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An R package for analyzing and modeling ranking data

Overview of attention for article published in BMC Medical Research Methodology, May 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 tweeters
q&a
1 Q&A thread

Citations

dimensions_citation
30 Dimensions

Readers on

mendeley
131 Mendeley
citeulike
1 CiteULike
Summary Twitter Q&A Dimensions citations
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Title
An R package for analyzing and modeling ranking data
Published in
BMC Medical Research Methodology, May 2013
DOI 10.1186/1471-2288-13-65
Pubmed ID
23672645
Authors

Paul H Lee, Philip LH Yu

Abstract

In medical informatics, psychology, market research and many other fields, researchers often need to analyze and model ranking data. However, there is no statistical software that provides tools for the comprehensive analysis of ranking data. Here, we present pmr, an R package for analyzing and modeling ranking data with a bundle of tools. The pmr package enables descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce model, distance-based model, and rank-ordered logit model), and the visualization of ranking data with multidimensional preference analysis.

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Twitter Demographics

The data shown below were collected from the profiles of 2 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 131 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 2%
Uganda 1 <1%
Germany 1 <1%
Sweden 1 <1%
United Kingdom 1 <1%
Unknown 125 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 22%
Student > Ph. D. Student 27 21%
Student > Master 11 8%
Student > Postgraduate 8 6%
Other 8 6%
Other 27 21%
Unknown 21 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 13%
Computer Science 14 11%
Psychology 11 8%
Medicine and Dentistry 11 8%
Social Sciences 10 8%
Other 44 34%
Unknown 24 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 13 March 2015.
All research outputs
#7,186,090
of 22,714,025 outputs
Outputs from BMC Medical Research Methodology
#1,066
of 2,003 outputs
Outputs of similar age
#62,577
of 194,052 outputs
Outputs of similar age from BMC Medical Research Methodology
#11
of 21 outputs
Altmetric has tracked 22,714,025 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 2,003 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 46th percentile – i.e., 46% 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 194,052 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 66% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.

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