<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Large-scale establishment surveys often exhibit substantial temporal or cross-sectional variability in their published standard errors. This article uses a framework defined by survey generalized variance functions to develop three sets of analytic tools for the evaluation of these patterns of variability. These tools are for (1) identification of predictor variables that explain some of the observed temporal and cross-sectional variability in published standard errors; (2) evaluation of the proportion of variability attributable to the abovementioned predictors, equation error and estimation error, respectively; and (3) comparison of equation error variances across groups defined by observable predictor variables. The primary ideas are motivated and illustrated by an application to the U.S. Current Employment Statistics program.</p></abstract>

Large-scale establishment surveys often exhibit substantial temporal or cross-sectional variability in their published standard errors. This article uses a framework defined by survey generalized variance functions to develop three sets of analytic tools for the evaluation of these patterns of variability. These tools are for (1) identification of predictor variables that explain some of the observed temporal and cross-sectional variability in published standard errors; (2) evaluation of the proportion of variability attributable to the abovementioned predictors, equation error and estimation error, respectively; and (3) comparison of equation error variances across groups defined by observable predictor variables. The primary ideas are motivated and illustrated by an application to the U.S. Current Employment Statistics program.

Analytic Tools for Evaluating Variability of Standard Errors in Large-Scale Establishment Surveys

U.S. Bureau of Labor Statistics-Office of Survey Methods Research, PSB 1950 2 Massachusetts Ave. N.E., Washington, DC, 20212, U.S.A.

U.S. Bureau of Labor Statistics-Office of Employment and Unemployment Statistics, Washington, DC, U.S.A.

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Large-scale establishment surveys often exhibit substantial temporal or cross-sectional variability in their published standard errors. This article uses a...

Analytic Tools for Evaluating Variability of Standard Errors in Large-Scale Establishment Surveys

Data publikacji: 11 gru 2014

Zakres stron: 787 - 810

Otrzymano: 01 gru 2012

Przyjęty: 01 lip 2014

DOI: https://doi.org/10.2478/jos-2014-0048

Słowa kluczowe
Degrees of freedom, design effect, generalized variance function (GVF), U.S. Current Employment Statistics program

© by MoonJung Cho

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Analytic Tools for Evaluating Variability of Standard Errors in Large-Scale Establishment Surveys

Data publikacji: 11 gru 2014

Zakres stron: 787 - 810

Otrzymano: 01 gru 2012

Przyjęty: 01 lip 2014

DOI: https://doi.org/10.2478/jos-2014-0048

Słowa kluczoweDegrees of freedom, design effect, generalized variance function (GVF), U.S. Current Employment Statistics program

© by MoonJung Cho

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Degrees of freedom, design effect, generalized variance function (GVF), U.S. Current Employment Statistics program