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Sample designs and sampling methods for the Collaborative Psychiatric Epidemiology Studies (CPES).

Steven G Heeringa1, James Wagner, Myriam Torres

  • 1Institute for Social Research, University of Michigan, Ann Arbor, 48104, USA. sheering@isr.umich.edu

International Journal of Methods in Psychiatric Research
|February 22, 2005
PubMed
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This study details probability sample designs for major psychiatric epidemiology surveys, including the National Comorbidity Survey Replication (NCS-R), National Study of American Life (NSAL), and National Latino and Asian American Study of Mental Health (NLAAS). It explains how these designs were adapted and refined based on cost, error, and field experience.

Area of Science:

  • Epidemiology
  • Survey Methodology
  • Psychiatry

Background:

  • The Collaborative Psychiatric Epidemiology Studies (CPES) encompass major national surveys.
  • These studies rely on established sampling methodologies from the University of Michigan Survey Research Center.

Purpose of the Study:

  • To provide an overview of probability sample designs and sampling methods used in CPES.
  • To detail the adaptation of general survey methods to specific study designs (NCS-R, NSAL, NLAAS).
  • To inform analysts and researchers about design characteristics, outcomes, and adaptations.

Main Methods:

  • Description of multi-stage sample designs and respondent selection procedures.
  • Discussion of adaptations of general sampling methods to specific CPES studies.

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  • Analysis of how survey cost and error structures influenced initial designs and subsequent adaptations.

    • Main Results:

    • Detailed examination of sample design characteristics and outcomes for NCS-R, NSAL, and NLAAS.
    • Explanation of modifications made to sampling procedures based on field experience.
    • Insights into the interplay between expected costs, error structures, and final sample realization.

    Conclusions:

    • The paper offers a comprehensive guide to the sampling strategies employed in CPES.
    • Understanding these design choices is crucial for accurate data analysis and planning future research.
    • Adaptations based on practical experience highlight the iterative nature of robust survey design.