Small Area Estimation of Latent Economic Well-beingCitation formats

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Small Area Estimation of Latent Economic Well-being. / Moretti, Angelo; Shlomo, Natalie; Sakshaug, Joseph W.

In: Sociological Methods and Research, Vol. 0, 0, 13.02.2019.

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Moretti, Angelo ; Shlomo, Natalie ; Sakshaug, Joseph W. / Small Area Estimation of Latent Economic Well-being. In: Sociological Methods and Research. 2019 ; Vol. 0.

Bibtex

@article{c05e0e13a6fe4449a062ba10ee855bbc,
title = "Small Area Estimation of Latent Economic Well-being",
abstract = "Small area estimation (SAE) plays a crucial role in the social sciences due to the growing need for reliable and accurate estimates for small domains. In the study of well-being, for example, policy makers need detailed information about the geographical distribution of a range of social indicators. We investigate data dimensionality reduction using factor analysis models and implement SAE on the factor scores under the empirical best linear unbiased prediction approach. We contrast this approach with the standard approach of providing a dashboard of indicators or a weighted average of indicators at the local level. We demonstrate the approach in a simulation study and a real data application based on the European Union Statistics for Income and Living Conditions for the municipalities of Tuscany.",
keywords = "composite estimation, direct estimation, EBLUP, factor analysis, factor scores, model-based estimation",
author = "Angelo Moretti and Natalie Shlomo and Sakshaug, {Joseph W.}",
year = "2019",
month = feb,
day = "13",
doi = "10.1177/0049124119826160",
language = "English",
volume = "0",
journal = "Sociological Methods & Research",
issn = "0049-1241",
publisher = "Sage Publications Ltd",

}

RIS

TY - JOUR

T1 - Small Area Estimation of Latent Economic Well-being

AU - Moretti, Angelo

AU - Shlomo, Natalie

AU - Sakshaug, Joseph W.

PY - 2019/2/13

Y1 - 2019/2/13

N2 - Small area estimation (SAE) plays a crucial role in the social sciences due to the growing need for reliable and accurate estimates for small domains. In the study of well-being, for example, policy makers need detailed information about the geographical distribution of a range of social indicators. We investigate data dimensionality reduction using factor analysis models and implement SAE on the factor scores under the empirical best linear unbiased prediction approach. We contrast this approach with the standard approach of providing a dashboard of indicators or a weighted average of indicators at the local level. We demonstrate the approach in a simulation study and a real data application based on the European Union Statistics for Income and Living Conditions for the municipalities of Tuscany.

AB - Small area estimation (SAE) plays a crucial role in the social sciences due to the growing need for reliable and accurate estimates for small domains. In the study of well-being, for example, policy makers need detailed information about the geographical distribution of a range of social indicators. We investigate data dimensionality reduction using factor analysis models and implement SAE on the factor scores under the empirical best linear unbiased prediction approach. We contrast this approach with the standard approach of providing a dashboard of indicators or a weighted average of indicators at the local level. We demonstrate the approach in a simulation study and a real data application based on the European Union Statistics for Income and Living Conditions for the municipalities of Tuscany.

KW - composite estimation

KW - direct estimation

KW - EBLUP

KW - factor analysis

KW - factor scores

KW - model-based estimation

UR - http://www.scopus.com/inward/record.url?scp=85061573144&partnerID=8YFLogxK

U2 - 10.1177/0049124119826160

DO - 10.1177/0049124119826160

M3 - Article

AN - SCOPUS:85061573144

VL - 0

JO - Sociological Methods & Research

JF - Sociological Methods & Research

SN - 0049-1241

M1 - 0

ER -