Title |
Robust Statistical Methods for Empirical Software Engineering
|
---|---|
Published in |
Empirical Software Engineering, June 2016
|
DOI | 10.1007/s10664-016-9437-5 |
Authors |
Barbara Kitchenham, Lech Madeyski, David Budgen, Jacky Keung, Pearl Brereton, Stuart Charters, Shirley Gibbs, Amnart Pohthong |
X Demographics
The data shown below were collected from the profiles of 18 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 | 3 | 17% |
Canada | 1 | 6% |
United Kingdom | 1 | 6% |
Sweden | 1 | 6% |
Netherlands | 1 | 6% |
Australia | 1 | 6% |
Japan | 1 | 6% |
India | 1 | 6% |
Brazil | 1 | 6% |
Other | 1 | 6% |
Unknown | 6 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 11 | 61% |
Members of the public | 5 | 28% |
Science communicators (journalists, bloggers, editors) | 2 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 201 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Brazil | 2 | <1% |
Spain | 1 | <1% |
United States | 1 | <1% |
Chile | 1 | <1% |
Unknown | 196 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 40 | 20% |
Student > Master | 38 | 19% |
Professor | 16 | 8% |
Professor > Associate Professor | 15 | 7% |
Student > Doctoral Student | 13 | 6% |
Other | 48 | 24% |
Unknown | 31 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 130 | 65% |
Engineering | 11 | 5% |
Business, Management and Accounting | 3 | 1% |
Mathematics | 2 | <1% |
Agricultural and Biological Sciences | 2 | <1% |
Other | 11 | 5% |
Unknown | 42 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 24 July 2019.
All research outputs
#3,034,732
of 26,017,215 outputs
Outputs from Empirical Software Engineering
#59
of 803 outputs
Outputs of similar age
#50,176
of 357,280 outputs
Outputs of similar age from Empirical Software Engineering
#1
of 11 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 803 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 92% 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 357,280 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 85% of its contemporaries.
We're also able to compare this research output to 11 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 90% of its contemporaries.