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:
ISBN: | 9781032220086 |
Publication date: | 27th May 2024 |
Author: | Qingzhao Yu, Bin Li |
Publisher: | Chapman & Hall/CRC an imprint of CRC Press |
Format: | Paperback |
Pagination: | 294 pages |
Series: | Chapman & Hall/CRC Biostatistics Series |
Genres: |
Probability and statistics |