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Titlebook: Missing Data; Analysis and Design John W. Graham Book 2012 Springer Science+Business Media New York 2012 Data analysis.Missing data.Monte C

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發(fā)表于 2025-3-21 17:39:12 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Missing Data
副標(biāo)題Analysis and Design
編輯John W. Graham
視頻videohttp://file.papertrans.cn/635/634780/634780.mp4
概述Enables non-statisticians to implement modern missing data procedures properly in their research.Contains easy-to-read information for readers of all levels.Utilizes an accompanying website.Includes s
叢書(shū)名稱Statistics for Social and Behavioral Sciences
圖書(shū)封面Titlebook: Missing Data; Analysis and Design John W. Graham Book 2012 Springer Science+Business Media New York 2012 Data analysis.Missing data.Monte C
描述.Missing data have long plagued those conducting applied research in the social, behavioral, and health sciences.? Good missing data analysis solutions are available, but practical information about implementation of these solutions has been lacking.? The objective of .Missing Data: Analysis and Design. is to enable investigators who are non-statisticians to implement modern missing data procedures properly in their research, and reap the benefits in terms of improved accuracy and statistical power..?.Missing Data: Analysis and Design. contains essential information for both beginners and advanced readers.? For researchers with limited missing data analysis experience, this book offers an easy-to-read introduction to the theoretical underpinnings of analysis of missing data; provides clear, step-by-step instructions for performing state-of-the-art multiple imputation analyses; and offers practical advice, based on over 20 years‘ experience, for avoiding and troubleshooting problems.? For more advanced readers, unique discussions of attrition, non-Monte-Carlo techniques for simulations involving missing data, evaluation of the benefits of auxiliary variables, and highly cost-effecti
出版日期Book 2012
關(guān)鍵詞Data analysis; Missing data; Monte Carlo; Multilevel data; Multiple imputation
版次1
doihttps://doi.org/10.1007/978-1-4614-4018-5
isbn_softcover978-1-4899-9573-5
isbn_ebook978-1-4614-4018-5Series ISSN 2199-7357 Series E-ISSN 2199-7365
issn_series 2199-7357
copyrightSpringer Science+Business Media New York 2012
The information of publication is updating

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Statistics for Social and Behavioral Scienceshttp://image.papertrans.cn/m/image/634780.jpg
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John W. GrahamEnables non-statisticians to implement modern missing data procedures properly in their research.Contains easy-to-read information for readers of all levels.Utilizes an accompanying website.Includes s
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Book 2012 practical advice, based on over 20 years‘ experience, for avoiding and troubleshooting problems.? For more advanced readers, unique discussions of attrition, non-Monte-Carlo techniques for simulations involving missing data, evaluation of the benefits of auxiliary variables, and highly cost-effecti
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2199-7357 rs of all levels.Utilizes an accompanying website.Includes s.Missing data have long plagued those conducting applied research in the social, behavioral, and health sciences.? Good missing data analysis solutions are available, but practical information about implementation of these solutions has bee
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Book 2012s are available, but practical information about implementation of these solutions has been lacking.? The objective of .Missing Data: Analysis and Design. is to enable investigators who are non-statisticians to implement modern missing data procedures properly in their research, and reap the benefit
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Nonclassical Phase Space Jumps and Optimal Spawningesses from radiationless transitions to electronic spectroscopy. In the classical picture, i.e., Condon approximation, nuclear motion is assumed frozen throughout the duration of electronic transitions. However, as is demonstrated in this chapter, position and momentum jumps can compete in determini
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