mathematics > probability & statistics / multivariate analysis
Structural Equation Modeling and Natural Systems
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This book presents an introduction to the methodology of structural equation modeling, illustrates its use, and goes on to argue that it has revolutionary implications for the study of natural systems. A major theme of this book is that we have, up to this point, attempted to study systems primarily using methods (such as the univariate model) that were designed only for considering individual processes. Understanding systems requires the capacity to examine simultaneous influences and responses. Structural equation modeling (SEM) has such capabilities. It also possesses many other traits that add strength to its utility as a means of making scientific progress. In light of the capabilities of SEM, it can be argued that much of ecological theory is currently locked in an immature state that impairs its relevance. It is further argued that the principles of SEM are capable of leading to the development and evaluation of multivariate theories of the sort vitally needed for the conservation of natural systems. Supplementary information can be found at the authors website, accessible via www.cambridge.org/9780521837422. - Details why multivariate analyses should be used to study ecological systems - Exposes unappreciated weakness in many current popular analyses - Emphasises the future methodological developments needed to advance our understanding of ecological systems
Grace, James B.: - James B. 'Jim' Grace obtained his Bachelor of Science degree from Presbyterian College, his Master's of Science degree from Clemson University, and his Ph.D. from Michigan State University. He served on the faculty at the University of Arkansas and later at Louisiana State University, where he reached the rank of Professor. He has, for the past several years, worked at the US Geological Survey's National Wetlands Research Center in Lafayette, Louisiana, USA where he is a Senior Research Ecologist. He holds an Adjunct Professorship at the University of Louisiana in the Biology Department.
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