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Dataset: CIS 2008 - Cross-sectional
The survey results are weighted in order to adjust for the sampling design and for unit non-response to produce valid results for the target population. In the case of non-response, the weightings adjust for statistically significant differences in the proportion of each of the three mutually exclusive types of innovative firms. Additional auxiliary information is also incorporated, if it is considered that this enhances the accuracy of the estimates. The basic method for adjusting for different probabilities of selection used in the sampling process is to use the inverse of the sampling fraction i.e. using the number of enterprises or employees. This is based on the figure Nh/nh where Nh is the total number of enterprises/employees in stratum h of the population and nh is the number of enterprises/employees in the realised sample in stratum h of the population, assuming that each unit in the stratum had the same inclusion probability. This automatically adjusts the sample weights of the respondents to compensate for unit non-response. However, if a non-response analysis was carried out (and the results indicate that there is a difference between respondents and non-respondents), then the results of the non-response analysis are also used when calculating the final weighting factors. One approach is to divide each stratum into a number of response homogeneity groups with (assumed) equal response probabilities within groups. A second approach could be to use auxiliary information at the estimation stage for reducing the non-response bias.
The following two weights are normally used:
- Weight - Weighting factors,
- Weightnr - Weights corrected for non-response bias
The integrated CIS 2008 dataset includes data from 16 participating countries:
- Czech Republic
Total N=127674. Total number of variables = 143.
The Community Innovation Survey is one of the four surveys explicitly mentioned in the Commission Regulation 831/2002. This means that the microdata received from the CIS are of sufficient interest for user for getting disseminated also as anonymised microdata set. The most applied anonymisation method is to reduce the level of detail of categorical variables by combining categories in order to obtain categories with more observations. Global recoding and micro aggregation were used. Based on previous experiences, the microdata for each country were aggregated to the NACE 2 digit level and to 3 size classes (small, medium, large). The regional dimension was collapsed and data were only aggregated at country level. Enterprise identifiers were removed. In addition to the enterprise identifier (Id), stratum A (strA) and stratum B (StrB) were removed from the database. Some other variables were recoded: Size06, Size08,C_Ho, Country, Nace_pro, Weight. The micro-aggregated database consists of the same number of units as kept in the original database. Formal identifiers such as the name or exact address of the enterprise are not part of the micro-aggregated database. The metric variables which were micro-aggregated: turnover in 2006, turnover in 2008, expenditure in intramural RD, expenditure in extramural RD, expenditure in acquisition of machinery, expenditure in other external knowledge and total innovation expenditure.
Eurostat 2009. The Community Innovation Survey 2008 Methodological recommendations. Avaliable at: on the Eurostat CIS 2008 DVD.
Eurostat 2009. CIS 2008 GENERAL DATA STRUCTURE FOR TRANSMISSION TO EUROSTAT. Avaliable at: on the Eurostat CIS 2008 DVD.
Eurostat 2011. The Sixth Community Innovation Survey CIS 2008 Methodology Of Anonymisation. Avaliable at: on the Eurostat CIS 2008 DVD.