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Missing Data Analysis Training Seminar

September 30 @ 9:00 am - October 2 @ 5:00 pm

This camp is an intensive THREE-DAY seminar on missing data analysis hosted at UCLA’s Math Sciences 5907 (meet first at Math Sciences 5628 Visualization Portal).

Instructor: Dr. Craig Enders,  Professor of Quantitative Psychology, UCLA

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 ranger 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.

Participants will receive an electronic copy of all course materials, including lecture slides, practice datasets, software scripts, relevant supporting documentation, and recommended readings.

Register with a valid UCLA email and get a 25% discount!

Professionals: $1095  $821.25 Students: $945 $708.75




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


September 30 @ 9:00 am
October 2 @ 5:00 pm


Math Sciences 5907
5907 Math Sciences
Los Angeles, CA 90095 United States
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