Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS (Hardcover) (ISBN-13: 9780367365479)

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 SAS introduces 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


Author: Qingzhao Yu, Bin Li
ISBN-10: 0367365472
ISBN-13: 9780367365479
Publisher: CRC Press
Language: English
Published: 03/21/2022
Pages: 294
Format: Hardcover
Weight: 1.30lbs
Size: 9.21h x 6.14w x 0.69d