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  • 1. Growing Regression Trees that Use Sampling Frame Covariates to Explore Response Burden for Use in Survey Design Archived
    Articles and reports: 11-522-X202100100024
    Description: The Economic Directorate of the U.S. Census Bureau is developing coordinated design and sample selection procedures for the Annual Integrated Economic Survey. The unified sample will replace the directorate’s existing practice of independently developing sampling frames and sampling procedures for a suite of separate annual surveys, which optimizes sample design features at the cost of increased response burden. Size attributes of business populations, e.g., revenues and employment, are highly skewed. A high percentage of companies operate in more than one industry. Therefore, many companies are sampled into multiple surveys compounding the response burden, especially for “medium sized” companies.

    This component of response burden is reduced by selecting a single coordinated sample but will not be completely alleviated. Response burden is a function of several factors, including (1) questionnaire length and complexity, (2) accessibility of data, (3) expected number of repeated measures, and (4) frequency of collection. The sample design can have profound effects on the third and fourth factors. To help inform decisions about the integrated sample design, we use regression trees to identify covariates from the sampling frame that are related to response burden. Using historic frame and response data from four independently sampled surveys, we test a variety of algorithms, then grow regression trees that explain relationships between expected levels of response burden (as measured by response rate) and frame covariates common to more than one survey. We validate initial findings by cross-validation, examining results over time. Finally, we make recommendations on how to incorporate our robust findings into the coordinated sample design.
    Release date: 2021-10-29
  • 2. Single stage cluster sampling in prevalence-incidence surveys: Some issues suggested by the Shanghai Survey of Alzheimer’s Disease and Dementia Archived
    Articles and reports: 12-001-X199200114492
    Description:

    The scenario considered here is that of a sample survey having the following two major objectives: (1) identification for future follow up studies of n^* subjects in each of H subdomains, and (2) estimation as of this time of conduct of the survey of the level of some characteristic in each of these subdomains. An additional constraint imposed here is that the sample design is restricted to single stage cluster sampling. A variation of single stage cluster sampling called telescopic single stage cluster sampling (TSSCS) had been proposed in an earlier paper (Levy et al. 1989) as a cost effective method of identifying n^* individuals in each sub domain and, in this article, we investigate the statistical properties of TSSCS in crossectional estimation of the level of a population characteristic. In particular, TSSCS is compared to ordinary single stage cluster sampling (OSSCS) with respect to the reliability of estimates at fixed cost. Motivation for this investigation comes from problems faced during the statistical design of the Shanghai Survey of Alzheimer’s Disease and Dementia (SSADD), an epidemiological study of the prevalence and incidence of Alzheimer’s disease and dementia.

    Release date: 1992-06-15
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  • 1. Growing Regression Trees that Use Sampling Frame Covariates to Explore Response Burden for Use in Survey Design Archived
    Articles and reports: 11-522-X202100100024
    Description: The Economic Directorate of the U.S. Census Bureau is developing coordinated design and sample selection procedures for the Annual Integrated Economic Survey. The unified sample will replace the directorate’s existing practice of independently developing sampling frames and sampling procedures for a suite of separate annual surveys, which optimizes sample design features at the cost of increased response burden. Size attributes of business populations, e.g., revenues and employment, are highly skewed. A high percentage of companies operate in more than one industry. Therefore, many companies are sampled into multiple surveys compounding the response burden, especially for “medium sized” companies.

    This component of response burden is reduced by selecting a single coordinated sample but will not be completely alleviated. Response burden is a function of several factors, including (1) questionnaire length and complexity, (2) accessibility of data, (3) expected number of repeated measures, and (4) frequency of collection. The sample design can have profound effects on the third and fourth factors. To help inform decisions about the integrated sample design, we use regression trees to identify covariates from the sampling frame that are related to response burden. Using historic frame and response data from four independently sampled surveys, we test a variety of algorithms, then grow regression trees that explain relationships between expected levels of response burden (as measured by response rate) and frame covariates common to more than one survey. We validate initial findings by cross-validation, examining results over time. Finally, we make recommendations on how to incorporate our robust findings into the coordinated sample design.
    Release date: 2021-10-29
  • 2. Single stage cluster sampling in prevalence-incidence surveys: Some issues suggested by the Shanghai Survey of Alzheimer’s Disease and Dementia Archived
    Articles and reports: 12-001-X199200114492
    Description:

    The scenario considered here is that of a sample survey having the following two major objectives: (1) identification for future follow up studies of n^* subjects in each of H subdomains, and (2) estimation as of this time of conduct of the survey of the level of some characteristic in each of these subdomains. An additional constraint imposed here is that the sample design is restricted to single stage cluster sampling. A variation of single stage cluster sampling called telescopic single stage cluster sampling (TSSCS) had been proposed in an earlier paper (Levy et al. 1989) as a cost effective method of identifying n^* individuals in each sub domain and, in this article, we investigate the statistical properties of TSSCS in crossectional estimation of the level of a population characteristic. In particular, TSSCS is compared to ordinary single stage cluster sampling (OSSCS) with respect to the reliability of estimates at fixed cost. Motivation for this investigation comes from problems faced during the statistical design of the Shanghai Survey of Alzheimer’s Disease and Dementia (SSADD), an epidemiological study of the prevalence and incidence of Alzheimer’s disease and dementia.

    Release date: 1992-06-15
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