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Weighting factor
No total weight usable for international comparison.
NATIONAL weighting procedures/ variables used for calculation of NATIONAL weights:
AU: A post-stratification or non-response weight was calculated with the aim of rebalancing the Australian sample so the weighted sample frequencies are equal to the expected frequencies in population in a three-way tabulation by age group (5 groups), sex, and highest level of education (5 levels). The cross tabulation from the Australian population was done using the 2011 Census Table Builder. In the sample not all individuals had complete information on age, sex and year of schooling. However imputations of weight were used to deal so that all individuals were assigned a weight even if they had missing data.
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 (Statbel). 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 analyzing data with weights, but with these data it is vitally important when comparing the regions or analyzing only one region separately.
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.
CL: A weighting procedure was applied in order to correct for distortions in the representativeness of the sample in four variables: gender, age (grouped categories), urbanity and region. This makes it possible to obtain a sample with characteristics similar to those of the national population. The weights are constructed by computing the ratio between the expected distribution and the observed one by crossing region, urbanity, gender and age groups. The expected distribution is obtained from the 2002 census data provided by the National Institute of Statistics. The results of the weighting slightly correct for problems of under and over representation among specific groups of the population.
CZ: Post-stratification weights used based on education, age, 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: weighting factor for Western Germany: 1.24958935; weighting factor for Eastern Germany: 0.52025301; recoding of the country variable is necessary (variable 'c_sample' can be used). The two weighting factors are stored in the weight variable.
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. 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.
GE: Variables gender and age used for the calculation of weight.
HR: No weighting.
HU: Weight to adjust for non-response bias (variable used for calculation of weight: sex, age, education, degree, type of settlement).
IL: No weighting.
IN: Multiple weighting done on following variables: Age, Gender, Social status, Income, Education.
IS: No weighting.
JP: No weighting.
KR: Post-stratification adjustment to the initial weight was done to correct potential non-response bias and coverage errors. Post-stratification adjustment cells were constructed using the following 4 variables with specific categories: Gender (Male, Female); Age (18-29, 30-39, 40-49, 50-59, 60 and over); Region (Seoul, Kyunggi, Kangwon/ Jeju, Chungchong, Kyungsang, Cholla); Urbanicity (Urban, Rural).
LT: No weighting.
LV: Weighting factor constructed according to the following five population characteristics: gender, age, nationality, region and place of settlement.
NO: 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 and above and region (NCR, Balance Luzon, Visayas, Mindanao). The weight applied was based on the 2016 census population. Computed by area.
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. The weighting procedure was based on the Census 2010.
SE: No weighting.
SI: No weighting.
SK: Weight constructed according to the following demography characteristics: sex, age group (18-29, 30-39, 40-49, 50-59, over 60), education (unfinished lower secondary education, lower secondary education, upper secondary education, upper secondary education enabling university entry, tertiary education), community size (up to 1.000, 1.001-5.000, 5.001-20.000, 20.001-100.000, over 100.000) and country structure (8 countries).
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.
TR: No weighting.
TW: The data were weighted using an iterative, proportional raking scheme. Each observation was weighted by gender, age, urbanization, and level of education. 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.
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 weights 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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