ responses.

The Data Management System is the single management point for all datasets involved (micro-data, paradata, organisation data, and indicators data), thus it is used to gather, store, interconnect and manage all collected R&D micro-data, the profiles of the organisations and the produced R&D indicators. Furthermore, it serves the following needs: data preservation and archiving (time-series), implementation of data validation & estimation workflows, real time automated generation of R&D indicators, data exporting (CSV, Excel, JSON etc.) and statistical reporting.

The dissemination of R&D indicators is accomplished through the SDMX Reference Implementation (SDMX-RI) which is the subsystem responsible for transmitting the produced R&D data to Eurostat.

To produce the extra non-mandatory variables, as defined in Modules 2.1 and 2.2, the following subsystems of the R&D Statistics Information System have been updated to become fully operational during the conduction of the R&D 2021 survey.

The update activities included both statistical and IT work.  

In the Online Data Collection System, new validation rules and features, relevant to the new fields and the non-mandatory variables, were added, to meet the need of collecting accurate and reliable data for the production of the corresponding statistical indicators. As an example, to check the consistency between the expenditure and the number of FTEs, the system calculates and depicts (in real time) the corresponding ratio (i.e., the current labour cost over the number of FTEs) for each of the five personnel categories defined in EKT’s R&D questionnaire (which are later utilized to compute the external/internal personnel breakdowns; see below).

The Data Management System (DMS) has been further developed and updated to encompass:

-          The identification of the indicators in the context of the SDMX specifications.

-          The update of EKT’s database that describes the SDMX indicators (created in MS Excel files).

-          The update of the DMS database in order to insert the new indicator’s metadata according to the SDMX structure.

-          The inclusion of the HES R&D Survey (microdata and statistical indicators) that was not previously supported (by the DMS) due to the increased data complexity of the sector. Although the particular update required a significant amount of effort, it allowed for the automated production of relevant R&D variables and statistical indicators. Moreover, it increased the efficiency of HES data processing. 

-          The definition of the calculations for each new indicator.

-          The initialisation of the corresponding DMS workflows in order to include the new indicators in the survey.

-          The creation of new validation rules (if needed).

-          The calculation of the new indicators.

It is important to note that several peripheral Python packages were, additionally, developed to support the DMS, for certain data processing and statistical needs:

1)            Additional layers of data validation:

-          Microdata level: a) cross-validation tests with respect to other sources (administrative data), b) time-series analyses using historical data from the R&D Survey, c) statistical tests (e.g., outlier detection and correlations between variables), d) logical and correctness tests.

-          Aggregate level: a) cross-validation tests with other countries’ data (e.g., data reported in the pilot studies, relevant to the current deliverable), b) logical and correctness tests (e.g., the total is equal to the sum of the components for each breakdown).

2)            Imputation and estimation tools: a) outlier corrections, b) strata imputation, c) variable estimation based on historical rates and strata rates (using linear programming techniques).   

3)            Calculation of statistical indicators: libraries to calculate all statistical indicators related to modules 2.1 and 2.2, including automated tools that correct for rounding errors (based on liner programming techniques).

4)            Interactive dashboard reporting: interactive visualization (e.g., bar charts) of the indicators (and their breakdowns) in absolute values and percentages and interactive comparison with data provided by other countries (e.g., data from the pilot studies).

Finally, the SDMX Reference Implementation (SDMX-RI) has been updated accordingly, to effectively incorporate the transmission of the new variables to Eurostat.

(i)            Validation rules for all questions and produced indicators concerning the new-added variables of Modules 2.1 and 2.2.

(ii)           Building-up the calculations for the new-added variables of Modules 2.1 and 2.2.

(iii)          Preparation of the SDMX (R&D V4) infrastructure in order to incorporate the new-added variables in the procedure of the national R&D production.

See below.

1)       Users' class codification

1- Institutions:
• European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
• in Member States, at the national or regional level: Ministries of Economy or Finance, other ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
• International organisations: OECD, UN, IMF, ILO, etc.

2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.

3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public.

4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.)

5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services)

6- Other (User class defined for national purposes, different from the previous classes.)

To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.

See below.

Mandatory variables: 100%

Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.

Criteria:

A) Obligatory data. Only 'Very Good' = 100%, Poor' >95%; 'Very Poor' <100% apply.

B) Optional data. 'Very Good' = 100%; 'Good' = >75%; 'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.

See below.

1) Y-start year, N – data not available

1) Y-start year, N – data not available

1) Y-start year, N – data not available

1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional').

2) Y-start year

Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).

Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:

1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.

2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:

a) Coverage errors,

b) Measurement errors,

c) Non response errors and

d) Processing errors.

Model assumption errors should be treated under the heading of the respective error they are trying to reduce.

1)  Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (5). In the event that errors of a particular type do not exist, is used the sign ‘-‘.

2)  The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.

1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys. Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.  

2) 'Good' = In the event that at least one out of the three criteria above described would not be fully met.

3) 'Satisfactory' = In the event that the average rate of response would be lower than 60% even by meeting the two remaining criteria.

4) 'Poor' = In the event that the average rate of response would be lower than 60% and at least one of the two remaining criteria would not be met.

5) 'Very Poor' = If all the three criteria are not met.

That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.

The main indicator used to measure sampling errors is the coefficient of variation (CV).
Definition of coefficient of variation:
CV= (Square root of the estimate of the sampling variance) / (Estimated value)

Not applicable.

Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.

Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.

a)       Description/assessment of coverage errors:

 No R&D performing units are considered missing. Therefore, coverage errors are considered negligible.

b)      Measures taken to reduce their effect:

 Not applicable.

Not applicable.

Not requested.

Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.

a)       Description/assessment of measurement errors:

The main difficulties that have been reported by respondents were: a) the separation of R&D from other activities, b) the breakdown of labour cost in all kinds of personnel.

b)      Measures taken to reduce their effect:

With regard to the Universities / University hospitals, the use of R&D coefficients has reduced significantly the errors reported by the respondents and concerned the separation of R&D activities from other activities.

- Guidelines have been included in the questionnaire and both electronic and telephone helpdesk was operating throughout the collection period to respond to enquiries by the respondents.

- In the electronic questionnaire a set of validation rules is added in order to help the respondents to complete the questionnaire. Also, a set of further information, guidelines and examples added in the electronic questionnaire in order to explain more the questions.

- A detailed question with the breakdown of the personnel is added in order to allow respondents to clearly report all the categories of personnel (external, internal, full-time, part-time, etc) and its breakdowns HCs, FTEs, Education level, Age group, Nationality.

- A detailed question about the labour costs of all personnel categories as explained above, which are then compared to the respective FTEs to assure the consistency of the reported figures.

- A mandatory questions relevant to R&D activities that can be answered as 'Yes/No' in order to proceed to the next section. The answers to these questions help respondents understand the concept of R&D and leads to the identification of R&D activities in their organisation.

- Regional analysis is also separated by type of personnel (researchers and other staff) for HCs / FTEs as also R&D expenditures by region. Users have to report except the R&D expenditures per region also the HCs and FTEs. Moreover, in cases where measurement errors have been detected during the validation phase (e.g. inconsistencies between personnel and expenditure data, peaks in time series), institutions were contacted by experienced staff to clarify misunderstandings, e.tc.

Moreover, in cases where measurement errors have been detected during the validation phase (e.g. inconsistencies between personnel and expenditure data, peaks in time series), institutions were contacted by experienced staff to clarify misunderstandings, e.tc.

Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.

There are two elements of non-response:

-Unit non-response which occurs when no data (or so little as to be unusable) are collected on a designated population unit.

-Item non-response which occurs when data only on some, but not all survey variables are collected on a designated population unit.

The extent of response (and accordingly of non response) is also measured with response rates. 

The main interest is to judge if the response from the target population was satisfactory by computing the un-weighted response rate.

Definition: Eligible are the survey units which indeed belong to the target population. Frame imperfections always leave the possibility that some units may not belong to the target population. Moreover, when there is no contact with certain units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.

Un-weighted Unit Non- Response Rate = 1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey) 

Definition:
Un-weighted Item Non-Response Rate (%) = 1-(Number of units with a response for the item) / (Total number of eligible , for the item, units in the sample) * 100

There was extensive communication with heads of the HES institutions as well as official bodies (such as the Plenary Meetings of the Rectors). E-mail invitations, accompanied with an official letter, signed by the Director of EKT, were sent to launch the survey. The e-mail invitations explained the purpose and mandatory nature of the survey.

The appropriate links to the online publications of EKT were also provided in the invitations to help respondents to better understand the use of the data they provide. Hard copies of the publications were also sent via post.

Statistical units were urged to respond via email reminders as well as systematic follow-up by phone and personal emails.

Between data collection and the beginning of statistical analysis, data must undergo a certain processing: coding, data entry, data editing, imputation, etc. Errors introduced at these stages are called processing errors. Data editing identifies inconsistencies or errors in the data.

Not requested.

Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.

Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)

a) End of reference period: 

December 2021 (T)

b) Date of first release of national data: 

October 2022 (T+10)

c) Lag (days):

10 months (300 days)

a) End of reference period: 

December 2021 (T)

b) Date of first release of national data: 

June 2023 (T+18)

c) Lag (days):

18 months (540 days)

Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.

Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release)

See below.

Not requested.

No deviations from FM recommendations and classifications. Therefore, R&D data for Greece are considered to be comparable with international R&D data.

The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197  or Frascati manual and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts/issues.

Given the change in the

definition of the HES in the

FM2015, private postsecondary institutions have been removed from the HES register and included either in BES or PNP register (starting reference year 2016). This change had not a serious effect on the R&D indicators given their small R&D activity.

The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual, where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.

See below.

See below.

1)       Breaks years are years for which data are not fully comparable to the previous period.

Are the data produced in the same way in the odd and even years? If no, please explain the main differences.

This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. The education statistics (UNESCO/OECD/Eurostat (UOE)) include R&D expenditure in tertiary educational institutions and follow the recommendations of the Frascati manual regarding the definition of R&D expenditure. Due to the differences in the coverage some differences in the two datasets (UOE questionnaire and the R&D HES surveys) are expected. However, there is a need to ensure that a harmonised approach is used for compiling data in the two domains. The two statistical domains should aim for a consistent use of R&D coefficients for splitting teaching and research time.

Not requested.

Based on the MOU between ELSTAT and EKT, microdata are sent annually to ELSTAT for inclusion in the National Accounts.

Not applicable.

See below.

This part compares key R&D variables as preliminary and final data.

(1)    Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).

(2)    Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).

The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible. 

1)       The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.

1)        T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)

Not requested.

Not requested.

Not requested.

Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.

See 12.3.3.

See below.

Validation processes run throughout all phases of the R&D survey. All institutions belonging to the target population (HES R&D Directory at EKT) are fully covered by a census survey. In order to eliminate any important missing information, an essential requirement of the R&D survey is to attain a 100% response rate by the core institutions of HES R&D Directory, namely all HES institutions which have a systematic R&D activity and contribute the major part of the final data (52 institutions).

The administrative data that are used for the compilation of R&D data as well as for validation / imputation purposes come from official reliable sources at national (governmental / other authorities) or European level (eCORDA database).

To ensure the validity of the data as completed by the respondents, validation checks are embedded into the online questionnaire to inform users, in real time, about the occurrence of errors (check totals, sub-totals, totals between questions, no of FTEs larger than no of HCs e.tc.).

Any inconsistencies in the reported data are checked in each questionnaire received, namely at the institutional level, as for example FTEs of internal R&D personnel vs R&D labour costs taking into account the average remuneration cost for each sector, proportion of other current costs vs labour costs, etc. Time series are also examined at the level of each responding unit and item peaks are cross-validated both with respondents and administrative sources (for example a significant increase in EC funding for a reporting unit is validated using the eCORDA database and communicated and verified by the respondent).

Thorough validation is carried out to check the coherence of the outputs produced. To this end, in addition to the extensive statistical checking, multiple official sources are used (as explained above, MIS data, eCORDA, etc.) to check the collected data against relevant data. The final outputs are interpreted using both tangible and tacit knowledge accumulated at EKT as well as sectoral studies produced by other national bodies.

See below.

No imputation.

Not requested.

Not requested.