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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 | Department of Cross-sectional Statistics |
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1.5. Contact mail address | Statistical Office of the Slovak Republic Lamacska cesta 3/C 840 05 Bratislava Slovakia |
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2.1. Metadata last certified | 01/07/2021 | ||
2.2. Metadata last posted | 31/05/2021 | ||
2.3. Metadata last update | 01/07/2021 |
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3.1. Data description | ||||||||||||||||||||||||||||||||||||||||||||||||
The Community Innovation Survey (CIS) is a survey about innovation activities in enterprises. The survey is designed to collect the information on different types of innovation, various aspects of the development of an innovation, objectives of innovation activities, sources of information, public funding or expenditure on innovation. It is aim is to measure the innovativeness of sectors and enable the analysis of the factors of innovation. The CIS provides statistics by type of innovators, economic activities and size class of enterprises. The survey is currently carried out every two years across the EU Member States, EFTA countries and EU candidate countries.
In order to ensure comparability across countries, Eurostat together with the countries developed a Harmonised Data Collection (HDC) questionnaire accompanied by a set of definitions and methodological recommendations.
CIS 2018 concepts and its underlying methodology are based on the Oslo Manual (2018) 4th Edition.
New review of the CIS2018 aims to meet several objectives : 1: Reduce subjectivity and biases in the main CIS indicators 2: Improve reporting about innovation activities and capabilities in the firm 3: Ensure international comparability (including compliance with the OM4) 4: Broaden the basis CIS information on enterprise management 5: Take better account the diversity of enterprises in the EU 6: Improve reporting about external drivers and enablers of innovation 7: Improve timeliness 8: Ensure the feasibility of data collection 9: Ensure continuity with the CIS 2016 10: Improve reporting about the output and impact of innovation
CIS 2018 is conducted under Commission Regulation No 995/2012. This Regulation defines the mandatory target population of the survey referring to enterprises in the Core NACE economic sectors (see section 3.3.) with at least 10 employees. Further activities may be covered on a voluntary basis in national datasets. Most statistics are based on the 3-year reference period (t, t-1, t-2), but some use only one calendar year (t or t-2).
Please consider CIS t to be the survey that refers to the same year of the quality report and CIS t-2 to be the previous survey e.g.: CIS 2018= CIS t then, CIS t-2=CIS 2016 |
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3.2. Classification system | ||||||||||||||||||||||||||||||||||||||||||||||||
Indicators related to the enterprises are classified by country, economic activity (NACE Rev. 2), size class of enterprises and type of innovation. The main typology of classification of enterprises in reference to innovation is the distinction between innovation-active enterprises (INN) and not innovation-active enterprises (NINN). The enterprise is considered as innovative (INN) if during the reference period it successfully introduced a product or business process innovation, had ongoing innovation activities, abandoned innovation activities or was engaged in in-house R&D or R&D contracted out. Non-innovative (NINN) enterprises had no innovation activity mentioned above whatsoever during the reference period. |
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3.3. Coverage - sector | ||||||||||||||||||||||||||||||||||||||||||||||||
CIS covers main economic sectors according to NACE Rev.2 broken down by size class of enterprises and type of innovation activity. |
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3.3.1. Main economic sectors covered - NACE Rev.2 | ||||||||||||||||||||||||||||||||||||||||||||||||
In accordance with Commission Regulation 995/2012 on innovation statistics, the following industries and services are included in the core target population. Results are made available with these following breakdowns : All NACE – Core NACE (NACE Rev. 2 sections & divisions B-C-D-E-46-H-J-K-71-72-73 )
CORE INDUSTRY (excluding construction) (NACE Rev. 2 SECTIONS B_C_D_E) 10-12: Manufacture of food products, beverages and tobacco 13-15: Manufacture of textiles, wearing apparel, leather and related products 16-18: Manufacture of wood, paper, printing and reproduction 20: Manufacture of chemicals and chemical products 21: Manufacture of basic pharmaceutical products and pharmaceutical preparations 19-22: Manufacture of petroleum, chemical, pharmaceutical, rubber and plastic products 23: Manufacture of other non-metallic mineral products 24: Manufacture of basic metals 25: Manufacture of fabricated metal products, except machinery and equipment 26: Manufacture of computer, electronic and optical products 25-30: Manufacture of fabricated metal products (except machinery and equipment), computer, electronic and optical products, electrical equipment, motor vehicles and other transport equipment 31-33: Manufacture of furniture; jewellery, musical instruments, toys; repair and installation of machinery and equipment
D: ELECTRICITY, GAS, STEAM AND AIR CONDITIONING SUPPLY
E: WATER SUPPLY; SEWERAGE, WASTE MANAGEMENT AND REMEDIATION ACTIVITIES 36: Water collection, treatment and supply 37-39: Sewerage, waste management, remediation activities
CORE SERVICES (NACE Rev. 2 sections & divisions 46-H-J-K-71-72-73)(NACE code in the tables = G46-M73_INN) 46: Wholesale trade, except of motor vehicles and motorcycles
H: TRANSPORTATION AND STORAGE 49-51: Land transport and transport via pipelines, water transport and air transport 52-53: Warehousing and support activities for transportation and postal and courier activities
J: INFORMATION AND COMMUNICATION 58: Publishing activities 61: Telecommunications 62: Computer programming, consultancy and related activities 63: Information service activities
K: FINANCIAL AND INSURANCE ACTIVITIES 64: Financial service activities, except insurance and pension funding 65: Insurance, reinsurance and pension funding, except compulsory social security 66: Activities auxiliary to financial services and insurance activities
M: PROFESSIONAL, SCIENTIFIC AND TECHNICAL ACTIVITIES 71: Architectural and engineering activities; technical testing and analysis 72: Scientific research and development 73: Advertising and market research 71-73: Architectural and engineering activities; technical testing and analysis; Scientific research and development; Advertising and market research
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3.3.1.1. Main economic sectors covered - NACE Rev.2 - national particularities | ||||||||||||||||||||||||||||||||||||||||||||||||
Covered NACE activities beyond the CR No. 995/2012 |
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3.3.2. Sector coverage - size class | ||||||||||||||||||||||||||||||||||||||||||||||||
In accordance with Commission Regulation 995/2012 on innovation statistics, the following size classes of enterprises according to number of employees are included in the core target population of the CIS:
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3.3.2.1. Sector coverage - size class - national particularities | ||||||||||||||||||||||||||||||||||||||||||||||||
No particularities. |
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3.4. Statistical concepts and definitions | ||||||||||||||||||||||||||||||||||||||||||||||||
The description of concepts, definitions and main statistical variables is available in CIS 2018 European metadata file (ESMS) Results of the community innovation survey 2018 (CIS2018) (inn_cis11) in Eurostat database. |
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3.5. Statistical unit | ||||||||||||||||||||||||||||||||||||||||||||||||
Entitiy for which the information is sought is the legal unit. Entity for which statistics are ultimately compiled is the legal unit. Legal units include legal persons whose existence is recognized by law independently of the individuals or institutions which may own them or are members of them. |
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3.6. Statistical population | ||||||||||||||||||||||||||||||||||||||||||||||||
Core target population are all enterprises in CORE NACE activities (see 3.3.1) with 10 or more employees. |
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3.7. Reference area | ||||||||||||||||||||||||||||||||||||||||||||||||
The measured statistical phenomenon relates to the total country SLOVAKIA. The regional dimension of national data is available at NUTS 2 level. |
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3.8. Coverage - Time | ||||||||||||||||||||||||||||||||||||||||||||||||
Several rounds of Community Innovation Survey have been conducted so far at two-year interval since end of 90’s. |
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3.8.1. Participation in the CIS waves | ||||||||||||||||||||||||||||||||||||||||||||||||
*two reference periods can be distinguished for CIS light: 2000-2002 and 2001-2003 |
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3.9. Base period | ||||||||||||||||||||||||||||||||||||||||||||||||
Not relevant. |
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CIS indicators are available according to 3 units of measure:
NR: Number for number of enterprises and number of persons employed. THS_EUR: Thousands of euros. All financial variables are provided in thousands of euros, i.e. Turnover or Innovation expenditure. PC: Percentage. The percentage is the ratio between the selected combinations of indicators. |
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For CIS 2018, the time covered by the survey is the 3-year period from the beginning of 2016 to the end of 2018. Some questions and indicators refer to one year — 2018. The list of indicators covering the 3-year period and referring to one year according to the HDC is available in the Annex section of the European metadata (ESMS). |
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Act No. 540/2001 Coll. on State Statistics as amended - adopted by the National Council of the Slovak Republic. |
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6.1. Institutional Mandate - legal acts and other agreements | |||
CIS surveys are based on the Commission Regulation No 995/2012, implementing Decision No 1608/2003/EC of the European Parliament and of the Council on the production and development of Community statistics on science and technology. This Regulation establishes innovation statistics on a statutory basis and makes the delivery of certain variables compulsory e.g. innovation activities, cooperation, development, expenditures and turnover (see the Regulation). Each survey wave may additionally include further variables. In addition, the Regulation defines the obligatory cross-coverage of economic sectors and size class of enterprises. |
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6.1.1. National legislation | |||
Act No. 540/2001 Coll. on State Statistics, as amended. https://slovak.statistics.sk/wps/wcm/connect/938bf3c4-f9f2-450d-80e1-18f0bedcb8f0/act_540_2001_en.pdf?MOD=AJPERES&CVID=jY9MIeC
Program of State Statistical Surveys, published for three years in the Collection of Laws of the SR. https://www.slov-lex.sk/pravne-predpisy/SK/ZZ/2020/292/20220101.html |
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6.2. Institutional Mandate - data sharing | |||
Not requested. |
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CIS data are transmitted to Eurostat via EDAMIS using the secured transmission system. |
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7.1. Confidentiality - policy | |||
Act No. 540/2001 on State Statistics and internal regulation on the protection of confidential statistical data. |
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7.2. Confidentiality - data treatment | |||
We have applied local suppression (primary and secondary), dominance rule (1, 90) (i.e. the turnover of the largest enterprise exceeds 90 % of the cell value) and minimum frequency rule (n=3). |
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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 |
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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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CIS is conducted and disseminated at two-year interval in pair years. |
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Accessibility and clarity refer to the simplicity and ease for users to access statistics using simple and user-friendly procedure, obtaining them in an expected form and within an acceptable time period, with the appropriate user information and assistance: a global context which finally enables them to make optimum use of the statistics. |
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10.1. Dissemination format - News release | |||||||||||||||
See below. |
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10.1.1. Availability of the releases | |||||||||||||||
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10.2. Dissemination format - Publications | |||||||||||||||
- Online database (containing all/most results) :Yes https://slovak.statistics.sk/wps/portal/ext/Databases/DATAcube_sk - Analytical publication (referring to all/most results) : Yes: Innovation activity of enterprises in the Slovak Republic 2016-2018 - Analytical publication (referring to specific results, e.g. only for one sector or one specific aspect) : Yes: 1 table in the Statistical Yearbook of the Statistical Office of the Slovak Republic |
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10.3. Dissemination format - online database | |||||||||||||||
https://slovak.statistics.sk/wps/portal/ext/Databases/DATAcube_sk |
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10.3.1. Data tables - consultations | |||||||||||||||
Not requested. |
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10.4. Dissemination format - microdata access | |||||||||||||||
Micro-data are not disseminated. Micro-data are provided only for scientific purposes according to the stated rules. Conditions for granting access to confidential statistical data for scientific purposes are provided on the website of the Statistical Office of the Slovak Republic. |
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10.4.1. Dissemination of microdata | |||||||||||||||
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10.5. Dissemination format - other | |||||||||||||||
No other forms of disseminations. |
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10.5.1. Metadata - consultations | |||||||||||||||
Not requested. |
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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 | |||||||||||||||
Quality reports for users are available on the website of the Statistical Office of the Slovak Republic, specifically at: https://slovak.statistics.sk/wps/portal/ext/metadata/qreports/ |
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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/9ca43aa4-bfaf-4101-9dae-5263aa834df7/ Prirucka_kvality.pdf?MOD=AJPERES&CVID=mu8R9IM&CVID=mu8R9IM 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 data quality of the CIS 2018 survey is considered to be good. Coverage of the survey, reference period, used methodology for sampling, data collection, checking and data processing followed the Eurostat methodology and recommendations. Results of the survey were transmitted to Eurostat in required SDMX forms via EDAMIS (tabulated data, micro-data and most of data going beyond the CR No. 995/2012) by the stated deadline. Main strengths of the survey: Weaknesses of the survey: Assessment of the CIS 2018 according to particular quality criteria is as follows: Relevance Subject covered by the CIS 2018 survey is highly relevant; obligatory modules and also most of voluntary modules were included. Accuracy Timeliness and punctuality Accessibility and clarity Comparability Coherence |
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12.1. Relevance - User Needs | |||||||||||||||||||||||||||||||||||||||
The content of the survey meets the needs of users. When deciding on the inclusion of indicators in the survey, the standard procedure used in the Statistical Office of the Slovak Republic is applied. We send information on the content of the survey for comments and for completion to individual central authorities and members of the Statistical Council. The suggestions obtained in this process are taken into account when creating the final questionairre. Consultations with specific users on their requirements for specific outputs show that the content of the survey is relevant. |
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12.1.1. Needs at national level | |||||||||||||||||||||||||||||||||||||||
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12.2. Relevance - User Satisfaction | |||||||||||||||||||||||||||||||||||||||
The user satisfaction survey has not been undertaken. The content of the innovation survey follows the common methodology of European countries. As no additional requests were addressed to the Statistical Office of the Slovak Republic to extend the content of the innovation survey. Communication with customers in case of specific requirements implies that users of innovation statistics are satisfied.
Since 2009, the Statistical Office of the Slovak Republic has carried out an overall customer satisfaction survey at two-year intervals. The purpose of the survey is to obtain information on users interest and opinion regarding provision and quality of statistical products and services. Result of the survey in 2020 is published on the website of the Statistical Office of the SR. https://slovak.statistics.sk/wps/portal/ext/aboutus/marketing/survey.of.satisfaction In this user satisfaction survey, innovation has been linked with areas of R&D, energy and environment. Average rate of satisfaction with these statistical areas was 76,7 %, but it does not accurately reflect the satisfaction with innovation statistics. |
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12.3. Completeness | |||||||||||||||||||||||||||||||||||||||
All compulsory and most of voluntary cells are provided in the output tabulation. Micro-data file were also provided to Eurostat. |
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12.3.1. Data completeness - rate | |||||||||||||||||||||||||||||||||||||||
Not requested. |
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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). |
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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 for CIS data is the coefficient of variation (CV).
Coefficient of Variation= (Square root of the estimate of the sampling variance) / (Estimated value) Formula:
where |
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13.2.1.1. Coefficient of variations for key variables | ||||||||||||||||||||||||||||||||||||||||||||||||||||
Coefficient of variation (%) for key variables by NACE categories and for enterprises with 10 and more employees
[1] = Coefficient of variation for the percentage of innovative enterprises (INN) in the total population of enterprises (ENT18) |
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13.2.1.2. Variance estimation method | ||||||||||||||||||||||||||||||||||||||||||||||||||||
The linearization method using Taylor expansion series was used for variance estimation. |
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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. |
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13.3.1.1. Over-coverage - rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||
Un-weighted over-coverage rate for Core NACE: 2,0 % Weighted over-coverage rate for Core NACE: 2,1 % |
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13.3.1.2. Common units - proportion | ||||||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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13.3.1.3. Under covered groups of the target population | ||||||||||||||||||||||||||||||||||||||||||||||||||||
Exact information on under-coverage is not available. We assume that the under-coverage could be slight only. |
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13.3.1.4. Coverage errors in coefficient variation | ||||||||||||||||||||||||||||||||||||||||||||||||||||
Effects of the overcoverage errors were taken into consideration when the updated weights were calculated. So, indirectly the CV (which use weights) incorporate affects of overcoverage errors. |
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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. The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
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13.3.2.1. Measures for reducing measurement errors | ||||||||||||||||||||||||||||||||||||||||||||||||||||
All errors caused by recording were corrected. Additional methodological explanations for respondents were included in the on-line questionnaire and also automatic controlls to alert respondents about wrong data or logical inconsistencies |
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13.3.3. Non response error | ||||||||||||||||||||||||||||||||||||||||||||||||||||
Non response occurs when a survey fails 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 types of non-response: 1) Unit non-response, which occurs when no data (or so little as to be unusable) are collected about a population unit designated for data collection. a) Un-weighted unit non-response rate (%) = 100*(Number of units with no response or not usable response) / (Total number of in-scope (eligible) units in the sample) b) Weighted unit non-response rate (%) = 100*(Number of weighted units with no response or not usable response) / (Total number of in-scope (eligible) units in the sample) 2) Item non-response, which occurs when only data on some, but not all survey data items are collected about a population unit designated for data collection. a) Un-weighted item non-response rate (%) = 100*(Number of units with no response at all for the item) / (Total number of eligible, for the item, units in the sample i.e. filters have to be taken into account) |
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13.3.3.1. Unit non-response - rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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13.3.3.1.1. Un-weighted and weighted unit non-response rate by NACE categories and for enterprises with 10 or more employees | ||||||||||||||||||||||||||||||||||||||||||||||||||||
Un-weighted and weighted unit non-response rate by NACE categories and for enterprises with 10 or more employees
The number of eligible units is the number of sample units, which indeed belong to the target population.
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13.3.3.1.2. Maximum number of recalls/reminders before coding | ||||||||||||||||||||||||||||||||||||||||||||||||||||
Generally, number of maximum reminders were 2 phone calls. In several cases more than 2 phone calls were conducted |
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13.3.3.2. Item non-response - rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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13.3.3.2.1. Item non-response rate for Turnover (in Core NACE: B-C-D-E-46-H-J-K-71-72-73 enterprises with 10 or more employees) | ||||||||||||||||||||||||||||||||||||||||||||||||||||
Item non-response rate for Turnover (in Core NACE: B-C-D-E-46-H-J-K-71-72-73 enterprises with 10 or more employees).
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13.3.3.2.2. Item non response rate for new questions | ||||||||||||||||||||||||||||||||||||||||||||||||||||
Item non-response rate for new questions in CIS t (in Core NACE: B-C-D-E-46-H-J-K-71-72-73 enterprises with 10 or more employees)
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13.3.4. Processing error | ||||||||||||||||||||||||||||||||||||||||||||||||||||
Number of coding errors is 0. Processing errors were corrected during the editing procedure. So, CVs do not incorporate the effects of processing errors |
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13.3.5. Model assumption error | ||||||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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Timeliness and punctuality refer to time and dates, but in a different manner. |
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14.1. Timeliness | |||
The timeliness of statistics reflects the length of time between data availability and the event or phenomenon they describe. |
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14.1.1. Time lag - first result | |||
Timeliness of national data – date of first release of national level : 18 months after the end of reference period |
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14.1.2. Time lag - final result | |||
Not requested. |
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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 | |||
Date of transmission of complete and validated data to Eurostat (Number of days between that date and 30 June 2020) : 6 days before the deadline |
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Comparability aims at measuring the impact of differences in applied statistical concepts and definitions on the comparison of statistics between geographical areas, non-geographical domains, or over time. The coherence of statistical outputs refers to the degree to which the statistical processes by which they were generated used the same concepts (classifications, definitions, and target populations) and harmonised methods. Coherent statistical outputs have the potential to be validly combined and used jointly. |
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15.1. Comparability - geographical | ||||||||||||||||||||
No problems of comparability between regions of the country. |
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15.1.1. Asymmetry for mirror flow statistics - coefficient | ||||||||||||||||||||
Not requested. |
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15.1.2. National questionnaire – compliance with Eurostat model questionnaire | ||||||||||||||||||||
Methodological deviations from the CIS Harmonised Data Collection (HDC)
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15.1.3. National questionnaire – additional questions | ||||||||||||||||||||
Methodological deviations from the CIS Harmonised Data Collection (HDC)
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15.2. Comparability - over time | ||||||||||||||||||||
Due to important methodological changes in CIS 2018 driven by Oslo Manual 2018, the data 2018 cannot be directly compared with previous CIS waves. |
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15.2.1. Length of comparable time series | ||||||||||||||||||||
Not requested. |
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15.3. Coherence - cross domain | ||||||||||||||||||||
See the comparison between SBS and CIS data in the section 15.3.3 below. |
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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 | ||||||||||||||||||||
Not requested. |
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15.3.3. Coherence – Structural Business Statistics (SBS) | ||||||||||||||||||||
This part compares key variables for aggregated CIS data with SBS data
* Numbers are to be provided for the last year of the reference period (t) |
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15.4. Coherence - internal | ||||||||||||||||||||
Not requested. |
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Confidential information on the production cost of the CIS. |
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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 | ||||||||||||||||||||||||||||||||||||||
See below. |
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18.1.1. Sampling frame (or census frame) | ||||||||||||||||||||||||||||||||||||||
The national statistical business register was used. |
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18.1.2. Sampling design | ||||||||||||||||||||||||||||||||||||||
Sample design is stratified with SRS in particular strata. The whole sample is divided into strata by crossing the number of employees (3 size groups – 10 to 49, 50 to 249, 250 or more), NACE 2 codes (NACE classification at the two digit level) and NUTS2 level. It gives a number of 660 strata, from which 475 are non-empty. These have to be divided into domains to calculate the proper sample sizes. We obtained 30 domains at NACE 1 level. There are 3 key indexes in CIS 2018 and the sample design must have taken into account all three to follow required precision of key estimates. |
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18.1.3. Target population and sample size | ||||||||||||||||||||||||||||||||||||||
The overal sample rate was 40,11 %. The initial weights were calculated as Nh/nh, where Nh was the total number of enterprises in stratum h of the population and nh was the number of enterprises in the sample in stratum h, assuming that each unit in the stratum had the same inclusion probability. Initial weights were updated after collection of questionnaires. |
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18.1.4. Data source for pre-filled variables | ||||||||||||||||||||||||||||||||||||||
Variables and indicators filled or prefilled from other sources.
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18.1.5. Data source and variables used for derivation and weighting | ||||||||||||||||||||||||||||||||||||||
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18.2. Frequency of data collection | ||||||||||||||||||||||||||||||||||||||
According to the Commission Regulation (UE) 995/2012, the innovation statistics shall be provided to Eurostat every two years in each even year. The data collection takes place every second year in year t-2 preceding the data provision. |
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18.3. Data collection | ||||||||||||||||||||||||||||||||||||||
See below. |
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18.3.1. Survey participation | ||||||||||||||||||||||||||||||||||||||
Mandatory survey included in the Program of State Statistical Surveys. |
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18.3.2. Survey type | ||||||||||||||||||||||||||||||||||||||
Combination of sample survey and census. |
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18.3.3. Combination of sample survey and census data | ||||||||||||||||||||||||||||||||||||||
Census for enterprises with 250 and more employees (i.e. for large enterprises) and sample survey for enterprises with 10-249 employees (i.e. for small and medium enterprises). |
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18.3.4. Census criteria | ||||||||||||||||||||||||||||||||||||||
The criterion to conduct a census is a size class (250 and more employees – large enterprises). However, if a particular stratum has less than six enterprises, then all the enterprises in this stratum are selected for the survey. |
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18.3.5. Data collection method | ||||||||||||||||||||||||||||||||||||||
Data collection method
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18.4. Data validation | ||||||||||||||||||||||||||||||||||||||
Not requested. |
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18.5. Data compilation | ||||||||||||||||||||||||||||||||||||||
Operations performed on data to derive new information according to a given set of rules. |
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18.5.1. Imputation - rate | ||||||||||||||||||||||||||||||||||||||
Imputation is the method of creating plausible (but artificial) substitute values for all those missing. Definition of imputation rate: Imputation rate (for the variable x) (%) = 100*(Number of replaced values) / (Total number of values for a given variable) Definition of weighted imputation rate: Weighted imputation rate= 100*(Number of total weighted replaced values) / (Total number of weighted values for a given variable) |
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18.5.1.1. Imputation rate for metric variables | ||||||||||||||||||||||||||||||||||||||
Imputation rate for metric variables by NACE categories and for enterprises with 10 or more employees:
(1) = Total turnover in the last year of the reference period (t) (TUR) (2) = Share of the turnover in the last year of the reference period (t) due to new or improved product new to the market in the total turnover for product innovative enterprises TUR_PRD_NEW_MKT/TUR(INNO_PRD) (3) = R&D expenditure performed in-house (EXP_INNO_RND_IH) |
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18.5.2. Weights calculation | ||||||||||||||||||||||||||||||||||||||
Weights calculation method for sample surveys
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18.6. Adjustment | ||||||||||||||||||||||||||||||||||||||
The calibration software itself is the Calif 4.0 application, which offers advanced web-designed graphical user interface. It is a powerful open-source tool which incorporates all current calibration methodologies. The NSI staff is encouraged to use this tool for calibration. For details see: https://slovak.statistics.sk/wps/portal/ext/products/software.tools https://github.com/SO-SR/Calif Inputs to the program body are: data themselves, specification of variables (which are categorical or numerical), marginal values which we want to calibrate on, degree of stratification, iteration method, lower and upper bounds for weights ratio, maximum number of iterations and a feasible tolerance. The program can stratify the calibration procedure according to defined stratification key. For this purpose we used stratification to NACE2 and size class levels (2D classification). For the calibration procedure variables turnover, number of employed persons and number of enterprises were used. |
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18.6.1. Seasonal adjustment | ||||||||||||||||||||||||||||||||||||||
Not requested. |
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