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Richard Valliant, Ph.D.
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®.
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What's inside
Syllabus
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.
Read more
Syllabus
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.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Examines modern techniques for weighting sample surveys, which is standard in this field
Suitable for individuals working with sampling and data collection
Teaches methods that are relevant to various stakeholders
Offers a comprehensive understanding of this topic
Implements R packages for practical examples
May require prerequisites in statistics
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Reviews summary
Handling missing data in surveys
According to learners, this course provides a solid foundation in dealing with missing data, particularly within the context of survey data. Many find the coverage of weighting techniques and imputation methods to be comprehensive for an introductory level. The sections covering R programming and its relevant packages for these tasks are frequently highlighted as a positive aspect, offering practical implementation guidance. However, some students note that the course can be quite theoretical and might require prior statistical knowledge. Coverage of Stata and SAS is mentioned but appears less detailed than R, which is a warning for users primarily relying on those platforms. Overall, it's seen as a useful course for professionals and students working with survey data.
Leans heavily towards theory, less hands-on.
"While the theory is well-covered, I wished there were more practical case studies or complex examples."
"The course focuses more on the 'why' than the 'how-to' in depth."
"Some sections felt a bit dry due to the theoretical nature of the content."
"Good for understanding concepts, but you'll need to practice implementation on your own."
"It provides the theoretical groundwork but could benefit from more real-world problem-solving."
Practical demos using R are very helpful.
"The R demos using packages like 'survey' and 'mice' were extremely helpful for practical application."
"Learning how to implement the methods in R was the most valuable part for me."
"The explanations of the R code and output were clear and easy to follow."
"I really appreciated the hands-on examples provided for using R with missing data."
"The R sections alone are worth the price of admission if you work with survey data."
Provides core concepts for missing data.
"The course provides a very solid foundation on the topics of weighting and imputation for missing data."
"I finally understand the underlying principles behind different imputation methods after taking this course."
"Excellent introduction to handling nonresponse and missing values in survey data analysis."
"This course gave me the core knowledge I needed to approach missing data problems confidently."
"It covers the fundamental theory quite well before diving into application."
R is emphasized over Stata/SAS.
"The coverage of Stata and SAS was minimal compared to R, which was disappointing for me as a Stata user."
"If you're not an R user, the software examples might not be as useful."
"I expected more balanced coverage across all three software packages mentioned."
"Focus is heavily on R; Stata and SAS felt like afterthoughts."
"Wish they had dedicated more time to demonstrations in Stata and SAS."
May be challenging for beginners.
"This course is definitely not for beginners; a strong background in statistics is highly recommended."
"Found it quite challenging at times without a deep understanding of statistical theory."
"It assumes prior knowledge of survey design and basic statistical concepts."
"Might be overwhelming if you are completely new to data analysis or statistics."
"Be prepared for dense statistical material if you don't have a stats background."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Dealing With Missing Data with these
activities:
Read 'Sampling: Design and Analysis' by Cochrane
Show steps
Cochrane's book provides a comprehensive overview of sampling theory and techniques.
View
Planning and Analysis of Observational Studies
on Amazon
Show steps
-
Read and understand the concepts of probability sampling
-
Learn about different sampling methods and their applications
-
Review methods for estimating population parameters from sample data
Review mathematics
Show steps
Review fundamental mathematical concepts to lay a strong foundation for understanding weighting and imputation techniques.
Browse courses on
Linear Algebra
Show steps
-
Revisit basic algebra concepts, such as vectors and matrices.
-
Practice solving linear equations and systems of equations.
-
Review differentiation and integration techniques in calculus.
-
Refresh your knowledge of probability and statistics, including concepts like mean, variance, and sampling distributions.
Review statistics
Show steps
Reviewing statistics will help you refresh your knowledge and prepare for the course's quantitative aspects.
Browse courses on
Statistics
Show steps
-
Review probability concepts (e.g., conditional probability, Bayes' theorem)
-
Revisit statistical inference (e.g., hypothesis testing, confidence intervals)
-
Brush up on regression analysis and model fitting
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Write a blog post on survey weighting
Show steps
Writing a blog post will help you synthesize your knowledge and explain weighting concepts to a broader audience.
Browse courses on
Weighting
Show steps
-
Choose a specific weighting topic to focus on
-
Research and gather relevant information
-
Write a clear and concise post that explains the concept
-
Publish the post on a relevant platform
Explore R packages for weighting and imputation
Show steps
Become familiar with the capabilities of R packages for implementing weighting and imputation techniques.
Browse courses on
R
Show steps
-
Install and load necessary R packages, such as sampling, survey, and PracTools.
-
Follow tutorials on using these packages for basic weighting and imputation tasks.
-
Experiment with different weighting and imputation methods using sample datasets.
Attend office hours
Show steps
Attending office hours provides an opportunity to ask questions and clarify concepts with the instructor.
Show steps
-
Attend office hours regularly
-
Bring specific questions or areas where you need clarification
-
Engage in active discussions and ask follow-up questions
Design a survey instrument
Show steps
Designing a survey instrument will provide practical experience in planning and executing a survey.
Browse courses on
Survey Design
Show steps
-
Identify the research objectives and target population
-
Develop survey questions that are clear, concise, and unbiased
-
Determine the appropriate sample size and sampling method
-
Pretest the survey instrument and make necessary revisions
Discuss weighting and imputation challenges
Show steps
Engage with peers to discuss real-world challenges and share insights on weighting and imputation techniques.
Show steps
-
Join or form a study group with other course participants.
-
Identify common challenges encountered in weighting and imputation.
-
Share experiences and strategies for overcoming these challenges.
-
Provide feedback and support to group members.
Solve weighting and imputation practice problems
Show steps
Reinforce knowledge and develop problem-solving skills in weighting and imputation through practice.
Show steps
-
Access practice problems from textbooks, online resources, or the course materials.
-
Solve problems independently, focusing on applying the correct techniques and formulas.
-
Check your solutions against provided answer keys or consult with the instructor or peers for feedback.
Write a research proposal
Show steps
Writing a research proposal will help you develop a clear and concise plan for your survey project.
Browse courses on
Research Proposal
Show steps
-
State the research problem and objectives
-
Review the literature and develop a theoretical framework
-
Describe the research design and methodology
-
Discuss the expected outcomes and implications
Summarize weighting and imputation techniques
Show steps
Reinforce understanding by summarizing key concepts and techniques related to weighting and imputation.
Show steps
-
Create a table summarizing different weighting methods, including formulas and examples.
-
Write a short essay describing imputation methods for missing data, discussing advantages and disadvantages.
-
Record a video explaining a specific weighting or imputation technique in detail.
Solve weighting and imputation problems
Show steps
Solving weighting and imputation problems will help you develop practical skills in these essential survey techniques.
Browse courses on
Weighting
Show steps
-
Calculate weights to adjust for nonresponse and coverage errors
-
Use different imputation methods to handle missing data
-
Assess the impact of weighting and imputation on survey estimates
Contribute to 'survey' package in R
Show steps
Contributing to the 'survey' package will provide hands-on experience in survey data analysis using R.
Browse courses on
R Programming
Show steps
-
Install the 'survey' package
-
Explore the package documentation and tutorials
-
Identify an area where you can contribute
-
Make a pull request with your proposed changes
Read 'Sampling: Design and Analysis' by Cochrane
Show steps
Cochrane's book provides a comprehensive overview of sampling theory and techniques.
View
Planning and Analysis of Observational Studies
on Amazon
Show steps
Show steps
Show steps
- Read and understand the concepts of probability sampling
- Learn about different sampling methods and their applications
- Review methods for estimating population parameters from sample data
Review mathematics
Show steps
Review fundamental mathematical concepts to lay a strong foundation for understanding weighting and imputation techniques.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Revisit basic algebra concepts, such as vectors and matrices.
- Practice solving linear equations and systems of equations.
- Review differentiation and integration techniques in calculus.
- Refresh your knowledge of probability and statistics, including concepts like mean, variance, and sampling distributions.
Review statistics
Show steps
Reviewing statistics will help you refresh your knowledge and prepare for the course's quantitative aspects.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review probability concepts (e.g., conditional probability, Bayes' theorem)
- Revisit statistical inference (e.g., hypothesis testing, confidence intervals)
- Brush up on regression analysis and model fitting
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Write a blog post on survey weighting
Show steps
Writing a blog post will help you synthesize your knowledge and explain weighting concepts to a broader audience.
Browse courses on
Weighting
Show steps
Show steps
Show steps
- Choose a specific weighting topic to focus on
- Research and gather relevant information
- Write a clear and concise post that explains the concept
- Publish the post on a relevant platform
Explore R packages for weighting and imputation
Show steps
Become familiar with the capabilities of R packages for implementing weighting and imputation techniques.
Browse courses on
R
Show steps
Show steps
Show steps
- Install and load necessary R packages, such as sampling, survey, and PracTools.
- Follow tutorials on using these packages for basic weighting and imputation tasks.
- Experiment with different weighting and imputation methods using sample datasets.
Attend office hours
Show steps
Attending office hours provides an opportunity to ask questions and clarify concepts with the instructor.
Show steps
Show steps
Show steps
- Attend office hours regularly
- Bring specific questions or areas where you need clarification
- Engage in active discussions and ask follow-up questions
Design a survey instrument
Show steps
Designing a survey instrument will provide practical experience in planning and executing a survey.
Browse courses on
Survey Design
Show steps
Show steps
Show steps
- Identify the research objectives and target population
- Develop survey questions that are clear, concise, and unbiased
- Determine the appropriate sample size and sampling method
- Pretest the survey instrument and make necessary revisions
Discuss weighting and imputation challenges
Show steps
Engage with peers to discuss real-world challenges and share insights on weighting and imputation techniques.
Show steps
Show steps
Show steps
- Join or form a study group with other course participants.
- Identify common challenges encountered in weighting and imputation.
- Share experiences and strategies for overcoming these challenges.
- Provide feedback and support to group members.
Solve weighting and imputation practice problems
Show steps
Reinforce knowledge and develop problem-solving skills in weighting and imputation through practice.
Show steps
Show steps
Show steps
- Access practice problems from textbooks, online resources, or the course materials.
- Solve problems independently, focusing on applying the correct techniques and formulas.
- Check your solutions against provided answer keys or consult with the instructor or peers for feedback.
Write a research proposal
Show steps
Writing a research proposal will help you develop a clear and concise plan for your survey project.
Browse courses on
Research Proposal
Show steps
Show steps
Show steps
- State the research problem and objectives
- Review the literature and develop a theoretical framework
- Describe the research design and methodology
- Discuss the expected outcomes and implications
Summarize weighting and imputation techniques
Show steps
Reinforce understanding by summarizing key concepts and techniques related to weighting and imputation.
Show steps
Show steps
Show steps
- Create a table summarizing different weighting methods, including formulas and examples.
- Write a short essay describing imputation methods for missing data, discussing advantages and disadvantages.
- Record a video explaining a specific weighting or imputation technique in detail.
Solve weighting and imputation problems
Show steps
Solving weighting and imputation problems will help you develop practical skills in these essential survey techniques.
Browse courses on
Weighting
Show steps
Show steps
Show steps
- Calculate weights to adjust for nonresponse and coverage errors
- Use different imputation methods to handle missing data
- Assess the impact of weighting and imputation on survey estimates
Contribute to 'survey' package in R
Show steps
Contributing to the 'survey' package will provide hands-on experience in survey data analysis using R.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Install the 'survey' package
- Explore the package documentation and tutorials
- Identify an area where you can contribute
- Make a pull request with your proposed changes
Career center
Learners who complete Dealing With Missing Data will develop knowledge and skills
that may be useful to these careers:
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to help businesses make better decisions. They may work with data from surveys, experiments, or other sources to identify trends and patterns. This course would be helpful for Operations Research Analysts because it would provide them with the skills and knowledge needed to collect and analyze data, and to make recommendations based on their findings.
Data Analyst
Data Analyst
Data Analysts use data to solve problems and make better decisions. They may work with data from a variety of sources, including surveys, experiments, and social media. This course would be helpful for Data Analysts because it would provide them with the skills and knowledge needed to collect and analyze data, and to make recommendations based on their findings.
Market Researcher
Market Researcher
Market Researchers study consumer behavior and preferences. They may use data from surveys, focus groups, and other sources to identify trends and patterns. This course would be helpful for Market Researchers because it would provide them with the skills and knowledge needed to collect and analyze data, and to make recommendations based on their findings.
Survey Researcher
Survey Researcher
Survey Researchers design and conduct surveys to collect data on a variety of topics. They may work with data from surveys to identify trends and patterns, and to make recommendations. This course would be helpful for Survey Researchers because it would provide them with the skills and knowledge needed to design and conduct surveys, and to analyze data.
Statistician
Statistician
Statisticians use data to solve problems and make better decisions. They may work with data from a variety of sources, including surveys, experiments, and social media. This course would be helpful for Statisticians because it would provide them with the skills and knowledge needed to collect and analyze data, and to make recommendations based on their findings.
Biostatistician
Biostatistician
Biostatisticians use data to solve problems in the field of medicine. They may work with data from clinical trials, observational studies, and other sources to identify trends and patterns. This course would be helpful for Biostatisticians because it would provide them with the skills and knowledge needed to collect and analyze data, and to make recommendations based on their findings.
Epidemiologist
Epidemiologist
Epidemiologists study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. This course would be helpful for Epidemiologists because it would provide them with the skills and knowledge needed to collect and analyze data, and to make recommendations based on their findings.
Public Health Analyst
Public Health Analyst
Public Health Analysts use data to improve the health of populations. They may work with data from a variety of sources, including surveys, vital records, and social media. This course would be helpful for Public Health Analysts because it would provide them with the skills and knowledge needed to collect and analyze data, and to make recommendations based on their findings.
Health Services Researcher
Health Services Researcher
Health Services Researchers study the organization, financing, and delivery of health care services. They may use data from a variety of sources, including surveys, claims data, and medical records. This course would be helpful for Health Services Researchers because it would provide them with the skills and knowledge needed to collect and analyze data, and to make recommendations based on their findings.
Policy Analyst
Policy Analyst
Policy Analysts use data to inform policy decisions. They may work with data from a variety of sources, including surveys, economic data, and social media. This course would be helpful for Policy Analysts because it would provide them with the skills and knowledge needed to collect and analyze data, and to make recommendations based on their findings.
Quantitative Researcher
Quantitative Researcher
Quantitative Researchers use data to answer research questions. They may work with data from a variety of sources, including surveys, experiments, and social media. This course would be helpful for Quantitative Researchers because it would provide them with the skills and knowledge needed to collect and analyze data, and to make recommendations based on their findings.
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make better decisions. They may work with data from a variety of sources, including surveys, experiments, and social media. This course would be helpful for Data Scientists because it would provide them with the skills and knowledge needed to collect and analyze data, and to make recommendations based on their findings.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models to solve problems and make better decisions. They may work with data from a variety of sources, including surveys, experiments, and social media. This course would be helpful for Machine Learning Engineers because it would provide them with the skills and knowledge needed to collect and analyze data, and to build and deploy machine learning models.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They may work with data from a variety of sources, including surveys, experiments, and social media. This course may be helpful for Software Engineers because it would provide them with the skills and knowledge needed to collect and analyze data, and to build software applications.
Computer Scientist
Computer Scientist
Computer Scientists design, develop, and maintain computer systems. They may work with data from a variety of sources, including surveys, experiments, and social media. This course may be helpful for Computer Scientists because it would provide them with the skills and knowledge needed to collect and analyze data, and to design and develop computer systems.
For more career information including salaries, visit:
OpenCourser.com/course/nqvqzp/dealing
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Dealing With Missing Data.
Save
Is considered a classic that provides a solid foundation for understanding sampling concepts, including entropy-based sampling.
Offers a detailed and comprehensive presentation of sampling techniques, with a particular emphasis on practical applications.
Provides a hands-on approach to survey sampling, with a focus on real-world survey design and analysis.
Offers a comprehensive overview of statistical methods for analyzing survey data, including the handling of missing data and the evaluation of survey error.
This handbook provides a comprehensive overview of survey research methods and techniques, covering a wide range of topics, including sampling, data collection, and analysis.
Focuses specifically on the topic of missing data in longitudinal studies, providing advanced techniques for Bayesian imputation.
Save
Offers a comprehensive examination of nonresponse in social science surveys, discussing the causes and consequences of nonresponse and providing strategies for addressing it.
Provides a foundational overview of survey sampling concepts and methods, suitable for students or individuals new to the field.
Offers a comprehensive treatment of sampling theory, covering both basic and advanced topics, including cluster sampling and adaptive sampling.
Provides a balanced coverage of sampling theory and practice, focusing on the selection and evaluation of samples.
Save
Covers a wide range of topics in survey sampling, including sampling design, estimation, and analysis, with an emphasis on practical applications.
This classic text provides a deep dive into the mathematical foundations of survey sampling, offering a comprehensive treatment of the subject.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nqvqzp/dealing
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Rating
3.8
Effort
17 hours to complete
Via
Coursera
Institution
University of Maryland, College Park
Instructor
Richard Valliant, Ph.D.
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
Survey Data Collection and Analytics . It's comprised of seven courses:
- Dealing With Missing Data (viewing)
- Framework for Data Collection and Analysis
- Combining and Analyzing Complex Data
- Questionnaire Design for Social Surveys
- Sampling People, Networks and Records
- Survey Data Collection and Analytics Project (Capstone)
- Data Collection: Online, Telephone and Face-to-face
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