OpenCourser brand icon
Sign in
Sorry, this page is no longer available
We may earn an affiliate commission when you visit our partners.
Course image
Course image
Coursera logo

Dealing With Missing Data

3.8 Filled star Filled star Filled star Half star Empty star
Based on 137 ratings
see reviews
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®.

Enroll now

Here's a deal for you

Save money when you learn with a deal that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until March 12, 2027
Save 40% Off 3 Months
Access thousands of top-rated learning programs with a Coursera Plus monthly subscription and take 40% off 3 months for a limited time.
Take
40%
off
Get offer

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

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

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

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
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
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
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
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
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
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
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
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
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
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
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
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
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

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 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.
See salaries and explore the career path for Operations Research 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.
See salaries and explore the career path for Data Analyst
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.
See salaries and explore the career path for Market 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.
See salaries and explore the career path for Survey Researcher
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.
See salaries and explore the career path for Statistician
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.
See salaries and explore the career path for Biostatistician
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.
See salaries and explore the career path for Epidemiologist
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.
See salaries and explore the career path for Public Health Analyst
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.
See salaries and explore the career path for Health Services Researcher
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.
See salaries and explore the career path for Policy Analyst
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.
See salaries and explore the career path for Quantitative Researcher
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.
See salaries and explore the career path for Data Scientist
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.
See salaries and explore the career path for Machine Learning 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.
See salaries and explore the career path for Software Engineer
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.
See salaries and explore the career path for Computer Scientist

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.
Cover image
Sampling
Save
Is considered a classic that provides a solid foundation for understanding sampling concepts, including entropy-based sampling.
Sampling: Design and Analysis (Chapman & Hall/CRC...
Hardcover
$$$
SAS® Software Companion for Sampling: Design and...
Kindle Edition
$$
R Companion for Sampling: Design and Analysis,...
Paperback
$$
Sampling: Design and Analysis (Chapman & Hall/CRC...
Paperback
$$
Sampling: Design and Analysis
Hardcover
$$$$
Sampling: Design and Analysis (Advanced Series)
Hardcover
$$$$
Cover image
Model Assisted Survey Sampling
Save
Offers a detailed and comprehensive presentation of sampling techniques, with a particular emphasis on practical applications.
Model Assisted Survey Sampling (Springer Series in...
Hardcover
$$$
Cover image
Elementary Survey Sampling
Save
Provides a hands-on approach to survey sampling, with a focus on real-world survey design and analysis.
Elementary Survey Sampling
Hardcover
$$$
Student Solutions Manual for...
Paperback
$$$
Elementary Survey Sampling
Kindle Edition
$$$
The Psychology of Survey Response
Save
Offers a comprehensive overview of statistical methods for analyzing survey data, including the handling of missing data and the evaluation of survey error.
The Psychology of Survey Response
Paperback
$$$
The Psychology of Survey Response
Kindle Edition
$$$
Cover image
The SAGE Handbook of Survey Methodology
Save
This handbook provides a comprehensive overview of survey research methods and techniques, covering a wide range of topics, including sampling, data collection, and analysis.
The SAGE Handbook of Survey Methodology
Kindle Edition
$$$$
Cover image
Flexible Imputation of Missing Data, Second Edition
Save
Focuses specifically on the topic of missing data in longitudinal studies, providing advanced techniques for Bayesian imputation.
Flexible Imputation of Missing Data, Second Edition...
Paperback
$$
Flexible Imputation of Missing Data, Second Edition...
Kindle Edition
$$
Cover image
Survey Methodology
Save
Offers a comprehensive examination of nonresponse in social science surveys, discussing the causes and consequences of nonresponse and providing strategies for addressing it.
Survey Methodology
Paperback
$$$
Survey Methodology (Wiley Series in Survey...
Paperback
$$
Cover image
Tests & Measurement for People Who (Think They)...
Save
Provides a foundational overview of survey sampling concepts and methods, suitable for students or individuals new to the field.
Tests & Measurement for People Who (Think They)...
Paperback
$$$
Tests & Measurement for People Who (Think They)...
Kindle Edition
$$$
Cover image
Elementary Survey Sampling
Save
Offers a comprehensive treatment of sampling theory, covering both basic and advanced topics, including cluster sampling and adaptive sampling.
Elementary Survey Sampling
Hardcover
$$$
Student Solutions Manual for...
Paperback
$$$
Elementary Survey Sampling
Kindle Edition
$$$
Cover image
Practical Tools for Designing and Weighting Survey...
Save
Provides a balanced coverage of sampling theory and practice, focusing on the selection and evaluation of samples.
Practical Tools for Designing and Weighting Survey...
Paperback
$$$
Practical Tools for Designing and Weighting Survey...
Hardcover
$$$$
Practical Tools for Designing and Weighting Survey...
Kindle Edition
$$$
Practical Tools for Designing and Weighting Survey...
Kindle Edition
$$$
Cover image
Survey Weights
Save
Covers a wide range of topics in survey sampling, including sampling design, estimation, and analysis, with an emphasis on practical applications.
Survey Weights: A Step-by-step Guide to Calculation
Paperback
$$$
Survey Weights: A Step-by-step Guide to Calculation
Kindle Edition
$$
Cover image
The New Economics for Industry, Government,...
Save
This classic text provides a deep dive into the mathematical foundations of survey sampling, offering a comprehensive treatment of the subject.
The New Economics for Industry, Government,...
Paperback
$$
DEMING Out of the Crisis, Reissue Edition + New...
Paperback
$$
The New Economics for Industry, Government,...
Kindle Edition
$$

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Here are nine courses similar to Dealing With Missing Data.
Parallel Programming in Java
Most relevant
Entrepreneurship I: Laying the Foundation
Most relevant
Positive Psychiatry and Mental Health
Most relevant
The People, Power, and Pride of Public Health
Most relevant
Accounting Analysis I: The Role of Accounting as an...
Most relevant
Leading for Equity, Diversity and Inclusion in Higher...
Most relevant
Cybersecurity Policy for Water and Electricity...
Most relevant
Electric Industry Operations and Markets
Most relevant
Epidemics
Most relevant

Coursera Buyer Protection & Guarantee

Verified partner policies & purchase protections

Accredited University Partner
Free Trial & Audit

7-Day Free Trial & Free Audit

7-day free trial on monthly Coursera Plus subscriptions; free audit option available on most individual courses to explore video lectures and readings at no charge.

Return & Refund Policy

14-Day Refund Guarantee

14-day refund window on annual Coursera Plus subscriptions and single-course purchases prior to certificate issuance. Monthly subscriptions do not offer refunds once billed.

Buyer Protections

Verified Academic & Industry Partner

Accredited university and industry certifications (e.g. Stanford, Google, IBM, Yale), secure SSL payment processing, and verifiable digital certificate links.

Key Purchase Protections
14-day refund window on annual Coursera Plus and single courses
7-day free trial on monthly Coursera Plus subscriptions
Free course audit option for lectures and readings
Verified certificates from top universities & companies
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Course image
Rating
3.8 Filled star Filled star Filled star Half star Empty star
Effort
17 hours to complete
Via
Coursera
Institution
University of Maryland, College Park
Instructor
Richard Valliant, Ph.D.
Language
English
Transcripts
Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова magyar nyelv Bahasa Indonesia ไทย қазақша اُردُو, فارسی বাংলা پښتو آذربایجان دیلی
This course belongs to a collection called Survey Data Collection and Analytics . It's comprised of seven courses:
  1. Dealing With Missing Data (viewing)
  2. Framework for Data Collection and Analysis
  3. Combining and Analyzing Complex Data
  4. Questionnaire Design for Social Surveys
  5. Sampling People, Networks and Records
  6. Survey Data Collection and Analytics Project (Capstone)
  7. Data Collection: Online, Telephone and Face-to-face
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
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
Enroll now to gain full access to Dealing With Missing Data.
Enroll now
Save for later
Add this course to your list. Find it anytime.
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser