1.1. Contact organisation
Statistical Office of the Slovak Republic
1.2. Contact organisation unit
Cross-sectional Statistics Department
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Lamacska cesta 3/C
840 05 Bratislava 45
Slovakia
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
2.1. Metadata last certified
30 October 2023
2.2. Metadata last posted
30 October 2023
2.3. Metadata last update
30 October 2023
3.1. Data description
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.
3.2. Classification system
- 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).
3.2.1. Additional classifications
| Additional classification used | Description |
| ISCO-08 | International Standard Classification of Occupations. |
| ISCED-2011 | International Standard Classification of Education. |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | Frascati manual methodology is used for identifying R&D. R&D covers three activities: basic research, applied research and experimental development. |
| Fields of Research and Development (FORD) | NSE and SSH are covered and separately available at the 1-digit level (6 main fields of science). From 2013 onwards, indicators (R&D personnel , R&D expenditure) are available at 2-digit level of FOS. |
| Socioeconomic objective (SEO by NABS) | SEO are covered and available at chapter level of NABS. NABS 12 and NABS 13 are available also at subchapter level. |
3.3.2. Sector institutional coverage
| Higher education sector | Corresponds to Frascati manual. Medical institutions complete the questionnaire only when performing R&D activities - tasks within the framework of a particular research programme. From reference year 2009 onwards, they were classified in the HES sector. |
| Tertiary education institution | Higher education institutions are defined by the Act No. 175/2008 on Higher Education as amended. Identification of the HES population is made according to the public information on the web-site of the Ministry of Education, Science, Research and Sport of the SR. |
| University and colleges: core of the sector | Included |
| University hospitals and clinics | Included are only teaching/training clinics. |
| HES Borderline institutions | In line with the FM. |
| Inclusion of units that primarily do not belong to HES | No |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Treatment in line with the Frascati Manual §2.122. |
| External R&D personnel | Treatment in line with the Frascati Manual §5.20-5.24. Doctoral students in daily form of study are included in total R&D personnel since 1994 data. External R&D personnel (all categories listed in Table 5.2 of the FM2015) are included from 2016 onwards, doctoral students are separately available and classified as external personnel. Master's students are not included. |
| Clinical trials | Not applicable. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Available. |
| Payments to rest of the world by sector - availability | Not available. |
3.3.5. Extramural R&D expenditures
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).
| Data collection on extramural R&D expenditure (Yes/No) | No |
| Method for separating extramural R&D expenditure from intramural R&D expenditure | |
| Difficulties to distinguish intramural from extramural R&D expenditure |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year. |
| Source of funds | Source of funds follows the Frascati Manual methodology. Source "Rest of the world, higher education sector" is not further divided to foreign branch campuses and other HE institutions. |
| Type of R&D | All 3 types of R&D available, basic research, applied research and experimental development. |
| Type of costs | From 1996 onwards, detailed breakdown by type of costs is available. Since 2018, more detailed breakdown of capital and current expenditure according to FM2015 is available. |
| Defence R&D - method for obtaining data on R&D expenditure | Only defence related R&D expenditure performed by the civil sector is surveyed. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Total number of persons during the calendar year. |
| Function | The classification into the three categories of personnel is by ISCO-08 classification. |
| Qualification | Questions on personnel by qualification were introduced in the 1994 questionnaire. Qualification at level ISCED-5B (First-stage tertiary education - practical) was included in the secondary education. From 2006 onwards, qualification at level ISCED 5B (ISCED 2011 554) is separately available. From 2016, qualification structure available for employees only. |
| Age | Available for researchers, for researchers employees in 2003-2015, for internal researchers from 2016 onwards. |
| Citizenship | Available for researchers, for researchers employees in 2003-2015, for internal researchers from 2016 onwards. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year. |
| Function | The classification into the three categories of personnel is by ISCO-08 classification. |
| Qualification | Questions on personnel by qualification were introduced in the 1994 questionnaire. Qualification at level ISCED-5B (First-stage tertiary education - practical) was included in the secondary education. From 2006 onwards, qualification at level ISCED 5B (ISCED 2011 554) is separately available. From 2016, qualification structure available for employees only. |
| Age | Not surveyed. |
| Citizenship | Not surveyed. |
3.4.2.3. FTE calculation
FTE is provided by units in the R&D questionnaire, it is calculated according to the formula:
sum of work-hours in R&D activities over the current year divided by 2000
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| Cross-classification of R&D personnel and researchers by occupation and qualification is available for employees. | HC, FTE | annually |
3.5. Statistical unit
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.
3.6. Statistical population
See below.
3.6.1. National target population
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.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
| Definition of the national target population | In the Slovak Republic, mission, tasks and status of higher education institutions are defined by the Act No. 175/2008 on Higher Education as amended. Identification of the HES population is made according to the public information on the web-site of the Ministry of Education, Science, Research and Sport of the SR. This information contains list of particular types of higher education institutions. |
|
| Estimation of the target population size | - 34 institutions providing formal tertiary education programmes (20 public, 3 state, 10 private and 1 foreign higher education institutions). The whole number of rectorates, faculties and centres under the direct control of or administrated by a tertiary education institution was 165. - 16 university hospitals |
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested. See point 3.4.
3.9. Base period
Not requested. The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
R&D indicators are available according to 3 units of measure:
R&D expenditure in thousand €.
Number of R&D personnel in HC as total number of persons engaged in R&D during the calendar year.
Number of R&D personnel in FTE as sum of work-hours in R&D activities over the current year divided by 2000.
Reference period is calendar year 2021
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
| Legal acts / agreements | 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 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. 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. Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020. |
| Nature of the “obligations” of responsible national organisations to produce statistics and report to international organisations | Yes, derived from the legal act. |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | Production of R&D statistics is governed by the general national statistical legislation. |
| Legal acts | Act No. 540/2001 Coll. on state statistics as amended; https://www.slov-lex.sk/pravne-predpisy/SK/ZZ/2001/540/ Program of State Statistical Surveys, published for three years in the Collection of Laws of the SR; https://www.slov-lex.sk/pravne-predpisy/SK/ZZ/2020/292/20230101 |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | Yes |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | Yes, derived from the legal act. |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | Laid down in the Act No. 540/2001 Coll. on state statistics as amended. |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | No access of third organisations to confidential data. |
| Planned changes of legislation | - |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- European Business Statistics Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
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:
Act No. 540/2001 Coll. on state statistics as amended.
Internal Directive on the Protection of Confidential Statistical Data (regulates the method of management and implementation of activities related to ensuring the protection of confidential statistical data in the Statistical Office of the Slovak Republic).
b) Confidentiality commitments of survey staff:
The survey staff signed the confidentiality commitment.
7.2. Confidentiality - data treatment
Confidential data are protected according to the CR (EC) No 322/97 and according to the national Act No 540/2001 on State Statistics as amended.
Internal methodological instruction of the Statistical Office of the Slovak Republic regulates specific methods and values of parameters used in the protection of confidential statistical data of surveys and data sets specified in the Directive on the protection of confidential statistical data.
Identifying confidential cells in aggregated data: minimum frequency rule (n=3) together with the k % dominance rule.
8.1. Release calendar
The Catalog of Publications is publicly available on the website of the Statistical Office of the Slovak Republic and it contains basic information on the issued titles, issue dates, periodicity and language version.
8.2. Release calendar access
https://slovak.statistics.sk/wps/portal/ext/products/publikacie/Kalendár zverejňovania publikácií/
8.3. Release policy - user access
Information on all new released publications is available on the website of the Statistical Office of the Slovak Republic. The release policy determines the availability of statistical data to all users at the same time.
Yearly data dissemination.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Content, format, links, ... | |
| Regular releases | N | |
| Ad-hoc releases | N |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1 | Content, format, links, ... |
| General publication/article (paper, online) |
Y | Yearbook of Science and Technology of the SR 2022 Statistical Yearbook of the SR 2022 – chapter on S&T&I Slovak Republic in figures 2022 – chapter on R&D |
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
On-line database of the Statistical Office of the Slovak Republic, https://slovak.statistics.sk/wps/portal/ext/Databases
The way to R&D data on this address: DATAcube. - Access to database DATAcube. - Multi-domain statistics - Science, technology and innovation - Research and Development
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
See below.
10.4.1. Provisions affecting the access
| Access rights to the information | Micro-data are provided only for scientific purposes according to the stated rules. Conditions for granting access to confidential statistical data for scientific purposes are provided on the website of the Statistical Office of the Slovak Republic. |
| Access cost policy | Payment required. |
| Micro-data anonymisation rules | Anonymized micro-data are provided to outside users for scientific purposes. Users (researchers) have to sign an agreement with the Statistical Office of the Slovak Republic that includes also data protection items. |
10.5. Dissemination format - other
See below.
10.5.1. Metadata - consultations
Not requested.
10.5.2. Availability of other dissemination means
| Dissemination means | Availability (Y/N)1 | Micro-data / Aggregate figures | Comments |
| Internet: main results available on the national statistical authority’s website | Y | Aggregate figures | |
| Data prepared for individual ad hoc requests | Y | Aggregate figures | |
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
Meta-information is available in on-line publication and on-line database, which includes description of indicators, definitions, survey methodology etc.
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
See below.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | Metadata on the statistical web-site and in publications, methodological explanation in the questionnaire. |
| Request on further clarification, most problematic issues | We have a few requests from data users for clarification. They are mainly about data breakdowns. |
| Measure to increase clarity | We permanently improve the methodological explanations in the survey questionnaire. |
| Impression of users on the clarity of the accompanying information to the data | According to the information from requests on the R&D data from users by phone or e-mail, we assume that accompanying methodological explanations to data is understandable for users on overall. |
11.1. Quality assurance
Statistical Office of the SR has established the system of quality management. Quality manual contains description of system of quality management and fulfillment of requirements of standard ISO 9001.
The application of the Quality manual in practice ensures that all activities with impact on the quality of statistical products are planned, managed, examined, evaluated and meet the requirements accepted in the customer order. Quality manual is available at: https://slovak.statistics.sk/wps/wcm/connect/b28fd6cb-76cf-4477-9a97-d9789b1fa429/Prirucka_kvality_2021.pdf
The basis of the whole system of quality management is the European Statistics Code of Practice.
11.2. Quality management - assessment
The overall quality of the HES R&D statistical outputs is very good. The survey methodology follows the Frascati manual recommendations and the national and international requirements. The R&D statistics complies with the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The R&D survey coverage, reference period, data collection, checking and data processing follow the Eurostat methodology and recommendations for production of the common R&D statistics of the EU member states. Results of the survey for the country total and by regions as well were transmitted to Eurostat. Transmission of R&D data to Eurostat was realised in the SDMX format via eDAMIS - the safe, secure procedure.
Main strengths of the survey:
- R&D survey is an annual exhaustive survey
- The survey methodology complies with the Frascati Manual methodology and the Eurostat/OECD harmonised R&D data collection
- All mandatory and most of optional indicators were introduced to the R&D survey
- Enterprises are contacted to consult errors and missing variables in all necessary cases
- The item-non response is equal to zero
Main activities undertaken to assure high quality of HES R&D statistics:
- Increase the response rate by several reminders
- Communications with respondents
- Use of best practices, quality guidelines, quality management activities used in the Statistical Office of the Slovak Republic according to ISO 9001.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
| 1 | The European Commission (DG RTD, DG ENTR); Eurostat |
Data used in publications and further development; Data used for dissemination in Eurostat on-line database and publications, preparation of EP and Council report etc. |
| 1 | Ministry of Economy; Ministry of Finance; Ministry of Education, Science, Research and Sport; Other Ministries; Government Office of the Slovak Republic; National Bank of Slovakia; Statistical Office of the Slovak Republic | Data used for policy making in the field of Science, Technology and Innovation, further for sectoral comparisons and international comparisons; Data used for storing in the database and published in national publications and on the web site |
| 1 | OECD, UN | Data used in databases, publications and international comparisons |
| 2 | Slovak Chamber of Commerce and Industry (SCCI); Regional Chambers of SCCI; Association of Industrial Research and Development Organisations |
Data used for analysis and comparisons; Data used for sectoral and regional comparisons; Data used for analysis and comparisons |
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.)
12.2. Relevance - User Satisfaction
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.
12.2.1. National Surveys and feedback
| Conduction of a user satisfaction survey or any other type of monitoring user satisfaction | The Statistical Office of the Slovak Republic carried out the user satisfaction survey in 2022, where also R&D statistics was included; https://slovak.statistics.sk/wps/portal/ext/aboutus/marketing/survey.of.satisfaction |
| User satisfaction survey specific for R&D statistics | No, the survey covered several statistical areas, where products of R&D statistics were included together with innovation, energy and environment statistics in one category. |
| Short description of the feedback received | Average rate of user satisfaction with products of these statistics was 68,9 %. No specific feedback for these statistics was in 2022. |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
not available
12.3.2. Completeness - overview
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.
| 5 (Very Good) |
4 (Good) |
3 (Satisfactory) |
2 (Poor) |
1 (Very poor) |
Reasons for missing cells |
|
| Preliminary variables | Y | |||||
| Obligatory data on R&D expenditure | Y | |||||
| Optional data on R&D expenditure | Y | |||||
| Obligatory data on R&D personnel | Y | |||||
| Optional data on R&D personnel | Y | Part of optional data on R&D personnel is available only for internal personnel. | ||||
| Regional data on R&D expenditure and R&D personnel | Y |
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%.
12.3.3. Data availability
See below.
12.3.3.1. Data availability - R&D Expenditure
| Availability1 | Frequency of data collection | Gap years – years with missing data | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Source of funds | Y-1994 | Annual | Extension of the structure of sources. 2000: introduction of funds from HE institutions, 2004: source from abroad divided to public and private, 2006: full breakdown of source from abroad according to the FM, 2018: extension of the survey from own sources in the GOV and PNP sectors. |
2000, 2004, 2006, 2018 | To be able to provide data in ESTAT/OECD questionnaires. | |
| Type of R&D | Y-1994 | Annual | ||||
| Type of costs | Y-1994 | Annual | 1996: Detailed breakdown of capital and current expenditure, 2016: Current R&D expenditure includes also costs related to external R&D personnel, 2018: Scholarship of PhD students is surveyed separately. |
1996, 2016, 2018 | To fulfill EU requirements. | |
| Socioeconomic objective | Y-1997 | Annual | ||||
| Region | Y-1997 | Annual | ||||
| FORD | Y-1996 | Annual | Introduction of 2-digit level of FOS | 2009 | To be able provide data for users. | |
| Type of institution | Y-2015 | Annual |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
| Availability1 | Frequency of data collection | Gap years – years with missing data | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | Y-2002 | Annual | ||||
| Function | Y-1994 | Annual | ||||
| Qualification | Y-1994 | Annual | Separation of the education level ISCED 5B (ISCED 2011 554) for employees only. | 2006 | To be able provide data for users. | |
| Age | Y-2003 | Annual | Extension for internal personnel, before for employees only. | 2016 | ||
| Citizenship | Y-2003 | Annual | Extension for internal personnel, before for employees only. | 2016 | ||
| Region | Y-1996 | Annual | ||||
| FORD | Y-1996 | Annual | Introduction of 2-digit level of FOS. | 2009 | To be able provide data for users. | |
| Type of institution | N |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
| Availability1 | Frequency of data collection | Gap years – years with missing data | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | Y-1994 | Annual | ||||
| Function | Y-1994 | Annual | ||||
| Qualification | Y-1994 | Annual | Separation of the education level ISCED 5B (ISCED 2011 554) for employees only. | 2006 | To be able provide data for users. | |
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y-1996 | Annual | ||||
| FORD | Y-1996 | Annual | Introduction of 2-digit level of FOS. | 2009 | To be able provide data for users. | |
| Type of institution | N |
1) Y-start year, N – data not available
12.3.3.4. Data availability - other
| Additional dimension/variable available at national level1) | Availability2 | Frequency of data collection | Breakdown variables |
Combinations of breakdown variables | Level of detail |
| Total R&D Expenditure and R&D Expenditure from government sources in selected areas of R&D: information and communication technologies, of which software; biotechnology; nanotechnologies and nanomaterials | Y-2006 | Annual |
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
13.1. Accuracy - overall
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.
13.1.1. Accuracy - Overall by 'Types of Error'
| Sampling errors | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
| Coverage errors | Measurement errors | Processing errors | Non response errors | ||||
| Total intramural R&D expenditure | - | - | - | - | - | - | No error known. |
| Total R&D personnel in FTE | - | - | - | - | - | - | No error known. |
| Researchers in FTE | - | - | - | - | - | - | No error known. |
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.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1 |
4 (Good)2 |
3 (Satisfactory)3 |
2 (Poor)4 |
1 (Very poor)5 |
| Total intramural R&D expenditure | X | ||||
| Total R&D personnel in FTE | X | ||||
| Researchers in FTE | X |
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.
13.2. Sampling error
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.
13.2.1. Sampling error - indicators
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)
13.2.1.1. Variance Estimation Method
Not applicable.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
| Business enterprise | - |
| Government | - |
| Higher education | - |
| Private non-profit | - |
| Rest of the world | - |
| Total | - |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
| Function | Researchers | - |
| Technicians | - | |
| Other support staff | - | |
| Qualification | ISCED 8 | - |
| ISCED 5-7 | - | |
| ISCED 4 and below | - |
13.3. Non-sampling error
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.
13.3.1. Coverage error
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:
Not relevant for census survey.
b) Measures taken to reduce their effect:
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
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 software for data collection and processing of data contains also checks to eliminate errors and logical inconsistencies. All errors and logical inconsistencies are consulted with respondents and corrected sequentially.
b) Measures taken to reduce their effect:
The R&D questionnaire contains detailed methodological explanation for filling in the particular modules. The regional department of the statistical office collects the questionnaires. Responsible persons of the regional office are instructed regularly about the improvements of the survey and necessary steps during the collection procedure.
13.3.3. Non response error
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.
13.3.3.1. Unit non-response - rate
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)
13.3.3.1.1. Un-weighted unit non-response rate
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
| 156 | 156 | 0,00 |
13.3.3.2. Item non-response - rate
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
13.3.3.2.1. Un-weighted item non-response rate
| R&D variable/breakdown | Item non-response rate (un-weighted) (%) | Comments |
| R&D Expenditure | 0% | census survey |
| R&D Personnel in FTE | 0% | census survey |
| Researchers in FTE | 0% | census survey |
13.3.3.3. Measures to increase response rate
In order to increase the response rate, non-respondents were connected by phone. Several phone reminders were realised in the necessary cases.
13.3.4. Processing error
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.
13.3.4.1. Identification of the main processing errors
| Data entry method applied | Data entry is realised by the software ISIS - ZBER that is own software of the Statistical Office of the Slovak Republic (SO SR). It is a database oriented program product that provides in the integrated form: · record keeping and evaluation of reporting duty · first data recording by the keyboard · data editing on the base of the defined checks · enables manual and automation data updating (auto-corrections) · protocols about its activity The central database storesData entry errors were corrected. Their number is not available. the data in the form of source micro-data (ISIS – ZBD). |
| Estimates of data entry errors | Data entry errors were corrected. Their number is not available. |
| Variables for which coding was performed | |
| Estimates of coding errors | On-line census survey. Number of coding errors is not available. Coding errors were corrected immediately. |
| Editing process and method | On-line census survey. Editing is carried out using the software that contains definitions of controls. All inconsistencies are corrected during the processing procedure. All missing information is amended and item non-response is equal to zero in the case of responded questionnaires. |
| Procedure used to correct errors | On-line census survey. In the case of occured errors detected by the software, respondents were re-contacted to discuss the errors and correct them. |
13.3.5. Model assumption error
Not requested.
14.1. Timeliness
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.
14.1.1. Time lag - first result
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: 31/12/2021
b) Date of first release of national data: 12/10/2022
c) Lag (months): 10
14.1.2. Time lag - final result
a) End of reference period: 31/12/2021
b) Date of first release of national data: 28/06/2023
c) Lag (months): 18
14.2. Punctuality
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.
14.2.1. Punctuality - delivery and publication
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release)
14.2.1.1. Deadline and date of data transmission
| Transmission of provisional data | Transmission of final data | |
| Legally defined deadline of data transmission (T+_ months) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | 10 | 18 |
| Delay (days) | 0 | 0 |
| Reasoning for delay |
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
No divergences from FM, from international classifications, no divergence in survey coverage.
15.1.3. Survey Concepts Issues
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.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
| R&D personnel | FM2015 Chapter 5 (mainly paragraph 5.2). | No | |
| Researcher | FM2015, § 5.35-5.39. | No | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat'EBS Methodological Manual on R&D Statistics). | No | |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No | |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly paragraph 4.2). | No | |
| Statistical unit | FM2015 §3.70 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Target population | FM2015 §9.6 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Sector coverage | FM2015 §3.67-3.69 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Hospitals and clinics | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Borderline research institutions | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Major fields of science and technology coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | No |
15.1.4. Deviations from recommendations
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.
| Methodological issues | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
| Data collection method | No | |
| Survey questionnaire / data collection form | No | |
| Cooperation with respondents | No | |
| Coverage of external funds | No | |
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | No | |
| Data processing methods | No | |
| Treatment of non-response | No | |
| Variance estimation | ||
| Method of deriving R&D coefficients | - | |
| Quality of R&D coefficients | - | |
| Data compilation of final and preliminary data | No |
15.2. Comparability - over time
See below.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1 | Nature of the breaks | |
| R&D personnel (HC) | From 1994 | 1994, 2016, 2018 | In 1994 Frascati definitions were adopted for the national R&D surveys. 2016: Inclusion of external personnel. Doctoral students are collected separately, previously with employees. 2018: Methodological change in content of the indicator R&D employees in head counts; it includes total number of R&D employees during the reference year, before number of R&D employees at the end of the reference year (as of December 31). |
| Function | From 1994 | ||
| Qualification | From 1994 | 2006, 2016 | Qualification at level ISCED 5B separately available, before 2006 included in the secondary education. 2016: for employees only |
| R&D personnel (FTE) | From 1994 | 1994, 2016 | In 1994 Frascati definitions were adopted for the national R&D surveys. 2016: Inclusion of external personnel. Doctoral students are collected separately, previously with employees. |
| Function | From 1994 | ||
| Qualification | From 1994 | 2006, 2016 | Qualification at level ISCED 5B separately available, before 2006 included in the secondary education. 2016: for employees only |
| R&D expenditure | From 1994 | 1994 | In 1994 Frascati definitions were adopted for the national R&D surveys. |
| Source of funds | From 1994 | 2006 | Introduction of detailed breakdown of R&D expenditure from source abroad according to the FM. |
| Type of costs | From 1994 | ||
| Type of R&D | From 1994 | ||
| Other | From 1994 | 2013 | Regional breakdown by NUTS2 is according to local units. |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.2.3. Collection of data in the even years
Are the data produced in the same way in the odd and even years? If no, please explain the main differences.
The data are produced in the same way in the odd and even years.
15.3. Coherence - cross domain
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.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Not available
15.3.3. National Coherence Assessments
| Variable name | R&D Statistics - Variable Value | Other national statistics - Variable value | Other national statistics - Source | Difference in values (of R&D statistics) | Explanation of / comments on difference |
| - | - | - | - | - | - |
15.3.4. Coherence – Education statistics
Statistical units of the HES sector are
- rectorates and faculties of the higher education institutions (legal units with NACE Rev.2=85420, faculties are not legal units, they have not an identification number in the statistical business register). Data of faculties are collected in separate questionnaires under the identification number of the rectorate and marked with a serial number for their distingushing;
- institutions not providing formal tertiary education programmes, but having their R&D activity under the direct control of or administered by a tertiary education institution (not legal units); their data are collected in separate questionnaires under the identification number of the rectorate and marked with a serial number for their distingushing;
- university hospitals (legal units).
15.4. Coherence - internal
See below.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total R&D expenditure – HERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
| Preliminary data (delivered at T+10) | 233420,711 | 9874,446 | 9554,879 |
| Final data (delivered T+18) | 233420,711 | 9874,446 | 9554,879 |
| Difference (of final data) | 0 | 0 | 0 |
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration (cost¨in national currency) | |
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | R&D labour costs / FTEs of internal R&D personnel = 16706,1 |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Other current costs for external R&D personnel / FTEs of external R&D personnel = 14052,2 |
(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.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | % sub-contracted1) | |
| Staff costs | Not separately available. | No work sub-contracted to third parties. |
| Data collection costs | Not separately available. | No work sub-contracted to third parties. |
| Other costs | Not separately available. | No work sub-contracted to third parties. |
| Total costs | Not separately available. | No work sub-contracted to third parties. |
| Comments on costs | ||
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
| Number of Respondents (R) | 156 | number of respondents only |
| Average Time required to complete the questionnaire in hours (T)1 | 7,28 | based on the response to a direct question |
| Average hourly cost (in national currency) of a respondent (C) | 10,43 | based on average labour costs in education |
| Total cost | 11845,14 | R x T x C |
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’)
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
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.
18.1.1. Data source – general information
| Survey name | Annual survey on research and development (In Slovak: Ročný podnikový výkaz o výskume a vývoji) |
| Type of survey | Census |
| Combination of sample survey and census data | |
| Combination of dedicated R&D and other survey(s) | |
| Sub-population A (covered by sampling) | |
| Sub-population B (covered by census) | |
| Variables the survey contributes to | All R&D variables requested by the European regulation. |
| Survey timetable-most recent implementation | Sending out of the questionnaire: middle of February, data collection date: middle of March, final data: end of July. |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | In universities: rectorates, faculties and centres under the direct control of or administrated by a tertiary education institution; university hospitals | ||
| Stratification variables (if any - for sample surveys only) | |||
| Stratification variable classes | |||
| Population size | |||
| Planned sample size | |||
| Sample selection mechanism (for sample surveys only) | |||
| Survey frame | |||
| Sample design | |||
| Sample size | |||
| Survey frame quality |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | No administrative data collection is carried out. |
| Description of collected data / statistics | |
| Reference period, in relation to the variables the survey contributes to |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | The only source for compilation of data transmitted to Eurostat is the R&D survey of the Statistical Office of the Slovak Republic (SO SR). The R&D questionnaire is collected from institutions providing formal tertiary education (rectorates and faculties of HE institutions), further from institutions not providing formal tertiary education programmes, but operating under the direct control of or administered by tertiary education institutions and from university hospitals. |
| Description of collected information | Information filled in the questionnaire by individual R&D units (universities) include number of R&D personnel (researchers, technicians and equivalent staff, supporting staff) by qualification and sex in HC and FTE. Teaching staff involved in research activities is also included. Researchers in HC are provided also by age and citizenship. Information from individual units concern also intramural expenditure on R&D, by type of R&D, source of funds, fields of science, socio-economic objectives of R&D performed. |
| Data collection method | The R&D survey of the Statistical Office of the Slovak Republic (SO SR) is an on-line survey. The statistical system of the Slovak Republic is decentralised, the Regional Department is charged to collect the questionnaires. The Cross-sectional Statistics Department of the SO SR is responsible for R&D statistics, provides the methodology and organises seminars for training of the regional staff. Monitoring of non-response is made by the regional staff during the collection period. The integrated statistical information system used for data collection contains also fuction for generation of reminders for statistical units. Reminders are sent twice to alert them to meet the survey deadline. Collection and checking of data is made by the regional staff, all further treatments are taken over by the R&D statistics staff of the SO SR. Administrative data sources are not used. |
| Time-use surveys for the calculation of R&D coefficients | |
| Realised sample size (per stratum) | |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | on-line survey |
| Incentives used for increasing response | |
| Follow-up of non-respondents | |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 1,0 |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
| R&D national questionnaire and explanatory notes in English: | 2021_SK_RD_questionnaire_VV6-01_EN.pdf |
| R&D national questionnaire and explanatory notes in the national language: | 2021_SK_RD_questionnaire_VV6-01_SK.pdf |
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: |
Annexes:
2021_SK_RD questionnaire_VV 6-01 in English
2021_SK_RD questionnaire_VV 6-01 in Slovak language
18.4. Data validation
Data validation is embedded in the integrated statistical information system (ISIS)of the Statistical Office of the Slovak Republic (SO SR).
When collecting data, the following checks are distinguished:
1- formal checks carried out automatically in the data collection process
2- informal checks to check the complexity and relationships between variables.
In terms of severity of errors, a distinction is made between:
I - Infomatic errors - provide additional information that is necessary for the process of data checking and correction. They provide information on possible exceedances of the set limits, partial non-response, etc.
Z - serious errors - refer to specific errors that must be corrected or justified by the reporting unit. These errors are consulted with the reporting unit and corrected by employees of the SO SR.
Controls and algorithms for creation of outputs, which ensure their required quality, are also defined in the ISIS system.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
No imputed data.
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | R&D surevey is an annual survey. Data are produced in the same way in the odd and even years. |
| Data compilation method - Preliminary data | R&D surevey is an annual survey. Data are produced in the same way in the odd and even years. |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | No such coefficients are used. |
| Revision policy for the coefficients | Not applicable. |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | Not applicable. |
18.5.4. Measurement issues
| Method of derivation of regional data | Until 2012 in all sectors, units are classified to regions according to their main location (by residence of the company or institutions). From 2013 onwards regional breakdown realised by local units. The new method did not cause significant change in data. |
| Coefficients used for estimation of the R&D share of more general expenditure items | Coefficients are not used. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Depreciation and VAT are exluded from R&D expenditure. |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | From 2006 onwards, GUF source of funds is separated from the direct government funds and it is reported by respondents in the R&D questionnaire. |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | No known differences. |
18.5.5. Weighting and estimation methods
| Description of weighting method | Weightening and estimation methods not used. Information collected by the statistical R&D survey from R&D performing units treated as final. |
| Description of the estimation method | Not applicable. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
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.
30 October 2023
See below.
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.
See below.
Not requested. R&D statistics cover national and regional data.
Reference period is calendar year 2021
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.
R&D indicators are available according to 3 units of measure:
R&D expenditure in thousand €.
Number of R&D personnel in HC as total number of persons engaged in R&D during the calendar year.
Number of R&D personnel in FTE as sum of work-hours in R&D activities over the current year divided by 2000.
See below.
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.
Yearly data dissemination.
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.
See below.
See below.
