Structural Equation Modeling: Is it Still Worth Learning?
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Abstract
Objective: this article aims to exemplify critical reading, rereading, and estimation of an alternative model using the summary data and covariance-based structural equation modeling (CB-SEM). Methods: we selected an article from a highly ranked journal that confirmed the importance of three out of eight antecedents of team performance. We then reanalyzed the article in three steps. Results: in step 1, we identified relationships that were not tested, or that were not significant, possibly due to multicollinearity (VIF > 3). In step 2, the correlation matrix indicated a discriminant validity issue and values that should have confirmed all hypotheses. In step 3, an alternative model was tested using CB-SEM and confirmed all hypotheses, with one of them as an indirect effect. Conclusions: structural equation modeling (SEM) is not just a statistical method for analyzing covariance structures. It is also a way of thinking about research and theory-building that involves abstract concepts and the imagination of ways to operationalize these constructs so that empirical research becomes possible.
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