关于此课程： 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®.
此课程适用人群： This course is aimed at undergraduates, graduate students, and working professionals who have an interest and need in preparing survey data for analysis and distribution to data users.
制作方： University of Maryland, College Park
教学方： Richard Valliant, Ph.D., Research Professor
Joint Program in Survey Methodology
| 基本信息 | 课程 5（共 7 门，Survey Data Collection and Analytics Specialization ） |
| 承诺学习时间 | 学习时长：4周，每周时间1-2小时 |
| 语言 | English |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
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.
7 个视频, 7 个阅读材料
- Reading: Class notes + additional reading
- 视频: Quantities to Estimate
- Reading: Class notes
- 视频: Goals of Estimation
- Reading: Class Notes
- 视频: Statistical Interpretation of Estimates
- Reading: Class Notes
- 视频: Coverage Problems
- Reading: Class Notes
- 视频: Improving Precision
- Reading: Class Notes
- 视频: Effects of Weighting on SEs
- Reading: Class Notes
已评分: Introductory quiz on weights
已评分: Quantities
已评分: Goals
已评分: Interpretation
已评分: Coverage
已评分: Improving precision
已评分: Effects on SEs
第 2 周
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.
6 个视频, 6 个阅读材料
- Reading: Class Notes
- 视频: Base Weights
- Reading: Class Notes
- 视频: Nonresponse Adjustments
- Reading: Class Notes
- 视频: Response Propensities
- Reading: Class Notes
- 视频: Tree algorithms
- Reading: Class Notes
- 视频: Calibration
- Reading: Class Notes
已评分: Overview
已评分: Base weights
已评分: Nonresponse
已评分: Trees
已评分: Calibration
第 3 周
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.
6 个视频, 5 个阅读材料, 3 个练习测验
- Reading: Class Notes
- 视频: Base Weights
- Reading: Class Notes + Software
- 视频: More on Base Weights
- Reading: Class Notes
- Practice Quiz: Quiz on base weights
- 视频: Nonresponse Adjustments
- Reading: Class Notes + Software for propensity classes
- Practice Quiz: Quiz on nonresponse adjustments
- 视频: Examples of Calibration
- 视频: Software for Poststratification
- Reading: Class Notes + Software for calibration
- Practice Quiz: Quiz on calibration and poststratification
已评分: Software
第 4 周
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.
6 个视频, 5 个阅读材料
- Reading: Class Notes
- 视频: Means and hotdeck
- Reading: Class Notes
- 视频: Regression Imputation
- Reading: Class Notes
- 视频: Effect on Variances
- Reading: Class Notes
- 视频: mice R package
- 视频: mice example
- Reading: Class Notes + mice R package
已评分: Reasons for imputing
已评分: Means and hot deck
已评分: Regression imputation
已评分: Effects on variances
已评分: Imputation software
Summary of Course 5
We briefly summarize the methods of weighting and imputation that were covered in Course 5.
1 个视频, 1 个阅读材料
- Reading: Class Notes
常见问题解答
运作方式
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制作方
University of Maryland, College Park
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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| 收到最终成绩 | 可用 |
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评分和审阅
Great course!
This is a higher level course. Good for beginners.
This course quite help to get as much reliable data as possible for any survey.
Prof. Richard Valliant, Ph.D. clearly enough explain all of these course materials. I will use these materials to dealing missing data on our census or survey. I believe that these materials were very helpful for me and my agency.
Thank you very much for all of this course.