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Cross-Survey Analysis to Estimate Low-Incidence Religious Groups

Author

Listed:
  • Elizabeth Tighe

    (Brandeis University, Waltham, MA, USA, tighe@brandeis.edu)

  • David Livert

    (Pennsylvania State University, Lehigh Valley, Center Valley, USA)

  • Melissa Barnett

    (Brandeis University, Waltham, MA, USA)

  • Leonard Saxe

    (Brandeis University, Waltham, MA, USA)

Abstract

Population-based surveys are of limited utility to estimate rare or low-incidence groups, particularly for those defined by religion or ethnicity not included in the U.S. Census. Methods of cross-survey analysis and small area estimation, however, can be used to provide reliable estimates of such low-incidence groups. To illustrate these methods, data from 50 national surveys are combined to examine the Jewish population in the United States. Hierarchical models are used to examine clustering of respondents within surveys and geographic regions. Bayesian analyses with Monte Carlo simulations are used to obtain pooled, state-level estimates poststratified by sex, race, education, and age to obtain certainty intervals about the estimates. This cross-survey approach provides a useful and practical analytic framework that can be generalized both to more extensive study of religion in the United States and to other social science problems in which single data sources are insufficient for reliable statistical inference.

Suggested Citation

  • Elizabeth Tighe & David Livert & Melissa Barnett & Leonard Saxe, 2010. "Cross-Survey Analysis to Estimate Low-Incidence Religious Groups," Sociological Methods & Research, , vol. 39(1), pages 56-82, August.
    • Handle: RePEc:sae:somere:v:39:y:2010:i:1:p:56-82
    DOI: 10.1177/0049124110366237
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    References listed on IDEAS

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    1. Klaus Larsen & Jørgen Holm Petersen & Esben Budtz-Jørgensen & Lars Endahl, 2000. "Interpreting Parameters in the Logistic Regression Model with Random Effects," Biometrics, The International Biometric Society, vol. 56(3), pages 909-914, September.
    2. Zheng, Tian & Salganik, Matthew J. & Gelman, Andrew, 2006. "How Many People Do You Know in Prison?: Using Overdispersion in Count Data to Estimate Social Structure in Networks," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 409-423, June.
    3. Park, David K. & Gelman, Andrew & Bafumi, Joseph, 2004. "Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls," Political Analysis, Cambridge University Press, vol. 12(4), pages 375-385.
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    Cited by:

    1. Michael S. Rendall & Bonnie Ghosh-Dastidar & Margaret M. Weden & Zafar Nazarov, 2011. "Multiple Imputation for Combined-Survey Estimation With Incomplete Regressors In One But Not Both Surveys," Working Papers WR-887-1, RAND Corporation.

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