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Mendeley readers
Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 20% |
| Ecuador | 1 | 20% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 65 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 | 60 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 26% |
| Researcher | 11 | 17% |
| Student > Master | 9 | 14% |
| Student > Doctoral Student | 4 | 6% |
| Professor | 3 | 5% |
| Other | 10 | 15% |
| Unknown | 11 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Social Sciences | 12 | 18% |
| Psychology | 8 | 12% |
| Mathematics | 5 | 8% |
| Computer Science | 3 | 5% |
| Physics and Astronomy | 3 | 5% |
| Other | 19 | 29% |
| Unknown | 15 | 23% |
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
#6,130,113
of 22,739,983 outputs
Outputs from PLOS ONE
#73,310
of 194,087 outputs
Outputs of similar age
#72,401
of 305,475 outputs
Outputs of similar age from PLOS ONE
#1,796
of 5,562 outputs
Altmetric has tracked 22,739,983 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 194,087 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one has gotten more attention than average, scoring higher than 61% 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 305,475 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 5,562 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 67% of its contemporaries.