This Advanced Training Institute highlights the range of approaches used in the analysis of data from experience sampling, ecological momentary assessment, daily diary, and other intensive longitudinal paradigms. The training is intended for faculty, postdoctoral fellows, and advanced graduate students in the behavioral and social sciences who are already familiar with these kinds of data and with basic multilevel modeling (e.g., at the level of a graduate-level introductory course).
The ATI will survey analytical techniques emerging from the intraindividual variability, multilevel modeling, dynamic systems, and data mining perspectives, as well as address important factors related to research design and the collection of intensive longitudinal data. See the tentative schedule.
Course materials include basic readings on the fundamental issues in analysis of intensive longitudinal data, lecture notes, and a full set of R scripts. The ATI includes lectures on theory and construction of models for intensive longitudinal data along with hands-on lab sections that examine how those models are specified, implemented in R, and fit to data. Participants are strongly encouraged to bring their own data and research problems, and a laptop equipped with R (within R Studio).
Course instructors include Kevin J. Grimm, Department of Psychology at Arizona State University; Nilam Ram, Departments of Human Development & Family Studies and of Psychology at The Pennsylvania State University; J.P. Laurenceau, Department of Psychological and Brain Sciences at University of Delaware; and Niall Bolger, Department of Psychology at Columbia University.
Applications are invited from investigators at the faculty/professional, postdoctoral, and advanced graduate student levels. The ATI is open to investigators from both within and outside of the United States.
The language of the course is English; no translation services will be provided.
The Advanced Training Institute on Analysis of Intensive Longitudinal Data: Experience Sampling and Ecological Momentary Assessment be held remotely. To provide maximum flexibility for participants, lecture presentations will be pre-recorded and made available at the start of the workshop. For each lecture, the instructors will host, via video conference, a half-hour online question & answer (Q&A) session and a one-hour programming (lab) session that covers code and implementation details.
We encourage participants to watch the lectures prior to the live sessions and bring questions. During the lab sessions, participants are encouraged to apply the longitudinal models reviewed in the lectures in analysis of their own data or to exemplar longitudinal data provided by instructors, and to ask questions about the programming and interpretation of the modeling results. A live round-table and office-hour type session will provide additional opportunity for interaction with and among the instructors.
Day 1: Introduction to Experience Sampling (EMA) and Intensive Longitudinal Data
- A: Introduction to intensive longitudinal data
- B: Q & A – Introduction to intensive longitudinal data
- C: Lab – Computer programming to describe & visualize EMA data
- D: Intraindividual Variation – Dynamic Characteristics
- E: Q & A – Intraindividual Variation – Dynamic Characteristics
- F: Lab – Computation of iMean, iSD, iMSSD, iEntropy, etc
Day 2: Modeling Intraindividual Covariation
- G: Multilevel modeling: Modeling of within-person process
- H: Q & A – Multilevel modeling: Modeling of within-person process
- I: Lab – Computer programming for basic multilevel model
- J: Multilevel modeling: Modeling categorical outcomes
- K: Q & A – Multilevel modeling: Modeling categorical outcomes
- L: Lab – Computer programming for generalized multilevel model
Day 3: Modeling Dyadic and Mediation Processes
- M: Multilevel extensions: Dyadic data analysis
- N: Q & A – Multilevel extensions: Dyadic data analysis
- O: Lab – Computer programming for dyadic multilevel models
- P: Multilevel extensions: Within-person mediation
- Q: Q & A – Multilevel extensions: Within-person mediation
- R: Lab – Computer programming for moderated 1-1-1 mediation
Day 4: Modeling Multivariate Systems and Machine Learning Approaches to ILD
- S: Multivariate dynamics and networks
- T: Q & A – Multivariate dynamics and networks
- U: Lab – Computer programming for network and VAR models
- V: Machine learning approaches to classification of time-series
- W: Q & A – Machine learning approaches to classification of time-series
- X: Lab – Computer programming for clustering and classifying
Day 5: Differential Equation Models and Other Extensions
- Y: Differential equation models, stability maintenance, coupling
- Z: Variance heterogeneity, new data, and future trends
- AA: Q & A – Modeling and data extensions