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Cited by 513

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

How Should We Estimate Public Opinion in The States?

Lax, Jeffrey R., Phillips, Justin H.·2009·American Journal of Political Science

Abstract

We compare two approaches for estimating state‐level public opinion: disaggregation by state of national surveys and a simulation approach using multilevel modeling of individual opinion and poststratification by population share. We present the first systematic assessment of the predictive accuracy of each and give practical advice about when and how each method should be used. To do so, we use an original data set of over 100 surveys on gay rights issues as well as 1988 presidential election data. Under optimal conditions, both methods work well, but multilevel modeling performs better generally. Compared to baseline opinion measures, it yields smaller errors, higher correlations, and more reliable estimates. Multilevel modeling is clearly superior when samples are smaller—indeed, one can accurately estimate state opinion using only a single large national survey. This greatly expands the scope of issues for which researchers can study subnational opinion directly or as an influence on policymaking.

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Metadata

Authors
Lax, J. R., & Phillips, J. H.
Venue
American Journal of Political Science
Publisher
Wiley
Published
2008-12-24
Volume
53
Issue
1
Pages
107-121
ISSN
  • 0092-5853 (ISSN-L)
  • 1540-5907
OpenAlex
W2142344524
Cited by
513
References
33
FWCI
13.35
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: How Should We Estimate Public Opinion in The States? https://app.cochat.ai/papers/10.1111/j.1540-5907.2008.00360.x
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Lax, J. R., & Phillips, J. H. (2009). How Should We Estimate Public Opinion in The States? American Journal of Political Science. https://doi.org/10.1111/j.1540-5907.2008.00360.x

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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.