Good data collection is built on good samples. But the samples can be chosen in many ways. Samples can be haphazard or convenient selections of persons, or records, or networks, or other units, but one questions the quality of such samples, especially what these selection methods mean for drawing good conclusions about a population after data collection and analysis is done. Samples can be more carefully selected based on a researcher’s judgment, but one then questions whether that judgment can be biased by personal factors. Samples can also be draw in statistically rigorous and careful ways, using random selection and control methods to provide sound representation and cost control. It is these last kinds of samples that will be discussed in this course. We will examine simple random sampling that can be used for sampling persons or records, cluster sampling that can be used to sample groups of persons or records or networks, stratification which can be applied to simple random and cluster samples, systematic selection, and stratified multistage samples. The course concludes with a brief overview of how to estimate and summarize the uncertainty of randomized sampling.
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TOP REVIEWS FROM SAMPLING PEOPLE, NETWORKS AND RECORDS
the most comprehensive course about sampling undoubtedly. Try to take quizzes as well in order to get the most of the course and materials.
This is a very good course and I especially liked the peer review assessement.
Very effective instructor who talks as if he's actually in class with you, rather than reading from slides.
I was very impressed with the course content as well as the expert presentation. This course has empowered with relevant and practical sampling skills that I will apply in the my work
About the Survey Data Collection and Analytics Specialization
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