Sampling Design: A Practical Approach

Gathering information about the characteristics of a population for each of its members is time consuming and costly. However, we can make inferences about the population with the help of samples. Sampling is a statistical procedure that is concerned with the collection of data on a part of a population of interest and subsequent extrapolation to make statistical inferences about the whole population, for example using sample means to estimate population means. Sampling is essential to enable collection of? the vast amount of information required for understanding the functioning of our society. It provides a solid basis for estimating unknown values and ratios, and for testing the validity of presumed relationships in different areas of science. At the same time, it is crucial in assuring the efficient use of the scarce resources generally available for data collection, while maintaining data quality. One important aspect of probability sampling is its ability to produce not only valid estimates of the parameters of interest but also of their margin of errors due to sample variability. This feature has no doubt greatly contributed to the wide acceptance of probability sampling as an objective tool of measurement among analysts and the public at large2. The ILO Department of Statistics, in collaboration with the ITCILO, proudly offered the Online course “Sampling Design: A Practical Approach”. This course is directly linked to the course “Weighting Methods & Strategies” planned to take place in the fall of 2023 as both courses complement each other. Both courses are considered a learning journey that qualifies the learner to understand comprehensively Sampling design & weighting. OBJECTIVES • Enhance understanding on household sample surveys and survey designs; • Provide insights about the principles and practices of sampling; • Enrich understanding of estimation theory, methods for probability sampling, and sampling frames; • Increase understanding of quality dimensions and calculation of sample size for • complex multi-stage designs. CONTENT • Overview of household sample surveys • Overview of survey designs • Introduction to principles and practices of sampling • Estimation Theory • Sampling frames • Use of master samples for household surveys • Introduction to the software R • Initial practice with R • Sampling designs and methods for probability sampling (advanced) • Selecting samples in complex designs with Probability Proportional to Size • Sample rotation and its impact on precision of estimates • Quality Dimensions • Calculation of sample size for simple and complex multi stage designs • Case studies on the advantages of stratification • Introduction to the R package for multistage sample design R2Beat • Initial practice on case studies with R2Beat • Illustration of case studies about the calculation of sample size given the desired precision, budget and other constraints NUMBER OF HOURS: 60