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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Survey Data Collection and Analytics
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Spezialisierung Survey Data Collection and Analytics University of MichiganÜber diesen Kurs
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Lehrplan - Was Sie in diesem Kurs lernen werden
Module 1: Sampling as a research tool
Mere randomization
Saving money using cluster sampling
Using auxiliary data to be more efficient
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Top-Bewertungen von SAMPLING PEOPLE, NETWORKS AND RECORDS
Best course of the specialization. Great instructor.
I gained solid foundations of sampling techniques from this course. The instructor is excellent, and the course content is very comprehensive.
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
Very useful course to get the foundation in understanding the sampling process
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