Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS

Third-variable effect refers to the effect transmitted by third-variables that intervene in the relationship between an exposure and a response variable. Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS provides general definitions of third-variable effects that are adaptable to all different types of response, exposure, or third-variables, allowing for simultaneous consideration of multiple third-variables and separation of the indirect effect carried by individual third-variables from the total effect. It includes parametric and nonparametric methods, multivariate and multiple third-variable effect analysis, multilevel mediation/confounding analysis, third-variable effect analysis with high-dimensional data, and moderation/interaction effect analysis within the third-variable analysis. R packages and SAS macros are provided to implement the proposed methods.

Format: Paperback / softback
Length: 294 pages
Publication date: 27 May 2024
Publisher: Taylor & Francis Ltd

Third-variable effect refers to the impact transmitted by third-variables that interfere with the connection between an exposure and a response variable. Distinguishing between the indirect effect of individual factors from multiple third-variables is a persistent challenge for contemporary researchers. Statistical Methods for Mediation, Confounding, and Moderation Analysis Using R and SAS offers comprehensive definitions of third-variable effects that can be applied to various types of responses, exposures, or third-variables. This approach enables the simultaneous consideration of multiple third-variables of different types, allowing for the separation of the indirect effect carried by individual third-variables from the total effect. Readers from all disciplines with a basic understanding of statistics will find this resource invaluable for analysis.

Key Features:

Parametric and Nonparametric Method in Third Variable Analysis: Statistical Methods for Mediation, Confounding, and Moderation Analysis Using R and SAS provides both parametric and nonparametric methods for third-variable analysis. Parametric methods assume a specific distribution of the data, while nonparametric methods do not make such assumptions.

Multivariate and Multiple Third-Variable Effect Analysis: The book covers multivariate and multiple third-variable effect analysis, which allows for the simultaneous consideration of multiple third-variables and their interactions.

Multilevel Mediation/Confounding Analysis: Multilevel mediation/confounding analysis is used to analyze data with multiple levels of mediation or confounding.

Third-Variable Effect Analysis with High-Dimensional Data: Statistical Methods for Mediation, Confounding, and Moderation Analysis Using R and SAS provides methods for analyzing data with high-dimensional data, which can be challenging due to the large number of variables.

Moderation/Interaction Effect Analysis within the Third-Variable Analysis: Statistical Methods for Mediation, Confounding, and Moderation Analysis Using R and SAS includes methods for analyzing moderation/interaction effects within the third-variable analysis.

R Packages and SAS Macros to Implement Methods Proposed in the Book: The book includes R packages and SAS macros that implement the methods proposed in the book. These packages and macros make it easy for researchers to apply the methods to their data.

In conclusion, Statistical Methods for Mediation, Confounding, and Moderation Analysis Using R and SAS is a valuable resource for researchers who want to analyze third-variable effects. The book offers comprehensive definitions of third-variable effects, provides both parametric and nonparametric methods, covers 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, and includes R packages and SAS macros to implement the methods proposed in the book. Readers from all disciplines with a basic understanding of statistics will find this resource invaluable for analysis.

Weight: 450g
Dimension: 233 x 156 x 21 (mm)
ISBN-13: 9781032220086