Sampling for Social and Population Surveys


Level of study: Career Based/Vocational

Inference from social and population surveys rely on a sufficiently large sample of respondents that are representative of the population of interest. Some biases in sample selection can be adjusted for after survey completion (using weights), but biases based on unobserved characteristics cannot be. Regardless, the more representative the sample, the more likely it is that researchers will be able to accurately make inference for the population of interest. Larger sample sizes have less uncertainty around their estimates, but this comes at the costs of interviewer/recruitment time and respondent burden. There are a number of ways to select samples including simple random samples, stratified samples, clustered samples, or non-probability methods.
Sampling methodology with a focus on practical ways to design a sample recruitment strategy
Critique a sampling method that has been used on a pre-existing survey
Learning outcomes
Upon successful completion, enrollees will have the knowledge and skills to:
Discuss the basic concepts of sampling for social and population surveys
Critique sampling methodologies for existing surveys
Design a simple sampling strategy that balances costs and error
Know the basic concepts for more advanced sampling strategies and where to obtain further information
Micro-credential stack information
This micro-credential may be undertaken as a stand-alone course.

Key Information
Visa Information
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