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Methods for calculating confidence and credible intervals for the residual between-study variance in random effects meta-regression models

Overview of attention for article published in BMC Medical Research Methodology, September 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

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5 tweeters

Citations

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36 Dimensions

Readers on

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80 Mendeley

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Title
Methods for calculating confidence and credible intervals for the residual between-study variance in random effects meta-regression models
Published in
BMC Medical Research Methodology, September 2014
DOI 10.1186/1471-2288-14-103
Pubmed ID
25196829
Authors

Dan Jackson, Rebecca Turner, Kirsty Rhodes, Wolfgang Viechtbauer

Abstract

Meta-regression is becoming increasingly used to model study level covariate effects. However this type of statistical analysis presents many difficulties and challenges. Here two methods for calculating confidence intervals for the magnitude of the residual between-study variance in random effects meta-regression models are developed. A further suggestion for calculating credible intervals using informative prior distributions for the residual between-study variance is presented.

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Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Macao 1 1%
United States 1 1%
Italy 1 1%
Unknown 76 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 21%
Researcher 11 14%
Student > Master 10 13%
Professor > Associate Professor 5 6%
Student > Postgraduate 4 5%
Other 16 20%
Unknown 17 21%
Readers by discipline Count As %
Medicine and Dentistry 15 19%
Psychology 10 13%
Mathematics 9 11%
Agricultural and Biological Sciences 3 4%
Computer Science 3 4%
Other 16 20%
Unknown 24 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 08 October 2018.
All research outputs
#12,709,447
of 22,763,032 outputs
Outputs from BMC Medical Research Methodology
#1,164
of 2,010 outputs
Outputs of similar age
#104,827
of 238,865 outputs
Outputs of similar age from BMC Medical Research Methodology
#8
of 17 outputs
Altmetric has tracked 22,763,032 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,010 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one is in the 41st percentile – i.e., 41% of its peers scored the same or lower than it.
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 238,865 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.

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