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Comparing Spatial and Multilevel Regression Models for Binary Outcomes in Neighborhood Studies.

Hongwei Xu1

  • 1Survey Research Center, University of Michigan, 426 Thompson St, 216 NU ISR Bldg, Ann Arbor, MI 48104, Telephone: +1 (734) 615-3552, , xuhongw@umich.edu.

Sociological Methodology
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Summary

Spatial models offer improved neighborhood effect research by accounting for spatial correlations, outperforming standard multilevel models in predictions. This enhances understanding of neighborhood and spatial effects in research.

Keywords:
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Area of Science:

  • Spatial statistics
  • Geographic analysis
  • Epidemiology

Background:

  • Standard multilevel regressions in neighborhood research often overlook spatial correlations, leading to inaccurate neighborhood effect inferences.
  • Spatial models explicitly address spatial correlations, improving estimations and predictions across locations.

Purpose of the Study:

  • To systematically compare spatial models (distance- and lattice-based) with standard multilevel models.
  • To evaluate model performance in estimating and predicting binary outcomes with within- and between-neighborhood correlations.

Main Methods:

  • Simulation analysis comparing spatial and multilevel models.
  • Assessment of fixed and random effects variance estimations.
  • Evaluation of out-of-sample predictive accuracy.

Main Results:

  • Spatial and multilevel models yield similar fixed effects estimates but differ in random effects variances.
  • Both standard multilevel and pure spatial models can overestimate random effects variances compared to hybrid models.
  • Spatial models show a slight advantage in out-of-sample predictions over standard multilevel models.

Conclusions:

  • Spatial models provide a more accurate approach to neighborhood research by incorporating spatial dependencies.
  • Distance-based spatial models can offer enhanced spatial information and predictive power.
  • The study highlights the utility of spatial modeling in analyzing complex spatial and neighborhood effects, as demonstrated with historical child mortality data.