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Happy software developers solve problems better: psychological measurements in empirical software engineering

Overview of attention for article published in PeerJ, March 2014
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

news
5 news outlets
blogs
1 blog
twitter
312 X users
patent
1 patent
facebook
6 Facebook pages
googleplus
7 Google+ users
reddit
2 Redditors

Citations

dimensions_citation
129 Dimensions

Readers on

mendeley
181 Mendeley
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Title
Happy software developers solve problems better: psychological measurements in empirical software engineering
Published in
PeerJ, March 2014
DOI 10.7717/peerj.289
Pubmed ID
Authors

Daniel Graziotin, Xiaofeng Wang, Pekka Abrahamsson

X Demographics

X Demographics

The data shown below were collected from the profiles of 312 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 181 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 2%
United States 2 1%
Japan 2 1%
France 1 <1%
Italy 1 <1%
Malaysia 1 <1%
Sweden 1 <1%
Germany 1 <1%
Switzerland 1 <1%
Other 1 <1%
Unknown 167 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 18%
Student > Master 30 17%
Student > Bachelor 20 11%
Researcher 18 10%
Student > Doctoral Student 12 7%
Other 29 16%
Unknown 39 22%
Readers by discipline Count As %
Computer Science 71 39%
Psychology 16 9%
Business, Management and Accounting 12 7%
Engineering 10 6%
Economics, Econometrics and Finance 4 2%
Other 20 11%
Unknown 48 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 323. 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 08 April 2024.
All research outputs
#106,898
of 25,923,151 outputs
Outputs from PeerJ
#133
of 15,398 outputs
Outputs of similar age
#812
of 236,239 outputs
Outputs of similar age from PeerJ
#4
of 126 outputs
Altmetric has tracked 25,923,151 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 15,398 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.2. This one has done particularly well, scoring higher than 99% 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 236,239 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 126 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.