Missing Data Analysis Training Seminar

Missing Data Analysis Seminar

An intermediate 3-day course introducing multiple imputation and maximum likelihood estimation for missing data.

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 ranger of situations than traditional missing data handling techniques.  The purpose of this course is to familiarize participants with ML and MI and to demonstrate the use of these techniques using popular software packages, with an emphasis on Mplus. The goal of this course is to provide participants with the skills necessary to understand and appropriately implement ML and MI 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.

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 14 – 16, 2017
Brea, CA – Embassy Suites North Orange County


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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 limited coverage of multilevel missing data will be provided in the context of multiple imputation.

Payment Options

$1,095 Faculty/Professional or $945 Student/Post-Doc


Learning Objectives

After engaging in this 3-day statistics training institute 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 pacakges.
  • Implement maximum likelihood estimation using the Mplus software program.
  • 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).

Syllabus

Thursday September 14, 2017
9:00-9:30 Welcome and introductions
9:30-10:45 Missing data mechanisms
10:45-11:00 Snack and refreshment break
11:00-12:30 Traditional missing data handling approaches
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 Univariate and multivariate imputation
Friday September 15, 2017
9:00-9:30 Q & A
9:30-10:45 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 imputation
3:00-3:15 Snack and refreshment break
3:15-5:00 Imputing composites: Scale scores and interactions
Saturday September 16, 2017
9:00-9:30 Q & A
9:30-10:45 Maximum likelihood estimation for complete data
10:45-11:00 Snack and refreshment break
11:00-12:30 Maximum likelihood missing data handling (FIML estimation)
12:30-1:30 Lunch break
1:30-3:00 Missing predictor variables, auxiliary variables
3:00-3:15 Snack and refreshment break
3:15-5:00 Lab exercises, one-on-one consultations with instructor

 Prerequisites

Required:

  • 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.
  • 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 focusses 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.

Software and Computer Support
Course Audience

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.  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.

Course Files

Below are links to course files for those who enrolled in the course. Please download these files onto your computer on the first day of the course. The files are password protected to respect the intellectual property rights of the instructors. By using your login information, you agree not to share your login information or the content protected by it.

Course files and downloads will be posted on the first day of class.

Statisics CoursesWhy Should You Attend?

  • Get 1 on 1 Consultation With Instructor
  • Professional Networking
  • Peer Socializing
  • Collaboration
  • All Course Resources
  • Breakfast (Embassy guests), Lunches, & Snacks Daily