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
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Programa especializado: Survey Data Collection and Analytics Universidad de Maryland en College ParkAcerca de este Curso
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Curso 5 de 7 en
Aprox. 18 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Español (Spanish)
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 5 de 7 en
Aprox. 18 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Español (Spanish)
ofrecido por
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.
Programa - Qué aprenderás en este curso
5 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.
5 horas para completar
7 videos (Total 48 minutos), 7 lecturas, 7 cuestionarios
4 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.
4 horas para completar
6 videos (Total 44 minutos), 6 lecturas, 5 cuestionarios
4 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.
4 horas para completar
6 videos (Total 64 minutos), 5 lecturas, 4 cuestionarios
4 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.
4 horas para completar
6 videos (Total 46 minutos), 5 lecturas, 5 cuestionarios
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
Reseñas
- 5 stars40,49 %
- 4 stars27,27 %
- 3 stars13,22 %
- 2 stars7,43 %
- 1 star11,57 %
Principales reseñas sobre DEALING WITH MISSING DATA
por MM4 de jun. de 2017
This course quite help to get as much reliable data as possible for any survey.
por HE24 de dic. de 2017
This is a higher level course. Good for beginners.
por MN13 de abr. de 2017
Aside from a little hiccup with one of the quiz questions during week 1, this course was worth my time.
por ZM19 de ago. de 2019
interesting material, well taught, lots of short quizzes to enforce understanding.
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.
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