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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/sampling-methods
课程评论：没有评论
课程名称：抽样：人群、网络与记录 课程概述：良好的数据收集依赖于优质的样本，而样本选择的方式多种多样。样本可能是随机或便利选择的个体、记录、网络或其他单元，但这种选择方法的质量常常受到质疑，特别是当数据收集和分析结束后，对总体得出结论的影响。样本也可以根据研究者的判断进行更细致的选择，但这时又会担心个人因素是否会导致偏见。本课程主要讨论采用统计学严格的方法进行抽样，包括随机选择和控制方法，以确保样本的有效性和成本控制。我们将探讨简单随机抽样、集群抽样、分层抽样、系统选择以及分层多阶段抽样。课程最后将简要介绍如何估计和总结随机抽样的不确定性。 课程大纲： - 模块1：作为研究工具的抽样 - 模块2：仅仅随机化 - 模块3：使用集群抽样节省成本 - 模块4：利用辅助数据提高效率 - 模块5：简化抽样 - 模块6：将所有知识整合起来 通过本课程，学员将学习到合理的样本选择方法，以提升数据收集的可靠性和有效性。
Name:Module 1: Sampling as a research tool
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Name:Mere randomization
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Name:Saving money using cluster sampling
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Name:Using auxiliary data to be more efficient
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Name:Simplified sampling
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Name:Pulling it all together
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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.
