intensive longitudinal data
Definition
Intensive longitudinal data refers to repeated measurements of psychological variables collected from individuals over time, enabling the estimation of individual-level network models and examination of how network properties vary across persons. This approach allows researchers to fit separate psychometric network models for each participant, capturing individual differences in the mutual associations between variables (nodes) and their connections (edges). By collecting multiple observations per person, intensive longitudinal datasets support the evaluation of network-derived metrics—including network-structure variables such as density and global strength, as well as network-informed variables based on node centrality—as predictors of psychological outcomes.
Sources: Johal & Rhemtulla (2024)
Related Terms
Applications
Intensive Longitudinal Data and Network Models
Intensive longitudinal datasets enable researchers to estimate individual network models for each participant rather than fitting a single network across an entire sample, thereby allowing variability in network properties across persons. This methodological approach is essential for examining whether network-derived metrics have predictive utility for psychological outcomes.
Sources: Johal & Rhemtulla (2024)
Intensive Longitudinal Data and Psychological Prediction
Intensive longitudinal data collected across multiple datasets have been used to evaluate which network-derived metrics best predict external psychological variables and outcomes.
Sources: Johal & Rhemtulla (2024)
