Evidence map›Paper›PMID 25284905›Full record

ArticleSociological methodology2014

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

Hongwei Xu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sociological methodology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Geospatial Science for the Environmental Epidemiology of Cancer in the Exposome Era.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2024
    Article
  5. Spatial Survival Model for COVID-19 in México.Healthcare (Basel, Switzerland) · 2024
    Article
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  7. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

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

Funding

Spatial Analysis CoreR24HD041020 · NICHD · BROWN UNIVERSITY · PI FOSTER, ANDREW D. · 2001 to 2016
$7.4M
Incorporating Immigrants and Minorities into Late 19th Century CitiesR01HD049493 · NICHD · BROWN UNIVERSITY · PI LOGAN, JOHN R · 2007 to 2009
$926k
NICHD NIH HHS R01 HD049493NICHD NIH HHS R24 HD041020
6 · The paper itself

Abstract

The standard multilevel regressions that are widely used in neighborhood research typically ignore potential between-neighborhood correlations due to underlying spatial processes, and hence produce inappropriate inferences about neighborhood effects. In contrast, spatial models make estimations and predictions across areas by explicitly modeling the spatial correlations among observations in different locations. A better understanding of the strengths and limitations of spatial models as compared to the standard multilevel model is needed to improve the research on neighborhood and spatial effects. This research systematically compares model estimations and predictions for binary outcomes between (distance- and lattice-based) spatial and the standard multilevel models in the presence of both within- and between-neighborhood correlations, through simulations. Results from simulation analysis reveal that the standard multilevel and spatial models produce similar estimates of fixed effects, but different estimates of random effects variances. Both the standard multilevel and pure spatial models tend to overestimate the corresponding random effects variances, compared to hybrid models when both non-spatial within neighborhood and spatial between-neighborhood effects exist. Spatial models also outperform the standard multilevel model by a narrow margin in case of fully out-of-sample predictions. Distance-based spatial models provide extra spatial information and have stronger predictive power than lattice-based models under certain circumstances. These merits of spatial modeling are exhibited in an empirical analysis of the child mortality data from 1880 Newark, New Jersey.

Indexed as

Bayesian inferencebinary outcomemultilevel regressionneighborhood effectsSpatial statistics

Identifiers

PMID25284905
PMCPMC4181593

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.