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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教学大纲 - 您将从这门课程中学到什么
周
1General 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.
7 个视频 （总计 48 分钟）, 7 个阅读材料, 7 个测验
7 个视频
Introduction10分钟
Quantities to Estimate8分钟
Goals of Estimation6分钟
Statistical Interpretation of Estimates10分钟
Coverage Problems5分钟
Improving Precision3分钟
Effects of Weighting on SEs2分钟
7 个阅读材料
Class notes + additional reading10分钟
Class notes10分钟
Class Notes10分钟
Class Notes10分钟
Class Notes10分钟
Class Notes10分钟
Class Notes10分钟
7 个练习
Introductory quiz on weights6分钟
Quantities4分钟
Goals6分钟
Interpretation6分钟
Coverage4分钟
Improving precision6分钟
Effects on SEs6分钟
周
2Specific 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.
6 个视频 （总计 44 分钟）, 6 个阅读材料, 5 个测验
6 个视频
Overview7分钟
Base Weights8分钟
Nonresponse Adjustments7分钟
Response Propensities4分钟
Tree algorithms10分钟
Calibration5分钟
6 个阅读材料
Class Notes10分钟
Class Notes10分钟
Class Notes10分钟
Class Notes10分钟
Class Notes10分钟
Class Notes10分钟
5 个练习
Overview6分钟
Base weights6分钟
Nonresponse4分钟
Trees4分钟
Calibration6分钟
周
3Implementing 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.
6 个视频 （总计 64 分钟）, 5 个阅读材料, 4 个测验
6 个视频
Software4分钟
Base Weights10分钟
More on Base Weights13分钟
Nonresponse Adjustments13分钟
Examples of Calibration7分钟
Software for Poststratification14分钟
5 个阅读材料
Class Notes10分钟
Class Notes + Software10分钟
Class Notes10分钟
Class Notes + Software for propensity classes10分钟
Class Notes + Software for calibration10分钟
4 个练习
Software4分钟
Quiz on base weights8分钟
Quiz on nonresponse adjustments6分钟
Quiz on calibration and poststratification8分钟
周
4Imputing 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.
6 个视频 （总计 46 分钟）, 5 个阅读材料, 5 个测验
6 个视频
Means and hotdeck7分钟
Regression Imputation6分钟
Effect on Variances9分钟
mice R package4分钟
mice example10分钟
5 个阅读材料
Class Notes10分钟
Class Notes10分钟
Class Notes10分钟
Class Notes10分钟
Class Notes + mice R package10分钟
5 个练习
Reasons for imputing6分钟
Means and hot deck4分钟
Regression imputation8分钟
Effects on variances8分钟
Imputation software12分钟
Summary of Course 5
We briefly summarize the methods of weighting and imputation that were covered in Course 5.
1 个视频 （总计 3 分钟）, 1 个阅读材料
1 个视频
Summary2分钟
1 个阅读材料
Class Notes10分钟
3.8
18 个审阅热门审阅
创建者 MM•Jun 5th 2017
This course quite help to get as much reliable data as possible for any survey.
讲师
Richard Valliant, Ph.D.
Research ProfessorJoint Program in Survey Methodology
关于 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.
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.
Faculty for this specialisation comes from the Michigan Program in Survey Methodology and the Joint Program in Survey Methodology, a collaboration between the University of Maryland, the University of Michigan, and the data collection firm Westat, founded by the National Science Foundation and the Interagency Consortium of Statistical Policy in the U.S. to educate the next generation of survey researchers, survey statisticians, and survey methodologists. In addition to this specialization we offer short courses, a summer school, certificates, master degrees as well as PhD programs.
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