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Cited by 332

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Multilevel Methods for Public Health Research

Subramanian, S. V., Jones, Kelvyn, Duncan, Craig·2003·Neighborhoods and Health

Abstract

This chapter begins by outlining the conceptual motivation behind multilevel analyses and by identifying a core set of research questions that this approach addresses. It then introduces the idea of multilevel structures and discusses simple and complex multilevel models. It emphasizes that the key strength of multilevel models lies in modeling heterogeneity at different levels and shows how multilevel models can be extended to additional contextual levels (e.g., neighborhoods nested within regions). The estimation procedures underlying such models are discussed, showing how a multilevel framework can provide a general, unified approach to data analysis and how this can be achieved by extensions to the basic hierarchical structure of individuals nested within contexts. The chapter concludes with a discussion of issues that researchers should be aware of when applying multilevel methods.

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Crossref · 10.1093/acprof:oso/9780195138382.003.0004

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Metadata

Authors
Subramanian, S. V., Jones, K., & Duncan, C.
Venue
Neighborhoods and Health
Published
2003-05-08
Pages
65-111
OpenAlex
W1849301062
Cited by
332
References
3
FWCI
110.33
Type
book chapter
Language
en
Open access
closed

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CoChat. (2026). Citation check: Multilevel Methods for Public Health Research. https://app.cochat.ai/papers/10.1093/acprof:oso/9780195138382.003.0004
The paper
Subramanian, S. V., Jones, K., & Duncan, C. (2003). Multilevel Methods for Public Health Research. Neighborhoods and Health. https://doi.org/10.1093/acprof%3Aoso/9780195138382.003.0004

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.