DUPRI will be hosting Chuck Huber from StataCorp on Tuesday, October 4 to give three talks on applied methods using Stata. The talks will be Introduction to Mediation Analysis, Multilevel/Longitudinal Modeling, and Latent Class Analysis. In addition to the talks, lunch will be provided. To attend this event, you must RSVP to Laura Satterfield no later than Monday, October 3.
Date: Tuesday, October 4, 2022
Location: Gross Hall, Room 230E
Time: 12PM - 4PM
Speaker: Chuck Huber is Director of Statistical Outreach at StataCorp and Adjunct Associate Professor of Biostatistics at the Texas A&M School of Public Health. In addition to working with Stata's team of software developers, he produces instructional videos for the Stata Youtube channel, writes blog entries, develops online NetCourses and gives talks about Stata at conferences and universities. Most of his current work is focused on statistical methods used by behavioral and health scientists. He has published in the areas of neurology, human and animal genetics, alcohol and drug abuse prevention, nutrition and birth defects.
Title: Introduction to Mediation Analysis (60 minutes)
Abstract: Mediation analysis is a statistical technique that is used to parse the direct, indirect, and total effect an independent variable (X) on a dependent variable (Y) in the presence of a mediating variable (M). For example, age (X) is associated with higher systolic blood pressure (Y). Age is also associated with higher weight (M) and increased weight is associated with higher systolic blood pressure (SBP). Age has a direct effect on SBP as well as an indirect effect on SBP through it's effect on weight. This talk will introduce the concepts of mediation analysis and demonstrate how to conduct mediation analysis using regression, structural equation modeling (SEM), and how to estimate bootstrap standard errors for mediation models with SEM.
Title: Multilevel/Longitudinal Modeling (60-90 minutes)
Abstract: In this talk I introduce the concepts and jargon of multilevel modeling for nested and longitudinal data. I also demonstrate how to fit multilevel/longitudinal models using Stata's -mixed- command, and how to visualize the results using Stata's -predict-, -twoway-, -margins-, and -marginsplot- commands. The 90 minute version of this talk includes a brief introduction to other Stata commands that can be used to fit multilevel models for binary, categorical, count, and survival data as well as multilevel structural equation models (SEMs).
Title: Latent Class Analysis Using Stata (60 minutes)
Abstract: Latent variables are a useful tool for modeling hypothetical contructs such as intelligence, ability, depression, and anxiety. Models for categorical latent variables are often called Latent Class Analysis (LCA) or Latent Profile Analysis (LPA). The levels of a categorical latent variable represent groups in the population and are called classes. We are interested in identifying and understanding these classes. LCA is characterized by discrete response variables while LPA is characterized by continuous response variables. This presentation will provide a brief introduction to LCA/LPA models and how to implement them using Stata.