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®.
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第 5 门课程（共 7 门）
完成时间大约为18 小时
英语（English）
字幕：英语（English）
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 5 门课程（共 7 门）
完成时间大约为18 小时
英语（English）
字幕：英语（English）
提供方
马里兰大学帕克分校
The University of Maryland is the state's flagship university and one of the nation's preeminent public research universities. A global leader in research, entrepreneurship and innovation, the university is home to more than 37,000 students, 9,000 faculty and staff, and 250 academic programs. Its faculty includes three Nobel laureates, three Pulitzer Prize winners, 47 members of the national academies and scores of Fulbright scholars. The institution has a $1.8 billion operating budget, secures $500 million annually in external research funding and recently completed a $1 billion fundraising campaign.
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完成时间为 5 小时
General Steps in Weighting
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.
完成时间为 5 小时
7 个视频 （总计 48 分钟）, 7 个阅读材料, 7 个测验
完成时间为 4 小时
Specific Steps
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.
完成时间为 4 小时
6 个视频 （总计 44 分钟）, 6 个阅读材料, 5 个测验
完成时间为 4 小时
Implementing the Steps
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.
完成时间为 4 小时
6 个视频 （总计 64 分钟）, 5 个阅读材料, 4 个测验
完成时间为 4 小时
Imputing for Missing Items
In most surveys there will be items for which respondents do not provide information, even though the respondent completed enough of the data collection instrument to be considered "complete". If only the cases with all items present are retained when fitting a model, quite a few cases may be excluded from the analysis. Imputing for the missing items avoids dropping the missing cases. We cover methods of doing the imputing and of reflecting the effects of imputations on standard errors in this module.
完成时间为 4 小时
6 个视频 （总计 46 分钟）, 5 个阅读材料, 5 个测验
完成时间为 13 分钟
Summary of Course 5
We briefly summarize the methods of weighting and imputation that were covered in Course 5.
完成时间为 13 分钟
1 个视频 （总计 3 分钟）, 1 个阅读材料
审阅
来自DEALING WITH MISSING DATA的热门评论
由 SS 提供Nov 18, 2017
This course was hard to follow, hard to complete (quizzes), poorly designed and with little useful content. In other words, not worth the money I paid for it!
由 MN 提供Apr 13, 2017
Aside from a little hiccup with one of the quiz questions during week 1, this course was worth my time.
由 ZM 提供Aug 19, 2019
interesting material, well taught, lots of short quizzes to enforce understanding.
由 MM 提供Jun 4, 2017
This course quite help to get as much reliable data as possible for any survey.
关于 Survey Data Collection and Analytics 专项课程
This specialization covers the fundamentals of surveys as used in market research, evaluation research, social science and political research, official government statistics, and many other topic domains. In six courses, you will learn the basics of questionnaire design, data collection methods, sampling design, dealing with missing values, making estimates, combining data from different sources, and the analysis of survey data. In the final Capstone Project, you’ll apply the skills learned throughout the specialization by analyzing and comparing multiple data sources.
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