| Title |
Studying Software Engineers: Data Collection Techniques for Software Field Studies
|
|---|---|
| Published in |
Empirical Software Engineering, July 2005
|
| DOI | 10.1007/s10664-005-1290-x |
| Authors |
Timothy C. Lethbridge, Susan Elliott Sim, Janice Singer |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 459 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 6 | 1% |
| Germany | 4 | <1% |
| United Kingdom | 4 | <1% |
| Canada | 4 | <1% |
| Finland | 4 | <1% |
| Sweden | 3 | <1% |
| Austria | 2 | <1% |
| Ireland | 2 | <1% |
| Portugal | 2 | <1% |
| Other | 12 | 3% |
| Unknown | 416 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 105 | 23% |
| Student > Master | 88 | 19% |
| Student > Bachelor | 34 | 7% |
| Researcher | 31 | 7% |
| Student > Doctoral Student | 28 | 6% |
| Other | 91 | 20% |
| Unknown | 82 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 262 | 57% |
| Engineering | 33 | 7% |
| Business, Management and Accounting | 23 | 5% |
| Social Sciences | 20 | 4% |
| Design | 5 | 1% |
| Other | 16 | 3% |
| Unknown | 100 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 May 2020.
All research outputs
#20,567,353
of 26,017,215 outputs
Outputs from Empirical Software Engineering
#631
of 804 outputs
Outputs of similar age
#66,232
of 70,617 outputs
Outputs of similar age from Empirical Software Engineering
#2
of 2 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 804 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 17th percentile – i.e., 17% of its peers scored the same or lower than it.
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 70,617 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.