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Attention Score in Context
| Title |
Model Evaluation in Generalized Structured Component Analysis Using Confirmatory Tetrad Analysis
|
|---|---|
| Published in |
Frontiers in Psychology, May 2017
|
| DOI | 10.3389/fpsyg.2017.00916 |
| Pubmed ID | |
| Authors |
Ji Hoon Ryoo, Heungsun Hwang |
| Abstract |
Generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling (SEM). GSCA regards weighted composites or components of indicators as proxies for latent variables and estimates model parameter via least squares without resorting to a distributional assumption such as multivariate normality of indicators. As with other SEM approaches, model evaluation is a crucial procedure in GSCA that is used to examine whether a hypothesized model is consistent with the data in hand. However, the few descriptive measures of model evaluation available for GSCA are limited to evaluating models in a more confirmatory manner. This study integrates confirmatory tetrad analysis (CTA) into GSCA for model evaluation or comparison. Although CTA has been used in factor-based SEM as an inferential statistic, CTA is actually more compatible with GSCA because it is completely free of the multivariate normality assumption. Utilizing empirical data collected for 18,174 students' social skills in an early childhood longitudinal study of 2010-11 kindergarten cohort, we demonstrate the capability and applicability of CTA in GSCA and compare its performance with existing measures for GSCA. |
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 % |
|---|---|---|
| Switzerland | 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 27 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 19% |
| Student > Master | 4 | 15% |
| Researcher | 2 | 7% |
| Lecturer | 1 | 4% |
| Student > Doctoral Student | 1 | 4% |
| Other | 4 | 15% |
| Unknown | 10 | 37% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 4 | 15% |
| Social Sciences | 3 | 11% |
| Computer Science | 2 | 7% |
| Business, Management and Accounting | 1 | 4% |
| Economics, Econometrics and Finance | 1 | 4% |
| Other | 3 | 11% |
| Unknown | 13 | 48% |
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 30 May 2017.
All research outputs
#20,425,762
of 22,977,819 outputs
Outputs from Frontiers in Psychology
#24,329
of 30,147 outputs
Outputs of similar age
#275,210
of 316,100 outputs
Outputs of similar age from Frontiers in Psychology
#550
of 607 outputs
Altmetric has tracked 22,977,819 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 30,147 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 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 607 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.