Spatially weighted multilevel modeling avoids unrealistic assumptions of independence between contextual units.

Evidence

All the indicators of the communal-level exposure to war events (both the ACLED and the TRACES) were computed as a spatially weighted mean, following a novel approach to multilevel modeling, which integrates spatially weighted context data (see Elcheroth et al. 2013). Spatial weighting corrects for the unrealistic assumption of spatial independency between the contextual units and increases the precision of the contextual indicators (Elcheroth et al. 2013). It allows to treat (potentially arbitrarily defined) boundaries of the upper level units (i.e., the TRACES areas) in a more flexible manner. — Sandra Penic, Guy Elcheroth, and Dario Spini, When Is Collective Exposure to War…, 81

Categorization

• DEFINE: Defines the spatially weighted mean approach and why naive multilevel modeling of region-level violence needs correction.
• XREF: connects to: Core social science concepts are inherently multidimensional and multilevel — Gary Goertz, Social Science Concepts and Measurement, 143

Broader Connections

• Core social science concepts are inherently multidimensional and multilevel #expands (0.78) — Gary Goertz, Social Science Concepts and Measurement, 143