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®.
Dealing With Missing Data
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Dealing With Missing Data
This course is part of Survey Data Collection and Analytics Specialization
Instructor: Richard Valliant, Ph.D.
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3.8
(133 reviews)
17 hours to complete
3 weeks at 5 hours a week
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Assessments
21 assignments
Taught in English
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This course is part of the Survey Data Collection and Analytics Specialization
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There are 5 modules in this course
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.
What's included
7 videos7 readings7 assignments
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.
What's included
6 videos6 readings5 assignments
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.
What's included
6 videos5 readings4 assignments
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.
What's included
6 videos5 readings5 assignments
We briefly summarize the methods of weighting and imputation that were covered in Course 5.
What's included
1 video1 reading
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