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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/data-collection-analytics-project
课程评论：没有评论
课程名称：结合与分析复杂数据 课程概述：本课程帮助学员学习如何使用调查权重来估计描述性统计数据，如均值和总量，以及更复杂的量，如线性和逻辑回归模型的参数。课程特别强调使用R®软件的能力，同时还将介绍记录连接和统计匹配的基础知识，这在结合来自不同来源的数据时变得尤为重要。结合数据集时可能会涉及伦理问题，课程对此进行了审视。可能需要获得个人的知情同意，以便允许其数据被关联。您还将了解到不同国家的法律要求的差异。 课程大纲： **第一部分：基础估计** 在完成本课程的第1和第2模块后，您将理解如何在处理调查数据时估计描述性统计，包括整体和子群体的统计。我们将回顾估计软件（R、Stata、SAS），并通过实例学习如何估计均值、比例和总量。您还将学习如何在线性、逻辑和其他模型中估计参数，并着重侧重于R软件的选项。模块3和4讨论如何将额外数据添加到分析中，这需要了解记录链接技术，以及获取数据连接许可的要求。 **第二部分：模型** 模块2涵盖如何使用调查数据估计线性和逻辑模型参数。完成本模块后，您将理解所用方法与非调查数据的方法的区别。我们还将探讨在估计模型参数的标准误时需要考虑的调查数据集特征。 **第三部分：记录链接** 模块从美国联邦统计系统中使用更多（链接的）行政记录的当前辩论开始，以及链接记录的一般动机。将给出多个示例，说明数据链接的实用性，并讨论记录链接的挑战。此外，还将简要概述关键的链接技术。 **第四部分：伦理** 本模块将讨论获得记录链接同意的关键问题。未能获得同意可能导致估计的偏差。将提供当前研究示例以及如何获得链接同意的实用建议。
Part: 1
Title:Basic Estimation
Description:After completing Modules 1 and 2 of this course you will understand how to estimate descriptive statistics, overall and for subgroups, when you deal with survey data. We will review software for estimation (R, Stata, SAS) with examples for how to estimate things like means, proportions, and totals. You will also learn how to estimate parameters in linear, logistic, and other models and learn software options with emphasis on R. Module 3 and 4 discuss how you can add additional data to your analysis. This requires knowing about record linkage techniques, and what it takes to get permission to link data.
Part: 2
Title:Models
Description:Module 2 covers how to estimate linear and logistic model parameters using survey data. After completing this module, you will understand how the methods used differ from the ones for non-survey data. We also cover the features of survey data sets that need to be accounted for when estimating standard errors of estimated model parameters.
Part: 3
Title:Record Linkage
Description:Module starts with the current debate on using more (linked) administrative records in the U.S. Federal Statistical System, and a general motivation for linking records. Several examples will be given on why it is useful to link data. Challenges of record linkage will be discussed. A brief overview over key linkage techniques is included as well.
Part: 4
Title:Ethics
Description:This module will discuss key issues in obtaining consent to record linkage. Failure to consent can lead to bias estimates. Current research examples will be given as well as practical suggestions on how to obtain linkage consent.
In this course you will learn how to use survey weights to estimate descriptive statistics, like means and totals, and more complicated quantities like model parameters for linear and logistic regressions. Software capabilities will be covered with R® receiving particular emphasis. The course will also cover the basics of record linkage and statistical matching—both of which are becoming more important as ways of combining data from different sources. Combining of datasets raises ethical issues which the course reviews. Informed consent may have to be obtained from persons to allow their data to be linked. You will learn about differences in the legal requirements in different countries.
