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Attention Score in Context
| Title |
Testing strong factorial invariance using three-level structural equation modeling
|
|---|---|
| Published in |
Frontiers in Psychology, July 2014
|
| DOI | 10.3389/fpsyg.2014.00745 |
| Pubmed ID | |
| Authors |
Suzanne Jak |
| Abstract |
Within structural equation modeling, the most prevalent model to investigate measurement bias is the multigroup model. Equal factor loadings and intercepts across groups in a multigroup model represent strong factorial invariance (absence of measurement bias) across groups. Although this approach is possible in principle, it is hardly practical when the number of groups is large or when the group size is relatively small. Jak et al. (2013) showed how strong factorial invariance across large numbers of groups can be tested in a multilevel structural equation modeling framework, by treating group as a random instead of a fixed variable. In the present study, this model is extended for use with three-level data. The proposed method is illustrated with an investigation of strong factorial invariance across 156 school classes and 50 schools in a Dutch dyscalculia test, using three-level structural equation modeling. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 33% |
| Switzerland | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| Netherlands | 1 | 2% |
| Unknown | 45 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 34% |
| Student > Doctoral Student | 7 | 15% |
| Researcher | 6 | 13% |
| Student > Postgraduate | 3 | 6% |
| Professor | 2 | 4% |
| Other | 8 | 17% |
| Unknown | 5 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Social Sciences | 12 | 26% |
| Psychology | 11 | 23% |
| Business, Management and Accounting | 6 | 13% |
| Environmental Science | 3 | 6% |
| Medicine and Dentistry | 3 | 6% |
| Other | 8 | 17% |
| Unknown | 4 | 9% |
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 25 July 2014.
All research outputs
#15,303,056
of 22,758,963 outputs
Outputs from Frontiers in Psychology
#18,587
of 29,672 outputs
Outputs of similar age
#132,402
of 228,769 outputs
Outputs of similar age from Frontiers in Psychology
#298
of 379 outputs
Altmetric has tracked 22,758,963 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 29,672 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 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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We're also able to compare this research output to 379 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.