| 期刊全称 | Applied Multiple Imputation | | 期刊简称 | Advantages, Pitfalls | | 影响因子2023 | Kristian Kleinke,Jost Reinecke,Martin Spiess | | 视频video | | | 发行地址 | Provides an introduction to missing data and multiple imputation for students and applied researchers.Features numerous step-by-step tutorials in R with supplementary R code and data sets.Discusses th | | 学科分类 | Statistics for Social and Behavioral Sciences | | 图书封面 |  | | 影响因子 | This book explores missing data techniques and provides a detailed and easy-to-read introduction to multiple imputation, covering the theoretical aspects of the topic and offering hands-on help with the implementation. It discusses the pros and cons of various techniques and concepts, including multiple imputation quality diagnostics, an important topic for practitioners. It also presents current research and new, practically relevant developments in the field, and demonstrates the use of recent multiple imputation techniques designed for situations where distributional assumptions of the classical multiple imputation solutions are violated. In addition, the book features numerous practical tutorials for widely used R software packages to generate multiple imputations (norm, pan and mice). The provided R code and data sets allow readers to reproduce all the examples and enhance their understanding of the procedures. This book is intended for social and health scientists and other quantitative researchers who analyze incompletely observed data sets, as well as master’s and PhD students with a sound basic knowledge of statistics. . | | Pindex | Textbook 2020 |
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发表于 2025-3-21 18:36:30
