Acerca de este curso: 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®.
Para quién es esta clase: 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.
Creada por: Universidad de Maryland en College Park
Enseñado por: Richard Valliant, Ph.D., Research Professor
Joint Program in Survey Methodology
| Información básica | Curso 5 de 7 en Survey Data Collection and Analytics Specialization |
| Compromiso | 4 weeks of study, 1-2 hours/week |
| Idioma | English |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
Programa
SEMANA 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 lecturas
- 閱讀: Class notes + additional reading
- Vídeo: Quantities to Estimate
- 閱讀: Class notes
- Vídeo: Goals of Estimation
- 閱讀: Class Notes
- Vídeo: Statistical Interpretation of Estimates
- 閱讀: Class Notes
- Vídeo: Coverage Problems
- 閱讀: Class Notes
- Vídeo: Improving Precision
- 閱讀: Class Notes
- Vídeo: Effects of Weighting on SEs
- 閱讀: Class Notes
Calificado: Introductory quiz on weights
Calificado: Quantities
Calificado: Goals
Calificado: Interpretation
Calificado: Coverage
Calificado: Improving precision
Calificado: Effects on SEs
SEMANA 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 lecturas
- 閱讀: Class Notes
- Vídeo: Base Weights
- 閱讀: Class Notes
- Vídeo: Nonresponse Adjustments
- 閱讀: Class Notes
- Vídeo: Response Propensities
- 閱讀: Class Notes
- Vídeo: Tree algorithms
- 閱讀: Class Notes
- Vídeo: Calibration
- 閱讀: Class Notes
Calificado: Overview
Calificado: Base weights
Calificado: Nonresponse
Calificado: Trees
Calificado: Calibration
SEMANA 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 lecturas, 3 cuestionarios de práctica
- 閱讀: Class Notes
- Vídeo: Base Weights
- 閱讀: Class Notes + Software
- Vídeo: More on Base Weights
- 閱讀: Class Notes
- 練習測驗: Quiz on base weights
- Vídeo: Nonresponse Adjustments
- 閱讀: Class Notes + Software for propensity classes
- 練習測驗: Quiz on nonresponse adjustments
- Vídeo: Examples of Calibration
- Vídeo: Software for Poststratification
- 閱讀: Class Notes + Software for calibration
- 練習測驗: Quiz on calibration and poststratification
Calificado: Software
SEMANA 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 lecturas
- 閱讀: Class Notes
- Vídeo: Means and hotdeck
- 閱讀: Class Notes
- Vídeo: Regression Imputation
- 閱讀: Class Notes
- Vídeo: Effect on Variances
- 閱讀: Class Notes
- Vídeo: mice R package
- Vídeo: mice example
- 閱讀: Class Notes + mice R package
Calificado: Reasons for imputing
Calificado: Means and hot deck
Calificado: Regression imputation
Calificado: Effects on variances
Calificado: Imputation software
Summary of Course 5
We briefly summarize the methods of weighting and imputation that were covered in Course 5.
1 video, 1 lectura
- 閱讀: Class Notes
Preguntas Frecuentes
Cómo funciona
課程作業
每門課程都像是一本互動的教科書，具有預先錄製的視頻、測驗和項目。
來自同學的幫助
與其他成千上萬的學生相聯繫，對想法進行辯論，討論課程材料，並尋求幫助來掌握概念。
證書
獲得正式認證的作業，並與朋友、同事和雇主分享您的成功。
Creadores
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.
Tarifa
| Comprar curso | |
|---|---|
| Accede a los materiales del curso | Disponible |
| Accede a los materiales con calificación | Disponible |
| Recibe una calificación final | Disponible |
| Obtén un Certificado de curso para compartir | Disponible |
Calificaciones y revisiones
Great course!
This is a higher level course. Good for beginners.
This course quite help to get as much reliable data as possible for any survey.
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Prof. Richard Valliant, Ph.D. clearly enough explain all of these course materials. I will use these materials to dealing missing data on our census or survey. I believe that these materials were very helpful for me and my agency.
Thank you very much for all of this course.