[Télécharger] Applied Missing Data Analysis (Methodology in the Social Sciences) (English Edition) de Craig K. Enders Pdf Ebook
Télécharger Applied Missing Data Analysis (Methodology in the Social Sciences) (English Edition) de Craig K. Enders Pdf Epub
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Auteur : Craig K. Enders
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Langue : Français, Anglais
Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and models for handling missing not at random (MNAR) data. Easy-to-follow examples and small simulated data sets illustrate the techniques and clarify the underlying principles. The companion website (www.appliedmissingdata.com) includes data files and syntax for the examples in the book as well as up-to-date information on software. The book is accessible to substantive researchers while providing a level of detail that will satisfy quantitative specialists.
Télécharger Applied Missing Data Analysis (Methodology in the Social Sciences) (English Edition) de Craig K. Enders En Ligne
Applied Missing Data Analysis. Methodology in the Social ~ Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and models for handling missing not at random (MNAR) data.
Applied missing data analysis (eBook, 2010) [WorldCat] ~ Methodology in the social sciences. Edition/Format: eBook: Document : EnglishView all editions and formats: Summary: Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum .
Applied Missing Data Analysis (Methodology in the Social ~ The author successfully achieved the goal of helping the reader to become familiar with basic concepts in missing data analysis procedures, and to feel comfortable using these procedures in a variety of practical and social science applications. It contains very useful examples and illustrations in the applied social sciences. In addition, those example and illustration datasets and detailed .
Applied Missing Data Analysis (Methodology in the Social ~ This book is a refreshing addition to the literature for applied social researchers and graduate students doing quantitative data analysis. It covers the full range of state-of-the-art methods of handling missing data in a clear and accessible manner, making it an excellent supplement or text for a graduate course on advanced, but widely used, statistical methods."--David R. Johnson, PhD .
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Data Analysis for the Social Sciences / SAGE Publications Ltd ~ Gathering data is the easy part of the empirical research process but often students do not think carefully enough about the analysis of their data before they start to gather it. This book gives clear guidance on the methodology and process of data analysis giving clear and concise approaches to data analysis methods and tools. A very useful addition to the methodological bookshelf.
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Statistical Analysis with Missing Data, Third Edition ~ Statistical Analysis with Missing Data, Third Edition starts by introducing readers to the subject and approaches toward solving it. It looks at the patterns and mechanisms that create the missing data, as well as a taxonomy of missing data. It then goes on to examine missing data in experiments, before discussing complete-case and available-case analysis, including weighting methods. The new .
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Introduction to Quantitative Data Analysis in the ~ Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences presents a clear and accessible introduction to the basics of quantitative data analysis and focuses on how to use statistical tests as a key tool for analyzing research data. The book presents the entire data analysis process as a cyclical, multiphase process and addresses the processes of exploratory analysis .
The Analysis of Social Science Data with Missing Values ~ Methods for handling missing data in social science data sets are reviewed. Limitations of common practical approaches, including complete-case analysis, available-case analysis and imputation, are illustrated on a simple missing-data problem with one complete and one incomplete variable. Two more principled approaches, namely maximum likelihood under a model for the data and missing-data .
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Missing Data Analysis in Practice - 1st Edition ~ Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. Drawing on his 25 years of experience researching, teaching, and consulting in quantitative areas, the author presents both frequentist and Bayesian perspectives. He describes easy-to-implement approaches, the underlying .
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Applied Missing Data Analysis Complete - video dailymotion ~ Easy-to-follow examples and small simulated data sets illustrate the techniques and clarify the underlying principles. The companion website (www.appliedmissingdata) includes data files and syntax for the examples in the book as well as up-to-date information on software. The book is accessible to substantive researchers while providing a level of detail that will satisfy quantitative .
Analyzing Social Science Data / SAGE Publications Ltd ~ Analyzing Social Science Data. guides students in: problems with the initial data; problems with the initial variables; how to handle too much data; how to generalize; problems of analyzing single variables; problems examining bivariate relationships; and problems examining multivariate relationships. The book is a tour de force in making data analysis manageable and rewarding for today's .
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