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.
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.
Daily Q & A Sessions denoted in bold.
- A: Introduction to intensive longitudinal data (Kevin) (40 min)
- B: EMA data in R – Management & Visualization (25 min)
- C: Q & A (6/7/2021, 12 pm ET / 9am PT)
- D: Intraindividual Variation – Dynamic Characteristics (Nilam)(83 min)
- E: Computing iMean, iSD, iMSSD, & iEntropy in R (16 min)
- F: Q & A (6/7/2021, 4pm ET / 1pm PT)
- G: Modeling within-person processes (J-P) (2 Parts, 115 min)
- H: Specifying multilevel models in R (Emily)(17 min)
- I: Q & A (6/8/2021, 12 pm ET / 9am PT)
- J: Modeling categorical and count outcomes (Niall) (4 Parts, 37 mins)
- K: Specifying generalized linear models in R (Katherine)(21 min)
- L: Q & A (6/8/2021, 4pm ET / 1pm PT)
- M: Dyadic Data Analysis (J-P) (95 min)
- N: Specifying dyadic multilevel models in R (Emily)(23 min)
- O: Q & A (6/9/2021, 12 pm ET / 9am PT)
- P: Within-person Mediation (Niall) (3 Parts, 43 min)
- Q: Specifying 1-1-1 mediation models in R (Katherine)(2 parts, 22 min)
- R: Q & A (6/9/2021, 4pm ET / 1pm PT)
- S: Multivariate Dynamics and Networks (58 min)
- T: Specifying network and VAR models in R (Xiao)(18 min)
- U: Q & A (6/10/2021, 12 pm ET / 9am PT)
- V: Machine Learning with Experience Sampling Data: CART (Kevin) (70 min)
- W: Specifying multilevel recursive partitioning models in R (Xiao)(13 min)
- X: Q & A (6/10/2021, 4pm ET / 1pm PT)
- Y: Dyadic Dynamics Systems Modeling (Katherine) (21 min)
- Z: Variance Heterogeneity Models (J-P) (38 min)
- AA: Q & A (6/11/2021, 12 pm ET / 9am PT)
- AB: Regularization with Experience Sampling Data: The Lasso (Kevin)(17 min)
- AC: Super-Intensive Longitudinal Data (Nilam) (41 min)
- AD: Q & A (6/11/2021, 4pm ET / 1pm PT)