{"product_id":"statistical-methods-for-mediation-confounding-and-moderation-analysis-using-r-and-sas-paperback","title":"Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eQingzhao Yu\u003c\/b\u003e (Author), \u003cb\u003eBin Li\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThird-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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003ci\u003eStatistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS \u003c\/i\u003e\u003c\/b\u003eintroduces 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.\u003c\/p\u003e\u003cp\u003eKey Features: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eParametric and nonparametric method in third variable analysis\u003c\/li\u003e \u003cli\u003eMultivariate and Multiple third-variable effect analysis\u003c\/li\u003e \u003cli\u003eMultilevel mediation\/confounding analysis\u003c\/li\u003e \u003cli\u003eThird-variable effect analysis with high-dimensional data Moderation\/Interaction effect analysis within the third-variable analysis\u003c\/li\u003e \u003cli\u003eR packages and SAS macros to implement methods proposed in the book\u003c\/li\u003e \u003c\/ul\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eQingzhao Yu is Professor in Biostatistics, Louisiana State University Health Sciences Center.\u003c\/p\u003e\u003cp\u003eBin Li is Associate Professor in Statistics, Louisiana State University.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 294\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.62 x 9.21 x 6.14 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e May 27, 2024\u003c\/div\u003e\n            ","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":52616437203226,"sku":"9781032220086","price":106.9,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1005\/5604\/6618\/files\/lKuljLsrjL9781032220086.webp?v=1785458778","url":"https:\/\/j-s-five-and-dime.myshopify.com\/products\/statistical-methods-for-mediation-confounding-and-moderation-analysis-using-r-and-sas-paperback","provider":"J \u0026 S Five and Dime","version":"1.0","type":"link"}