Weighting Methods & Strategies

Weighting is one of the major components in survey sampling. For a given sample survey, to each unit of the selected sample is attached a weight that is used to obtain estimates of population parameters of interest (e.g. means, totals, rates). The weighting process usually involves three steps: (i) obtain the design weights, which account for sample selection; (ii) adjust these weights to compensate for nonresponse; (iii) adjust the weights so that the estimates coincide to some population figures known from external trusted sources 1. The principle behind estimation in a probability survey is that each sample unit represents not only itself, but also several units of the survey population. The design weight of a unit usually refers to the average number of units in the population that each sampled unit represents. This weight is determined by the sampling method and is an important part of the estimation process. While the design weights can be used for estimation, most surveys produce a set of estimation weights by adjusting the design weights to improve accuracy of the final estimates. Once the final estimation weights have been calculated, they are applied to the sample data in order to compute estimates 2. The ILO Department of Statistics, in collaboration with the ITCILO, is proud to offer the Online course “Weighting Methods & Strategies”. OBJECTIVES The main objective of the course is to “enhance understanding and capacities of ILO constituents and social partners to design household surveys and to process sample data in line with best methodological practices.” The course will enhance the knowledge of participants on the different weighting techniques highlighting their pros and cons through practical cases studies. More specifically, the course aims to: • Enrich understanding of different weighting methods (i.e. post-stratification and calibration); • Provide insights about different weighting strategies, included the treatment of unit non-response; • Improve capacity to calculate final weights and precision of estimates; • Provide practical case studies on weighting sample data using either poststratification or calibration making use of complex constraints using different sets of benchmarks available for different population sub-groups and/or for different geographical domains. CONTENT • Overview of household sample surveys • Frequency of data collection • Sample rotation • Wave Approach • Design weights • Weighting methods – Post-stratification – Calibration • Weighting strategies • Introduction to the software R • Initial practice with R The R Project for Statistical Computing https://www.r-project.org/ • Organizing folders and subfolders for processing sample data according to the GSBPM • Introduction to the R package ReGenesees (for post-stratification and calibration) • Illustration of practical exercises for participants • Illustration of practical exercises for participants • Unit Non-Response • Analysis of unit non-response • Treatment of unit non-response Quality Dimensions • Use of an R package to calculate standard errors and confidence intervals (for levels, ratios and rates) and design effect ACHIEVEMENT Submission of all the course exercises.