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Medicine: Mind the Gap, Optimizing Inferences Using Principled Missing Data Treatments

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Air date: Wednesday, June 29, 2016, 11:00:00 AM
Time displayed is Eastern Time, Washington DC Local
Views: Total views: 114, (85 Live, 29 On-demand)
Category: Medicine: Mind the Gap
Runtime: 00:58:38
Description: Medicine: Mind the Gap

Missing data are a common problem for prevention research and improperly handling missing data can severely compromise the validity of a study’s inferences. In this webinar, Dr. Little will highlight the power and utility of modern principled treatments for missing data to optimize inferences. He will discuss the three issues that occur when data go missing and the three mechanisms that give rise to missing data. Dr. Little will also address how modern principled treatments are capable of remedying the otherwise deleterious effects of unplanned missing data. He will emphasize the importance of the missing data modeling plan as a precursor to any data analytic modeling plan. Lastly, Dr. Little will show how experimentally manipulated missing data can increase the validity and reduce the costs of research.

Todd D. Little, Ph.D., is a Professor and Director of the Research, Evaluation, Measurement, and Statistics program at Texas Tech University and Director of the Institute for Measurement, Methodology, Analysis, and Policy. He is internationally recognized for his quantitative work on various aspects of applied structural equation modeling (e.g., modern missing data treatments, indicator selection, parceling, modeling developmental processes) as well as his substantive developmental research (e.g., action-control processes and motivation, coping, self-regulation). In 2001, Dr. Little was elected to membership in the Society for Multivariate Experimental Psychology, and in 2009, he was elected President of the American Psychological Association’s (APA) Division 5 (Evaluation, Measurement, and Statistics). He founded, organizes, and teaches in the internationally renowned “Stats Camps” (see statscamp.org). Dr. Little is a Fellow in the APA, the Association for Psychological Science, and the American Association for the Advancement of Science. In 2013, he received the Cohen Award from the APA’s Division 5 for distinguished contributions to teaching and mentoring. In 2015, he received the inaugural Distinguished Contributions to Mentoring of Developmental Scientists Award from the Society for Research in Child Development. As an interdisciplinary-oriented collaborator, Dr. Little has published with over 340 persons from around the world in over 65 different peer-reviewed journals. His work has garnered over 17,000 citations with an H-index of 67 and an I-10 index of 151. Dr. Little published Longitudinal Structural Equation Modeling in 2013, and he has edited five books related to methodology including the Oxford Handbook of Quantitative Methods and the Guildford Handbook of Developmental Research Methods (with Brett Laursen and Noel Card).

For more information go to https://prevention.nih.gov/programs-events/medicine-mind-the-gap

The survey evaluation link is https://www.surveymonkey.com/r/RZL29N7
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NLM Title: Optimizing inferences using principled missing data treatments / Todd D. Little.
Author: Little, Todd D.
National Institutes of Health (U.S.),
Publisher:
Abstract: (CIT): Missing data are a common problem for prevention research and improperly handling missing data can severely compromise the validity of a study's inferences. In this webinar, Dr. Little will highlight the power and utility of modern principled treatments for missing data to optimize inferences. He will discuss the three issues that occur when data go missing and the three mechanisms that give rise to missing data. Dr. Little will also address how modern principled treatments are capable of remedying the otherwise deleterious effects of unplanned missing data. He will emphasize the importance of the missing data modeling plan as a precursor to any data analytic modeling plan. Lastly, Dr. Little will show how experimentally manipulated missing data can increase the validity and reduce the costs of research.
Subjects: Biomedical Research--methods
Data Accuracy
Data Interpretation, Statistical
Research Design
Publication Types: Lectures
Webcasts
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NLM Classification: W 20.5
NLM ID: 101689999
CIT Live ID: 19255
Permanent link: https://videocast.nih.gov/launch.asp?19774