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®.
Este curso forma parte de Programa Especializado - Survey Data Collection and Analytics
Dealing With Missing Data
ofrecido por
Programa Especializado - Survey Data Collection and Analytics Universidad de Maryland en College Park
Acerca de este Curso
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Fechas límite flexibles
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Aprox. 9 horas para completar
Sugerido: 4 weeks of study, 1-2 hours/week
Inglés (English)
Subtítulos: Inglés (English)
Programa - Qué aprenderás en este curso
3 horas para completar
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.
3 horas para completar
7 videos (Total 48 minutos), 7 lecturas, 7 cuestionarios
7 videos
Introduction10m
Quantities to Estimate8m
Goals of Estimation6m
Statistical Interpretation of Estimates10m
Coverage Problems5m
Improving Precision3m
Effects of Weighting on SEs2m
7 lecturas
Class notes + additional reading10m
Class notes10m
Class Notes10m
Class Notes10m
Class Notes10m
Class Notes10m
Class Notes10m
7 ejercicios de práctica
Introductory quiz on weights6m
Quantities4m
Goals6m
Interpretation6m
Coverage4m
Improving precision6m
Effects on SEs6m
2 horas para completar
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.
2 horas para completar
6 videos (Total 44 minutos), 6 lecturas, 5 cuestionarios
6 videos
Overview7m
Base Weights8m
Nonresponse Adjustments7m
Response Propensities4m
Tree algorithms10m
Calibration5m
6 lecturas
Class Notes10m
Class Notes10m
Class Notes10m
Class Notes10m
Class Notes10m
Class Notes10m
5 ejercicios de práctica
Overview6m
Base weights6m
Nonresponse4m
Trees4m
Calibration6m
2 horas para completar
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.
2 horas para completar
6 videos (Total 64 minutos), 5 lecturas, 4 cuestionarios
6 videos
Software4m
Base Weights10m
More on Base Weights13m
Nonresponse Adjustments13m
Examples of Calibration7m
Software for Poststratification14m
5 lecturas
Class Notes10m
Class Notes + Software10m
Class Notes10m
Class Notes + Software for propensity classes10m
Class Notes + Software for calibration10m
4 ejercicios de práctica
Software4m
Quiz on base weights8m
Quiz on nonresponse adjustments6m
Quiz on calibration and poststratification8m
2 horas para completar
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.
2 horas para completar
6 videos (Total 46 minutos), 5 lecturas, 5 cuestionarios
6 videos
Means and hotdeck7m
Regression Imputation6m
Effect on Variances9m
mice R package4m
mice example10m
5 lecturas
Class Notes10m
Class Notes10m
Class Notes10m
Class Notes10m
Class Notes + mice R package10m
5 ejercicios de práctica
Reasons for imputing6m
Means and hot deck4m
Regression imputation8m
Effects on variances8m
Imputation software12m
13 minutos para completar
Summary of Course 5
We briefly summarize the methods of weighting and imputation that were covered in Course 5.
13 minutos para completar
1 video (Total 3 minutos), 1 lectura
1 video
Summary2m
1 lectura
Class Notes10m
Principales revisiones sobre Dealing With Missing Data
interesting material, well taught, lots of short quizzes to enforce understanding.
This course quite help to get as much reliable data as possible for any survey.
Instructor
Richard Valliant, Ph.D.
Research ProfessorJoint Program in Survey Methodology
Acerca de Universidad de Maryland en 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.
Acerca de Programa especializado Survey Data Collection and Analytics
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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