Missing Data Using R - Online Course
A 4-Week On-Demand Seminar Taught by
Paul AllisonMonday, February 22 —
Monday, March 22, 2027
Each Monday you will receive an email with instructions for the following week.
All course materials are available 24 hours a day. Materials will be accessible for an additional 2 weeks after the official close on March 22.
If 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.
The course takes place online in a series of four weekly installments of videos, readings, and exercises, and requires about 6-8 hours/week. You may participate at your own convenience; there are no set times when you are required to be online.
This four-week course can be accessed through any recent web browser on most computers and mobile devices, including iPhones, iPads, and Android devices. It consists of several modules, which contain videos of the 3-day livestream version of the course in its entirety. There are also weekly exercises that ask you to apply what you’ve learned.
There is also an online discussion forum where you can post questions or comments about any aspect of the course. All questions will be promptly answered by Dr. Allison.
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.
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.
Computing
This is a hands-on course. To get the most out of it, we strongly recommend using 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 get the most out of it, we strongly recommend using 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?
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. However, you do not need to know matrix algebra, calculus, or likelihood theory.
You should be at least moderately proficient in one of these statistical software programs: R, Stata, or SAS.
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. However, you do not need to know matrix algebra, calculus, or likelihood theory.
You should be at least moderately proficient in one of these statistical software programs: R, Stata, or SAS.
Seminar outline
- Assumptions for missing data methods
- Problems with conventional methods
- Maximum likelihood (ML)
- ML with the expectation-maximization (EM) algorithm
- Full-information ML
- ML for contingency tables
- Multiple imputation (MI)
- MI under a multivariate normal model
- MI with R
- MI with categorical and non-normal data
- Interactions and nonlinearities
- Using auxiliary variables
- Other parametric approaches to MI
- Linear hypotheses and likelihood ratio tests
- Nonparametric and partially parametric methods
- Fully conditional models
- MI and ML for non-ignorable missing data
- Fully Bayesian analysis
- Assumptions for missing data methods
- Problems with conventional methods
- Maximum likelihood (ML)
- ML with the expectation-maximization (EM) algorithm
- Full-information ML
- ML for contingency tables
- Multiple imputation (MI)
- MI under a multivariate normal model
- MI with R
- MI with categorical and non-normal data
- Interactions and nonlinearities
- Using auxiliary variables
- Other parametric approaches to MI
- Linear hypotheses and likelihood ratio tests
- Nonparametric and partially parametric methods
- Fully conditional models
- MI and ML for non-ignorable missing data
- Fully Bayesian analysis
Registration instructions
The fee of $695 USD includes all course materials. All major credit cards are accepted.
This course is hosted on a platform called DigitalChalk. To register, you’ll need to go to statisticalhorizons.digitalchalk.com and click on Create Account. Then you will enter your name and email address, and create a password. Be sure to save your password because you will need it to log in to the course itself.
When you have created your account, you’ll be taken to your new home page. Click on the Register Now button (or click the Catalog icon on the left-hand column), and you’ll see “Missing Data Using R” as one of the available courses. At the bottom of the box for that course, click the green button Add to Cart. Next, click the green button at the top that says Checkout. You will then be prompted for your credit card information.
When you have finished the payment process, you will be taken back to your home page. Click on Dashboard to see Missing Data Using R. When the course begins on February 22, you can click the play button to get started.
Need funding from your employer or institution? Use our funding request template.
The fee of $695 USD includes all course materials. All major credit cards are accepted.
This course is hosted on a platform called DigitalChalk. To register, you’ll need to go to statisticalhorizons.digitalchalk.com and click on Create Account. Then you will enter your name and email address, and create a password. Be sure to save your password because you will need it to log in to the course itself.
When you have created your account, you’ll be taken to your new home page. Click on the Register Now button (or click the Catalog icon on the left-hand column), and you’ll see “Missing Data Using R” as one of the available courses. At the bottom of the box for that course, click the green button Add to Cart. Next, click the green button at the top that says Checkout. You will then be prompted for your credit card information.
When you have finished the payment process, you will be taken back to your home page. Click on Dashboard to see Missing Data Using R. When the course begins on February 22, you can click the play button to get started.
Need funding from your employer or institution? Use our funding request template.