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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Main Authors: Cho MoonJung, Eltinge John L., Gershunskaya Julie, Huff Larry
Format: Article
Language:English
Published: Sciendo 2014-12-01
Series:Journal of Official Statistics
Subjects:
Online Access:https://doi.org/10.2478/jos-2014-0048
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spelling 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
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