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Applied Missing Data Analysis
 
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Applied Missing Data Analysis [Print Replica] [Format Kindle]

Craig K. Enders

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Présentation de l'éditeur

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.

Biographie de l'auteur

Craig K. Enders, Department of Psychology, Arizona State University, Tempe, USA


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Amazon.com: 5.0 étoiles sur 5  5 commentaires
4 internautes sur 4 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Very helpful 14 avril 2012
Par Dimitri Shvorob - Publié sur Amazon.com
Format:Relié
High marks to an accessible, effective and just nice-to-look-at book - with a supporting website to boot. I suppose its main competitor is the SAGE-series pamphlet by Paul Allison; a nice concise overview of the field, it is however not a viable alternative to "Applied missing data analysis" if you want to get your hands dirty. Readers with econometrics background, be forewarned about relatively brief coverage of MNAR models. More generally, you will need to go to other references for information about specific missing-data models in specific contexts (duh), but as a substantial generalist introduction, this one is hard to beat.
2 internautes sur 2 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Indispensable 12 septembre 2013
Par Adam B. Cohen - Publié sur Amazon.com
Format:Relié
This is an indispensable book for anyone doing empirical research. So useful - I misplaced the book, but then used FIML, and it reappeared.
1 internautes sur 1 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Excellent Comprehensive Source on Missing Data Analyses 21 octobre 2011
Par Researching Gal - Publié sur Amazon.com
Format:Relié
This is an excellent book that provides a well written comprehensive overview of missing data analyses. The book is clearly written and examples are easy to follow. Since missing data is such an universal problem, anyone conducting statistical analyses should read this book.
5.0 étoiles sur 5 Great book 19 juin 2014
Par Hamed Farshbaf Dadgour - Publié sur Amazon.com
Format:Relié|Achat vérifié
I have been working on a project at work that requires me to handle missing data. I did sone initial research and bought this book based on the good Amazon reviews. I very much liked the book and its approach. Very clear problem statements, explanations and applications in real-world problems.
1 internautes sur 2 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Where could I find the missing data? Applied missing data analysis by Enders, 2010 27 septembre 2013
Par Dr. Gabriel Liberman - Publié sur Amazon.com
Format:Relié|Achat vérifié
I recently dived into problems of missing data. Intuitively, data analysts would be suspicious about missing data and many times would prefer to delete these records as whole, e.g. respondents to longitudinal surveys respond to the first two repeats but not to the third. The questions is what is better, to run more robust likelihood functions (full information maximum likelihood) which tend not to converge, or to impute missing data based on the data themselves? Enders takes us along the process of the later option – how to identify missing data and why to consider replacement, what are the better techniques to impute missing values for each case we handle? Enders supports his text with many examples and charts that explain the process outcome. He also tests the different suggested procedures by means of simulations and shows the power of each and mainly, how good imputation strategy almost does not bias the original data. On the trade-off between larger sample size and small bias, randomly imputing missing data makes a great difference. Statistical models run the variance/covariance matrix, thus the effect of each record is small, that is why imputations adds just little bias to the final model we use. I use the book heavily in my statistical work and find it helpful every time I face problems of missing data and want to convince the collector of the data that imputations are harmless. Dr. Gabriel Liberman –Data-Graph Statistical Consulting at: www.data-graph.com .
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