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Weighting factor
No total weight usable for international comparism.
NATIONAL weighting procedures/ variables used for calculation of NATIONAL weights:
AT AU: Age, Sex, Education are used to adjust for non-response bias. CH DK IL IS JP KR LT NO SE SI TR: No weighting BE: In order to deliver one integrated harmonized data file for Belgium post stratification weights were calculated based upon age (three age groups: 18-39; 40-59; 60+), sex, and geographical classification (NUTS1 Flemish Region, Walloon Region, Brussels Capital Region). These characteristics are known for all sampled units and the population distributions are published by the Belgian Institute for National Statistics (ADS). The age-sex distribution in each region is reproduced by the weights as well as the distribution over the regions. The result of this weighting procedure in combination with the sampling design is that the weights for respondents of the Brussels Capital Region are much smaller than the weights for the respondents of the Flemish and Walloon regions. The use of adequate software is always necessary when analysing data with weights, but with these data it is vitally important when comparing the regions or analysing only one region separately. The weights were scaled to make the weighted sample size equal to the unweighted sample size. CL: A weighting procedure is applied in order to correct for distortions in the representativeness of the sample as regards three variables of interest: Gender, Age, (grouped in five categories: 18-24 years, 25-34, 35-44, 45-54, 55 or older) and Urbanity (classification of place of residence as urban or rural). This makes it possible to obtain a sample with characteristics similar to those of the population. The weights are constructed by calculating the quotient between the expected distribution and that observed in the cross between Urbanity, Gender and Age. The expected distribution is obtained from the 2002 census data provided by the National Institute of Statistics. The result of the weighting slightly corrects for problems of under- and over-representation among certain specific groups of the population. CZ: Data include design weight calibrated to number of cases. Post-stratification weights were also used - based on RIM Weighting (NUTS2 and 3 education levels, NUTS2 and 4 location size categories, 5 age categories and gender. DE: Two separate German samples: the sample for eastern Germany deliberately over-samples the five eastern federal states. If all of Germany is taken as the unit of analysis (rather than the eastern and western states), design weight is necessary (already stored in the weight variable: weighting factor for Western Germany: 1.219448672; weighting factor for Eastern Germany: 0.549798906). ES: Weights have been used to compensate for non-response. They have been calculated using the two variables initially used for the stratification of the sample: Autonomous Communities and Size of Municipality. FI: The design of the survey was systematic sampling. In order to improve the efficiency of estimation and to reduce bias due to non-response a calibration method was used for the creation of the weight. The following marginal distributions of the population were used: 1) gender (male, female), 2) age classes (15–24, 25–34,…, 65–74), 3) NUTS3 regions with following modifications: the Greater Helsinki Area was dealt as a separate region, 4) type of community (urban - semi-urban - rural). FR: Post stratification weighting computed on sex, age (4 groups: 18 to 29, 30 to 39, 40 to 54, 55 or more) and occupation (6 groups: Farmers, Tradesmen, Shopkeepers and Business Owners; Managers and Secondary/ University Teachers; Intermediate Professions, White Collar Workers; Blue Collar Workers; Unemployed) GB: Selection weights are required because not all the units covered in the survey had the same probability of selection. The weighting reflects the relative selection probabilities of the individual at the three main stages of selection: address, DU and individual. At each stage the selection weights were trimmed to avoid a small number of very high or very low weights in the sample; such weights would inflate standard errors, reducing the precision of the survey estimates and causing the weighted sample to be less efficient. Less than one per cent of the selection weights were trimmed at each stage. GE: Variables gender and age used for the calculation of weight. HR: The data is weighted in order to make corrections in the distribution of age so that they match the last census estimates. HU: The weighting factor has been built by considing the common frequencies of four demographic factors – gender, age group (18-39, 40-59, 60 and older), type of settlement (Budapest, city, village) and educational level (elementary, secondary, higher). Marginal statistics are derived from the latest Census data of the Central Statistical Office 2011. IN: Multiple weighing done on following variables: Age, Gender, Social status, Income, Education. NL: Three weights (www1*www2*www3) used: www1 adjusts for difference from sample frame with respect to location, no name, no phone, foreign name, education level of neighbourhood and for household size; www2 adjusts for difference due to within-household replacement with respect to age, sex, position in household, education, main activity and www3 adjusts for differences with turnout and results of last national elections. PH: Weighting variable is based on people aged 18 and above and region (NCR, Balance Luzon, Visayas, Mindanao). The weight applied was based on the 2010 census population. Computed by area. PL: Because of household sample, the weighting factor was calculated using two stages. First stage equalized probabilities of being chosen for all respondents. Second stage was an IPF (Iterative Proportional Fitting) algorithm. It includes four variables: 1. sex (male, female), 2. age categories (18-24, 25-34, 35-49, 50-59, 60 and more years), 3. type of the place of living (urban/ rural), 4. voivodship (16 voivodships corresponding to administrative division of Poland). 5 iteration were needed to comply with the population-based distributions with precision of 7 digits after dot. Weighting procedure assumes that after weighting the sample size is equal to the number of completed interviews. RU: 9 socio-demographic groups based on the variables sex, age and education were used for each region/ strata for the calculation of weight. The weighting procedure is based on the Census 2010. SK: Weight constructed according to the following population characteristics: sex, age (6 categories: 18-24, 25-34, 35-44, 45-54, 55-64, 65 +), education (4 categories), nationality (3 categories: Slovak, Hungarian, other), size of community (7 categories: up to 1000, 1001–2000, 2001-5000, 5001-20000, 20001-50000, 50001-100000, over 100000) and county structure (8 categories). TW: The data was weighted using an iterative proportional raking scheme. For each respondent, sample data were weighted by sex, age, urbanization and education-degree groups. Weights were then generated to match the population characteristics of Taiwan. US: Weights adjust for area-level non-response, number of adults in HU, and the sub-sampling of non-respondents. It also essentially maintains the original sample size. VE: The weight variable was constructed by multiplying the probability of selection by the post-stratification by sex and age. ZA: Explicit stratification variables: Province, population group and geography type (viz. urban formal, urban informal, tribal and rural formal, including commercial farms). Non-response adjustment = number of drawn HHs per EA (census enumerator area) / number of responding HHs (i.e. where a person 16+ is successfully interviewed) provided that at least 50% of households responded. Otherwise two similar (i.e. in the same explicit stratum) and neighbouring EAs are combined and a combined adjustment factor calculated. Person and household variables are benchmarked, for persons using province, population group, gender and 5 age groups (16-24, 25-34, 35-49, 50-50 and 60 and older) as benchmark variables and for households using province and population group of the respondent in the household. The marginal totals for the benchmark variables are obtained from the applicable midyear estimates as published by Statistics South Africa.
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Das International Social Survey Programme (ISSP) ist ein internationales Kooperationsprogramm, das jährlich eine Umfrage zu sozialwissenschaftlich relevanten Themen durchführt. Seit 1985 stellt das ISSP internationale Datensätze bereit, die internationale und kulturübergreifende sozialwissenschaftliche Forschung über Zeit ermöglichen.
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Die ISSP-Module befassen sich sehr ausführlich mit verschiedenen Aspekten der Staatsbürgerschaft, wobei sowohl eine rechtliche (Rechte und Pflichten) als auch eine psychologische (Identität) Dimension angesprochen wird. Zentrale Themen dieser Erhebungen sind daher Bürgerrechte, Bürgerpflichten, Partizipation, Toleranz, Gruppenzugehörigkeit, Vertrauen (sowohl soziales als auch politisches Vertrauen), Befähigung, politisches Interesse, Bewertung von Institutionen, Zufriedenheit mit der Demokratie und globale vs. nationale Staatsbürgerschaft.
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The ISSP-modules deal in great detail with several aspects of citizenship addressing a legal (rights and duties) as well as a psychological (identity) dimension. Therefore, central themes of these surveys are citizen rights, citizen obligations, participation, tolerance, group membership, trust (social trust as well as political trust), empowerment, political interest, evaluation of institutions, satisfaction with democracy and global vs. national citizenship.
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The International Social Survey Programme (ISSP) is a continuing annual program of cross-national survey collaboration, covering a wide range of topics important for social science research. Since 1985 the ISSP provides international data sets, enabling cross-cultural and cross-temporal research.
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