Missing Data Using R (for students) - Online Course
A 3-Day Livestream Seminar Taught by
Paul Allison10:00am-12:30pm (convert to your local time)
1:30pm-3:30pm
This seminar is intended to give students the opportunity to learn Missing Data from an expert instructor, Dr. Paul Allison, at a special price of $295 (email info@statisticalhorizons.com for the student discount code). Non-students are welcome to register at the regular rate of $995.
Based on Dr. Allison’s book Missing Data, this course covers the theory and practice of multiple imputation and maximum likelihood.
If you are 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.
Starting March 2, we are offering this seminar as a 3-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions, separated by a 1-hour break. Hands-on computing exercises will be assigned at the end of each day’s session and reviewed the next day. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. 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. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
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: norm2, 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: norm2, 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 any student 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 any student 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 student fee of $295 and non-student fee of $995 includes all course materials. Students can get the discounted rate by emailing info@statisticalhorizons.com using your university email address.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The student fee of $295 and non-student fee of $995 includes all course materials. Students can get the discounted rate by emailing info@statisticalhorizons.com using your university email address.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.