Missing Data Using R - Online Course
A 4-Day Livestream Seminar Taught by
Paul AllisonIf you’re using conventional methods for handling missing data, you may be missing out. Conventional methods for missing data, like listwise deletion or regression imputation, are prone to three serious problems:
- Inefficient use of the available information, leading to low power and Type II errors.
- Biased estimates of standard errors, leading to incorrect p-values.
- Biased parameter estimates, due to failure to adjust for selectivity in missing data.
More accurate and reliable results can be obtained with maximum likelihood or multiple imputation.
Although these newer methods for handling missing data have been around for more than two decades, they have only become practical with the introduction of widely available and user friendly software.
*We understand that scheduling is difficult during this unpredictable time. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions.
More details about the course content
Maximum likelihood and multiple imputation have very similar statistical properties. If the assumptions are met, they are approximately unbiased and efficient. What’s remarkable is that these newer methods depend on less demanding assumptions than those required for older methods for handling missing data.
Maximum likelihood is available for linear regression, logistic regression, Cox regression, and regression for count data. Multiple imputation can be used for virtually any statistical problem.
This course will cover the theory and practice of both maximum likelihood and multiple imputation. These methods will be demonstrated using four packages in R: norm, lavaan, jomo and mice. Slides and exercises using SAS and Stata are also available to participants on request.
Maximum likelihood and multiple imputation have very similar statistical properties. If the assumptions are met, they are approximately unbiased and efficient. What’s remarkable is that these newer methods depend on less demanding assumptions than those required for older methods for handling missing data.
Maximum likelihood is available for linear regression, logistic regression, Cox regression, and regression for count data. Multiple imputation can be used for virtually any statistical problem.
This course will cover the theory and practice of both maximum likelihood and multiple imputation. These methods will be demonstrated using four packages in R: norm, lavaan, jomo and mice. Slides and exercises using SAS and Stata are also available to participants on request.
Computing
This is a hands-on course. To optimally benefit, you are strongly encouraged to use a computer with a recent version of R and RStudio installed. Mplus and LEM will also be used in the section on maximum likelihood estimation, but will not be used for exercises. For those who prefer SAS or Stata, slides and exercises using these packages can be downloaded from the course site.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
This is a hands-on course. To optimally benefit, you are strongly encouraged to use a computer with a recent version of R and RStudio installed. Mplus and LEM will also be used in the section on maximum likelihood estimation, but will not be used for exercises. For those who prefer SAS or Stata, slides and exercises using these packages can be downloaded from the course site.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
Who should register?
Virtually anyone who does statistical analysis can benefit from new methods for handling missing data. To optimally benefit from this course, you should have a good working knowledge of the principles and practice of linear regression, as well as elementary statistical inference. But you do not need to know matrix algebra, calculus, or likelihood theory. You should be at least moderately proficient at using one of these packages: R, Stata, or SAS.
Virtually anyone who does statistical analysis can benefit from new methods for handling missing data. To optimally benefit from this course, you should have a good working knowledge of the principles and practice of linear regression, as well as elementary statistical inference. But you do not need to know matrix algebra, calculus, or likelihood theory. You should be at least moderately proficient at using one of these packages: R, Stata, or SAS.
Seminar outline
- Basics and Assumptions
- Traditional Methods
- Maximum Likelihood (ML)
- ML for Quantitative Variables
- Full Information Maximum Likelihood (FIML)
- Multiple Imputation Basics
- MI for Non-Monotone Missing Data
- Options for MCMC
- Fully Conditional Specification
- EMB Method
- Multivariate Inference and Nonlinearity
- Panel Data and Other Clustered data
- Nonignorable Missing Data
- Final Things
- Basics and Assumptions
- Traditional Methods
- Maximum Likelihood (ML)
- ML for Quantitative Variables
- Full Information Maximum Likelihood (FIML)
- Multiple Imputation Basics
- MI for Non-Monotone Missing Data
- Options for MCMC
- Fully Conditional Specification
- EMB Method
- Multivariate Inference and Nonlinearity
- Panel Data and Other Clustered data
- Nonignorable Missing Data
- Final Things
Payment information
The fee of $895 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $895 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.