Applied Missing Data Analysis, Second Edition
Autor Craig K. Endersen Limba Engleză Hardback – 28 oct 2022
Începând cu analiza tiparelor și mecanismelor datelor lipsă din primul capitol, Applied Missing Data Analysis, Second Edition se impune ca o resursă fundamentală pentru cercetătorii care navighează complexitatea seturilor de date incomplete. Observăm că această a doua ediție nu este doar o actualizare minoră, ci o reconstrucție aproape integrală a volumului original, adaptată la progresele metodologice din ultimul deceniu. Autorul, Craig K. Enders, reușește să traducă literatura tehnică de ultimă oră în ghiduri accesibile, punând un accent deosebit pe integrarea metodelor Bayesiene și a regresiilor factorizate.
Structura cărții este riguros organizată pentru a facilita progresia de la conceptele de bază la tehnici avansate. Primele secțiuni fundamentează înțelegerea distribuțiilor de probabilitate și a funcțiilor de verosimilitate, esențiale pentru stăpânirea estimării prin verosimilitate maximă (MLE). Ulterior, volumul explorează inovații precum imputarea bazată pe modele și metodele specifice pentru datele care nu sunt lipsă la întâmplare (MNAR). Apreciem în mod deosebit modul în care conceptele teoretice sunt ancorate în realitate prin studii de caz relevante din științele sociale și sănătate, oferind cititorului un context aplicat imediat.
Cititorii familiarizați cu Missing Data de Paul D Allison vor aprecia în acest volum profunzimea tehnică suplimentară și acoperirea extinsă a metodelor moderne care au apărut după publicarea textelor clasice. În timp ce alte lucrări se concentrează pe o singură paradigmă, Applied Missing Data Analysis, Second Edition oferă o perspectivă echilibrată între MLE și analiza Bayesiană, transformând teoria abstractă în instrumente de lucru pentru cercetarea empirică. Este o resursă esențială pentru oricine dorește să depășească metodele rudimentare de eliminare a cazurilor incomplete, asigurând validitatea și rigoarea analizelor statistice.
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Specificații
ISBN-10: 1462549861
Pagini: 546
Dimensiuni: 178 x 254 x 32 mm
Greutate: 1.08 kg
Ediția:2 ed
Editura: Guilford Publications
Colecția Guilford Press
De ce să citești această carte
Această ediție este indispensabilă cercetătorilor și studenților la masterat sau doctorat care lucrează cu date din psihologie sau științe sociale. Câștigați acces la cele mai noi strategii de imputare și analiză Bayesiană, explicate fără jargon excesiv. Este manualul care transformă problema datelor lipsă dintr-un obstacol metodologic într-o oportunitate de a aplica tehnici statistice de înaltă precizie.
Despre autor
Craig K. Enders este un expert recunoscut în metodologia statistică, specializat în tratarea datelor incomplete. Activitatea sa academică și de cercetare se concentrează pe dezvoltarea și aplicarea metodelor de estimare prin verosimilitate maximă și imputare multiplă în științele comportamentale. Prin lucrarea sa, Applied Missing Data Analysis, Second Edition, publicată la Guilford Publications, Enders a devenit o voce autoritară în domeniu, fiind apreciat pentru capacitatea de a face accesibile concepte statistice complexe pentru o audiență largă de cercetători aplicați.
Notă biografică
Cuprins
1.1 Chapter Overview
1.2 Missing Data Patterns
1.3 Missing Data Mechanisms
1.4 Diagnosing Missing Data Mechanisms
1.5 Auxiliary Variables
1.6 Analysis Example: Preparing for Missing Data Handling
1.7 Older Missing Data Methods
1.8 Comparing Missing Data Methods via Simulation
1.9 Planned Missing Data
1.10 Power Analyses for Planned Missingness Designs
1.11 Summary and Recommended Readings
2. Maximum Likelihood Estimation
2.1 Chapter Overview
2.2 Probability Distributions versus Likelihood Functions
2.3 The Univariate Normal Distribution
2.4 Estimating Unknown Parameters
2.5 Getting an Analytic Solution
2.6 Estimating Standard Errors
2.7 Information Matrix and Parameter Covariance Matrix
2.8 Alternative Approaches to Estimating Standard Errors
2.9 Iterative Optimization Algorithms
2.10 Linear Regression
2.11 Significance Tests
2.12 Multivariate Normal Data
2.13 Categorical Outcomes: Logistic and Probit Regression
2.14 Summary and Recommended Readings
3. Maximum Likelihood Estimation with Missing Data
3.1 Chapter Overview
3.2 The Multivariate Normal Distribution Revisited
3.3 How Do Incomplete Data Records Help?
3.4 Standard Errors with Incomplete Data
3.5 The Expectation Maximization Algorithm
3.6 Linear Regression
3.7 Significance Testing
3.8 Interaction Effects
3.9 Curvilinear Effects
3.10 Auxiliary Variables
3.11 Categorical Outcomes
3.12 Summary and Recommended Readings
4. Bayesian Estimation
4.1 Chapter Overview
4.2 What Makes Bayesian Statistics Different?
4.3 Conceptual Overview of Bayesian Estimation
4.4 Bayes’ Theorem
4.5 The Univariate Normal Distribution
4.6 MCMC Estimation with the Gibbs Sampler
4.7 Estimating the Mean and Variance with MCMC
4.8 Linear Regression
4.9 Assessing Convergence of the Gibbs Sampler
4.10 Multivariate Normal Data
4.11 Summary and Recommended Readings
5. Bayesian Estimation with Missing Data
5.1 Chapter Overview
5.2 Imputing an Incomplete Outcome Variable
5.3 Linear Regression
5.4 Interaction Effects
5.5 Inspecting Imputations
5.6 The Metropolis–Hastings Algorithm
5.7 Curvilinear Effects
5.8 Auxiliary Variables
5.9 Multivariate Normal Data
5.10 Summary and Recommended Readings
6. Bayesian Estimation for Categorical Variables
6.1 Chapter Overview
6.2 Latent Response Formulation for Categorical Variables
6.3 Regression with a Binary Outcome
6.4 Regression with an Ordinal Outcome
6.5 Binary and Ordinal Predictor Variables
6.6 Latent Response Formulation for Nominal Variables
6.7 Regression with a Nominal Outcome
6.8 Nominal Predictor Variables
6.9 Logistic Regression
6.10 Summary and Recommended Readings
7. Multiple Imputation
7.1 Chapter Overview
7.2 Agnostic versus Model-Based Multiple Imputation
7.3 Joint Model Imputation
7.4 Fully Conditional Specification
7.5 Analyzing Multiply-Imputed Data Sets
7.6 Pooling Parameter Estimates
7.7 Pooling Standard Errors
7.8 Test Statistic and Confidence Intervals
7.9 When Might Multiple Imputation Give Different Answers?
7.10 Interaction and Curvilinear Effects Revisited
7.11 Model-Based Imputation
7.12 Multivariate Significance Tests
7.13 Summary and Recommended Readings
8. Multilevel Missing Data
8.1 Chapter Overview
8.2 Random Intercept Regression Models
8.3 Random Coefficient Models
8.4 Multilevel Interaction Effects
8.5 Three-Level Models
8.6 Multiple Imputation
8.7 Joint Model Imputation
8.8 Fully Conditional Specification Imputation
8.9 Maximum Likelihood Estimation
8.10 Summary and Recommended Readings
9. Missing Not at Random Processes
9.1 Chapter Overview
9.2 Missing Not at Random Processes Revisited
9.3 Major Modeling Frameworks
9.4 Selection Models for Multiple Regression
9.5 Model Comparisons and Individual Influence Diagnostics
9.6 Selection Model Analysis Examples
9.7 Pattern Mixture Models for Multiple Regression
9.8 Pattern Mixture Model Analysis Examples
9.9 Longitudinal Data Analyses
9.10 Diggle–Kenward Selection Model
9.11 Shared Parameter (Random Coefficient) Selection Model
9.12 Random Coefficient Pattern Mixture Models
9.13 Longitudinal Data Analysis Examples
9.14 Summary and Recommended Readings
10. Special Topics and Applications
10.1 Chapter Overview
10.2 Descriptive Summaries, Correlations, and Subgroups
10.3 Non-Normal Predictor Variables
10.4 Non-Normal Outcome Variables
10.5 Mediation and Indirect Effects
10.6 Structural Equation Models
10.7 Scale Scores and Missing Questionnaire Items
10.8 Interactions with Scales
10.9 Longitudinal Data Analyses
10.10 Regression with a Count Outcome
10.11 Power Analyses for Growth Models with Missing Data
10.12 Summary and Recommended Readings
11. Wrap-Up
11.1 Chapter Overview
11.2 Choosing a Missing Data-Handling Procedure
11.3 Software Landscape
11.4 Reporting Results from a Missing Data Analysis
11.5 Final Thoughts and Recommended Readings
Appendix. Data Set Descriptions
Author Index
Subject Index
About the Author
Descriere scurtă
New to This Edition
*Expanded coverage of Bayesian estimation, including a new chapter on incomplete categorical variables.
*New chapters on factored regressions, model-based imputation strategies, multilevel missing data-handling methods, missing not at random analyses, and other timely topics.
*Presents cutting-edge methods developed since the 2010 first edition; includes dozens of new data analysis examples.
*Most of the book is entirely new.
Recenzii
"Approaches for dealing with missing data have progressed greatly in the statistical and methodological literatures, and the second edition of this exemplary book thoroughly presents and synthesizes these developments. The book makes sophisticated statistics amazingly accessible and offers a great deal to a wide audience, including statisticians, data analysts, substantive researchers, and quantitative students. I learn something new (or better understand something I thought I knew) every time I pick up this book! The presentation of how to report results from a missing data analysis, which gives explicit examples of such reporting for a wide variety of scenarios, is particularly useful. With an abundance of examples, figures, and illustrations to enhance the crystal-clear exposition, this is the 'go-to' book for dealing with missing data in statistical modeling."--Donald Hedeker, PhD, Department of Public Health Sciences, University of Chicago
"Thorough, cutting-edge, and far and away the clearest text available on missing data analysis. Written by a world-renowned expert who is a gifted instructor, this book is accessible enough for applied researchers with introductory statistics and regression knowledge, is an outstanding text for a missing data course, or can be used to fill gaps in methodologists’ understanding of the notoriously opaque missing data literature. For researchers who learned 'modern' missing data-handling methods years ago--much has changed. For instance, the second edition will bring you up to speed on how to accommodate missingness in conjunction with non-normal and discrete outcomes, nonlinear and interactive relationships, and multilevel structures; choose among non-model-based versus model-based multiple imputation methods; and conceptualize and implement sensitivity analyses to assess the impact of alternative missing data assumptions. Reading this book feels like being guided by the author through a comprehensive one-on-one workshop. A gift to the field!"--Sonya K. Sterba, PhD, Professor of Psychology and Director, Quantitative Methods Program, Vanderbilt University
“Simply stated, this is the best textbook available on missing data analysis. The book provides comprehensive coverage, is highly accessible, and is written by one of the experts in the field. The concepts involved in missing data analysis are complex, and it is obvious that Enders takes the 'teaching mission' seriously. The writing is clear, the figures and tables are very helpful in promoting understanding, and the simulations developed for the text are helpful in conveying the strengths and weaknesses of various missing data treatments. The excellent companion website provides important, updated resources for teaching and learning. The software scripts available on the website are very useful for researchers wishing to apply the missing data methods to real data."--Keenan A. Pituch, PhD, Edson College of Nursing and Health Innovation, Arizona State University-The book is well written….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. (on the first edition)--American Statistician, 8/1/2011