| Title |
Fixed- and random-effects meta-analytic structural equation modeling: Examples and analyses in R
|
|---|---|
| Published in |
Behavior Research Methods, June 2013
|
| DOI | 10.3758/s13428-013-0361-y |
| Pubmed ID | |
| Authors |
Mike W.-L. Cheung |
| Abstract |
Meta-analytic structural equation modeling (MASEM) combines the ideas of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Cheung and Chan (Psychological Methods 10:40-64, 2005b, Structural Equation Modeling 16:28-53, 2009) proposed a two-stage structural equation modeling (TSSEM) approach to conducting MASEM that was based on a fixed-effects model by assuming that all studies have the same population correlation or covariance matrices. The main objective of this article is to extend the TSSEM approach to a random-effects model by the inclusion of study-specific random effects. Another objective is to demonstrate the procedures with two examples using the metaSEM package implemented in the R statistical environment. Issues related to and future directions for MASEM are discussed. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 50% |
| Japan | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 149 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 2% |
| Brazil | 2 | 1% |
| Portugal | 1 | <1% |
| Germany | 1 | <1% |
| Macao | 1 | <1% |
| Russia | 1 | <1% |
| Singapore | 1 | <1% |
| Unknown | 139 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 39 | 26% |
| Student > Master | 17 | 11% |
| Researcher | 14 | 9% |
| Student > Bachelor | 12 | 8% |
| Student > Doctoral Student | 11 | 7% |
| Other | 28 | 19% |
| Unknown | 28 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 51 | 34% |
| Business, Management and Accounting | 20 | 13% |
| Agricultural and Biological Sciences | 10 | 7% |
| Social Sciences | 9 | 6% |
| Medicine and Dentistry | 5 | 3% |
| Other | 18 | 12% |
| Unknown | 36 | 24% |
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 23 September 2016.
All research outputs
#7,205,295
of 25,374,647 outputs
Outputs from Behavior Research Methods
#895
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Outputs of similar age
#58,004
of 208,852 outputs
Outputs of similar age from Behavior Research Methods
#4
of 15 outputs
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We're also able to compare this research output to 15 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.