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Structural equation modeling for decomposing rank-dependent indicators of socioeconomic inequality of health: an empirical study

Overview of attention for article published in Health Economics Review, December 2016
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Mentioned by

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3 X users
facebook
1 Facebook page
reddit
1 Redditor

Citations

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22 Dimensions

Readers on

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92 Mendeley
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Title
Structural equation modeling for decomposing rank-dependent indicators of socioeconomic inequality of health: an empirical study
Published in
Health Economics Review, December 2016
DOI 10.1186/s13561-016-0134-2
Pubmed ID
Authors

Roselinde Kessels, Guido Erreygers

Abstract

We present a flexible structural equation modeling (SEM) framework for the regression-based decomposition of rank-dependent indicators of socioeconomic inequality of health and compare it with simple ordinary least squares (OLS) regression. The SEM framework forms the basis for a proper use of the most prominent one- and two-dimensional decompositions and provides an argument for using the bivariate multiple regression model for two-dimensional decomposition. Within the SEM framework, the two-dimensional decomposition integrates the feedback mechanism between health and socioeconomic status and allows for different sets of determinants of these variables. We illustrate the SEM approach and its outperformance of OLS using data from the 2011 Ethiopian Demographic and Health Survey.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Belgium 1 1%
Unknown 91 99%

Demographic breakdown

Readers by professional status Count As %
Lecturer 22 24%
Student > Master 14 15%
Researcher 9 10%
Student > Ph. D. Student 8 9%
Student > Bachelor 4 4%
Other 11 12%
Unknown 24 26%
Readers by discipline Count As %
Nursing and Health Professions 28 30%
Economics, Econometrics and Finance 14 15%
Social Sciences 6 7%
Medicine and Dentistry 5 5%
Agricultural and Biological Sciences 3 3%
Other 11 12%
Unknown 25 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 January 2017.
All research outputs
#14,275,790
of 23,322,966 outputs
Outputs from Health Economics Review
#199
of 442 outputs
Outputs of similar age
#223,908
of 422,157 outputs
Outputs of similar age from Health Economics Review
#8
of 13 outputs
Altmetric has tracked 23,322,966 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 442 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.8. This one has gotten more attention than average, scoring higher than 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 422,157 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.