Using temporal network methods to reveal the idiographic nature of development

Area of Science:

• Developmental Psychology
• Neuroscience
• Quantitative Methods

Background:

• Developmental science predominantly relies on average trajectories and group generalizations.
• There is a significant gap in methodologies for making individual-level inferences in developmental research.
• Individual uniqueness is central to developmental theories, yet methods often overlook this.

Purpose of the Study:

• To advocate for and explicate the advantages of an idiographic approach in developmental science.
• To demonstrate how studying within-person variation in intensive longitudinal data yields individual-level insights.
• To compare idiographic methods with traditional mean-based analyses across developmental domains.

Main Methods:

• Utilizing intensive longitudinal data (e.g., daily assessments, functional neuroimages).
• Comparing traditional mean-based analysis techniques with idiographic approaches.
• Applying group iterative multiple model estimation (GIMME) to create youth-specific temporal networks.

Main Results:

• Idiographic approaches reveal different empirical conclusions compared to mean-based methods.
• GIMME successfully models directional relationships between brain regions or behaviors over time.
• Benefits of the idiographic approach are illustrated across three developmental domains.

Conclusions:

• An idiographic approach is crucial for understanding individual youth development.
• Intensive longitudinal data and advanced techniques like GIMME enable personalized inferences.
• Future research should prioritize study designs and analyses that optimize individual-level understanding.