返回到 Sampling People, Networks and Records
学生对 University of Michigan 提供的 Sampling People, Networks and Records 的评价和反馈
106 个评分
课程概述
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.
热门审阅
LM
Oct 4, 2020
This is a very good course and I especially liked the peer review assessement.
KD
May 20, 2021
Very effective instructor who talks as if he's actually in class with you, rather than reading from slides.
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26 - Sampling People, Networks and Records 的 31 个评论(共 31 个)
创建者 Anna B R
•Jan 29, 2018
Great course
创建者 Saurav J
•May 30, 2021
good
创建者 KESHAB K S
•Mar 14, 2021
Comprehensive Knowledge is provided in the course. Highly skilled faculty member.
创建者 Quinn R
•Nov 7, 2020
Poorly delivered, disorganized, no clear explanations of mathematics needed, caters more to solely auditory learners (very few and mostly unhelpful visuals).
创建者 SJ W
•Nov 5, 2022
Poor lecturer and the homework can be difficult. Slides are not easy to follow.
创建者 Teodoro R H A
•Sep 14, 2024
too hard