Report
Gambling Survey for Great Britain - Year 1 (2023), wave 2 report: Official statistics
Gambling Survey for Great Britain - Year 1 (2023), wave 2 report
Weighting strategy
The data was weighted to take account of non-response, bias, and improve representativeness. As there was no disproportionate sampling, selection weights were not required. The weighting method consisted of two stages1.
A logistic regression model for number of responses within a household (run for households with more than one eligible adult).
A calibration to population estimates.
For the first stage, forward and backward stepwise logistic regression models were used to test which variables were predictive of the number of responses within a household. This model was run only for households with more than one eligible adult. Area-level variables (from the 2021 census for England and Wales and the 2011 census for Scotland) and household-level variables were tested, where we had both a household level and area level version of a given characteristic the household level version was preferred, that is if household income and index of multiple deprivation were both predictive of number of responses, only household income would be used). The final regression model included all variables that were significant in stepwise regressions: household income and household type, this is notably fewer variables than Year 1 Wave 1, and is for at least three reasons, our new preference of using household level variables over area level, household type capturing much of the variance that multiple other variables would otherwise capture, and chance variables that were significant last time were not as significant this time. Region of residence was also included in the model, as it is well established in literature that response rates vary by region.
The predicted probabilities from this model were used to create response weights for households with more than one eligible adult. Weights were checked for outliers and left untrimmed. Weights for responding households with only one eligible adult were set to 1.
The response weights were then calibrated to estimates of the eligible population, residents of Great Britain aged 18 and over. Calibration weighting adjusts the weights so that characteristics of the weighted achieved sample match population estimates, reducing bias. The following variables were included in the calibration: age categories by sex, region, the Index of Multiple Deprivation (IMD) percentiles (quintiles for England and bitiles for Wales and Scotland), tenure, and ethnicity.
Estimates of the Great Britain population by age, sex, and region of residence were taken from Office for National Statistics (ONS) 2022 mid-year population estimates, for England and Wales Population estimates for the UK, England, Wales, Scotland and Northern Ireland - Office for National Statistics (opens in new tab). Population estimates for IMD percentiles within each country were taken from ONS England and Wales (opens in new tab) and National Records of Scotland (opens in new tab). Population estimates for tenure and ethnicity were taken from the most recent Labour Force Survey performance and quality monitoring report: April to June 2023 (opens in new tab).
After calibration, the weights were checked for outliers and the two highest weights were trimmed. The final weight for the 4,985 productive individuals has a design effect of 1.26, an effective sample size of 3,971, and efficiency of 80 percent.
References
1This same method was also used to weight Experimental Phase data, with the notable difference that highest level of education has not been included in the calibration variables for official statistics data collection. This is because the qualification questions in the Gambling Survey for Great Britain are too different to those included in the Labour Force Survey (LFS) to be confident that they are measuring the same thing. Both the experimental phase and GSGB year 1 response datasets show significant divergence in education profiles compared to LFS estimates. Therefore, calibration to LFS estimates of education would not be reliable and has the potential to increase bias rather than reduce it. Alternative high quality estimates of education levels are not available.
GSGB Year 1 (2023), wave 2 report - Questionnaire completion times Next section
GSGB Appendix A - Year 1 (2023), wave 2 online questionnaire
Last updated: 10 March 2025
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Data points within this report have been updated. For further information visit the notes section within Statistics on gambling participation – Year 1 (2023), wave 2: Official statistics.