(Why) Should We Use SEM? Pros and Cons of Structural Equation Modeling
By Christof Nachtigall, Ulf Kroehne, F. Funke, Rolf Steyer, F. Schiller. 2003
250 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/db5942456d12909877d62cc971395bab251d31e0
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Citations in the corpus
- Structural equation modeling in medical research: a primer (2010, background): “In psychology, for example, the citation frequency of SEM has steadily increased from 164 in 1994 to 343 in 2000 and then to 742 in the last year (based on the citation frequency of SEM and M[ANOVA] of PsychINFO database 1970-2010) [4,5].”
- Expanding the Technology Acceptance Model with the Inclusion of Trust, Social Influence, and Health Valuation to Determine the Predictors of German Users’ Willingness to Continue using a Fitness App: A Structural Equation Modeling Approach (2018, background): “SEM allows the analysis of the dependencies of latent variables, offering the opportunity to analyze the relationships of psychological constructs without measurement errors (Nachtigall, Kroehne, Funke, & Steyer, 2003).”
- Moderating Effects of Tourists’ Novelty-Seeking Tendencies on Destination Image, Visitor Satisfaction, and Short- and Long-Term Revisit Intentions (2013, methodology): “AMOS has become one of the most commonly used SEM software applications to analyze a system of regression equations (Nachtigall et al. 2003) and is becoming increasingly popular in tourism research (see, Hallak, Brown, and Lindsay 2012).”
- Workload, generic and work–family specific social supports and job stress: Mediating role of work–family and family–work conflict (2016, background): “…and job stress D ow nl oa de d by T É L U Q A t 1 3: 00 1 8 Ja nu ar y 20 18 ( PT ) graphical interface is the most user-friendly because it requires essentially the ability “to drag and drop boxes (manifest variables) and eggs (latent variables) on the screen” (Nachtigall et al., 2003, p. 12).”