Applications of structural equation modeling (SEM) in ecological studies: an updated review

By Yi Fan, Jiquan Chen, Gabriela Shirkey, Ranjeet John, S. Wu, Hogeun Park, Changliang Shao. Ecological Processes, 2016

1,297 citations recorded by the source corpus.

AimsThis review was developed to introduce the essential components and variants of structural equation modeling (SEM), synthesize the common issues in SEM applications, and share our views on SEM’s future in ecological research.MethodsWe searched the Web of Science on SEM applications in ecological studies from 1999 through 2016 and summarized the potential of SEMs, with a special focus on unexplored uses in ecology. We also analyzed and discussed the common issues with SEM applications in previous publications and presented our view for its future applications.ResultsWe searched and found 146 relevant publications on SEM applications in ecological studies. We found that five SEM variants had not commenly been applied in ecology, including the latent growth curve model, Bayesian SEM, partial least square SEM, hierarchical SEM, and variable/model selection. We identified ten common issues in SEM applications including strength of causal assumption, specification of feedback loops, selection of models and variables, identification of models, methods of estimation, explanation of latent variables, selection of fit indices, report of results, estimation of sample size, and the fit of model.ConclusionsIn previous ecological studies, measurements of latent variables, explanations of model parameters, and reports of key statistics were commonly overlooked, while several advanced uses of SEM had been ignored overall. With the increasing availability of data, the use of SEM holds immense potential for ecologists in the future.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a4f69b9a9130397e3c90014f4dd935b9b56df32f

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Citations in the corpus

A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation (2020, methodology): “…used to validate the measurement model and model fit indices such as Chi-square value ( χ 2), degrees of freedom (df), Comparative Fit Index (CFI), Tuckers Lewis Index (TLI) and Root Mean Square Error of Approximation (RMSEA) was used as per the guidelines (Hu and Bentler, 1999; Fan et al., 2016).”
A review of the methodological misconceptions and guidelines related to the application of structural equation modeling: A Malaysian scenario (2017, methodology): “We would recommend some good articles on the subject matter, and they are authored by Hair et al. (2017a), Fan et al. (2016), Green et al. (2016), Brown (2015), Fabrigar and Wegener (2011) and Winter, Dodou, and Wieringa (2009).”