Temporal Analysis: By tracking the same individuals over time, researchers can observe the sequence of events, which helps establish temporal relationships between risk factors and outcomes. Understanding Developmental Changes: Longitudinal studies are particularly useful for understanding how diseases and health behaviors evolve over the lifespan. Reduced Recall Bias: Since data are collected prospectively, participants are less likely to forget past behaviors or exposures, reducing recall bias. Identifying Causal Relationships: When combined with appropriate statistical methods, longitudinal data can help infer causality more effectively than cross-sectional data.
