Structural Equation Modeling For Travel Behavior Research

By T. Golob. 2001

1,095 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8c072bab426658af5f131634f56dd3c76f074163

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In Transportation

Citations in the corpus

Structural equation modelling and regression analysis in tourism research (2012, background): “SEM also allows for the modelling of mediating variables (Golob, 2003).”
Self‐Selection: A Key to a Better Understanding of Location Choices, Travel Behaviour and Transport Externalities? (2009, background): “The advantages of SEM are nowadays generally recognized—see Golob ( 2003 ) for a general overview of SEM applications in transport, or Mokhtarian and Cao ( 2008 ) for the application of SEM for research into residential self‐selection.”
Do satisfying walking and cycling trips result in more future trips with active travel modes? An exploratory study (2019, background): “SEM makes it possible to examine multiple relationships within a set of variables in which a given variable can be outcome (dependent variable) in one set of relationships and simultaneously predictor of outcomes (explanatory variable) in other relationships, making it possible to quantify direct an”
Intentions to use bike-sharing for holiday cycling: An application of the Theory of Planned Behavior (2015, methodology): “This methodology and its application in travel behavior research in the last three decades were reviewed by Golob (2003).”
Impact of the Perceived Image of Celebrity Endorsers on Tourists’ Intentions to Visit (2014, background): “…to improve model fit were not conducted, because data-driven model modifications tend to capitalize on chance and features of the sample, making it less likely that the model will be generalizable to other samples (Golob 2003; MacCallum, Rosnowski, and Necowitz 1992; Silvia and MacCallum 1988).”