multi-level modeling
Definition
Multi-level modeling refers to statistical methods that simultaneously estimate within-person and between-person effects from intensive longitudinal data, allowing researchers to distinguish variance and covariance structures that operate at different levels of analysis. These methods are essential for separating effects generated by fluctuations within individuals across time from effects that reflect stable differences between individuals, such as distinguishing day-to-day mood correlations from correlations between average mood states. A key challenge in multi-level modeling is that observed relationships between person-wise means can be biased by within-person dynamics; observed correlations between person-means depend on both the true between-person correlations and the within-person correlations, meaning spurious between-person correlations can be induced by within-person effects alone, particularly when sample sizes per person are small and between-person variance is low.
Sources: Haslbeck & Epskamp (2024)
Related Terms
Applications
Multi-level Modeling and Intensive Longitudinal Data
Multi-level modeling is fundamentally designed to analyze intensive longitudinal data, in which multiple subjects are measured repeatedly over time, enabling the separation of within-person and between-person variance that cross-sectional data cannot provide. The method addresses a core limitation of cross-sectional designs by allowing researchers to distinguish effects generated by fluctuations within individuals from effects that represent stable differences between individuals.
Sources: Haslbeck & Epskamp (2024)
Multi-level Modeling and Vector Autoregressive Models
Multi-level vector autoregressive (VAR) models are a specific application of multi-level modeling that estimates both within-person temporal and contemporaneous effects and between-person relationships from longitudinal data. Person-wise sample means can introduce bias in between-person parameters when within-person correlations are present.
Sources: Haslbeck & Epskamp (2024)
