↓ Skip to main content

Bayesian Thurstonian models for ranking data using JAGS

Overview of attention for article published in Behavior Research Methods, March 2013
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
1 X user
wikipedia
1 Wikipedia page
q&a
1 Q&A thread

Readers on

mendeley
33 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Article details
Title
Bayesian Thurstonian models for ranking data using JAGS
Published in
Behavior Research Methods, March 2013
DOI 10.3758/s13428-012-0300-3
Pubmed ID
Authors
Abstract

A Thurstonian model for ranking data assumes that observed rankings are consistent with those of a set of underlying continuous variables. This model is appealing since it renders ranking data amenable to familiar models for continuous response variables-namely, linear regression models. To date, however, the use of Thurstonian models for ranking data has been very rare in practice. One reason for this may be that inferences based on these models require specialized technical methods. These methods have been developed to address computational challenges involved in these models but are not easy to implement without considerable technical expertise and are not widely available in software packages. To address this limitation, we show that Bayesian Thurstonian models for ranking data can be very easily implemented with the JAGS software package. We provide JAGS model files for Thurstonian ranking models for general use, discuss their implementation, and illustrate their use in analyses.

Login to access the Attention Digest and the Sentiment Analysis related to this output.

Timeline Attention over time Attention Score history
Login to access the full charts related to this output.
Activity
Login to access the full charts related to this output.
X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Login to view Mendeley reader trends over time.

Geographical breakdown

Geographical breakdown
Country Count As %
Germany 1 3%
Canada 1 3%
Unknown 31 94%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 11 33%
Researcher 3 9%
Professor > Associate Professor 3 9%
Student > Bachelor 2 6%
Professor 2 6%
Other 5 15%
Unknown 7 21%
Readers by discipline
Readers by discipline Count As %
Psychology 14 42%
Social Sciences 5 15%
Agricultural and Biological Sciences 2 6%
Linguistics 1 3%
Computer Science 1 3%
Other 2 6%
Unknown 8 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 10 November 2019.
All research outputs
#6,307,921
of 29,164,447 outputs
Outputs from Behavior Research Methods
#684
of 2,198 outputs
Outputs of similar age
#47,971
of 220,914 outputs
Outputs of similar age from Behavior Research Methods
#5
of 20 outputs
Altmetric has tracked 29,164,447 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 2,198 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.1. This one has gotten more attention than average, scoring higher than 68% 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 220,914 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.