APS
2026 APS Annual Convention
New Developments In Psychological Time Series Modeling
We showcase four new methodological developments for intensive longitudinal psychological data: multilevel Hidden Markov Models for latent state dynamics, joint models linking daily processes to clinical outcomes and dropout, Bayesian multilevel VAR models with uncertainty for network inference, and a tutorial on systematic model checking for time series models.
Chairs & Discussants
- Jonas HaslbeckChair
University of Amsterdam - Emmeke AartsCoChair
Utrecht University
Presentations
- Uncovering Dynamics In Psychological Time Series with Multilevel Hidden Markov ModelsEmmeke Aarts
- Predicting Dropout In Intensive Longitudinal Data: Extending the Joint Model for Autocorrelated DataFridtjof Peterson
- Using Features of Dynamic Networks to Guide Treatment Selection and Outcome Prediction: The Central Role of UncertaintyBjörn Siepe
- Model Checking for Vector Autoregressive ModelsJonas Haslbeck
