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

The third-variable effect is 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 introduces general definitions of third-variable effects that are adaptable to all different types of response, exposure, or third-variables. It offers a valuable resource for readers of all disciplines familiar with introductory statistics, with parametric and nonparametric methods, multivariate and multiple third-variable effect analysis, multilevel mediation/confounding analysis, and third-variable effect analysis with high-dimensional data.

Format: Hardback
Length: 278 pages
Publication date: 14 March 2022
Publisher: Taylor & Francis Ltd

The third-variable effect pertains to the influence exerted by external factors that intervene in the association between an exposure and a response variable. Distinguishing between the indirect effect of individual factors and the combined impact of multiple third-variables poses a persistent challenge for contemporary researchers.

Statistical Methods for Mediation, Confounding, and Moderation Analysis Using R and SAS provides comprehensive definitions of third-variable effects that are adaptable to diverse response types (categorical or continuous), exposure, and third-variables. By employing this approach, multiple third-variables of different types can be simultaneously accounted for, enabling the separation of the indirect effect carried by individual third-variables from the overall effect. This book is a valuable resource for scholars from all disciplines with a foundational understanding of introductory statistics, as it offers insightful analysis techniques.

Key Features:
Parametric and Nonparametric Methods in Third Variable Analysis: The book encompasses both parametric and nonparametric methods for third-variable analysis, offering flexibility to researchers depending on their data characteristics and research objectives.

Multivariate and Multiple Third-Variable Effect Analysis: It delves into the analysis of multiple third-variables, allowing researchers to explore the interplay and interactions between these variables and their effects on the outcome.

Multilevel Mediation/Confounding Analysis: The book provides comprehensive coverage of multilevel mediation/confounding analysis, which is crucial for examining the indirect effects of multiple mediators or confounders on the relationship between exposure and response variables.

Third-Variable Effect Analysis with High-Dimensional Data: It addresses the analysis of third-variable effects in high-dimensional data scenarios, where the number of variables exceeds the sample size, presenting challenges in traditional statistical methods.

Moderation/Interaction Effect Analysis within the Third-Variable Analysis: The book explores the moderation/interaction effect analysis within the third-variable framework, shedding light on how third-variables moderate or interact with other variables to influence the relationship between exposure and response.

R Packages and SAS Macros to Implement Methods Proposed in the Book: The book includes R packages and SAS macros that facilitate the implementation of the methods proposed throughout, making it easier for researchers to apply these techniques to their own data sets.

Weight: 574g
Dimension: 163 x 241 x 23 (mm)
ISBN-13: 9780367365479