Statistical Analysis With Missing Data

Statistical Analysis With Missing Data Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Statistical Analysis With Missing Data book. This book definitely worth reading, it is an incredibly well-written.

Statistical Analysis with Missing Data

Roderick J. A. Little,Donald B. Rubin
Statistical Analysis with Missing Data Book PDF

Author : Roderick J. A. Little,Donald B. Rubin
Publisher : John Wiley & Sons
Page : 462 pages
File Size : 43,9 Mb
Release : 2019-04-23
Category : Mathematics
ISBN : 9780470526798

Get Book

Statistical Analysis with Missing Data by Roderick J. A. Little,Donald B. Rubin Pdf

An up-to-date, comprehensive treatment of a classic text on missing data in statistics The topic of missing data has gained considerable attention in recent decades. This new edition by two acknowledged experts on the subject offers an up-to-date account of practical methodology for handling missing data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism, and then they apply the theory to a wide range of important missing data problems. 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 edition expands its coverage to include recent work on topics such as nonresponse in sample surveys, causal inference, diagnostic methods, and sensitivity analysis, among a host of other topics. An updated “classic” written by renowned authorities on the subject Features over 150 exercises (including many new ones) Covers recent work on important methods like multiple imputation, robust alternatives to weighting, and Bayesian methods Revises previous topics based on past student feedback and class experience Contains an updated and expanded bibliography The authors were awarded The Karl Pearson Prize in 2017 by the International Statistical Institute, for a research contribution that has had profound influence on statistical theory, methodology or applications. Their work "has been no less than defining and transforming." (ISI) Statistical Analysis with Missing Data, Third Edition is an ideal textbook for upper undergraduate and/or beginning graduate level students of the subject. It is also an excellent source of information for applied statisticians and practitioners in government and industry.

Statistical Analysis with Missing Data

Roderick J. A. Little,Donald B. Rubin
Statistical Analysis with Missing Data Book PDF

Author : Roderick J. A. Little,Donald B. Rubin
Publisher : John Wiley & Sons
Page : 463 pages
File Size : 54,9 Mb
Release : 2019-03-21
Category : Mathematics
ISBN : 9781118595695

Get Book

Statistical Analysis with Missing Data by Roderick J. A. Little,Donald B. Rubin Pdf

An up-to-date, comprehensive treatment of a classic text on missing data in statistics The topic of missing data has gained considerable attention in recent decades. This new edition by two acknowledged experts on the subject offers an up-to-date account of practical methodology for handling missing data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism, and then they apply the theory to a wide range of important missing data problems. 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 edition expands its coverage to include recent work on topics such as nonresponse in sample surveys, causal inference, diagnostic methods, and sensitivity analysis, among a host of other topics. An updated “classic” written by renowned authorities on the subject Features over 150 exercises (including many new ones) Covers recent work on important methods like multiple imputation, robust alternatives to weighting, and Bayesian methods Revises previous topics based on past student feedback and class experience Contains an updated and expanded bibliography The authors were awarded The Karl Pearson Prize in 2017 by the International Statistical Institute, for a research contribution that has had profound influence on statistical theory, methodology or applications. Their work "has been no less than defining and transforming." (ISI) Statistical Analysis with Missing Data, Third Edition is an ideal textbook for upper undergraduate and/or beginning graduate level students of the subject. It is also an excellent source of information for applied statisticians and practitioners in government and industry.

Statistical Power Analysis with Missing Data

Adam Davey,Jyoti "Tina" Savla
Statistical Power Analysis with Missing Data Book PDF

Author : Adam Davey,Jyoti "Tina" Savla
Publisher : Routledge
Page : 328 pages
File Size : 50,8 Mb
Release : 2009-08-20
Category : Psychology
ISBN : 9781135269302

Get Book

Statistical Power Analysis with Missing Data by Adam Davey,Jyoti "Tina" Savla Pdf

Statistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling. It answers many practical questions such as: How missing data affects the statistical power in a study How much power is likely with different amounts and types of missing data How to increase the power of a design in the presence of missing data, and How to identify the most powerful design in the presence of missing data. Points of Reflection encourage readers to stop and test their understanding of the material. Try Me sections test one’s ability to apply the material. Troubleshooting Tips help to prevent commonly encountered problems. Exercises reinforce content and Additional Readings provide sources for delving more deeply into selected topics. Numerous examples demonstrate the book’s application to a variety of disciplines. Each issue is accompanied by its potential strengths and shortcomings and examples using a variety of software packages (SAS, SPSS, Stata, LISREL, AMOS, and MPlus). Syntax is provided using a single software program to promote continuity but in each case, parallel syntax using the other packages is presented in appendixes. Routines, data sets, syntax files, and links to student versions of software packages are found at www.psypress.com/davey. The worked examples in Part 2 also provide results from a wider set of estimated models. These tables, and accompanying syntax, can be used to estimate statistical power or required sample size for similar problems under a wide range of conditions. Class-tested at Temple, Virginia Tech, and Miami University of Ohio, this brief text is an ideal supplement for graduate courses in applied statistics, statistics II, intermediate or advanced statistics, experimental design, structural equation modeling, power analysis, and research methods taught in departments of psychology, human development, education, sociology, nursing, social work, gerontology and other social and health sciences. The book’s applied approach will also appeal to researchers in these areas. Sections covering Fundamentals, Applications, and Extensions are designed to take readers from first steps to mastery.

Statistical Analysis with Missing Data

Roderick J. A. Little,Donald B. Rubin
Statistical Analysis with Missing Data Book PDF

Author : Roderick J. A. Little,Donald B. Rubin
Publisher : John Wiley & Sons
Page : 358 pages
File Size : 42,8 Mb
Release : 2014-08-25
Category : Mathematics
ISBN : 9781118625880

Get Book

Statistical Analysis with Missing Data by Roderick J. A. Little,Donald B. Rubin Pdf

* Emphasizes the latest trends in the field. * Includes a new chapter on evolving methods. * Provides updated or revised material in most of the chapters.

Missing Data

John W. Graham
Missing Data Book PDF

Author : John W. Graham
Publisher : Springer Science & Business Media
Page : 303 pages
File Size : 54,7 Mb
Release : 2012-06-08
Category : Social Science
ISBN : 9781461440185

Get Book

Missing Data by John W. Graham Pdf

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-effective planned missing data designs are provided. The author lays out missing data theory in a plain English style that is accessible and precise. Most analysis described in the book are conducted using the well-known statistical software packages SAS and SPSS, supplemented by Norm 2.03 and associated Java-based automation utilities. A related web site contains free downloads of the supplementary software, as well as sample empirical data sets and a variety of practical exercises described in the book to enhance and reinforce the reader’s learning experience. Missing Data: Analysis and Design and its web site work together to enable beginners to gain confidence in their ability to conduct missing data analysis, and more advanced readers to expand their skill set.

Flexible Imputation of Missing Data, Second Edition

Stef van Buuren
Flexible Imputation of Missing Data Second Edition Book PDF

Author : Stef van Buuren
Publisher : CRC Press
Page : 444 pages
File Size : 40,5 Mb
Release : 2018-07-17
Category : Mathematics
ISBN : 9780429960352

Get Book

Flexible Imputation of Missing Data, Second Edition by Stef van Buuren Pdf

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem. This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field. This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data.

Handbook of Missing Data Methodology

Geert Molenberghs,Garrett Fitzmaurice,Michael G. Kenward,Anastasios Tsiatis,Geert Verbeke
Handbook of Missing Data Methodology Book PDF

Author : Geert Molenberghs,Garrett Fitzmaurice,Michael G. Kenward,Anastasios Tsiatis,Geert Verbeke
Publisher : CRC Press
Page : 590 pages
File Size : 44,8 Mb
Release : 2014-11-06
Category : Mathematics
ISBN : 9781439854624

Get Book

Handbook of Missing Data Methodology by Geert Molenberghs,Garrett Fitzmaurice,Michael G. Kenward,Anastasios Tsiatis,Geert Verbeke Pdf

Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area, Handbook of Missing Data Methodology presents many methodological advances and t

Statistical Analysis With Missing Data

Roderick J. A. Little,Donald B. Rubin
Statistical Analysis With Missing Data Book PDF

Author : Roderick J. A. Little,Donald B. Rubin
Publisher : Unknown
Page : 312 pages
File Size : 52,5 Mb
Release : 1987-05-11
Category : Mathematics
ISBN : UCSD:31822002419430

Get Book

Statistical Analysis With Missing Data by Roderick J. A. Little,Donald B. Rubin Pdf

Blending theory and application, this study reviews historical approaches to the subject and provides rigorous yet simple methods for multivariate analysis with missing values.

Missing Data

Paul D. Allison
Missing Data Book PDF

Author : Paul D. Allison
Publisher : SAGE Publications
Page : 100 pages
File Size : 55,9 Mb
Release : 2024-05-08
Category : Social Science
ISBN : 9781071962527

Get Book

Missing Data by Paul D. Allison Pdf

Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables is missing for some cases. Why is this a problem? Because most statistical methods presume that every case has information on all the variables to be included in the analysis. Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has been relying on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data.

Statistical Methods for Handling Incomplete Data

Jae Kwang Kim,Jun Shao
Statistical Methods for Handling Incomplete Data Book PDF

Author : Jae Kwang Kim,Jun Shao
Publisher : CRC Press
Page : 380 pages
File Size : 55,6 Mb
Release : 2021-11-19
Category : Mathematics
ISBN : 9781000466294

Get Book

Statistical Methods for Handling Incomplete Data by Jae Kwang Kim,Jun Shao Pdf

Due to recent theoretical findings and advances in statistical computing, there has been a rapid development of techniques and applications in the area of missing data analysis. Statistical Methods for Handling Incomplete Data covers the most up-to-date statistical theories and computational methods for analyzing incomplete data. Features Uses the mean score equation as a building block for developing the theory for missing data analysis Provides comprehensive coverage of computational techniques for missing data analysis Presents a rigorous treatment of imputation techniques, including multiple imputation fractional imputation Explores the most recent advances of the propensity score method and estimation techniques for nonignorable missing data Describes a survey sampling application Updated with a new chapter on Data Integration Now includes a chapter on Advanced Topics, including kernel ridge regression imputation and neural network model imputation The book is primarily aimed at researchers and graduate students from statistics, and could be used as a reference by applied researchers with a good quantitative background. It includes many real data examples and simulated examples to help readers understand the methodologies.

The Prevention and Treatment of Missing Data in Clinical Trials

National Research Council,Division of Behavioral and Social Sciences and Education,Committee on National Statistics,Panel on Handling Missing Data in Clinical Trials
The Prevention and Treatment of Missing Data in Clinical Trials Book PDF

Author : National Research Council,Division of Behavioral and Social Sciences and Education,Committee on National Statistics,Panel on Handling Missing Data in Clinical Trials
Publisher : National Academies Press
Page : 163 pages
File Size : 49,6 Mb
Release : 2010-12-21
Category : Medical
ISBN : 9780309186513

Get Book

The Prevention and Treatment of Missing Data in Clinical Trials by National Research Council,Division of Behavioral and Social Sciences and Education,Committee on National Statistics,Panel on Handling Missing Data in Clinical Trials Pdf

Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable. The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data.

Applied Missing Data Analysis

Craig K. Enders
Applied Missing Data Analysis Book PDF

Author : Craig K. Enders
Publisher : Guilford Press
Page : 401 pages
File Size : 47,9 Mb
Release : 2010-04-23
Category : Psychology
ISBN : 9781606236390

Get Book

Applied Missing Data Analysis by Craig K. Enders Pdf

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 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. This book will appeal to researchers and graduate students in psychology, education, management, family studies, public health, sociology, and political science. It will also serve as a supplemental text for doctoral-level courses or seminars in advanced quantitative methods, survey analysis, longitudinal data analysis, and multilevel modeling, and as a primary text for doctoral-level courses or seminars in missing data.

Secondary Analysis of Electronic Health Records

MIT Critical Data
Secondary Analysis of Electronic Health Records Book PDF

Author : MIT Critical Data
Publisher : Springer
Page : 427 pages
File Size : 45,8 Mb
Release : 2016-09-09
Category : Medical
ISBN : 9783319437422

Get Book

Secondary Analysis of Electronic Health Records by MIT Critical Data Pdf

This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.

Multiple Imputation of Missing Data Using SAS

Patricia Berglund,Steven G. Heeringa
Multiple Imputation of Missing Data Using SAS Book PDF

Author : Patricia Berglund,Steven G. Heeringa
Publisher : SAS Institute
Page : 164 pages
File Size : 54,6 Mb
Release : 2014-07-01
Category : Computers
ISBN : 9781629592039

Get Book

Multiple Imputation of Missing Data Using SAS by Patricia Berglund,Steven G. Heeringa Pdf

Find guidance on using SAS for multiple imputation and solving common missing data issues. Multiple Imputation of Missing Data Using SAS provides both theoretical background and constructive solutions for those working with incomplete data sets in an engaging example-driven format. It offers practical instruction on the use of SAS for multiple imputation and provides numerous examples that use a variety of public release data sets with applications to survey data. Written for users with an intermediate background in SAS programming and statistics, this book is an excellent resource for anyone seeking guidance on multiple imputation. The authors cover the MI and MIANALYZE procedures in detail, along with other procedures used for analysis of complete data sets. They guide analysts through the multiple imputation process, including evaluation of missing data patterns, choice of an imputation method, execution of the process, and interpretation of results. Topics discussed include how to deal with missing data problems in a statistically appropriate manner, how to intelligently select an imputation method, how to incorporate the uncertainty introduced by the imputation process, and how to incorporate the complex sample design (if appropriate) through use of the SAS SURVEY procedures. Discover the theoretical background and see extensive applications of the multiple imputation process in action. This book is part of the SAS Press program.

Statistical Methods for Handling Incomplete Data

Jae Kwang Kim,Jun Shao
Statistical Methods for Handling Incomplete Data Book PDF

Author : Jae Kwang Kim,Jun Shao
Publisher : CRC Press
Page : 221 pages
File Size : 42,7 Mb
Release : 2013-07-23
Category : Mathematics
ISBN : 9781482205077

Get Book

Statistical Methods for Handling Incomplete Data by Jae Kwang Kim,Jun Shao Pdf

Along with many examples, this text covers the most up-to-date statistical theories and computational methods for analyzing incomplete data. It presents a thorough treatment of statistical theories of likelihood-based inference with missing data. It also discusses numerous computational techniques and theories on imputation and extensively covers methods involving propensity score weighting, nonignorable missing data, longitudinal missing data, survey sampling, and statistical matching. Some of the research ideas introduced can be developed further for specific applications.