|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/missing-data
课程评论：没有评论
课程名称：处理缺失数据 概述：本课程将介绍样本调查加权的步骤，包括针对非响应的调整方法以及使用调查外部数据进行校准。讨论的技术包括使用估计响应倾向的调整、后分层加权、比例加权和一般回归估计。还将讨论缺失项值的替代填补技术。关于加权和填补，各种统计软件包的功能也会进行介绍，包括R®、Stata®和SAS®。 课程大纲：
第一部分
标题：加权的一般步骤
描述：权重用于将样本扩展到人口，为此，权重可以修正调查框中的覆盖错误，调整非响应，并通过结合协变量来降低估计量的方差。本模块涵盖了实现这一目标所需的一系列步骤。
第二部分
标题：具体步骤
描述：加权的具体步骤包括计算基础权重、在不确定某些案例的资格时进行调整、调整非响应以及使用协变量校准样本与外部人口控制。本部分将具体细化一般步骤。
第三部分
标题：实施步骤
描述：软件在实施步骤中至关重要，R系统是提供免费例程的优秀来源。本模块介绍几个R软件包，包括sampling、survey和PracTools，用于选择样本和计算权重。
第四部分
标题：课程总结
描述：我们简要总结了在课程中涉及的加权和填补方法。
Part: 1
Title:General Steps in Weighting
Description:Weights are used to expand a sample to a population. To accomplish this, the weights may correct for coverage errors in the sampling frame, adjust for nonresponse, and reduce variances of estimators by incorporating covariates. The series of steps needed to do this are covered in Module 1.
Part: 2
Title:Specific Steps
Description:Specific steps in weighting include computing base weights, adjusting if there are cases whose eligibility we are unsure of, adjusting for nonresponse, and using covariates to calibrate the sample to external population controls. We flesh out the general steps with specific details here.
Part: 3
Title:Implementing the Steps
Description:Software is critical to implementing the steps, but the R system is an excellent source of free routines. This module covers several R packages, including sampling, survey, and PracTools that will select samples and compute weights.
Part: 4
Title:Summary of Course 5
Description:We briefly summarize the methods of weighting and imputation that were covered in Course 5.
This course will cover the steps used in weighting sample surveys, including methods for adjusting for nonresponse and using data external to the survey for calibration. Among the techniques discussed are adjustments using estimated response propensities, poststratification, raking, and general regression estimation. Alternative techniques for imputing values for missing items will be discussed. For both weighting and imputation, the capabilities of different statistical software packages will be covered, including R®, Stata®, and SAS®.
