Maria Antonella Coman - Academia.edu (original) (raw)

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Papers by Maria Antonella Coman

Research paper thumbnail of A Review of Graphical Approaches to Common Statistical Analyses. The Omnipresence of Latent Variables in Statistics

We provide a comprehensive review of simple and advanced statistical analyses using an intuitive ... more We provide a comprehensive review of simple and advanced statistical analyses using an intuitive visual approach explicitly modeling Latent Variables (LV). This method can better illuminate what is assumed in each analytical method and what is actually estimated, by translating the causal relationships embedded in the graphical models in equation form. We recommend the graphical display rooted in the century old
path analysis, that details all parameters of each statistical model, and suggest labeling that clarifies what is given vs. what is estimated.
We link in the process classical and modern analyses under the
encompassing broader umbrella of Generalized Latent Variable
Modeling, and demonstrate that LVs are omnipresent in all statistical
approaches, yet until directly ‘seeing’ them in visual graphical displays, they are unnecessarily overlooked. The advantages of directly modeling LVs are shown with examples of analyses from the Active8 intervention designed to increase physical activity.

Research paper thumbnail of A Review of Graphical Approaches to Common Statistical Analyses. The Omnipresence of Latent Variables in Statistics

We provide a comprehensive review of simple and advanced statistical analyses using an intuitive ... more We provide a comprehensive review of simple and advanced statistical analyses using an intuitive visual approach explicitly modeling Latent Variables (LV). This method can better illuminate what is assumed in each analytical method and what is actually estimated, by translating the causal relationships embedded in the graphical models in equation form. We recommend the graphical display rooted in the century old
path analysis, that details all parameters of each statistical model, and suggest labeling that clarifies what is given vs. what is estimated.
We link in the process classical and modern analyses under the
encompassing broader umbrella of Generalized Latent Variable
Modeling, and demonstrate that LVs are omnipresent in all statistical
approaches, yet until directly ‘seeing’ them in visual graphical displays, they are unnecessarily overlooked. The advantages of directly modeling LVs are shown with examples of analyses from the Active8 intervention designed to increase physical activity.

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