2018
DOI: 10.31234/osf.io/j65th
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Some Reflections on Combining Meta-Analysis and Structural Equation Modeling

Abstract: Meta-analysis and structural equation modeling (SEM) are two of the most prominent statistical techniques employed in the behavioral, medical, and social sciences. They each have their own well-established research communities, terminologies, statistical models, software packages, and journals (Research Synthesis Methods and Structural Equation Modeling: A Multidisciplinary Journal). In this paper, I will provide some personal reflections on combining meta-analysis and SEM in the forms of meta-analytic SEM (MA… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
33
0
4

Year Published

2022
2022
2024
2024

Publication Types

Select...
6
1

Relationship

0
7

Authors

Journals

citations
Cited by 19 publications
(37 citation statements)
references
References 48 publications
0
33
0
4
Order By: Relevance
“…Therefore, the dataset contained some repeated data entries from the same study, and corresponding multiple eCO 2 or drought treatments with the same aCO 2 or well-watered treatments. The non-independent observations were tackled using the 'shifting the unit of analysis' approach (Cheung, 2015;Liang et al, 2020) in Section 2.3. In our database, species were categorized by photosynthetic pathways (C 3 plant and C 4 plant, C 3 herb and C 4 herb, C 3 grass and C 4 grass, and C 3 crop and C 4 crop) and plant growth forms (woody plant and C 3 herb, tree and shrub, and C 3 grass and C 3 forb).…”
Section: Literature Searching and Data Compilingmentioning
confidence: 99%
See 1 more Smart Citation
“…Therefore, the dataset contained some repeated data entries from the same study, and corresponding multiple eCO 2 or drought treatments with the same aCO 2 or well-watered treatments. The non-independent observations were tackled using the 'shifting the unit of analysis' approach (Cheung, 2015;Liang et al, 2020) in Section 2.3. In our database, species were categorized by photosynthetic pathways (C 3 plant and C 4 plant, C 3 herb and C 4 herb, C 3 grass and C 4 grass, and C 3 crop and C 4 crop) and plant growth forms (woody plant and C 3 herb, tree and shrub, and C 3 grass and C 3 forb).…”
Section: Literature Searching and Data Compilingmentioning
confidence: 99%
“…The 'shifting the unit of analysis' approach (Cheung, 2015) was used to tackle the non-independent observations described above. The initial weight (w) of each observation was calculated as:…”
Section: Independence and Weightsmentioning
confidence: 99%
“…Hence, we used multivariate meta-analysis models to take into account the dependencies that result from including multiple outcome variables measured in the same sample (e.g., Cheung, 2019;Riley, 2009). We applied the Fisher's z-transformation to the correlation coefficients to obtain an effect size measure that is normally distributed, which is a requirement for the multivariate meta-analysis model (e.g., Cheung, 2015). After the analysis, we back-transformed the Fisher's z scores into correlation coefficients and reported the correlation coefficients in the Results section.…”
Section: Discussionmentioning
confidence: 99%
“…However, this was not feasible in our meta-analysis, because many of the relevant effect sizes were not reported in most included studies. Similarly, we could not use meta-analytic structural equation modeling (e.g., Cheung, 2015) because a large portion of the relevant data points were missing values. Estimating causal effects and fitting complex structural equation models is more feasible in a primary study or a mega-analysis (i.e., the pooling of raw data from multiple studies; e.g., Eisenhauer, 2021) than in a meta-analysis.…”
Section: Limitations and Future Directionsmentioning
confidence: 99%
“…Also, three-level mixed-effects models were fitted to explore the relationship between the effect sizes and a group of potentially moderator variables. Pseudo-R 2 estimations (Cheung, 2015) were performed for each parameter and variance component through the equations,…”
Section: Data Extractionmentioning
confidence: 99%