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For any question on data and metadata, please contact: Eurostat user support |
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1.1. Contact organisation | Statistical Office of the Slovak Republic |
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1.2. Contact organisation unit | Cross-sectional Statistics Department |
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1.5. Contact mail address | Statistical Office of the Slovak Republic Lamacska cesta 3/C 840 05 Bratislava 45 Slovakia |
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2.1. Metadata last certified | 30/10/2023 | ||
2.2. Metadata last posted | 30/10/2023 | ||
2.3. Metadata last update | 30/10/2023 |
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3.1. Data description | ||||||||||||||||||
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise 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 business enterprise sector consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by 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 by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics. (EBS Methodological Manual on R&D Statistics).
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. |
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3.2. Classification system | ||||||||||||||||||
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3.2.1. Additional classifications | ||||||||||||||||||
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3.3. Coverage - sector | ||||||||||||||||||
See below. |
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3.3.1. General coverage | ||||||||||||||||||
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3.3.2. Sector institutional coverage | ||||||||||||||||||
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3.3.3. R&D variable coverage | ||||||||||||||||||
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3.3.4. International R&D transactions | ||||||||||||||||||
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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 enterprise) is not included in intramural R&D performance totals (FM, §4.12).
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3.4. Statistical concepts and definitions | ||||||||||||||||||
See below. |
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3.4.1. R&D expenditure | ||||||||||||||||||
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3.4.2. R&D personnel | ||||||||||||||||||
See below. |
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3.4.2.1. R&D personnel – Head Counts (HC) | ||||||||||||||||||
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3.4.2.2. R&D personnel – Full Time Equivalent (FTE) | ||||||||||||||||||
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3.4.2.3. FTE calculation | ||||||||||||||||||
FTE is provided by units in the R&D questionnaire, it is calculated according to the formula: |
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3.4.2.4. R&D personnel - Cross-classification by function and qualification | ||||||||||||||||||
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3.5. Statistical unit | ||||||||||||||||||
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, – if there are deviations please explain. |
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3.6. Statistical population | ||||||||||||||||||
See below. |
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3.6.1. National target population | ||||||||||||||||||
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 Business Enterprise Sector should consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. In practice however, countries in their R&D surveys might exclude some enterprises for which R&D activities are deemed to be non-existent or negligible, in order to limit the response burden or due to budgetary constraints.
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3.6.2. Frame population – Description | ||||||||||||||||||
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.
1) i.e. enterprises previously not known or not supposed to perform R&D |
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3.7. Reference area | ||||||||||||||||||
Not requested. R&D statistics cover national and regional data. |
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3.8. Coverage - Time | ||||||||||||||||||
Not requested. See point 3.4. |
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3.9. Base period | ||||||||||||||||||
Not requested. |
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R&D indicators are available in these units of measure: R&D expenditure in thousand € Number of R&D personnel in HC as total number of persons engaged in R&D during the calendar year. Number of R&D personnel in FTE as sum of work-hours in R&D activities over the current year divided by 2000. Number of R&D performing institutional units. Concentration of R&D expenditure and R&D personnel in %. |
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Reference period is calendar year 2021 |
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6.1. Institutional Mandate - legal acts and other agreements | ||||||||||||||
See below. |
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6.1.1. European legislation | ||||||||||||||
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6.1.2. National legislation | ||||||||||||||
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6.1.3. Standards and manuals | ||||||||||||||
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development |
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6.2. Institutional Mandate - data sharing | ||||||||||||||
Not requested. |
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7.1. Confidentiality - policy | |||
Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes. A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
a) Confidentiality protection required by law: Act No. 540/2001 Coll. on state statistics as amended. Internal Directive on the Protection of Confidential Statistical Data (regulates the method of management and implementation of activities related to ensuring the protection of confidential statistical data in the Statistical Office of the Slovak Republic).
b) Confidentiality commitments of survey staff: The survey staff signed the confidentiality commitment. |
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7.2. Confidentiality - data treatment | |||
Confidential data are protected according to the CR (EC) No 322/97 and according to the national Act No 540/2001 on State Statistics as amended. Internal methodological instruction of the Statistical Office of the Slovak Republic regulates specific methods and values of parameters used in the protection of confidential statistical data of surveys and data sets specified in the Directive on the protection of confidential statistical data. Identifying confidential cells in aggregated data: minimum frequency rule (n=3) together with the k % dominance rule. |
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8.1. Release calendar | |||
The Catalog of Publications is publicly available on the website of the Statistical Office of the Slovak Republic and it contains basic information on the issued titles, issue dates, periodicity and language version. |
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8.2. Release calendar access | |||
https://slovak.statistics.sk/wps/portal/ext/products/publikacie/Kalendár zverejňovania publikácií |
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8.3. Release policy - user access | |||
Information on all new released publications is available on the website of the Statistical Office of the Slovak Republic. The release policy determines the availability of statistical data to all users at the same time. |
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Yearly data dissemination. |
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10.1. Dissemination format - News release | ||||||||||||||||
See below. |
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10.1.1. Availability of the releases | ||||||||||||||||
1) Y - Yes, N – No |
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10.2. Dissemination format - Publications | ||||||||||||||||
See below. |
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10.2.1. Availability of means of dissemination | ||||||||||||||||
1) Y – Yes, N - No |
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10.3. Dissemination format - online database | ||||||||||||||||
On-line database of the Statistical Office of the Slovak Republic, https://slovak.statistics.sk/wps/portal/ext/Databases The way to R&D data on this address: DATAcube. - Access to database DATAcube. - Multi-domain statistics - Science, technology and innovation - Research and Development |
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10.3.1. Data tables - consultations | ||||||||||||||||
Not requested. |
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10.4. Dissemination format - microdata access | ||||||||||||||||
See below. |
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10.4.1. Provisions affecting the access | ||||||||||||||||
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10.5. Dissemination format - other | ||||||||||||||||
See below. |
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10.5.1. Metadata - consultations | ||||||||||||||||
Not requested. |
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10.5.2. Availability of other dissemination means | ||||||||||||||||
1) Y – Yes, N - No |
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10.6. Documentation on methodology | ||||||||||||||||
Meta-information is available in on-line publication and on-line database, which includes description of indicators, definitions, survey methodology etc. |
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10.6.1. Metadata completeness - rate | ||||||||||||||||
Not requested. |
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10.7. Quality management - documentation | ||||||||||||||||
See below. |
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10.7.1. Information and clarity | ||||||||||||||||
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11.1. Quality assurance | |||
Statistical Office of the SR has established the system of quality management. Quality manual contains description of system of quality management and fulfillment of requirements of standard ISO 9001. The application of the Quality manual in practice ensures that all activities with impact on the quality of statistical products are planned, managed, examined, evaluated and meet the requirements accepted in the customer order. Quality manual is available at: https://slovak.statistics.sk/wps/wcm/connect/b28fd6cb-76cf-4477-9a97-d9789b1fa429/Prirucka_kvality_2021.pdf?MOD=AJPERES&CVID=nTynTCM&CVID=nTynTCM The basis of the whole system of quality management is the European Statistics Code of Practice. |
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11.2. Quality management - assessment | |||
The overall quality of the business R&D statistical outputs is very good. The survey methodology follows the Frascati manual recommendations and the national and international requirements. The R&D statistics complies with the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The R&D survey coverage, reference period, data collection, checking and data processing follow the Eurostat methodology and recommendations for production of the common R&D statistics of the EU member states. Results of the survey for the country total and by regions as well were transmitted to Eurostat. Transmission of R&D data to Eurostat was realised in the SDMX format via eDAMIS - the safe, secure procedure. Main strengths of the survey: Main activities undertaken to assure high quality of business R&D statistics: |
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12.1. Relevance - User Needs | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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12.1.1. Needs at national level | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Users' class codification 1- Institutions: 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. ) |
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12.2. Relevance - User Satisfaction | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys. |
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12.2.1. National Surveys and feedback | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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12.3. Completeness | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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12.3.1. Data completeness - rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not available. |
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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.
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%. |
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12.3.3. Data availability | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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12.3.3.1. Data availability - R&D Expenditure | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Y-start year, N – data not available |
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12.3.3.2. Data availability - R&D Personnel (HC) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Y-start year, N – data not available |
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12.3.3.3. Data availability - R&D Personnel (FTE) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Y-start year, N – data not available |
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12.3.3.4. Data availability - other | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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'), if R&D data for BES are collected for additional breakdowns or/and at more detailed level than requested. 2) Y-start year |
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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. |
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13.1.1. Accuracy - Overall by 'Types of Error' | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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. |
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13.1.2. Assessment of the accuracy with regard to the main indicators | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 (BES R&D). 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. |
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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. |
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13.2.1. Sampling error - indicators | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
The main indicator used to measure sampling errors is the coefficient of variation (CV). |
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13.2.1.1. Variance Estimation Method | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not applicable. |
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13.2.1.2. Coefficient of variation for key variables by NACE | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Industry sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43) 2) Services sector (NACE Rev 2.: 45-47,49-53,55-56,58-63,64-66 68,69-75,77-82,84,85,86-88,90-93,94-96,97-98,99)
Does not apply. Census survey. |
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13.2.1.3. Coefficient of variation for key variables by Size Class | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Does not apply. Census survey. |
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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. |
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13.3.1. Coverage error | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Coverage errors (or frame errors) are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
a) Description/assessment of coverage errors: Not relevant for census survey.
b) Measures taken to reduce their effect:
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13.3.1.1. Over-coverage - rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Magnitude of error (%) = (Observed Value-True Value)/ True Value (%) |
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13.3.1.1.1. Over-coverage rate - groups | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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13.3.1.2. Common units - proportion | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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13.3.1.3. Frame misclassification rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Misclassification rate measures the percentage of enterprises that changed stratum between the time the frame was last updated and the time the survey was carried out. It is defined as the number of enterprises that changed stratum divided by the number of enterprises which belong to the stratum, according to the frame. The rate can be estimated based on the characteristics of the surveyed enterprises.
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13.3.2. Measurement error | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
a) Description/assessment of measurement errors: The software for data collection and processing of data contains also checks to eliminate errors and logical inconsistencies. All errors and logical inconsistencies are consulted with respondents and corrected sequentially.
b) Measures taken to reduce their effect: The R&D questionnaire contains detailed methodological explanation for filling in the particular modules. The regional department of the statistical office collects the questionnaires. Responsible persons of the regional office are instructed regularly about the improvements of the survey and necessary steps during the collection procedure. |
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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. |
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13.3.3.1. Unit non-response - rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
The main interest is to judge if the response from the target population was satisfying by computing the weighted and un-weighted response rate. |
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13.3.3.1.1. Unit non-response rates by Size Class | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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13.3.3.1.2. Unit non-response rates by NACE | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43) 2) Services (NACE Rev 2.: 45-47,49-53,55-56,58-63,64-66 68,69-75,77-82,84,85,86-88,90-93,94-96,97-98,99) |
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13.3.3.1.3. Recalls/Reminders description | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
R&D survey is a census survey. Reminders were sent by the electronic system used for data collection, the Integrated Statistical Information System (ISIS). In the ISIS, two automatic warnings are incorporated. In addition, some reminders were made by phone. Information on the number of telephone recalls is not available. |
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13.3.3.1.4. Unit non-response survey | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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13.3.3.2. Item non-response - rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Definition: |
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13.3.3.2.1. Un-weighted item non-response rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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13.3.3.3. Magnitude of errors due to non-response | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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. |
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13.3.4.1. Identification of the main processing errors | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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13.3.5. Model assumption error | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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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. |
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14.1.1. Time lag - first result | |||||||||||||||
Time lag between the end of reference period and the release date of the results:
a) End of reference period: 31/12/2021 b) Date of first release of national data: 12/10/2022 c) Lag (months): 10 |
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14.1.2. Time lag - final result | |||||||||||||||
a) End of reference period: 31/12/2021 b) Date of first release of national data: 28/06/2023 c) Lag (months): 18 |
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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. |
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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). |
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14.2.1.1. Deadline and date of data transmission | |||||||||||||||
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15.1. Comparability - geographical | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.1.1. Asymmetry for mirror flow statistics - coefficient | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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15.1.2. General issues of comparability | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
No divergences from FM, from international classification, no divergence in survey coverage. |
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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.
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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.
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15.2. Comparability - over time | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.2.1. Length of comparable time series | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.2.2. Breaks in time series | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Breaks years are years for which data are not fully comparable to the previous period. |
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15.2.3. Collection of data in the even years | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
The data are produced in the same way in the odd and even years. |
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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. Intramural R & D expenditure (code 230101 in the Commission Implementing Regulation (EU) 2020/1197) and R & D personnel (code 230201) are surveyed also in foreign-controlled EU enterprises statistics (inward FATS). The Community innovation survey 2020 (CIS2020) (inn_cis12) (europa.eu) also collects the R&D expenditure of enterprises that form the coverage of the CIS2020 survey. |
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15.3.1. Coherence - sub annual and annual statistics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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15.3.2. Coherence - National Accounts | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Classification ESA2010 is used in R&D statistics for definition of sectors. BES sector includes institutional units classified in S.11 and S.12 with exclusion of those units included in the HES. R&D survey data are regularly used in SNA calculations. |
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15.3.3. National Coherence Assessments | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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15.3.4. Coherence – Foreign-controlled EU enterprises – inward FATS | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Indicators for inward FATS are processed from R&D survey data which ensures full coherence. R&D data and FATS data are collected in different department of the statistical office. R&D data are collected in the Cross-sectional Statistics Department (as also CIS data) and FATS data are collected in Methodology and Synthesis of Business Statistics Department (as also SBS data). |
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15.4. Coherence - internal | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.4.1. Comparison between preliminary and final data | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
This part compares key R&D variables as preliminary and final data.
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15.4.2. Consistency between R&D personnel and expenditure | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
(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). |
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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. |
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16.1. Costs summary | |||||||||||||||||||||
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies. |
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16.2. Components of burden and description of how these estimates were reached | |||||||||||||||||||||
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’) |
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17.1. Data revision - policy | |||
Not requested. |
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17.2. Data revision - practice | |||
Not requested. |
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17.2.1. Data revision - average size | |||
Not requested. |
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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. |
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18.1.1. Data source – general information | ||||||||||||||||||||||||||||||||||||||||||||
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18.1.2. Sample/census survey information | ||||||||||||||||||||||||||||||||||||||||||||
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18.1.3. Information on collection of administrative data or of pre-compiled statistics | ||||||||||||||||||||||||||||||||||||||||||||
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18.2. Frequency of data collection | ||||||||||||||||||||||||||||||||||||||||||||
See 12.3.3. |
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18.3. Data collection | ||||||||||||||||||||||||||||||||||||||||||||
See below. |
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18.3.1. Data collection overview | ||||||||||||||||||||||||||||||||||||||||||||
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18.3.2. Questionnaire and other documents | ||||||||||||||||||||||||||||||||||||||||||||
Annexes: 2021_SK_RD questionnaire_VV 6-01 in English 2021_SK_RD questionnaire_VV 6-01 in Slovak language |
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18.4. Data validation | ||||||||||||||||||||||||||||||||||||||||||||
Data validation is embedded in the integrated statistical information system (ISIS) of the Statistical Office of the Slovak Republic (SO SR).
When collecting data, the following checks are distinguished:
1- formal checks carried out automatically in the data collection process
2- informal checks to check the complexity and relationships between variables.
In terms of severity of errors, a distinction is made between:
I - Infomatic errors - provide additional information that is necessary for the process of data checking and correction. They provide information on possible exceedances of the set limits, partial non-response, etc.
Z - Serious errors - refer to specific errors that must be corrected or justified by the reporting unit. These errors are consulted with the reporting unit and corrected by employees of the SO SR.
Controls and algorithms for creation of outputs, which ensure their required quality, are also defined in the ISIS system.
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18.5. Data compilation | ||||||||||||||||||||||||||||||||||||||||||||
See below. |
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18.5.1. Imputation - rate | ||||||||||||||||||||||||||||||||||||||||||||
Imputation is the method of creating plausible (but artificial) substitute values for all those missing. |
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18.5.1.1. Imputation rate (un-weighted) (%) by Size class | ||||||||||||||||||||||||||||||||||||||||||||
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18.5.1.2. Imputation rate (un-weighted) (%) by NACE | ||||||||||||||||||||||||||||||||||||||||||||
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43) 2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
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18.5.2. Data compilation methods | ||||||||||||||||||||||||||||||||||||||||||||
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18.5.3. Measurement issues | ||||||||||||||||||||||||||||||||||||||||||||
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18.5.4. Weighting and estimation methods | ||||||||||||||||||||||||||||||||||||||||||||
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18.6. Adjustment | ||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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18.6.1. Seasonal adjustment | ||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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