Document Type
Article
Disciplines
Medicine and Health Sciences
Abstract
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 ActiveS intervention designed to increase physical activity.
Recommended Citation
Coman, Emil N.; Fifield, Judith; and Coman, Maria A., "A Review of Graphical Approaches to Common Statistical Analyses : The Omnipresence of Latent Variables in Statistics" (2015). UCHC Articles - Research. 295.
https://digitalcommons.lib.uconn.edu/uchcres_articles/295
Comments
Int J Clin Biostat Biom. Author manuscript; available in PMC 2015 Dec 16. Published in final edited form as: Int J Clin Biostat Biom. 2015; 1(1): 1–9. PMCID: PMC4680982 NIHMSID: NIHMS732009 This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.