1.1. Contact organisation
Statistics Norway
1.2. Contact organisation unit
Statistics Norway, Division for R&D, technology and business dynamics
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Statistics Norway, PB 2633 St. Hanshaugen, NO-0131 Oslo, Norway
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Not required.
31 October 2025
2.1. Metadata last certified
31 October 2025
2.2. Metadata last posted
31 January 2025
2.3. Metadata last update
31 October 2025
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.
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
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.
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);
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011.
3.3. Coverage - sector
See below.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
Definition used in line with the Frascati Manual.
3.3.2. Sector institutional coverage
| Tertiary education institution | All public and private higher education institutions with R&D, 11 universities, 6 public and 3 private universities of applied sciences, 4 state university colleges, 5 private colleges and 4 other colleges. |
|---|---|
| University and colleges: core of the sector | Included according to FM § 3.67 |
| University hospitals and clinics | Included according to FM. |
| Inclusion of units that primarily do not belong to HES and the borderline cases |
Not applicable. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Included in line with FM $12.122. |
|---|---|
| External R&D personnel | Not available for this sector |
| Clinical trials: compliance with the recommendations in the Frascati Manual §2.61. | No special effort has been made to deal with clinical trials |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | From adjusted accounting data some information is available, especially on EU-funding. We have better data for years with R&D survey (every other year), when respondents adjust prefilled accounting data |
|---|---|
| Payments to rest of the world by sector - availability | Not available |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual (FM), 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 applicable |
| 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 | Every second year (odd years) |
|---|---|
| Source of funds | Source of funds In line with FM. Data on internal/external are collected (almost everything is external for HES). No data on transfer/exchange funds yet |
| Type of R&D | In line with FM. Available every second year; main survey years |
| Type of costs | In line with FM. But no information on capitalised computer software or other intellectual property products |
| Defence R&D - method for obtaining data on R&D expenditure | Not available |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Calendar year (per October 1), also available in the years between survey year. We maintain a register of R&D personnel in HES (and GOV) |
|---|---|
| Function | In line with FM. Researchers and support staff. Classified according to employment category |
| Qualification | Available |
| Age | Available |
| Citizenship | Available (linking register of R&D personnel with population statistics) |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year (per October 1), also available in the years between survey years. |
|---|---|
| Function | Researchers and other support staff (based on employment categories of HC) |
| Qualification | Not available |
| Age | Not available |
| Citizenship | Not available |
3.4.2.3. FTE calculation
Based on time-use surveys approximately every fifth year, and information from R&D survey (every second year)
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 | All HE institutions known or supposed to perform R&D | All HE institutions known or supposed to perform R&D |
| Estimation of the target population size | 33 higher education institutions in 2023, almost 400 institutes/departments | 33 higher education institutions in 2023, almost 400 institutes/departments |
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested, see concept 12.3.3 (Data availability).
3.9. Base period
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 expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
Metadata refers to reference years 2023 and 2024. Survey is conducted every second year (odd years)
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 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 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. The transmission of R&D data is mandatory for Member States and EEA countries.
The 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.
6.1.2. National legislation
| Existence of R&D specific statistical legislation | No, but there is a statistical programme where R&D statistics is mentioned together with all other national statistics. Governed by the general national statistical legislation |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | No |
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
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.
At the level of the ESS, the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
- Confidentiality protection required by law: Yes.
- Confidentiality commitments of survey staff: Yes.
7.2. Confidentiality - data treatment
We follow Eurostat's Code of practice and make sure it is not possible to identify units at detailed level. Behind every cell in tables, statistical data there must be at least 3 units. Exceptions may occur when data cover public institutions, data is publicly available, or by consent. In cases of doubt, this is decided in each case.
8.1. Release calendar
Calendar available on website.
8.2. Release calendar access
For Eurostat this is: Release calendar - Eurostat (europa.eu)
For Statistics Norway the release calender is available under "New statistics" on the front page: National Release Calendar
8.3. Release policy - user access
Data are available for all users at the same time
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
Full survey and more details are avaiable every second year (odd years) and main figures annually
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Links | |
|---|---|---|
| Regular releases | Y | Regular Release on HES R&D |
| Ad-hoc releases | Y | Releases on R&D in HES |
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 | Links |
|---|---|---|
| General publication/article | Y | Figures are also made available through the publication “Report on Science & Technology Indicators for Norway”. Variables for all performing sectors as well as time series; Report on Statistical Indicators |
| Specific paper publication (e.g. sectoral provided to enterprises) | Y | On an irregular basis shorter articles or reports are produced at Statistics Norway and i.e. in Forskningspolitikk «Research Policy» an independent Norwegian magazine for analysis and debate on research, innovation and higher education in Norway and the Nordic area. |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Statistics Norway has an online statdata bank with figures on R&D: National Database on R&D Indicators
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
10.4.1. Provisions affecting the access
| Access rights to the micro-data | Public authorities, researchers at approved institutions and employees/students at Norwegian higher education institutions |
|---|---|
| Access cost policy | Full cost |
| Micro-data anonymisation rules | Micro-data from the R&D statistics is available to researchers if rules and regulations are met |
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 | Aggregated figures | Main results available at Statistics Norway´s websites |
| Data prepared for individual ad hoc requests | Y | Microdata / aggregate figures | |
| Other |
1) Y – Yes, N - No
10.6. Documentation on methodology
Methodology of R&D survey "Statistics on Research and development in the higher education sector" at webpage R&D Indicators in HES
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 is published as a parallel to the press release. The press release addressess key points and comparisons over time. Information is further ensured by, including contact persons' names, phone numbers and e-mail addresses. Furthermore, figures accompany key tables for clarity purposes |
|---|---|
| Requests on further clarification, most problematic issues | Sometimes by email or at user meetings. Most problematic issues are questions on comparability between countries, what is included in funding sources, etc. |
11.1. Quality assurance
At Eurostat level, the common quality framework of the European Statistical System (ESS) is composed of the European Statistics Code of Practice, the Quality Assurance Framework of the ESS, and the general quality management principles (such as continuous interaction with users, continuous improvement, integration, and harmonisation).
At Statistics Norway the production of HES R&D statistics is taking place in cooperation with stakeholder as well as contact persons at the higher education institutions. We arrange user meetings, have an advisory committee for R&D statistics, and frequent contact with respondents. We also discuss quality issues with international colleagues, especially the group of Nordic R&D statistical producers. At Statistics Norway, these are the guidelines all official statistics follow and report to on an annual basis:
Annual Report on Quailty in Official Statistics
11.2. Quality management - assessment
The methodology for R&D statistics in the HES is considered to be of high quality. Compilation includes extensive quality control and testing, comparisons with previous surveys as well as with data from other sources.
The web questionnaires are to a high degree prefilled with institutions’ accounting data. Ongoing work to improve accounting data for this purpose, cooperation with HEI on this.
Users sometimes request data more frequently and with more details than we can provide and they may have questions regarding what is included in different categories and reasons for increase/decrease in numbers.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1 | Ministry of Education and Research | Data used for policy assessment and policy creation, white papers |
| 1 | Ministry of Health and Care | Monitor R&D in hospitals, develop strategies for R&D |
| 1 | The Research Council of Norway | Data used for benchmarking, research policy issues, evaluations |
| 1 | Committee for Gender Balance and Diversity in Research (KIF) | Data on gender and diversity in research personnel |
| 1 | OECD/EU | Pilotstudies |
| 2 | Trade union for researchers, knowledge workers and students | Data used for knowledgebase and policy creation |
| 3 | Media | Data to inform public |
| 4 | Researchers and students | Data for analytical purposes |
| 8 | Higher education institutions | Data for benchmarking with other institutions |
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 | Statistics Norway does not undertake a national user satisfaction survey per se. Instead, meetings are held with key users. We have an advisory board for R&D statistics. At these meetings the users are encouraged to evaluate previous surveys, as well as suggest changes or amendments to future surveys. |
|---|---|
| User satisfaction survey specific for R&D statistics | With the introduction of a new survey tool we plan survey testing |
| Short description of the feedback received | There is always a request for more detailed breakdowns, often more details than we can deliver. This refers most often to data on specific subjects. In such cases we some times conduct additional surveys to the ordinary R&D surveys and combine the data (last regarding coronapandemic). Users often want data at an earlier stage, that means before data are released. Often conflict between user needs and response burden. But the users are mostly satisfied with R&D statistics in the HES. For research personnel we have published new Tables by linking to data on citizenship, temporary employment and social background, |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
100% all HES mandatory datasets transmited.
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.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | Not applicable |
| Obligatory data on R&D expenditure | Not applicable |
| Optional data on R&D expenditure | Not applicable |
| Obligatory data on R&D personnel | Not applicable |
| Optional data on R&D personnel | Not applicable |
| Regional data on R&D expenditure and R&D personnel | Not applicable |
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 | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Source of funds | Y-1970 | every second year (odd years), total annual | Not applicable | Not applicable | Not applicable | Not applicable |
| Type of R&D | Y-1970 | every second year (odd years), total annual | Not applicable | Not applicable | Not applicable | Not applicable |
| Type of costs | Y-1970 | every second year (odd years), total annual | Not applicable | Not applicable | Not applicable | Not applicable |
| Socioeconomic objective | Y-1970-2005 | every second year until 2005 | 2005-present | Not surveyed after 2005 | Not surveyed after 2005 | National areas of policy priority surveyed instead |
| Region | Y-1970 | every second year (odd years), total annual | Not applicable | Not applicable | Not applicable | Not applicable |
| FORD | Y-1970 | every second year (odd years), total annual | Not applicable | Not applicable | Not applicable | Not applicable |
| Type of institution | Y-1970 | every second year (odd years), total annual | Not applicable | Not applicable | Not applicable | Not applicable |
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 | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y-1961 | Annually | Not applicable | Not applicable | Not applicable | Not applicable |
| Function | Y-1961 | Annually | Not applicable | Not applicable | Not applicable | Not applicable |
| Qualification | Y-1961 | Annually | Not applicable | Not applicable | Not applicable | Not applicable |
| Age | Y-1961 | Annually | Not applicable | Not applicable | Not applicable | Not applicable |
| Citizenship | Y-2023 | Annually from 2023, ad-hoc before that | Annually from 2023, ad-hoc before that | Annually from 2023, ad-hoc before that | Annually from 2023, ad-hoc before that | Not fixed part of register of R&D personnel |
| Region | Y-1961 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| FORD | Y-1961 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Type of institution | Y-1961 | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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 | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | N | Not available | Not available | Not applicable | Not applicable | Can be estimated |
| Function | Y-1961 | every second year | Not applicable | Not applicable | Not applicable | Not applicable |
| Qualification | N | every second year | Not applicable | Not applicable | Not applicable | Not applicable |
| Age | N | every second year | Not applicable | Not applicable | Not applicable | Not applicable |
| Citizenship | N | every second year | Not applicable | Not applicable | Not applicable | Not applicable |
| Region | Y-1961 | every second year | even years | Not applicable | Not applicable | Not applicable |
| FORD | Y-1961 | every second year | even years | Not applicable | Not applicable | Not applicable |
| Type of institution | Y-1961 | every second year | even years | Not applicable | Not applicable | Not applicable |
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 |
|---|---|---|---|---|---|
| Parents' level of education (social background) | Y-2012 | Register data annually | Mother or father has long tertiary education Mother or father has short tertiary education Mother or father has upper secondary education Mother or father has primary and lower secondary education |
FORD, age, gender, institution | Not appliciable |
| Type of employment | Y-2016 | Register data annually | Fixed and temporary employment | FORD, institutions, position, gender | Not appliciable |
| Thematic and technology areas | Y-1995 | Every second year | 18 policy priorities | By performing sector | Not applicable |
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
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| Educational level (i.e. PhD is available, also Masters and Bachelor level) | HC | Annual |
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:
- 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.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- 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 errors1) | 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 | : | : | : | 1 | 2 | : | +/- |
| Total R&D personnel in FTE | : | : | 1 | 1 | : | 1 | +/- |
| Researchers in FTE | : | : | 1 | 1 | : | 1 | +/- |
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 (6). If errors of a particular type do not exist, the sign ‘:‘ is used.
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' = If at least one out of the three criteria described above is not fully met.
3) 'Satisfactory' = If the average rate of response is lower than 60%, even by meeting the two remaining criteria.
4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is 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
See below.
13.2.1.1. Variance Estimation Method
Not available.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
|---|---|
| Business enterprise | Not available |
| Government | Not available |
| Not availableHigher education | Not available |
| Private non-profit | Not available |
| Rest of the world | Not available |
| Total | Not available |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
|---|---|---|
| Occupation | Researchers | Not available |
| Technicians | Not available | |
| Other support staff | Not available | |
| Qualification | ISCED 8 | Not available |
| ISCED 5-7 | Not available | |
| ISCED 4 and below | Not available |
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.
- Description/assessment of coverage errors: Not applicable.
- Measures taken to reduce their effect: Not applicable.
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.
- Description/assessment of measurement errors: Respondents may fail to understand instructions, there might be a new contact person and strategic answers may also occour.
- Measures taken to reduce their effect: We prefill questionnaire with accounting data and check the results (with previus answers). We work on minimizing erros through increased use of programming.
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)] * 100
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) |
|---|---|---|
| 344 | 369 | 25 |
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 Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
|---|---|---|---|
| Item non-response rate (un-weighted) (%) | 15% | 15% | 15% |
| Comments | Census survey | Census survey | Census survey |
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 | Some of the data is entered manually from questionnaire and directly into the R&D database. Punching errors may occur, but the errors are minimized through control and testing and cannot be estimated. |
|---|---|
| Estimates of data entry errors | Automatic controls have reduced errors to a minimum, not possible to estimate. |
| Variables for which coding was performed | Source of funds |
| Estimates of coding errors | Punching errors may occur, but the errors are minimized through control and testing and cannot be estimated. We work on further minimizing errors through increased use of programming. |
| Editing process and method | Editing is performed both manually and automatically. |
| Procedure used to correct errors | After receiving the questionnaires from the units, figures are checked thoroughly. This reduces the number of errors to a minimum. During the data revision, answers from previous surveys are used as a reference. Consistency checks are undertaken. Where discrepancies are found, the auditor either corrects obvious mistakes, or contacts the units to rule out mistakes and misconceptions. |
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)
- End of reference period: 31 December 2023.
- Date of first release of national data: 31 October 2024.
- Lag (days): 300.
14.1.2. Time lag - final result
- End of reference period: 31 December 2021.
- Date of first release of national data: 31 October 2024.
- Lag (days): 300.
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 | 10 |
| Delay (days) | no delays | no delays |
| Reasoning for delay | no delays | no delays |
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
As far as we know, we are in line with the Frascati Manual.
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 (FM) 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 sub-chapter 5.2). | No deviation | Not applicable |
| Researcher | FM2015, § 5.35-5.39. | No deviation | Not applicable |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | Not applicable |
| 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 deviation | Not applicable |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No deviation | Not applicable |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly sub-chapter 4.2). | No deviation | Not applicable |
| Statistical unit | FM2015 §3.70 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | Not applicable |
| Target population | FM2015 §9.6 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | Not applicable |
| Sector coverage | FM2015 §3.67-3.69 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | Not applicable |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | Not applicable |
| 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 deviation | Not applicable |
| 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 deviation | Not applicable |
| Major fields of science and technology coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | Not applicable |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | Not applicable |
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 (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection method | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | Not applicable |
| Survey questionnaire / data collection form | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | Not applicable |
| Cooperation with respondents | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | Not applicable |
| Coverage of external funds | FM2015 Chapter 9 (mainly sub-chapter 9.4). | No deviation | Not applicable |
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | FM2015 Chapter 9 (mainly sub-chapter 9.4). | No deviation | Not applicable |
| Data processing methods | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | Not applicable |
| Treatment of non-response | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | Not applicable |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | No deviation | Not applicable |
| Method of deriving R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | Not applicable |
| Quality of R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | Not applicable |
| Data compilation of final and preliminary data | Reg. 2020/1197: Annex 1, Table 18 | No deviation | In 2023 our preliminary data = 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) | 1970-2023 | 1991 and 2007 | 1991 Personnel in central administration units of higher education are not included; however, the cost of such personnel is included in other current R&D expenditure (in line with the Frascati Manual). 2007 New survey method for hospitals |
| Function | 1970-2023 | See above | See above |
| Qualification | 1970-2023 | See above | See above |
| R&D personnel (FTE) | 1970-2023 | 1991 and 2007 | 1991 Personnel in central administration units of higher education are not included; however, the cost of such personnel is included in other current R&D expenditure (in line with the Frascati Manual). 2007 New survey method for hospitals |
| Function | 1970-2023 | See above | See above |
| Qualification | 1970-2023 | See above | See above |
| R&D expenditure | 1970-2023 | 1991 and 2007 | 1991 Personnel in central administration units of higher education are not included; however, the cost of such personnel is included in other current R&D expenditure (in line with the Frascati Manual). 2007 New survey method for hospitals |
| Source of funds | 1970-2023 | See above | See above |
| Type of costs | 1970-2023 | See above | See above |
| Type of R&D | 1970-2023 | See above | See above |
| Other | Not applicable | Not applicable | Not applicable |
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
HERD are based on a survey every second year with detailed statistics for odd years. In even years main figures are produced based on annual update of research personnel data and accounting data.
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 (FM) 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. Coherence – Education statistics
In Norway, almost all academic personnel are involved in both R&D and teaching activities. Differences in data are related to the different data sources: For UOE Statistics Norway is collecting data from Database for Statistics on Higher Education (DBH), the administrative systems of various higher education institutions, and the State Education Loan Fund. UOE data include more position categories than R&D statistics which is also based on data collected from the higher education institutions and DBH. A few small institutions with very little R&D are included in UOE data but not in R&D statistics. In UOE data positions with very low vacancy rates are included (<25%). In R&D statistics (according to guidelines) these positions are excluded, but university hospitals are included. There is also a difference in that education statistics is based on academic year, while R&D statistics is based on calendar year.
The difference in expenditure is related to the above-mentioned differences in personnel and institutions included. For UOE financial reports from central and local governments are compiled in accordance with the European System of Accounts (ESA95) and Classification of the functions of government (COFOG). R&D statistics are based on survey data and administrative data as described in the metadata.
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) | 29576 | 17409 | 13993 | |
| Final data (delivered T+18) | 29576 | 17409 | 13993 | |
| Difference (of final data) | 0 | 0 | 0 |
Comments:
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanation of consistency issues if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | 16417/17409=0,943 | Internal R&D personnel=total R&D personnel in HES. In Norway, also grant holders/research fellows are employed and hence included in these numbers. |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Not available | Internal R&D personnel=total R&D personnel in HES. |
(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) | Cost for the NSI in time use/person/day | |
|---|---|---|
| Staff costs | Not available | Not available |
| Data collection costs | Not available | Not available |
| Other costs | Not available | Not available |
| Total costs | Not available | Not available |
The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
Comments on costs:
It is not possible to calculate the costs for producing R&D statistics in the Higher education sector. This activity is integrated in other tasks, and the persons involved in compiling the statistics work on other projects as well.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | Not available | Not available |
| Average Time required to complete the questionnaire in hours (T)1) | Not available | Not available |
| Average hourly cost (in national currency) of a respondent (C) | Not available | Not available |
| Total cost | Not available | 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. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
18.1.1. Data source – general information
R&D survey in HES, administrative data and time-use survey (approximately every fifth year).
18.1.2. Sample/census survey information
| Sampling unit | All higher education units with R&D |
|---|---|
| Stratification variables (if any - for sample surveys only) | Not applicable |
| Stratification variable classes | Not applicable |
| Population size | 32 higher education institutions and 6 health trusts (university hospitals) (2023) |
| Planned sample size | Not applicable |
| Sample selection mechanism (for sample surveys only) | Not applicable |
| Survey frame | Not applicable |
| Sample design | Not applicable |
| Sample size | Not applicable |
| Survey frame quality | Census survey of high quality |
| Variables the survey contributes to | R&D expenditure, FTE by qualification, field of R&D, funding sources, type of R&D, R&D coefficient of external funding, National variables: information on foreign doctorate holders, share of R&D activity that involved international cooperation, share of R&D activity relevant for business, share of R&D devoted to 18 policy priority ares |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Administrative data collected from higher education institutions that are used to prefill the R&D questionnaires to the institute/department level. Administrative data also from the Research Council of Norway. |
|---|---|
| Description of collected data / statistics | Customized accounting data and personnel data from higher education institutions. |
| Reference period, in relation to the variables the administrative source contributes to | Detailed accounting data only in odd years, personnel data every year. In the future we plan to collect detalied accounting data annually to increase the number of variables we can estimate in years without survey |
| Variables the administrative source contributes to | Numbers to prefill in R&D survey and HC for R&D personnel |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider |
|
|---|---|
| Description of collected information | 1. Each university/college institute/department:
2. Time-use survey 3. Central government accounting system:
4. Central university/college/university hospital administrations
5. Financing bodies:
6. The Directorate of Public Construction and Property:
|
| Data collection method | 1. Each university/college institute/department: Questionnaires sent to heads of institutes/departments 2. Time-use questionnaire to each tenured person in HES approximately every 5. year 3. Central government accounting system:
|
| Time-use surveys for the calculation of R&D coefficients | Time-use questionnaire to each tenured person in HES approximately every 5. year (last for 2021, next for 2025) |
| Realised sample size (per stratum) | Not applicable |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Link to questionnaire distrebuted by email to respondents |
| Incentives used for increasing response | Email reminders and some times by phone if it is a respondent of a large group with much external funding |
| Follow-up of non-respondents | Non-respondents are contacted by e-mail and phone. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Not applicable |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 85 % of the higher education institutes respond to survey |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | Not applicable |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | Sporreskjema UoH 2023_EN |
| R&D national questionnaire and explanatory notes in the national language: | Screenshot uoh-skjema 2023_FINAL |
| Other relevant documentation of national methodology in English: | Guidance in English |
| Other relevant documentation of national methodology in the national language: | Guidance in Norwegian |
Annexes:
Questionnaire HERD 2023
Questionnaire HERD 2023_in Norwegian
Guidance in English
Guidance in Norwegian
18.4. Data validation
Statistics are compared with with previous years and are confronted with other relevant data (both internal and external) inconsistencies in the statistics are also investigated.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100/ (Total number of possible records for x)
18.5.2. Data compilation methods
| Data compilation method - Final data | In the years between survey years R&D statistics are compiled/estimated from personnel data and R&D coefficients (from the latest survey), accounting data. We also receive expenditure and personnel data from the Research Council of Norway. Data on investments in land and buildings come from the Directorate of Public Construction and Property. |
|---|---|
| Data compilation method - Preliminary data | Preliminary R&D statistics have been compiled/estimated by the same methods as Final data (between survey years), see above. For 2023 preliminary R&D statistics have been = final statistics. |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | Time-use survey to tenured personnel at higher education institutions, R&D coefficients are calculated from these data. |
|---|---|
| Revision policy for the coefficients | In accordance with FM2015 we conduct time-use surveys with maximum five year´s intervals. Survey in use for 2023 figures was conducted for 2021. The next survey is for the academic year 2024-2025. |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | Decreasing response rate. |
18.5.4. Measurement issues
| Method of derivation of regional data | By geographical location of HEI unit |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | HES: time-use-surveys approximately every fifth year (2021 and next for 2024-2025). R&D coefficients on external funding from survey. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Excl. depreciation (in the guidelines). |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | GUF: basic funding from Ministry of Education and Research (and from Ministry of Health and Care services) Direct government: The Research Council of Norway, funding from counties/municipalities, project funding from ministries |
18.5.5. Weighting and estimation methods
| Description of weighting method | Not applicable |
|---|---|
| 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.
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
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.
31 October 2025
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.
Metadata refers to reference years 2023 and 2024. Survey is conducted every second year (odd years)
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:
- 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.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
Full survey and more details are avaiable every second year (odd years) and main figures annually
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.
