2018 Summer Institute: Analysis of Ecological Momentary Assessment Data
Topic: Analysis of Ecological Momentary Assessment Data Using Multilevel Modeling and Time-Varying Effect Modeling
Date: June 28 - 29, 2018
Venue: Penn State, University Park, PA
The goal of this two-day workshop is to provide attendees with the theoretical background and applied skills necessary to identify and address innovative and interesting research questions in intensive longitudinal data streams such as daily diary and ecological momentary assessment (EMA) data using multilevel modeling (MLM) and time-varying effect modeling (TVEM). By the end of the workshop, participants will have fit several multilevel and time-varying effect models in SAS and will have had the opportunity to fit and interpret preliminary models using their own data.
Workshop time will be spent in lecture, software demonstrations, computer exercises, and discussion. At the workshop, participants will be provided with a hard copy of all lecture notes, select computer exercises and output, and suggested reading lists for future reference. SAS software will be used in the course, including native SAS procedures for analyzing multilevel models (PROC MIXED and PROC GLIMMIX) and the SAS TVEM macro, a downloadable supplement to SAS developed at the Penn State Methodology Center. Time will be reserved for participants to apply the concepts learned in class to their own data, and the presenters will be available for consultation during that period.
Basic familiarity with linear and logistic regression and the SAS software will be helpful.
Participants are strongly encouraged to bring a laptop so that they can conduct the computer exercises and analyze their own data. To conduct analyses at the workshop, SAS Version 9 for Windows must be installed on the laptop prior to arrival. In addition, approximately one week prior to the workshop participants will be sent an email requesting that they download the TVEM SAS macro suite. Participants must verify that any data use agreements permit them to bring their own data to the workshop. Simulated data will be made available to those who do not bring their own data.
Topics to be covered
- Conceptual introduction to multilevel modeling (MLM) and time-varying effect modeling (TVEM)
- Two-level MLM for daily diary and ecological momentary assessment (EMA) data
- Extension to three-level MLM for EMA data
- TVEM for EMA data: overview and applications (time of day, time relative to event, time since treatment)
- Analyses using participants’ own data, presenters available for consultation
Enrollment is limited to 40 participants to maintain an informal atmosphere and to encourage interaction between and among the presenters and participants. We give priority to individuals who are involved in drug abuse prevention and treatment research or HIV research, who have the appropriate statistical background to get the most out of the Institute, and for whom the topic is directly and immediately relevant to their current work. We also aim to maximize geographic and minority representation.
The application window has closed. Applicants will be notified about decisions by Friday, April 13.
Once accepted, participants will be emailed instructions about how to register. The registration fee of $395 for the two-day Institute covers all instruction, program materials, and breakfast and lunch each day. A block of rooms at the Nittany Lion Inn will be available for lodging. Further information will be sent after acceptance to the Institute.
Participants are encouraged to bring their own laptop computers for conducting exercises.
Review our refund, access, and cancellation policies.
Stephanie Lanza, Ph.D.
Professor of Biobehavioral Health, Director of the Edna Bennett Pierce Prevention Research Center, Principal Investigator at The Methodology Center, Penn State
Dr. Lanza has a background in research methods, human development, and substance use and comorbid behaviors, with more than 100 papers appearing in top methodological and applied journals. She is co-author of the book Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences and led the development of PROC LCA & PROC LTA, SAS procedures for fitting latent class and latent transition models. Her methodological research interests include advances in finite mixture modeling and time-varying effect modeling to address innovative research questions in behavioral research, particularly those best addressed using intensive longitudinal data. She is passionate about disseminating these methods to health, behavioral, and social science researchers and has organized many NIH-funded dissemination conferences, taught more than 30 intensive hands-on workshops, and written tutorial articles to enable applied researchers to use the latest methods in their own work.
Michael Russell, Ph.D.
Assistant Professor of Biobehavioral Health, Investigator at The Methodology Center, Penn State
Dr. Russell’s research is focused on understanding the connections between stress, affect, and health behaviors in day-to-day life using advanced statistical modeling (multilevel and time-varying effect modeling) and ambulatory assessment methods (daily diaries, ecological momentary assessments (EMA), and wearable biosensors). He is currently leading a data collection effort that combines EMA and wearable biosensors for alcohol intoxication to understand the causes and consequences of young-adult heavy drinking episodes in daily life. Dr. Russell has a strong commitment to teaching and mentoring other health researchers in advanced analytic methods, as evidenced by numerous invited talks and workshops focused on advanced MLM, TVEM, and the analysis of intensive longitudinal data. His work has been published in a variety of top journals, including Annals of Behavioral Medicine, Development and Psychopathology, Journal of Adolescent Health, Prevention Science, Drug and Alcohol Dependence, and Psychology of Addictive Behaviors.
The Pennsylvania State University, University Park campus
Funding for this conference was made possible by award number R13 DA020334 from the National Institute on Drug Abuse. The views expressed in written conference materials or publications and by speakers and moderators do not necessarily reflect the official views and/or policies of the Department of Health and Human Services; nor does mention of trade names, commercial practices, or organizations imply endorsement by the U.S. Government.
- 2017 - Statistical Power Analysis for Intensive Longitudinal Studies by Jean-Philippe Laurenceau and Niall Bolger
- 2016 - Ecological Momentary Assessment (EMA): Investigating Biopsychosocial Processes in Context by Joshua Smyth, Kristin Heron, and Michael Russell
- 2015 - An Introduction to Time-Varying Effect Modeling by Stephanie T. Lanza and Sara Vasilenko
- 2014 - Experimental Design and Analysis Methods for Developing Adaptive Interventions: Getting SMART by Daniel Almirall and Inbal Nahum-Shani
- 2013 - Introduction to Latent Class Analysis by Stephanie Lanza and Bethany Bray
- 2012 - Causal Inference by Donna Coffman
- 2011 - The Multiphase Optimization Strategy (MOST) by Linda Collins
- 2010 - Analysis of Longitudinal Dyadic Data by Niall Bolger and Jean-Philippe Laurenceau
- 2009 - Latent Class and Latent Transition Analysis by Linda Collins and Stephanie Lanza
- 2008 - Statistical Mediation Analysis by David MacKinnon
- 2007 - Mixed Models and Practical Tools for Causal Inference by Donald Hedeker and Joseph Schafer
- 2006 - Causal Inference by Christopher Winship and Felix Elwert
- 2005 - Survival Analysis by Paul Allison
- 2004 - Analyzing Developmental Trajectories by Daniel Nagin
- 2003 - Modeling Change and Event Occurrence by Judith Singer and John Willett
- 2002 - Missing Data by Joseph Schafer
- 2001 - Longitudinal Modeling with MPlus by Bengt Muthén and Linda Muthén
- 2000 - Integrating Design and Analysis and Mixed-Effect Models by Richard Campbell, Paras Mehta, and Donald Hedeker
- 1999 - Structural Equation Modeling by John McArdle
- 1998 - Categorical Data Analysis by David Rindskopf and Linda Collins
- 1997 - Hierarchical Linear Models and Missing Data Analysis by Stephen Raudenbush and Joseph Schafer
- 1996 - Analysis of Stage Sequential Development by Linda Collins, Peter Molenaar, and Han van der Maas