The Analysis of Intensive Longitudinal Data: APA Science Training Sessions from CenterStat

September-October, 2022

With the evolution and ever greater penetration of smartphones, other personal electronic devices, and wearable technologies, scientists are increasingly able to collect and analyze Intensive Longitudinal Data (ILD). The extensive sampling of observations for each person in ILD provides researchers with unique opportunities to study psychological processes as they unfold in real time. To introduce participants to the design and analysis of research with ILD, the American Psychological Association (APA) hosted a series of free online Science Training Sessions on The Collection and Analysis of Intensive Longitudinal Data from August 31 to October 11, 2022. Within this series, CenterStat instructors Jean-Philippe Laurenceau, Daniel Bauer, and Patrick Curran provided four webinars focused on the conceptualization and implementation of ILD methods and analyses within multilevel modeling and dynamic structural equation frameworks. More detailed descriptions of these webinars are provided below, along with video recordings and downloadable PDFs of the presentation materials.

Instructors

Daniel J. Bauer, Ph.D.

Dan Bauer is a Professor and the Director of the L.L. Thurstone Psychometric Laboratory in the Department of Psychology and Neuroscience at the University of North Carolina. He teaches primarily graduate-level courses in statistical methods, for which he has won multiple teaching awards. Read More

Patrick J. Curran, Ph.D.

Patrick Curran is a Professor in the L.L. Thurstone Psychometric Laboratory in the Department of Psychology and Neuroscience at the University of North Carolina at Chapel Hill. Patrick has dedicated much of his career to the teaching and dissemination of advanced quantitative methods and has won awards in recognition of teaching excellence. Read More

Jean-Philippe Laurenceau, Ph.D.

Jean-Philippe Laurenceau, is the Unidel A. Gilchrist Sparks III Chair in the Social Sciences and Professor of Psychological & Brain Sciences at the University of Delaware where he teaches doctoral courses on regression analysis, multilevel modeling, structural equation modeling, and applied longitudinal data analysis. Read More

Workshop Details

Introduction to Intensive Longitudinal Data Methods

Instructor: Jean-Philippe Laurenceau

September 15, 2022

This session provides an overview and definition of intensive longitudinal methods that focuses on the importance of within-person change processes. In this session, Jean-Philippe discusses rhe strengths of intensive longitudinal methods, the basic research questions that can be addressed with intensive longitudinal data, and issues of data structure.

Intensive Longitudinal Data: Methodological Challenges and Opportunities

Instructors: Daniel J. Bauer & Patrick J. Curran

October 4, 2022

Intensive longitudinal data (ILD) is a broad term used to describe a variety of types of data structures, although it most typically implies a moderately large number of repeated assessments (e.g., 20 or more) taken on a modest number of individuals (e.g., 50 to 100). ILD provides powerful information with which to test theoretically-derived research hypotheses about intra-individual variability and change that would be difficult or impossible to assess with other data structures such as conventional panel data (e.g., three to six repeated measures taken on samples of 200 or more participants). However, there are also methodological and data analytic challenges presented by ILD that do not arise in more traditional repeated measures settings. In this session, Patrick and Dan describe both the challenges and opportunities of ILD.

Intensive Longitudinal Data: A Multilevel Modeling Perspective

Instructors: Daniel J. Bauer & Patrick J. Curran

October 6, 2022

Recent developments have demonstrated the MLM is uniquely well suited to incorporate many of the complexities that arise when analyzing intensively longitudinal data (ILD). In this lecture, we demonstrate how MLMs can accommodate the unique features of ILD and be used to test a variety of research hypotheses, including modeling time trends that may vary between persons and testing the effects of both time-invariant (person-level) and time-varying (observation-level) predictors. For time-varying predictors, the MLM allows for the isolation of within- versus between-person effects and for the possibility that within-person effects may differ across individuals.

Intensive Longitudinal Data: A Dynamic Structural Equation Modeling Perspective

Instructors: Daniel J. Bauer & Patrick J. Curran

October 11, 2022

Recent developments in dynamic structural equation modeling (DSEM) have allowed for the assessment of new kinds of research questions with ILD. DSEM combines features from more traditional time-series analysis, multilevel modeling, and structural equation modeling. DSEM is especially well suited to the analysis of stable within-person processes, for which it allows for the modeling of complex relationships among multiple outcomes as well as the incorporation of latent variables to remove biases due to measurement error. Key goals are often to characterize the inertia (carry-over) of a process over time, as well as the spill-over (lead/lag effects) from one process to another. In this presentation, we provide a general overview of the DSEM approach to ILD and discuss the application and interpretation of the DSEM results with respect to psychological theory.

Reviews

CenterStat workshops routinely receive excellent reviews.  Here is a sampling from our free 3-day structural equation modeling workshop:

Dan and Patrick do an awesome job at breaking down complex material and provide great applicable examples that really help with understanding and clarifying the concepts. The notes and materials provided are excellent and very detailed! 

The workshop had a relaxed environment with plenty of banter. It was easy to follow the step-by-step workshop. As a result, I was able to learn a lot in a short amount of time.

Clear examples, software demonstrations/code, nice pacing (I appreciated the background information!). I was also extremely impressed by how responsive Dan/Patrick/Ethan were to participant questions! 

Using real data to show examples and providing notes detailed for actually conducting the analyses later. The balance of what the theory is with the practical application is what my previous stats training often lacked (too little application).

The key strength was the clarity of communication and the balance between technicalities and intuition. Dr. Curran & Dr. Bauer did not shy away from technical aspects of the content but also communicated about them as intuitively as possible.

Dr. Curran and Dr. Bauer's style of lecture made complex statistical concepts straightforward and non-intimating, the lecture notes were very clear and the ability to ask questions via chat during lecture was amazing.  

I have a much better understanding of what data modeling is. Before the workshop, I knew how to do SEM and even wrote a paper currently under review using SEM, but I didn't really understand what I was doing, why I was doing it, how it was being done

High quality materials and explanations. Very engaging to listen to.

The instructors were incredible. The way they taught complex concepts made things that were difficult for me to grasp previously magically fall into place.  Concepts were clear and analogies were entertaining and on point!

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