About this course: 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®.
Who is this class for: 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.
Created by: University of Maryland, College Park
Taught by: Richard Valliant, Ph.D., Research Professor
Joint Program in Survey Methodology
| Basic Info | Course 5 of 7 in the Survey Data Collection and Analytics Specialization |
| Commitment | 4 weeks of study, 1-2 hours/week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
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 videos, 7 readings
- Video: Introduction
- Reading: Class notes + additional reading
- Video: Quantities to Estimate
- Reading: Class notes
- Video: Goals of Estimation
- Reading: Class Notes
- Video: Statistical Interpretation of Estimates
- Reading: Class Notes
- Video: Coverage Problems
- Reading: Class Notes
- Video: Improving Precision
- Reading: Class Notes
- Video: Effects of Weighting on SEs
- Reading: Class Notes
Graded: Introductory quiz on weights
Graded: Quantities
Graded: Goals
Graded: Interpretation
Graded: Coverage
Graded: Improving precision
Graded: Effects on SEs
WEEK 2
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 videos, 6 readings
- Video: Overview
- Reading: Class Notes
- Video: Base Weights
- Reading: Class Notes
- Video: Nonresponse Adjustments
- Reading: Class Notes
- Video: Response Propensities
- Reading: Class Notes
- Video: Tree algorithms
- Reading: Class Notes
- Video: Calibration
- Reading: Class Notes
Graded: Overview
Graded: Base weights
Graded: Nonresponse
Graded: Trees
Graded: Calibration
WEEK 3
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 videos, 5 readings, 3 practice quizzes
- Video: Software
- Reading: Class Notes
- Video: Base Weights
- Reading: Class Notes + Software
- Video: More on Base Weights
- Reading: Class Notes
- Practice Quiz: Quiz on base weights
- Video: Nonresponse Adjustments
- Reading: Class Notes + Software for propensity classes
- Practice Quiz: Quiz on nonresponse adjustments
- Video: Examples of Calibration
- Video: Software for Poststratification
- Reading: Class Notes + Software for calibration
- Practice Quiz: Quiz on calibration and poststratification
Graded: Software
WEEK 4
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 videos, 5 readings
- Video: Reasons for Imputation
- Reading: Class Notes
- Video: Means and hotdeck
- Reading: Class Notes
- Video: Regression Imputation
- Reading: Class Notes
- Video: Effect on Variances
- Reading: Class Notes
- Video: mice R package
- Video: mice example
- Reading: Class Notes + mice R package
Graded: Reasons for imputing
Graded: Means and hot deck
Graded: Regression imputation
Graded: Effects on variances
Graded: Imputation software
Summary of Course 5
We briefly summarize the methods of weighting and imputation that were covered in Course 5.
1 video, 1 reading
- Video: Summary
- Reading: Class Notes
FAQs
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Creators
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.
Pricing
| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
Ratings and Reviews
Excellent review of relevant material.
Good knowledge about Non-responses!
it is very informative
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This course quite help to get as much reliable data as possible for any survey.