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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
| Autorius: | Qingzhao Yu, Bin Li, |
| Leidėjas: | Chapman and Hall/CRC |
| Išleidimo metai: | 2022 |
| Knygos puslapių skaičius: | 294 |
| ISBN-10: | 0367365472 |
| ISBN-13: | 9780367365479 |
| Formatas: | 240 x 161 x 20 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
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