This is the first module of nine-course modules.

Overview of this Webinar Series

STRENGTHENING CAUSAL INFERENCE IN BEHAVIORAL OBESITY RESEARCH

The identification of causal relations is fundamental to a science of intervention and prevention. Obesity is a major problem for which much progress in understanding, treatment, and prevention remains to be made. Understanding which social and behavioral factors cause variations in adiposity and which other factors cause variations is vital to producing, evaluating, and selecting among intervention and prevention strategies as well as to understanding obesity’s root causes, requiring input from disciplines including statistics, economics, psychology, epidemiology, mathematics, philosophy, and in some cases behavioral or statistical genetics.

This webinar features:

Key underlying and fundamental principles of causal inference in obesity research
Applying a broad range of techniques in obesity research
Tailored approaches to specific and varying situations in obesity research

Webinar Archives

Please begin by meeting our webinar hosts.

Introductory Remarks

Intro to Statistical Inference

Intro to Energy Balance and Laws of Thermodynamics

Study Designs & Quantifying Effect and Association Size

We would like to thank our sponsors for their support:

NIH Disclaimer:
This material is based upon work supported by the National Institutes of Health under Grant No. (R25HL124208). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Institutes of Health.

Module 1: Introduction to Basic Language, Terms, and Concepts in Statistics and Design