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- All Subjects: Structural equation modeling
- Creators: Thompson, Marilyn
- Creators: Cham, Hei Ning
- Creators: Dolby, Greer A.
- Member of: ASU Electronic Theses and Dissertations
In terms of the realized values, the behavior of the CCC and R2 functions were generally consistent with prior research. However, as diagnostics, these functions were extremely conservative even when some aspect of the data was unaccounted for. In contrast, the conditional SGDDM (SGDDMC), LR, and LRT were generally sensitive to the underspecifications investigated in this work on all outcomes considered. Although the proportions of extreme PPP-values for these functions tended to increase in null situations for non-normal data, this behavior may have reflected the true misfit that resulted from the specification of normal prior distributions. Importantly, the LR and the SGDDMC to a greater extent exhibited some potential for untangling the sources of data-model misfit. Owing to connections of growth curve models to the more fundamental frameworks of multilevel modeling, structural equation models with a mean structure, and Bayesian hierarchical models, the results of the current work may have broader implications that warrant further research.
Structural Equation Modeling (SEM) is a multivariate analysis methodology that could potentially be utilized to examine the barrier effect that river systems have on genetic differentiation. In this project, river systems are split into the variables of Daily Average Discharge, Average River Width, and Seasonality measurements and regressed onto the genetic differentiation, measured as Fst. This data was collected from the USGS database (U.S. Geological Survey, 2020), sequencing files from differing literature, or Google Earth measurements. Different Structural Equation Modeling models are used to model different system structures as well as compare it to more traditional methodologies like Generalized Linear Modeling and Generalized Linear Mixed Modeling. Ultimately results were limited by the small sample size, however, interesting patterns still emerged from the models. The SE models indicate that Discharge plays a primary role in the genetic differentiation of adjacent river populations. In addition to this, the results demonstrate how quantification of indirect effects, particularly those relating to discharge, give more informative interpretations than traditional multivariate statistics alone. These findings prompt further investigations into this potential methodology.