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Mendeley readers
Attention Score in Context
| Title |
Recent developments in maximum likelihood estimation of MTMM models for categorical data
|
|---|---|
| Published in |
Frontiers in Psychology, April 2014
|
| DOI | 10.3389/fpsyg.2014.00269 |
| Pubmed ID | |
| Authors |
Minjeong Jeon, Frank Rijmen |
| Abstract |
Maximum likelihood (ML) estimation of categorical multitrait-multimethod (MTMM) data is challenging because the likelihood involves high-dimensional integrals over the crossed method and trait factors, with no known closed-form solution. The purpose of the study is to introduce three newly developed ML methods that are eligible for estimating MTMM models with categorical responses: Variational maximization-maximization (e.g., Rijmen and Jeon, 2013), alternating imputation posterior (e.g., Cho and Rabe-Hesketh, 2011), and Monte Carlo local likelihood (e.g., Jeon et al., under revision). Each method is briefly described and its applicability for MTMM models with categorical data are discussed. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 16 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Lecturer | 2 | 13% |
| Student > Doctoral Student | 2 | 13% |
| Student > Ph. D. Student | 2 | 13% |
| Professor | 2 | 13% |
| Student > Master | 2 | 13% |
| Other | 2 | 13% |
| Unknown | 4 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 5 | 31% |
| Computer Science | 2 | 13% |
| Business, Management and Accounting | 1 | 6% |
| Economics, Econometrics and Finance | 1 | 6% |
| Agricultural and Biological Sciences | 1 | 6% |
| Other | 2 | 13% |
| Unknown | 4 | 25% |
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 08 April 2014.
All research outputs
#17,719,424
of 22,753,345 outputs
Outputs from Frontiers in Psychology
#20,318
of 29,641 outputs
Outputs of similar age
#157,893
of 228,038 outputs
Outputs of similar age from Frontiers in Psychology
#216
of 281 outputs
Altmetric has tracked 22,753,345 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 29,641 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one is in the 25th percentile – i.e., 25% 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 228,038 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 281 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.