1.1. Contact organisation
Turkish Statistical Institute (TurkStat)
1.2. Contact organisation unit
Sectoral Statistics Department, Science and Technology Statistics Group
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Devlet Mah.Necatibey Cad. No:114 06420 Çankaya/ANKARA
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
5 December 2023
2.2. Metadata last posted
5 December 2023
2.3. Metadata last update
5 December 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 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 |
| ISCED-2011 | Used International Standard Classification of Education (ISCED-2011) for educational status of R&D personnel. |
| NUTS | Level 1 and Level 2 |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | There is no deviation, main concepts and definitions used for the production of R&D statistics are given by the Frascati Manual. |
| Fields of Research and Development (FORD) | There is no deviation, main concepts and definitions used for the production of R&D statistics are given by the Frascati Manual; Natural sciences |
| Socioeconomic objective (SEO by NABS) | All SEO included. No deviation in SEO classification from FM2015. |
3.3.2. Sector institutional coverage
| Higher education sector | |
| Tertiary education institution | All public and private universities |
| University and colleges: core of the sector | Included |
| University hospitals and clinics | Included (as are their associated research centres and institutes. ) |
| HES Borderline institutions | not applicable |
| Inclusion of units that primarily do not belong to HES |
3.3.3. R&D variable coverage
| R&D administration and other support activities | These costs are included in R&D "overhead costs". |
| External R&D personnel | Not available |
| Clinical trials | When R&D is the primary purpose of the clinical trials, included in R&D data (phase 1, 2 and 3) |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Complied with Frascati Manual. |
| 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 | Not available |
| Difficulties to distinguish intramural from extramural R&D expenditure | Not applicable |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year. |
| Source of funds | All elements requested by FM are included. |
| Type of R&D | All types are included |
| Type of costs | All types are included |
| Defence R&D - method for obtaining data on R&D expenditure | No specific method for the sector |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | 1990 |
| Function | The number of researchers is available for higher education R&D personnel. For technicians/equivalent staff: The lack of scope in higher education sector was eliminated with the addition of technicians/equivalent staff and other supporting staff which were compiled For researchers : Due to increasing of new administrative records regarding R&D data inrecent years, the calculation method was revised following the recommendations of the Frascati Manual, and the new method was integrated into the system. In 2022, both the calculation method was changed in the light of new administrative record data and the time use research results were revised to cover the years 2015 and 2021. FTE questionnaire and documents regarding the revision were shared in the attachment. |
| Qualification | Compiled based on ISCED classsification |
| Age | Categorized by age groups |
| Citizenship | Not available. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | 1990 |
| Function | The number of researchers is available for higher education R&D personnel. For technicians/equivalent staff: The lack of scope in higher education sector was eliminated with the addition of technicians/equivalent staff and other supporting staff which were compiled For researchers : Due to increasing of new administrative records regarding R&D data inrecent years, the calculation method was revised following the recommendations of the Frascati Manual, and the new method was integrated into the system. In 2022, both the calculation method was changed in the light of new administrative record data and the time use research results were revised to cover the years 2015 and 2021. FTE questionnaire and documents regarding the revision were shared in the attachment. |
| Qualification | Compiled based on ISCED classsification |
| Age | Categorized by age groups |
| Citizenship | Not available. |
3.4.2.3. FTE calculation
Administrative records used for the calculation of researchers for the higher education sector and Time Use Survey results (2015 -2021) were revised.
Accordingly, head count and full-time equivalent figures by occupation have been updated for the years 2015-2021
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| Aggregated cross-table for occupation and qualification | HC | Annual |
| Aggregated cross-table for occupation and qualification | FTE | Annual |
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.
All public and foundation universities are included.
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 | All public and foundation universities for the reference year. | Academic data base is used for compile to academicians information for public universities. |
| Estimation of the target population size |
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.
National currency, National currency per inhabitant, Purchasing Power Standard per inhabitant, Percentage of gross domestic product and Percentage of total R&D expenditure.
R&D personnel data is available in full-time equivalent (FTE), in head count (HC).
Reference period is the calendar year.
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 | The data is compiled in accordance with the needs of the EUROSTAT and OECD. Data is collected according to the Law No. 5429. The law obliges responding units to fill out questionnaires in order to produce the necessary statistics in line with needs. The data is collected to be used only for statistical work, privacy is guaranteed by Law No. 5429. It can not be used as evidence for emergence of any liability or investigation. This confidentiality is the legal responsibility of the Turkish Statistical Institute (TUIK). |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | Not available |
| Legal acts | Law No. 5429. |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | The information is collected to be used only for statistical work, privacy is guaranteed by Law No. 5429. It can not be used as evidence for emergence of any liability or investigation. This privacy is the legal responsibility of Turkish Statistical Institute. |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | The information is collected to be used only for statistical work, privacy is guaranteed by Law No. 5429. It can not be used as evidence for emergence of any liability or investigation. This privacy is the legal responsibility of Turkish Statistical Institute. |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | |
| Planned changes of legislation | Not available |
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: According to Articles 13,14 and 15 of Law No. 5429
b) Confidentiality commitments of survey staff: Law No. 5429
7.2. Confidentiality - data treatment
The information is collected to be used only for statistical work, privacy is guaranteed by Law No. 5429. It can not be used as evidence for emergence of any liability or investigation. This privacy is the legal responsibility of Turkish Statistical Institute.
8.1. Release calendar
From 1990 and onwards, R&D data are published annually.
8.2. Release calendar access
https://www.tuik.gov.tr/Kurumsal/Veri_Takvimi
8.3. Release policy - user access
It can be reached the contents of "Press Release" "Statistical Tables", "Databases", "Reports" and "Metadata" via https://www.tuik.gov.tr/Kurumsal/Bilgiye_Nasil_Erisilir link after you choose the related topic in "Statistics" menu.
Moreover, it can be reached many information available in international or local level via "Regional Statistics", "Province Indicators", "International Selected Indicators" applications take place in "E-Services" menu using TurkStat Website.
Annual.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Content, format, links, ... | |
| Regular releases | Yes | https://data.tuik.gov.tr/Kategori/GetKategori?p=bilgi-teknolojileri-ve-bilgi-toplumu-102&dil=1 |
| Ad-hoc releases |
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) |
N | |
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
The statistical tables published online is available on the website to observe the time series.
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 | Not available |
| Access cost policy | Not available |
| Micro-data anonymisation rules |
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 | ||
| Data prepared for individual ad hoc requests | Y | Online request form filled by users |
|
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
It can be reach detailed information via https://data.tuik.gov.tr/Kategori/GetKategori?p=bilgi-teknolojileri-ve-bilgi-toplumu-102&dil=2
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.) | R&D data is published on web with national metadata file |
| Request on further clarification, most problematic issues | All the required explanations are available on metadata file. |
| Measure to increase clarity | No |
| Impression of users on the clarity of the accompanying information to the data | N/A |
11.1. Quality assurance
The number and variety of current administrative records are used for R&D calculations. The administrative records also use for control of R&D web questionnaire.
The web questionnaire contains a large number of automatic plausibility checks. Data also is guaranteed according to law 5429.
TurkStat is ensuring that the statistical practices used to compile national R&D data are in compliance with Frascati Manual recommendations.
Quality evaluation of R&D statistics is carried out based on the information provided in the national and international quality reports
11.2. Quality management - assessment
During Frascati Manual revision process, TurkStat was in close cooperation with EU Member States and Eurostat.
The questionnaires sent by OECD and Eurostat were examined, the breakdowns that were not collecting were specified. Afterwards, the variables / breakdowns were included in the revised questionnaires.
Considering the updated manual and additional data requirements, data collection methods and reporting process were enhanced in a similar way to the other national statistical offices.
The survey has high response rates (2021: 100%) and the intensive check mechanism using for to guarantee a very high data quality,
As a result, quality of the R&D data is very good. The methodological measures taken are in compliance with the Frascati manual recommendations.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
| Institutions | -International organisations • OECD • Eurostat - National organisations • The Supreme Council for Science, Technology and Innovation Policies • Ministry of Science, Industry and Technology • The Scientific and Technological Research Council of Turkey • Technology Development Foundation |
-International organisations
- National organisations: Strategic goals, grant schemes, research project, government allocations for R&D activities. |
| Media | National and regional media | Press release results |
| Researchers and students | Researchers and students need statistics, analyses, ad hoc services, access to specific data. | Statistics, analyses, access to specific data in Data Research Centre |
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 | No. |
| User satisfaction survey specific for R&D statistics | Not applicable. |
| Short description of the feedback received | Not applicable. |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
Completeness of statistics is good.(100%)
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 | x | |||||
| Obligatory data on R&D expenditure | x | |||||
| Optional data on R&D expenditure | x | |||||
| Obligatory data on R&D personnel | x | |||||
| Optional data on R&D personnel | ||||||
| Regional data on R&D expenditure and R&D personnel | x |
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-1990 | Annual | ||||
| Type of R&D | N | |||||
| Type of costs | Y-1990 | Annual | ||||
| Socioeconomic objective | N | |||||
| Region | Y-2010 | Annual | ||||
| FORD | Y-1990 | Annual | ||||
| Type of institution | Y-1990 | 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-1990 | Annual | Technician and equavalent R&D personnel and other supporting staff were added ın HES in 2016 and onwards. Head count and full-time equivalent figures of R&D researcher personnel by occupation have been revised for the years 2015-2021. |
2016,2021 | Due to increasing of new administrative records regarding R&D data in recent years, the calculation method was revised following the recommendations of the Frascati Manual, and the new method was integrated into the system. In 2021, both the calculation method was changed in the light of new administrative record data and the time use research results were revised to cover the years 2015 and 2021. FTE questionnaire and documents regarding the revision were shared in the attachment. |
|
| Function | Y-1990 | Annual | Technician and equavalent R&D personnel and other supporting staff were added ın HES in 2016 and onwards. Head count and full-time equivalent figures of R&D researcher personnel by occupation have been revised for the years 2015-2021. |
2016,2021 | Due to increasing of new administrative records regarding R&D data in recent years, the calculation method was revised following the recommendations of the Frascati Manual, and the new method was integrated into the system. In 2021, both the calculation method was changed in the light of new administrative record data and the time use research results were revised to cover the years 2015 and 2021. FTE questionnaire and documents regarding the revision were shared in the attachment. |
|
| Qualification | Y-1990 | Annual | ||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y-2010 | Annual | Technician and equavalent R&D personnel and other supporting staff were added ın HES in 2016 and onwards. Head count and full-time equivalent figures of R&D researcher personnel by occupation have been revised for the years 2015-2021. |
2016,2021 | Due to increasing of new administrative records regarding R&D data in recent years, the calculation method was revised following the recommendations of the Frascati Manual, and the new method was integrated into the system. In 2021, both the calculation method was changed in the light of new administrative record data and the time use research results were revised to cover the years 2015 and 2021. FTE questionnaire and documents regarding the revision were shared in the attachment. |
|
| FORD | Y-1990 | Annual | ||||
| Type of institution | Y-1990 | Annual |
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-1990 | Annual | Technician and equavalent R&D personnel and other supporting staff were added ın HES in 2016 and onwards. Head count and full-time equivalent figures of R&D researcher personnel by occupation have been revised for the years 2015-2021. |
2016,2021 | Due to increasing of new administrative records regarding R&D data in recent years, the calculation method was revised following the recommendations of the Frascati Manual, and the new method was integrated into the system. In 2021, both the calculation method was changed in the light of new administrative record data and the time use research results were revised to cover the years 2015 and 2021. FTE questionnaire and documents regarding the revision were shared in the attachment. |
|
| Function | Y-1990 | Annual | Technician and equavalent R&D personnel and other supporting staff were added ın HES in 2016 and onwards. Head count and full-time equivalent figures of R&D researcher personnel by occupation have been revised for the years 2015-2021. |
2016,2021 | Due to increasing of new administrative records regarding R&D data in recent years, the calculation method was revised following the recommendations of the Frascati Manual, and the new method was integrated into the system. In 2021, both the calculation method was changed in the light of new administrative record data and the time use research results were revised to cover the years 2015 and 2021. FTE questionnaire and documents regarding the revision were shared in the attachment. |
|
| Qualification | Y-1990 | Annual | ||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y-2010 | Annual | Technician and equavalent R&D personnel and other supporting staff were added ın HES in 2016 and onwards. Head count and full-time equivalent figures of R&D researcher personnel by occupation have been revised for the years 2015-2021. |
2016,2021 | Due to increasing of new administrative records regarding R&D data in recent years, the calculation method was revised following the recommendations of the Frascati Manual, and the new method was integrated into the system. In 2021, both the calculation method was changed in the light of new administrative record data and the time use research results were revised to cover the years 2015 and 2021. FTE questionnaire and documents regarding the revision were shared in the attachment. |
|
| FORD | Y-1990 | Annual | ||||
| Type of institution | Y-1990 | Annual |
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 |
| There is no other dimension/variable available at national level. |
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 | Sampling method is not used. the calculation based on administrative data. | ||||||
| Total R&D personnel in FTE | Sampling method is not used. the calculation based on administrative data. | ||||||
| Researchers in FTE | Sampling method is not used. the calculation based on administrative data. |
||||||
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
Does not apply. Census survey.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
| Business enterprise | Does not apply. Census survey. |
| Government | Does not apply. Census survey. |
| Higher education | Does not apply. Census survey. |
| Private non-profit | Does not apply. Census survey. |
| Rest of the world | Does not apply. Census survey. |
| Total | Does not apply. Census survey. |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
| Function | Researchers | Does not apply. Census survey. |
| Technicians | Does not apply. Census survey. | |
| Other support staff | Does not apply. Census survey. | |
| Qualification | ISCED 8 | Does not apply. Census survey. |
| ISCED 5-7 | Does not apply. Census survey. | |
| ISCED 4 and below | Does not apply. Census survey. |
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: No such errors known.
b) Measures taken to reduce their effect:
13.3.1.1. Over-coverage - rate
No such errors known. (Census)
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: No such errors known.
b) Measures taken to reduce their effect:
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) |
| Sampling method is not used. the calculation based on administrative data. As a result, There is no response unit. |
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 |
| Census survey | There is no non-response rate | |
13.3.3.3. Measures to increase response rate
Census, The information is collected to be used only for statistical work, privacy is guaranteed by Law No. 5429.
Not answering the questionnaire is prohibited by law. If the questionnaire is not filled out in a transparent, accurate and in time , the responding unit encounter penalty due to relevant law.
It can not be used as evidence for emergence of any liability or investigation. This privacy is the legal responsibility of Turkish Statistical Institute.
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 | Web survey and administrative records. |
| Estimates of data entry errors | There is no data entry errors due to data entering software program |
| Variables for which coding was performed | Not applicable |
| Estimates of coding errors | There is no estimates of coding errors. |
| Editing process and method | There are no editing rates available. |
| Procedure used to correct errors | Data entering software program and accuracy analysis performed |
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: There is no first results
b) Date of first release of national data:
c) Lag (days):
14.1.2. Time lag - final result
a) End of reference period: 2021
b) Date of first release of national data: 19.10.2022
c) Lag (days): 292
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 | 12 |
| Actual date of transmission of the data (T+x months) | ||
| Delay (days) | ||
| 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 problems regarding international comparability known.
Main concepts and definitions are used for the production of R&D statistics are given by the Frascati Manual.
External R&D personnel and external R&D expenditure are not compiled.
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 | No | |
| Method of deriving R&D coefficients | No | |
| Quality of R&D coefficients | No | |
| Data compilation of final and preliminary data | Yes | Only for final data. |
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) | 1990 and onwars | 2016,2021 | Technician and equavalent R&D personnel and other supporting staff were added ın HES in 2016 and onwards. Head count and full-time equivalent figures of R&D researcher personnel by occupation have been revised for the years 2015-2021. FTE questionnaire and documents regarding the revision were shared in the attachment. |
| Function | 1990 and onwars | 2016,2021 | Technician and equavalent R&D personnel and other supporting staff were added ın HES in 2016 and onwards. Head count and full-time equivalent figures of R&D researcher personnel by occupation have been revised for the years 2015-2021. FTE questionnaire and documents regarding the revision were shared in the attachment. |
| Qualification | 1990 and onwars | 2016,2021 | Technician and equavalent R&D personnel and other supporting staff were added ın HES in 2016 and onwards. Head count and full-time equivalent figures of R&D researcher personnel by occupation have been revised for the years 2015-2021. FTE questionnaire and documents regarding the revision were shared in the attachment. |
| R&D personnel (FTE) | 1990 and onwars | 2016,2021 | Technician and equavalent R&D personnel and other supporting staff were added ın HES in 2016 and onwards. Head count and full-time equivalent figures of R&D researcher personnel by occupation have been revised for the years 2015-2021. FTE questionnaire and documents regarding the revision were shared in the attachment. |
| Function | 1990 and onwars | 2016,2021 | Technician and equavalent R&D personnel and other supporting staff were added ın HES in 2016 and onwards. Head count and full-time equivalent figures of R&D researcher personnel by occupation have been revised for the years 2015-2021. FTE questionnaire and documents regarding the revision were shared in the attachment. |
| Qualification | 1990 and onwars | 2016,2021 | Technician and equavalent R&D personnel and other supporting staff were added ın HES in 2016 and onwards. Head count and full-time equivalent figures of R&D researcher personnel by occupation have been revised for the years 2015-2021. FTE questionnaire and documents regarding the revision were shared in the attachment. |
| R&D expenditure | 1990 and onwars | 2016,2021 | Due to number and variety of current administrative records regarding R&D data have increased in recent years the calculation method was revised following the recommendations of the Frascati Manual, and the new method was integrated into the system. In accordance with the new calculation method, the R&D indicators were updated between 2015 and 2021. |
| Source of funds | 1990 and onwars | 2016,2021 | Due to number and variety of current administrative records regarding R&D data have increased in recent years the calculation method was revised following the recommendations of the Frascati Manual, and the new method was integrated into the system. In accordance with the new calculation method, the R&D indicators were updated between 2015 and 2021. |
| Type of costs | 1990 and onwars | 2016,2021 | Due to number and variety of current administrative records regarding R&D data have increased in recent years the calculation method was revised following the recommendations of the Frascati Manual, and the new method was integrated into the system. In accordance with the new calculation method, the R&D indicators were updated between 2015 and 2021. |
| Type of R&D | 1990 and onwars | 2016,2021 | Due to number and variety of current administrative records regarding R&D data have increased in recent years the calculation method was revised following the recommendations of the Frascati Manual, and the new method was integrated into the system. In accordance with the new calculation method, the R&D indicators were updated between 2015 and 2021. |
| Other | 1990 and onwars | 2016,2021 | Due to number and variety of current administrative records regarding R&D data have increased in recent years the calculation method was revised following the recommendations of the Frascati Manual, and the new method was integrated into the system. In accordance with the new calculation method, the R&D indicators were updated between 2015 and 2021. |
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.
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
R&D statistics are produced according to System of National Accounts (SNA) and Frascati Manual 2015. Fallowing provisional data on R&D expenditure are provided to National Accounts Unit :
*Gross domestic expenditure on R&D by sector and type of cost
*Gross domestic expenditure on R&D by sector of performance and by source of funds
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 |
| There are no other statistics for which data from HES can be compared with |
15.3.4. Coherence – Education statistics
The distribution of HES sector expenditures according to economic classification is coherent with education statistics.
The budget and revolving funds data for universities and also information about academics are used through the same databases.
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) | Not applicable | Not applicable | Not applicable |
| Final data (delivered T+18) | Only final data are transmitted to Eurostat. | Only final data are transmitted to Eurostat. | Only final data are transmitted to Eurostat. |
| Difference (of final data) |
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) | 134.192 TRY researcher labor costs per FTE ( 19.449.226.228 total RD academician costs / 144935 total academican in R&D ). |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | No distinction between internal and external R&D personnel available. |
(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 available. | |
| Data collection costs | Not available. | |
| Other costs | Not available. | |
| Total costs | Not available. | |
| Comments on costs | ||
| HES R&D statistic is not compiled through survey . The calculation method based on administrative records. | ||
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) | There is no respondents . The calculation method based on administrative records. | |
| Average Time required to complete the questionnaire in hours (T)1 | Not available. | |
| Average hourly cost (in national currency) of a respondent (C) | There is no respondents . The calculation method based on administrative records. | |
| Total cost | Not available. |
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 | Higher Education R&D Activities Survey for private universities R&D projects survey for public universities R&D Technician and Equivalent Personnel and Other Support Personnel Survey Academic personnel R&D Projects |
| Type of survey | Census survey (via web survey) Census survey (via web survey) Census survey Administrative data from Higher Education Council (YOK) Administrative data from The Scientific and Technological Council (TUBITAK) |
| Combination of sample survey and census data | Not applicable. |
| 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 | Number of researchers (in FTE for R&D) |
| Survey timetable-most recent implementation | Not applicable. |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | All public and foundation universities. | ||
| Stratification variables (if any - for sample surveys only) | Does not apply. | ||
| Stratification variable classes | Does not apply. | ||
| Population size | 168108 (personnel inforamtion)/202 universities | ||
| Planned sample size | 168108 (personnel inforamtion)/202 universities | ||
| Sample selection mechanism (for sample surveys only) | Does not apply. | ||
| Survey frame | All public and foundation universities and academician database. | ||
| Sample design | Does not apply. | ||
| Sample size | 168108 (personnel inforamtion)/202 universities | ||
| Survey frame quality | Very good. |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | 1.Budget data, 2. Social Security Institution 3. Payroll data, 4.Revolving fund data for universities, 5. Academician database, 6. TUBITAK Project information, 7. Web survey for university R&D project information, 8. Web survey for technician and other support personel information, 9.Investment survey for foundation universities, 10. FTE survey results. 11.PhD student information (it is obtained from Council of Higher Education website) |
| Description of collected data / statistics | Wage, academician information,project information etc. |
| Reference period, in relation to the variables the survey contributes to | 2021 |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Private universities Public universities Higher Education Council TUBITAK Social Security Institution data base |
| Description of collected information | The following informations are calculated: -Intramural R&D expenditures by types of expenditure, by types of R&D, by FORD -Source of funds of intramural R&D expenditures by types of funding flow (transfer-exchange) -Extramural R&D expenditures by type of flow (transfer-exchange) -Average gross wages of academicians by academic title -Information on R&D personnel other than academicians (Technician and Equivalent staff and Other Support stuff) The following information is requested (Census survey): -Information on R&D projects (expenditures, financial sources) -Information on R&D personnel other than academicians (Technician and Equivalent staff and Other Support stuff) R&D projects supported by TUBITAK |
| Data collection method | Web based survey Web based survey Sampling method (Via Web survey) Administrative source |
| Time-use surveys for the calculation of R&D coefficients | Times use survey was conducted in 2022: a. -Frame population:156812 -Sample size:28582 (18% of target population) -Sampling method: Stratified by academic title and FORD -Response rate:81% -Stand-alone web survey b. -Conducted in 2022 (Reference year 2021) -Two specified weeks (one typical week during the lecture period and another week in the lecture-free period) c.It is based on the principle that researchers distribute their own time in accordance with Frascati Manual. Requested time proportion: -R&D -Teaching for graduate level -Teaching for postgraduate / doctorate level -Supervision of students -Administration -Other work Higher education academic personnel database (name, sex, age, university/faculty/department, academic title, educational status) |
| Realised sample size (per stratum) | The questionnaire for the research is attached. |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Web questionnaires. |
| Incentives used for increasing response | It is a mandatory questionnaire to be filled. In order to increase the response rate, the responding units were informed via message and e-mail before the start of the study. |
| Follow-up of non-respondents | Through the regional directorates, the units that did not respond were contacted and asked to fill out the questionnaire. The main reason for the non-response is that the academic database was not up-to-date. Studies on this subject have been carried out and the academic database has been updated. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Weight correction was made. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 81% |
| 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: | FTE questionnaire for the research is attached. Most of the R&D HES is calculated based on administrative records. |
| R&D national questionnaire and explanatory notes in the national language: | -Research and Development Activity Survey in Higher Education Sector, 2021 (Yükseköğretim Kesimi Araştırma Geliştirme Faaliyetleri İstatistikleri Soru Formu, 2021) -Researchers' Tıme Use Survey On R&D In Hıgher Educatıon,2022(Yükseköğretim Kesiminde Araştırmacıların Ar-Ge Faaliyetleri Zaman Kullanımı Araştırması Soru Formu, 2012) |
| Other relevant documentation of national methodology in English: | Revision report. |
| Other relevant documentation of national methodology in the national language: | Revision report (Revision report) |
18.4. Data validation
R&D data is checked for consistency and compared with previously calculated data before publication. Suspected errors are questioned and reported to the authorities.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
There is no imputation due to the census structure.
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | The final data transmission is T+12. |
| Data compilation method - Preliminary data | No preliminary data |
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 available |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | Not available |
18.5.4. Measurement issues
| Method of derivation of regional data | Units are classified to the region of their main location. |
| Coefficients used for estimation of the R&D share of more general expenditure items | - |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Depreciation is excluded from R&D expenditure, VAT included. |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | Funds from GUF and funds from other non-GUF government sources are collected separately in the survey. |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | No deviations known. |
18.5.5. Weighting and estimation methods
| Description of weighting method | Does not apply. Census. |
| Description of the estimation method | Does not apply. Census. |
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.
5 December 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.
All public and foundation universities are included.
See below.
Not requested. R&D statistics cover national and regional data.
Reference period is the calendar 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.
National currency, National currency per inhabitant, Purchasing Power Standard per inhabitant, Percentage of gross domestic product and Percentage of total R&D expenditure.
R&D personnel data is available in full-time equivalent (FTE), in head count (HC).
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.
Annual.
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.
