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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 (77th 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
24 Dimensions

Readers on

mendeley
43 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 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Croatia 1 2%
Australia 1 2%
United Kingdom 1 2%
United States 1 2%
Sweden 1 2%
Unknown 38 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 26%
Researcher 10 23%
Student > Master 6 14%
Student > Bachelor 4 9%
Professor 3 7%
Other 9 21%
Readers by discipline Count As %
Psychology 7 16%
Social Sciences 7 16%
Physics and Astronomy 5 12%
Mathematics 4 9%
Biochemistry, Genetics and Molecular Biology 3 7%
Other 17 40%

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 11 December 2016.
All research outputs
#3,364,540
of 13,460,924 outputs
Outputs from PLoS ONE
#41,819
of 143,178 outputs
Outputs of similar age
#54,685
of 244,861 outputs
Outputs of similar age from PLoS ONE
#1,922
of 8,127 outputs
Altmetric has tracked 13,460,924 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 143,178 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.1. This one has gotten more attention than average, scoring higher than 70% 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 244,861 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 77% of its contemporaries.
We're also able to compare this research output to 8,127 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.