Analysis

All (2) ((2 results))

• 1. A hypothesis test of linear regression coefficients with survey data Archived

  Articles and reports: 12-001-X199400214421

  Description:

  This paper discusses testing a single hypothesis about linear regression coefficients based on sample survey data. It suggests that when the design-based linearization variance estimator for a regression coefficient is used it should be adjusted to reduce its slight model bias and that a Satterthwaite-like estimation of its effective degrees of freedom be made. A very important special case of this analysis is its application to domain means.

  Release date: 1994-12-15

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• 2. Jackknife variance estimation of imputed survey data Archived

  Articles and reports: 12-001-X199400114433

  Description:

  Imputation is a common technique employed by survey-taking organizations in order to address the problem of item nonresponse. While in most of the cases the resulting completed data sets provide good estimates of means and totals, the corresponding variances are often grossly underestimated. A number of methods to remedy this problem exists, but most of them depend on the sampling design and the imputation method. Recently, Rao (1992), and Rao and Shao (1992) have proposed a unified jackknife approach to variance estimation of imputed data sets. The present paper explores this technique empirically, using a real population of businesses, under a simple random sampling design and a uniform nonresponse mechanism. Extensions to stratified multistage sample designs are considered, and the performance of the proposed variance estimator under non-uniform response mechanisms is briefly investigated.

  Release date: 1994-06-15

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Articles and reports (2) ((2 results))

• 1. A hypothesis test of linear regression coefficients with survey data Archived

  Articles and reports: 12-001-X199400214421

  Description:

  This paper discusses testing a single hypothesis about linear regression coefficients based on sample survey data. It suggests that when the design-based linearization variance estimator for a regression coefficient is used it should be adjusted to reduce its slight model bias and that a Satterthwaite-like estimation of its effective degrees of freedom be made. A very important special case of this analysis is its application to domain means.

  Release date: 1994-12-15

  ---

• 2. Jackknife variance estimation of imputed survey data Archived

  Articles and reports: 12-001-X199400114433

  Description:

  Imputation is a common technique employed by survey-taking organizations in order to address the problem of item nonresponse. While in most of the cases the resulting completed data sets provide good estimates of means and totals, the corresponding variances are often grossly underestimated. A number of methods to remedy this problem exists, but most of them depend on the sampling design and the imputation method. Recently, Rao (1992), and Rao and Shao (1992) have proposed a unified jackknife approach to variance estimation of imputed data sets. The present paper explores this technique empirically, using a real population of businesses, under a simple random sampling design and a uniform nonresponse mechanism. Extensions to stratified multistage sample designs are considered, and the performance of the proposed variance estimator under non-uniform response mechanisms is briefly investigated.

  Release date: 1994-06-15

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