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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- 5 stars72.04%
- 4 stars19.35%
- 3 stars2.15%
- 2 stars1.07%
- 1 star5.37%
SAMPLING PEOPLE, NETWORKS AND RECORDS의 최상위 리뷰
Very useful course to get the foundation in understanding the sampling process
Very effective instructor who talks as if he's actually in class with you, rather than reading from slides.
Best course of the specialization. Great instructor.
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
Survey Data Collection and Analytics 특화 과정 정보
This specialization covers the fundamentals of surveys as used in market research, evaluation research, social science and political research, official government statistics, and many other topic domains. In six courses, you will learn the basics of questionnaire design, data collection methods, sampling design, dealing with missing values, making estimates, combining data from different sources, and the analysis of survey data. In the final Capstone Project, you’ll apply the skills learned throughout the specialization by analyzing and comparing multiple data sources.
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