Missing Data Analysis Training Seminar

Missing Data Statistics Training Seminar

An intermediate 3-day course introducing multiple imputation for missing data.

Listwise deletion or mean substitution are not your answer; they bias your results! There have been continued and substantial advances in missing data handling procedures over the past 25 years. We will be instructing you on the most up-to-date methods, including maximum likelihood estimation, Bayesian estimation, and, multiple imputation. These procedures use all available data to mitigate the loss of power, improve accuracy, and enhance generalizability.

You will be presented a mixture of theoretical information and applied examples, providing you with the skills necessary to understand and appropriately implement these procedures in your own research.

Participants will receive an electronic copy of all course materials, including lecture slides, practice datasets, software scripts, relevant supporting documentation, and recommended readings. Participants will also have access to a video recording of the course.

Fall Camp: September 30 – October 2, 2019
UCLA Math Sciences Building – Room 5907


September 30, 2019 at 9AM

The Stats Camp will take place on the UCLA Campus. Please book your accommodation as soon as possible. The cost of accommodation is not included in the fee for the Stats Camp.

Parking Location – Guests must drive to any information kiosk on campus and provide the event name or the confirmation in order to receive their courtesy permit. They must display this on their dashboard, then proceed to park in any non-reserved stall in Lot 2.

Travel Info:

Hotels are listed below in order of proximity to the UCLA Stats Camp course location:

Marriott Hotel
Hotel Angeleno

For those who may fly in, there is also a “Fly Away” bus that runs from LAX and LAS to Westwood.


Payment Options

$1,295 Faculty/Professional or $945 Student/Post-Doc

View Upcoming Statistics Courses Here

Course Overview

There have been substantial methodological advances in the area of missing data analyses during the last 25 years. Methodologists currently regard maximum likelihood estimation (ML) and multiple imputation (MI) as two state of the art missing data handling procedures.  These two procedures are advantageous because they use all available data, thereby mitigating the loss of power from missing data.  Moreover, these techniques make less strict assumptions about the cause of missing data, thereby providing accurate estimates and significance tests in a wider range of situations than traditional missing data handling techniques.  The purpose of this course is to familiarize participants with MI (and to a less extent, ML) and to demonstrate the use of these techniques using popular software packages. The goal of this course is to provide participants with the skills necessary to understand and appropriately implement MI and ML in their own research. To this end, the course will provide a mixture of theoretical information and computer applications. The course content will be accessible to researchers with a foundation in multiple regression.

Topics Include:

  • Missing data mechanisms
  • Traditional and ad hoc missing data approaches
  • Multiple imputation for continuous, nominal, and ordinal variables
  • Maximum likelihood estimation for continuous variables

Note: This course will focus primarily on single-level analyses, although some coverage of multilevel missing data will be provided in the context of multiple imputation.

Learning Objectives

After engaging in course lectures and discussions as well as completing the hands-on practice activities with real data, participants will be able to:

  • Explain the meaning of missing at random (MAR), missing completely at random (MCAR), and not missing at random (NMAR).
  • Understand the shortcomings of older ad hoc missing data approaches.
  • Generate multiply imputed data sets using the Blimp software package, and analyze the imputations with a variety of standard analysis packages.
  • Implement maximum likelihood estimation using Mplus or lavaan software programs.
  • Interpret and describe the results from a missing data analysis.

Participants will also complete the course with a foundation for future learning about more advanced missing data handling topics (e.g., multiple imputation for multilevel missing data).


Monday September 30, 2019
9:00-9:30 Welcome and introductions
9:30-10:45 Missing data mechanisms
10:45-11:00 Snack and refreshment break
11:00-12:00 Traditional missing data handling approaches
12:00-12:30 Modern approaches: Likelihood, multiple imputation, Bayesian estimation
12:30-1:30 Lunch break
1:30-2:30 Bayesian Estimation and MCMC for linear regression
2:30-3:00 MCMC convergence
3:00-3:15 Snack and refreshment break
3:15-5:00 Lab exercise
Tuesday October 1, 2019
9:00-10:00 Multiple Imputation: Imputation Phase
10:00-10:45 Multiple imputation with Blimp
10:45-11:00 Snack and refreshment break
11:00-12:30 Analyzing multiply imputed data: Pooling and significance testing
12:30-1:30 Lunch break
1:30-3:00 Categorical variables and questionnaire data
3:00-3:15 Snack and refreshment break
3:15-5:00 Lab exercise
Wednesday October 2, 2019
9:00-10:00 Model-based imputation for interaction effects
10:00-10:45 Model-based imputation with Blimp
10:45-11:00 Snack and refreshment break
11:00-12:30 Multilevel growth model
12:30-1:30 Lunch break
1:30-3:00 Longitudinal missing data
3:00-3:15 Snack and refreshment break
3:15-4:00 Longitudinal missing data continued
4:00-5:00 Lab exercise



  • Advanced proficiency in multiple linear regression.
  • Intermediate proficiency with at least one statistical software package (e.g., SPSS, SAS, R, etc.).

Not required but advantageous:

  • At least limited experience (e.g., graduate-level course) with multivariate data analysis.
  • At least limited experience (e.g., graduate-level course) with continuous latent variable models, e.g., exploratory and confirmatory factor analysis (EFA; CFA) and structural equation modeling (SEM).

No level of proficiency beyond basic awareness is assumed for skills related to:

  • Data analysis using Blimp.
  • Data analysis using Mplus, R.
  • Advanced mathematical or statistical topics such as matrix algebra or likelihood theory.

Instructor: Craig Enders, Ph.D.

Missing Data Analysis Statistics Training Course

Craig Enders, Ph.D., is a Professor in the Department of Psychology at UCLA where he is a member of the Quantitative program area, where he teaches graduate-level courses in missing data analyses and longitudinal modeling.  Dr. Enders received his Ph.D. in psychometrics from the University of Nebraska and was previously a faculty member of Arizona State’s quantitative psychology program.  Dr. Enders’ research largely focuses on the the development and application of missing data analyses, particularly multiple imputation.  He currently serves as the PI for an Institute of Educational Sciences-funded grant to build and develop the Blimp software application used in the course.  Enders’ book, Applied Missing Data Analysis, was published with Guilford Press in 2010 and is currently under revision for a second edition.

Software and Computer Support

Participants need to bring a laptop computer with Wi-Fi capabilities.

All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.

Instruction will be provided for the methods using the Blimp software package, which is available for free download for Mac and Windows environment at www.appliedmissingdata/multilevel-imputation. Virtually all general-use software packages can analyze multiply imputed data, and syntax examples will be provided for R, SPSS, Stata, and Mplus. Additionally, the course will rely on the most current version of Mplus (base program with mixture add-on or base program with combination add-on). Mplus is available for Windows, Mac, and Linux environments.  Many of the software examples can be executed using the free demo version of Mplus, and information for purchasing a personal license can be found at www.statmodel.com.

Seminar Files

Seminar files and downloads will be posted on the first day of class. Please download these files onto your computer at the beginning of the first day. The files are password protected to respect the intellectual property rights of the instructors. By using your login information, you agree not to share login information or the content protected by it.

Seminar Certificate

Upon completion of the course you may request an official Stats Camp print-ready PDF certificate.  Please contact us here to request a digital copy.

Why Should You Attend?

  • Get 1 on 1 Consultation With Instructor
  • Professional Networking
  • Peer Socializing
  • Collaboration
  • All Course Resources
  • Lunches & Snacks Daily