longitudinal data analysis
Definition
Longitudinal data analysis refers to the statistical examination of data collected from multiple subjects measured repeatedly across time points, enabling researchers to distinguish between within-person effects (fluctuations within individuals over time) and between-person effects (stable differences across individuals). A critical issue in longitudinal data analysis is the accurate separation of these two sources of variation, as methods that use person-wise sample means—aggregated averages for each individual—to estimate between-person relationships can be biased by within-person correlations and dynamics. Specifically, observed correlations between person-wise means may reflect within-person temporal patterns rather than true between-person relationships, a bias that is particularly severe when the number of time points per person is low, between-person variance is small, and within-person effects are strong. To avoid such bias, longitudinal data analysis benefits from approaches that jointly estimate within- and between-person parameters simultaneously rather than relying on stepwise estimation strategies.
Sources: Haslbeck & Epskamp (2024)
Related Terms
Applications
Longitudinal Data Analysis and Multilevel Vector Autoregressive Models
Multilevel vector autoregressive models are used in longitudinal data analysis to separate within-person temporal relationships from between-person effects. However, stepwise approaches that use person-wise sample means to estimate between-person relationships can produce biased estimates of between-person correlations due to contamination from within-person correlations.
Sources: Haslbeck & Epskamp (2024)
Longitudinal Data Analysis and Within-person Centered Data
Within-person centering using person-wise sample means is a computational strategy employed in longitudinal data analysis to estimate within-person effects and relationships. The variance-covariance structure of these sample means is partly determined by within-person deviations, which can bias the between-person parameters estimated from relationships between the centered means.
Sources: Haslbeck & Epskamp (2024)
