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Bayesian Dynamical Systems Modelling in the Social Sciences

Overview of attention for article published in PLOS ONE, January 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
6 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
39 Dimensions

Readers on

mendeley
49 Mendeley
citeulike
1 CiteULike
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Title
Bayesian Dynamical Systems Modelling in the Social Sciences
Published in
PLOS ONE, January 2014
DOI 10.1371/journal.pone.0086468
Pubmed ID
Authors

Shyam Ranganathan, Viktoria Spaiser, Richard P. Mann, David J. T. Sumpter

Abstract

Data arising from social systems is often highly complex, involving non-linear relationships between the macro-level variables that characterize these systems. We present a method for analyzing this type of longitudinal or panel data using differential equations. We identify the best non-linear functions that capture interactions between variables, employing Bayes factor to decide how many interaction terms should be included in the model. This method punishes overly complicated models and identifies models with the most explanatory power. We illustrate our approach on the classic example of relating democracy and economic growth, identifying non-linear relationships between these two variables. We show how multiple variables and variable lags can be accounted for and provide a toolbox in R to implement our approach.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Australia 1 2%
Sweden 1 2%
United Kingdom 1 2%
United States 1 2%
Croatia 1 2%
Unknown 44 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 29%
Researcher 11 22%
Student > Master 7 14%
Student > Doctoral Student 4 8%
Professor 3 6%
Other 6 12%
Unknown 4 8%
Readers by discipline Count As %
Social Sciences 10 20%
Psychology 8 16%
Mathematics 4 8%
Physics and Astronomy 3 6%
Business, Management and Accounting 3 6%
Other 14 29%
Unknown 7 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 12 April 2021.
All research outputs
#4,588,018
of 17,709,629 outputs
Outputs from PLOS ONE
#49,971
of 166,376 outputs
Outputs of similar age
#60,544
of 264,398 outputs
Outputs of similar age from PLOS ONE
#1,978
of 8,115 outputs
Altmetric has tracked 17,709,629 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 166,376 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.6. This one has gotten more attention than average, scoring higher than 69% 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 264,398 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 76% of its contemporaries.
We're also able to compare this research output to 8,115 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.