Introduction to mediation analysis with structural equation modeling
By Douglas Gunzler, Tian Chen, Pan Wu, Hui Zhang. Shanghai Archives of Psychiatry, 2013
753 citations recorded by the source corpus.
In mediation, we consider an intermediate variable, called the mediator, that helps explain how or why an independent variable influences an outcome. In the context of a treatment study, it is often of great interest to identify and study the mechanisms by which an intervention achieves its effect. By investigating mediational processes that clarify how the treatment achieves the study outcome, not only can we further our understanding of the pathology of the disease and the mechanisms of treatment, but we may also be able to identify alternative, more efficient, intervention strategies. For example, a tobacco prevention program may teach participants how to stop taking smoking breaks at work (the intervention) which changes their social norms about tobacco use (the intermediate mediator) and subsequently leads to a reduction in smoking behavior (study outcome).
Subfields: Clinical Psychology, Sociology and Political Science, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/7f8eee2e72bcc597e589237281e49c43eb2ddf59
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Citations in the corpus
- Improving employee productivity through work engagement: Evidence from higher education sector (2016, methodology): “Additionally, SEM is the preferred method to test causal relationships between any two or more variables making it easy to articulate research hypothesis (Gunzler et al., 2013).”
- Students' Entrepreneurial Intentions: The Role of Prior Learning Experiences and Emotional, Social, and Cognitive Competencies* (2018, methodology): “We carried out a mediation analysis (Gunzler et al. 2013) for testing H5, and the proposed fully mediated model was tested against partially mediated and nonmediated models (Table 6).”