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商品詳細
Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS.
・ISBN 978-1-032-22008-6 paper GB£ 50.99
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| 著者・編者 | Yu, Qingzhao / Li, Bin, |
|---|---|
| シリーズ | Chapman & Hall/CRC Biostatistics Series |
| 出版社 | (Chapman & Hall/CRC, UK) |
| 出版年月 | 2024 |
| ページ数 | 294 pp. |
| 言語 | ENG |
| ニュース番号 | ＜M25-6533＞ |
解説
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