University of Maryland, College Park

Dealing With Missing Data

This course is part of Survey Data Collection and Analytics Specialization

Taught in English

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Course

Gain insight into a topic and learn the fundamentals

3.8

(130 reviews)

17 hours (approximately)
Flexible schedule
Learn at your own pace

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Assessments

21 quizzes

Course

Gain insight into a topic and learn the fundamentals

3.8

(130 reviews)

17 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Survey Data Collection and Analytics Specialization
When you enroll in this course, you'll also be enrolled in this 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 quizzes

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 quizzes

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 quizzes

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 quizzes

We briefly summarize the methods of weighting and imputation that were covered in Course 5.

What's included

1 video1 reading

Instructor

Instructor ratings
3.3 (11 ratings)
Richard Valliant, Ph.D.
University of Maryland, College Park
5 Courses16,242 learners

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3.8

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