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Weighting factor
No total weight usable for international comparison.
NATIONAL weighting procedures/ variables used for calculation of NATIONAL weights:
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AT: Combination of design weight for size of household and post-stratification weight for 1) federal states, 2) age-cohorts by sex, 3) level of education.
AU: The data were weighted to account for non-response (AGE, SEX, and EDUCATIONAL ATTAINMENT derived from AU_DEGR).
CH: The sample frame is individual based and the sampling is pure random, so that every resident in Switzerland has equal chance to participate - all weights are equal to 1. No adjustment for non-response bias.
CN: First design weight computed and then post-stratification adjusted based on gender, age group, education, and urban/ rural.
CZ: Post-stratification weights used based on highest education level, age group, gender and size of municipality for regions (NUTS3).
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. The two weighting factors are stored in the weight variable (weighting factor for Western Germany: 1.21803499; weighting factor for Eastern Germany: 0.53501930).
DK: Post-stratification weight based on sex and age (age groups: 18-25, 26-35, 36-45, 46-55, 56-65, older than 65).
ES: Design weight to adjust for unequal selection probabilities + Weight to adjust for non-response (variables used for calculation of weight: Autonomous Community (ES_REG), Size of habitat).
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 weights. The following marginal distributions of the population were used: 1) gender, 2) age classes, 3) NUTS3 regions, 4) type of community.
FR: Post stratification weighting computed on sex, age (4 groups : 18 to 29 years old, 30 to 39 years old, 40 to 54 years old, 55 years old 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-GBN: The weighting strategy takes into account 1) Selection weights to correct for unequal probability of selection at 3 levels, address, household and individual. 2) Non-response model, to correct for unequal probability of responding among different sub-groups within the population (A number of area level and interviewer observation varialbes were used to model response). 3) Calibration weighting, to adjust the final non-response weight so that the weighted sample matched the population in terms of age, sex and region (This applies to the whole BSA dataset, of which the ISSP dataset is a part, thus, for the ISSP data, weighted base=1.582 and unweighted base=1.595).
HR: No weighting.
HU: Weight to adjust for non-response bias (variables used for calculation of weight: gender, age, level of education, type of settlement).
IL: No weighting.
IN: Weight to adjust for non-response bias - multiple weighting done on following variables: Age, Gender, Social status, Income, Education.
IS: No weighting.
JP: No weighting.
LT: Post-stratification weight used to make survey sample representative of the population by gender, age and type/ size of settlement size.
MX: No weighting.
NZ: Weighting factor adjusts for unequal selection probabilities and for non-response bias in terms of sex, age, deprivation (neighbourhood deprivation quintiles), urbanicity, occupation and, Auckland (either respondent lived/ did not live in the Auckland region).
PH: Weighting variable is based on people aged 18+ (voting age) and area/ region (NCR, Balance Luzon, Visayas, Mindanao). The weight applied was based on the 2018 census population.
RU: The procedure of weighting was aimed at minimizing the sum of squares of the deviation of weighted survey data and statistical data by each of 9 socio-demographic groups by sex, age, education in each region/ strata.
SE: No weighting.
SI: No weighting.
SK: Weight was constructed according to the stratification criteria (8 counties and 3 community/ municipality categories by size) following demography characteristics: age group (7 categories), education (4 categories) and sex. No characteristics were combined in the procedure. An internal program of the fieldwork agency was used for weighting.
SR: Weighting factor adjusts for non-response bias in terms of a) district and b) household composition (gender, age, education, main activity, relationship to head of household).
TH: No weighting.
TW: The data was weighted including design weight and 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: The US weight variable takes into consideration a) the sub-sampling of non-respondents, and b) the number of adults in the household. It also essentially maintains the original sample size.
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 weights are benchmarked, for persons using province, population group, gender and different age groups (i.e. 16-19, 20-24, 25-34, 35-44, 45-54, 55-64 and 65 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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