Third-variable effect refers to the effect transmitted by third-variables that intervene in the relationship between an exposure and a response variable. Differentiating between the indirect effect of individual factors from multiple third-variables is a constant problem for modern researchers.



Third-variable effect refers to the effect transmitted by third-variables that intervene in the relationship between an exposure and a response variable. Differentiating between the indirect effect of individual factors from multiple third-variables is a constant problem for modern researchers.
Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SASintroduces general definitions of third-variable effects that are adaptable to all different types of response (categorical or continuous), exposure, or third-variables. Using this method, multiple third- variables of different types can be considered simultaneously, and the indirect effect carried by individual third-variables can be separated from the total effect. Readers of all disciplines familiar with introductory statistics will find this a valuable resource for analysis.
Key Features

• Parametric and nonparametric method in third variable analysis
  
• Multivariate and Multiple third-variable effect analysis
  
• Multilevel mediation/confounding analysis
  
• Third-variable effect analysis with high-dimensional data Moderation/Interaction effect analysis within the third-variable analysis
  
• R packages and SAS macros to implement methods proposed in the book
  

RapidGator


NitroFlare