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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Comparison of the binary logistic and skewed logistic (Scobit) models of injury severity in motor vehicle collisions
|
|---|---|
| Published in |
Accident Analysis & Prevention, December 2015
|
| DOI | 10.1016/j.aap.2015.12.009 |
| Pubmed ID | |
| Authors |
Richard Tay |
| Abstract |
The binary logistic model has been extensively used to analyze traffic collision and injury data where the outcome of interest has two categories. However, the assumption of a symmetric distribution may not be a desirable property in some cases, especially when there is a significant imbalance in the two categories of outcome. This study compares the standard binary logistic model with the skewed logistic model in two cases in which the symmetry assumption is violated in one but not the other case. The differences in the estimates, and thus the marginal effects obtained, are significant when the assumption of symmetry is violated. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 3 | 50% |
| Unknown | 3 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 67% |
| Scientists | 1 | 17% |
| Practitioners (doctors, other healthcare professionals) | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 37 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 16% |
| Student > Master | 6 | 16% |
| Researcher | 4 | 11% |
| Student > Bachelor | 3 | 8% |
| Student > Doctoral Student | 2 | 5% |
| Other | 6 | 16% |
| Unknown | 10 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 17 | 46% |
| Computer Science | 2 | 5% |
| Business, Management and Accounting | 1 | 3% |
| Environmental Science | 1 | 3% |
| Chemical Engineering | 1 | 3% |
| Other | 4 | 11% |
| Unknown | 11 | 30% |
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 20 February 2018.
All research outputs
#6,866,293
of 25,373,627 outputs
Outputs from Accident Analysis & Prevention
#1,305
of 4,178 outputs
Outputs of similar age
#98,395
of 396,423 outputs
Outputs of similar age from Accident Analysis & Prevention
#25
of 93 outputs
Altmetric has tracked 25,373,627 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 4,178 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. This one has gotten more attention than average, scoring higher than 68% 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 396,423 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 93 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 73% of its contemporaries.