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The data was subjected to multi-group analyzes with structural equations modeling.
These were then analysed using two scientific tools: concept mapping and structural equations modeling.
We use structural equations modeling to test 10 major hypotheses and more than two third of the 28 subhypotheses are supported.
Second, we used the averaged effects to complete a correlation matrix, which was used to test the cross-lagged effects between BU and WE, using structural equations modeling.
Soil microbial community composition was characterized using phospholipid fatty acid (PLFA) analysis, and explicitly linked to environmental data using structural equations modeling (SEM).
A correlational study conducted in an academic software users' CoP and involving structural equations modeling validates the model, suggesting thus a research line that is worth further pursuing.
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Data were analyzed using structural equation modeling.
Data were analyzed through structural equation modeling.
Structural equation modeling analysis supported these hypotheses.
Hypotheses are tested using structural equation modeling.
Research model was empirically examined using structural equation modeling.
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