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Mendeley readers
Attention Score in Context
| Title |
Tuning multiple imputation by predictive mean matching and local residual draws
|
|---|---|
| Published in |
BMC Medical Research Methodology, June 2014
|
| DOI | 10.1186/1471-2288-14-75 |
| Pubmed ID | |
| Authors |
Tim P Morris, Ian R White, Patrick Royston |
| Abstract |
Multiple imputation is a commonly used method for handling incomplete covariates as it can provide valid inference when data are missing at random. This depends on being able to correctly specify the parametric model used to impute missing values, which may be difficult in many realistic settings. Imputation by predictive mean matching (PMM) borrows an observed value from a donor with a similar predictive mean; imputation by local residual draws (LRD) instead borrows the donor's residual. Both methods relax some assumptions of parametric imputation, promising greater robustness when the imputation model is misspecified. |
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 Kingdom | 4 | 80% |
| Unknown | 1 | 20% |
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 277 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | <1% |
| Netherlands | 1 | <1% |
| Vietnam | 1 | <1% |
| Brazil | 1 | <1% |
| South Africa | 1 | <1% |
| Canada | 1 | <1% |
| Spain | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 269 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 63 | 23% |
| Researcher | 40 | 14% |
| Student > Master | 33 | 12% |
| Student > Bachelor | 18 | 6% |
| Student > Doctoral Student | 17 | 6% |
| Other | 44 | 16% |
| Unknown | 62 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 53 | 19% |
| Social Sciences | 23 | 8% |
| Mathematics | 22 | 8% |
| Agricultural and Biological Sciences | 20 | 7% |
| Psychology | 17 | 6% |
| Other | 64 | 23% |
| Unknown | 78 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 February 2023.
All research outputs
#6,194,427
of 25,292,378 outputs
Outputs from BMC Medical Research Methodology
#862
of 2,257 outputs
Outputs of similar age
#53,514
of 234,809 outputs
Outputs of similar age from BMC Medical Research Methodology
#10
of 31 outputs
Altmetric has tracked 25,292,378 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,257 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.4. 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 234,809 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 77% of its contemporaries.
We're also able to compare this research output to 31 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 70% of its contemporaries.