This course offers a comprehensive introduction to sampling techniques used in statistical research and data analysis. It begins with foundational concepts such as terms and definitions, population and sample, parameters and statistics, and the differences between sampling errors and non-sampling errors.
Students will explore both probability and non-probability sampling methods, with emphasis on the design and selection of appropriate sampling techniques for various types of research.
The course provides in-depth study of key probability sampling techniques:
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Simple Random Sampling: Definition, selection process, estimators, sample size determination, advantages, and disadvantages.
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Systematic Random Sampling: How to select a sample, appropriate estimators, sample size calculation, and pros and cons.
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Stratified Random Sampling: Stratification methods, allocation of sample size among strata, estimators used, and its strengths and weaknesses.
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Single-Stage Cluster Sampling: Sampling procedure, estimator use, sample size, and a discussion of its advantages and disadvantages.