Methods Workshop

2025 Methods Workshop

Dynamic Structural Equation Models for Intensive Longitudinal Data

This workshop covers foundational topics in dynamic structural equation modeling (DSEM). DSEM is a framework that combines aspects of multilevel modeling, structural equation modeling, and time-series analysis to model intensive longitudinal data (roughly defined as 15 or more repeated measures on the same individuals over a relatively short timespan such as a few days or weeks). This type of data is becoming increasingly common as technological advances like smartphones and wearables continue to transform how data are collected, how studies are designed, and research questions that can be asked. Whereas traditional longitudinal models focus on growth over longer durations, intensive longitudinal models focus on momentary changes over short durations. For instance, a growth model may be interested in how anxiety changes over 12 months, but an intensive longitudinal model may be interested in why anxiety was low at 12pm, spiked at 4pm and receded at 8pm.

The emphasis of this course is to build solid foundations for (1) how intensive longitudinal data differ from traditional longitudinal data, (2) the opportunities and unique research questions that can be asked and answered with DSEM and intensive longitudinal data, (3) the conceptual underpinning of DSEM and how it can handle distinct features of intensive longitudinal data, and (4) how to leverage concepts from multilevel and structural equation modeling to build models that capture idiosyncratic features of intensive longitudinal data and designs. 

May 21st 11:00 am to 5:00 pm & May 22nd 8:00 am to 12:00pm
In-person in Pullman (Cleveland Hall) and available on Zoom for other campuses.

Presented by the Learning Performance Research Center and Educational Psychology program.

Presenter: Dr. Daniel McNeish

Dr. Dan McNeish is Professor of Quantitative Psychology at Arizona State University. His methodological research is interested in time-series, multilevel, and measurement modeling. He has published over 100 peer-reviewed articles and his body of work has been acknowledged with 6 early career awards, including the APA’s Distinguished Scientific Award for Early Career Contributions. He has been named a Highly Cited Researcher by ISI/Web of Science every year since 2022 for producing the most high-impact papers in psychology/psychiatry and a Stanford/Elsevier analysis ranked him as having the 8th highest research impact among all researchers in the field of Social Science Methods in 2024. He has been a section or associate editor at two top-tier methods journals (Multivariate Behavioral Research, Behavior Research Methods) and he has been supported by a variety of funding agencies including IES, 5 institutes of NIH, the US Navy Research Lab, and the Wellcome Trust.

Cost

Registration cost is $25.

Registration includes workshop, course materials, and refreshments (in-person).

Please click here to register!

  • 2024 – Holmes Finch, Ball State University – Examining Heterogeneity with Mixture Models
  • 2023 – Gregory Hancock, University of Maryland – Structural Equation Modeling Methods for Longitudinal Data
  • 2022 – Tracy Sweet, University of Maryland – An Introduction to Machine Learning for the Social Sciences
  • 2021 – Tenko Raykov, Michigan State University –  Longitudinal Modeling and Missing Data Analysis
  • 2019 – Matthew McCrudden, Pennsylvania State University – Mixed Methods
  • 2018 – Gregory Hancock, University of Maryland – Latent Growth Curve Modeling
  • 2017 – Roy Levy, Arizona State University – Foundations of Bayesian Statistical Modeling
  • 2016 – Todd D. Little, Texas Tech University – Measurement, design, and analysis issues in longitudinal modeling with a particular focus on the longitudinal CFA model as the basis for both panel and latent growth curve modeling.
  • 2015 – Gregory Hancock, University of Maryland – Second Course in Structural Equation Modeling
  • 2014 – Bethany Bray, Pennsylvania State University – An Introduction to Latent Class and Latent Profile Analysis
  • 2013 – Gregory Hancock, University of Maryland – A First Course in Structural Equation Modeling