Analytic Tools for Evaluating Variability of Standard Errors in Large-Scale Establishment Surveys
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 varia...
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Online Access: | https://doi.org/10.2478/jos-2014-0048 |
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doaj-1db754b707884fef8f7753bb04bee9202021-09-06T19:41:47ZengSciendoJournal of Official Statistics2001-73672014-12-0130478781010.2478/jos-2014-0048jos-2014-0048Analytic Tools for Evaluating Variability of Standard Errors in Large-Scale Establishment SurveysCho MoonJung0Eltinge John L.1Gershunskaya Julie2Huff Larry3U.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 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.U.S. Bureau of Labor Statistics-Office of Employment and Unemployment Statistics, Washington, DC, U.S.A.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.https://doi.org/10.2478/jos-2014-0048degrees of freedomdesign effectgeneralized variance function (gvf)u.s. current employment statistics program |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Cho MoonJung Eltinge John L. Gershunskaya Julie Huff Larry |
spellingShingle |
Cho MoonJung Eltinge John L. Gershunskaya Julie Huff Larry Analytic Tools for Evaluating Variability of Standard Errors in Large-Scale Establishment Surveys Journal of Official Statistics degrees of freedom design effect generalized variance function (gvf) u.s. current employment statistics program |
author_facet |
Cho MoonJung Eltinge John L. Gershunskaya Julie Huff Larry |
author_sort |
Cho MoonJung |
title |
Analytic Tools for Evaluating Variability of Standard Errors in Large-Scale Establishment Surveys |
title_short |
Analytic Tools for Evaluating Variability of Standard Errors in Large-Scale Establishment Surveys |
title_full |
Analytic Tools for Evaluating Variability of Standard Errors in Large-Scale Establishment Surveys |
title_fullStr |
Analytic Tools for Evaluating Variability of Standard Errors in Large-Scale Establishment Surveys |
title_full_unstemmed |
Analytic Tools for Evaluating Variability of Standard Errors in Large-Scale Establishment Surveys |
title_sort |
analytic tools for evaluating variability of standard errors in large-scale establishment surveys |
publisher |
Sciendo |
series |
Journal of Official Statistics |
issn |
2001-7367 |
publishDate |
2014-12-01 |
description |
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. |
topic |
degrees of freedom design effect generalized variance function (gvf) u.s. current employment statistics program |
url |
https://doi.org/10.2478/jos-2014-0048 |
work_keys_str_mv |
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