Analyzing Intensive Longitudinal Data

June 13-17, 2022
Replay Access: 1 year from end of workshop
Instructors: Jean-Philippe Laurenceau & Niall Bolger

Student: $795
Professional: $995

Category: Livestream, Workshops

Analyzing Intensive Longitudinal Data is a five-day workshop devoted to the analysis and interpretation of data from studies involving many repeated measurements on individuals and dyads. These studies include experience sampling, daily diary, ecological momentary assessment and ambulatory psychophysiological studies. They allow researchers to understand people's thoughts, emotions, behaviors, and physiology in their natural contexts. Typically, they involve data collected over the course of hours, days, and weeks that can reveal life as it is actually lived and provide insights that are not possible using conventional experimental or survey research methods. Intensive longitudinal data, however, present data analytic challenges stemming from the multiple levels of analysis and temporal dependencies in the data. Extensions of multilevel or mixed-effects models for longitudinal data (e.g., multiple outcomes, lagged dependent variables as predictors, residual autocorrelation) can take account of these complexities, and the goal of the workshop is to provide training in their use.

Drawn from the presenters' 2013 Guilford Press book, Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research (, course topics will include: (a) introduction and overview of intensive longitudinal methods and designs; (b) analyzing the time course of continuous outcomes; (c) analyzing within-person causal processes for continuous outcomes; (d) modeling dyadic intensive longitudinal data; (e) modeling the time course and causal processes for categorical outcomes; (f) psychometrics of intensive longitudinal data; (g) within-person statistical mediation; and (h) power analyses for intensive longitudinal designs. The workshop will consist of lectures and software demonstrations with example datasets. Software demonstrations will be conducted using Mplus, SPSS, and R.


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

Niall Bolger, Ph.D.

Niall Bolger is Professor of Psychology and former Chairperson of the Department of Psychology at Columbia University where he teaches a graduate statistics sequence on linear and mixed models as tools for psychological research. He also teaches classes on research methods and psychophysiological methods and analysis. Read More

Hear the instructors talk about the class in this video interview conducted in 2021 (please note that the dates for this year differ).

Workshop Details


In an effort to continually improve our instruction we obtain student evaluations with each course offering. Here is a sample of reviews from Jean-Philippe and Niall's prior offerings of Analyzing Intensive Longitudinal Data:

This workshop had so many strengths! I am so thankful for JP, Niall, and Ana [the teaching assistant] for all of their hard work. The material they covered is very difficult, but they explained it so well. I loved all of their examples, and I am so thankful for the lecture slides as well as the SPSS, R, and Mplus notes. Also, I think that JP, Niall, and Ana did a great job at answering questions both live and using the Q&A box.

Balances well the emphasis on theory and background of the topic and practical implementation. Approach is for researchers rather than statisticians (which I as a researcher really appreciate).

Niall and JP are experts in their field and they recognize the complexity of this analytical approach. They knew where to explain in more detail and paced themselves extremely well for us to follow along and ensure that we understood the concepts, methods, and approaches.

The panelists work hard to answer every question that is asked. The panelists were VERY knowledgeable and helpful! I was never made felt like I asked a dumb question.

The instruction style was clear, and I especially appreciated the way that the instructors provided software code and walked us through the sample code.

This was a great course - I would highly recommend to anyone analyzing intensive longitudinal data!


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