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For any question on data and metadata, please contact: Eurostat user support |
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1.1. Contact organisation | National Statistical Institute |
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1.2. Contact organisation unit | Labour Statistics, Research and Development, Innovation and Information Society Department |
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1.5. Contact mail address | 2 Panayot Volov Street, Sofia 1038, Bulgaria |
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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 higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics(EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries. Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology. |
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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) 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 | ||||||||||||
Data on R&D personnel in FTE are provided by the reporting units, based on given guidelines. |
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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 is the institutional unit 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 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.
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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. 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. |
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R&D expenditure - thousand BGN; R&D personnel - numbers in HC and FTE. |
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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 - European Business Statistics Methodological Manual on R&D Statistics |
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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: The national Statistics Act (Chapter 6 “Protection of secrecy”) guarantees the protection of statistical confidentiality and the use of individual data of enterprises for statistical purposes only. b) Confidentiality commitments of survey staff: Confidentiality commitments of survey staff are specified in the national Statistics Act, Article 27. |
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7.2. Confidentiality - data treatment | |||
The two criteria (national confidentiality rules) applied for defining cells with direct disclosure risk (primary confidentiality), according to which certain data cannot be made public or released, are specified in Article 25 of the national Statistics Act: ‘statistical information, which aggregates data about less than three statistical units or about a population, in which the relative share of the value of a surveyed parameter of a single unit exceeds 85 per cent of the total volume of such parameter for all units in the population’. Secondary confidentiality treatment is carried out in way making it impossible to recalculate confidential data by subtraction from row and column aggregates. |
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8.1. Release calendar | |||
R&D data are published in accordance with the deadline specified in the Release Calendar presenting the results of the statistical surveys carried out by the NSI, which is publicly accessible on the NSI website. |
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8.2. Release calendar access | |||
The Release Calendar presenting the results of the statistical surveys carried out by the NSI of Bulgaria is available at the following link: Release Calendar | National statistical institute (nsi.bg) |
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8.3. Release policy - user access | |||
The release policy of the NSI determines the dissemination of statistical data on R&D to all users at the same time - standard tables for free access. The users are informed that the data are being released by a press release. Specific data/breakdowns are provided to everyone with a specific request. |
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R&D data are disseminated annually. |
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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 | ||||||||||||||||
R&D data set is available in online database - Information System for online requests for statistical information (INFOSTAT) - at the following link: https://infostat.nsi.bg/infostat/pages/module.jsf?x_2=88 |
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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 | ||||||||||||||||
National reference metadata file is available on NSI website. Research and development activity (R&D) | National statistical institute (nsi.bg) |
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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 | |||
NSI of Bulgaria follows the recommendations on organization and quality management provided in the European Statistics Code of Practice (CoP) and implements the guidelines given in the European Statistical System Quality Assurance Framework (QAF). As part of the European Statistical System (ESS), NSI of Bulgaria endorses the Quality Declaration of the ESS. Quality management system established in NSI (respectively of R&D statistics domain) is in conformity with the requirements of ISO 9001:2015. More information on the quality assurance and procedures that describe the quality policy in NSI of Bulgaria can be found on the NSI website at the following link: Quality | National statistical institute (nsi.bg) |
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11.2. Quality management - assessment | |||
Quality checks are conducted throughout the entire statistical production process of R&D data on HES, including comparisons with data from the previous survey, valid values checks, consistency and completeness checks, etc. The overall quality of the R&D data on HES is assessed as very good. Data for all obligatory and optional R&D variables on HES are annually produced strictly on time. As far as the R&D survey in Bulgaria is a census, sampling error does not exist, misclassification rate=0, completeness is 100%, data accuracy is very good as the response rate is 98,2%. Coherence between preliminary and final HES R&D data is very good as both data series are obtained from one and the same survey source. |
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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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
100%. |
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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. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
Criteria: A) Obligatory data. Only 'Very Good' = 100%, Poor' >95%; 'Very Poor' <100% apply. B) Optional data. 'Very Good' = 100%; 'Good' = >75%; 'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%. |
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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'). 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. 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. Census survey. |
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13.2.1.2. Coefficient of variation for R&D expenditure by source of funds | ||||||||||||||||||||||||||||||||||||
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13.2.1.3. Coefficient of variation for R&D expenditure by function and qualification | ||||||||||||||||||||||||||||||||||||
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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 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: There are not coverage errors - no divergences between the target population and the population actually surveyed. b) Measures taken to reduce their effect: No need of such measures. |
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13.3.1.1. Over-coverage - rate | ||||||||||||||||||||||||||||||||||||
0%. HES data are not collected from units that do not belong to the target population. |
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13.3.1.2. Common units - proportion | ||||||||||||||||||||||||||||||||||||
Not requested. |
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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: There are not indications that such type of errors exist in R&D survey. b) Measures taken to reduce their effect: In order respondents correctly to understand and apply R&D concepts, the questionnaire is attended with comprehensive methodological explanatory notes fully in line with FM2015 recommendations. The NSI staff conducting the R&D survey is experienced and well trained. |
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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 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) |
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13.3.3.1.1. Un-weighted unit non-response rate | ||||||||||||||||||||||||||||||||||||
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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. Measures to increase response rate | ||||||||||||||||||||||||||||||||||||
The potential R&D performing units that do not return the filled in R&D questionnaires within the deadlines defined are shortly after that reminded about their legal obligations to provide the required statistics by phone and/or by e-mail. The number of recalls/reminders normally are two or maximum three. |
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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 December 2021. b) Date of first release of national data: 31 October 2022. c) Lag (days): 303 |
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14.1.2. Time lag - final result | |||||||||||||||
a) End of reference period: 31 December 2021. b) Date of first release of national data: 28 February 2023. c) Lag (days): 423 |
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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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Compliance is achieved with FM2015 methodological recommendations in terms of applied concepts and definitions of the observed variables, type and coverage of the survey, data collection method, unit of observation, reference period. No problems regarding international comparability known. |
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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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Are the data produced in the same way in the odd and even years? If no, please explain the main differences. R&D 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. The education statistics (UNESCO/OECD/Eurostat (UOE)) include R&D expenditure in tertiary educational institutions and follow the recommendations of the Frascati manual regarding the definition of R&D expenditure. Due to the differences in the coverage some differences in the two datasets (UOE questionnaire and the R&D HES surveys) are expected. However, there is a need to ensure that a harmonised approach is used for compiling data in the two domains. The two statistical domains should aim for a consistent use of R&D coefficients for splitting teaching and research time. |
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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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data from R&D survey are used for estimating R&D as capital formation in the National Accounts. |
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15.3.3. National Coherence Assessments | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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15.3.4. Coherence – Education statistics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
There are no differences between R&D expenditure data in the education statistics and the R&D data collection. HERD data obtained from R&D survey are used for UOE questionnaire. |
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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 | ||||||||||||||||||||||||||||||||||||||||||||
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18.4. Data validation | ||||||||||||||||||||||||||||||||||||||||||||
During data validation process several types of checks are carried out including valid values checks, consistency and completeness checks, comparisons with data from the previous R&D surveys. |
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18.5. Data compilation | ||||||||||||||||||||||||||||||||||||||||||||
See below. |
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18.5.1. Imputation - rate | ||||||||||||||||||||||||||||||||||||||||||||
Imputation is not performed. |
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18.5.2. Data compilation methods | ||||||||||||||||||||||||||||||||||||||||||||
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18.5.3. Methodology for derivation of R&D coefficients | ||||||||||||||||||||||||||||||||||||||||||||
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18.5.4. Measurement issues | ||||||||||||||||||||||||||||||||||||||||||||
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18.5.5. 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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R&D QUESTIONNAIRE 2021 in Bulgarian |