
关键信息
关于内容
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.
课程大纲
- Week 1 - Module 1: Sampling as a research tool
- Week 2 - Mere randomization
- Week 3 - Saving money using cluster sampling
- Week 4 - Using auxiliary data to be more efficient
- Week 5 - Simplified sampling
- Week 6 - Pulling it all together
教师
James M Lepkowski
Research Professor
Survey Research Center, Institute for Social Research
内容设计师

密歇根大学(UM,UMich 或简称密歇根)是一所公立研究型大学,位于美国密歇根州安阿伯市。该大学成立于 1817 年,是密歇根州历史最悠久、规模最大的大学。
密歇根大学的使命是为密歇根州和全世界人民服务,在创造、交流、保存和应用学术知识、艺术和价值观方面发挥领导作用,培养挑战现在和丰富未来的领导者和公民。
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Coursera是一家数字公司,提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。
Coursera与顶尖大学和组织合作,在线提供一些课程,并提供许多科目的课程,包括:物理,工程,人文,医学,生物学,社会科学,数学,商业,计算机科学,数字营销,数据科学 和其他科目。