Applied Missing Data Analysis

Hardcover (Print)
Used and New from Other Sellers
Used and New from Other Sellers
from $41.50
Usually ships in 1-2 business days
(Save 38%)
Other sellers (Hardcover)
  • All (14) from $41.50   
  • New (9) from $60.16   
  • Used (5) from $41.50   

Overview

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 stimulated 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.

Read More Show Less

Editorial Reviews

From the Publisher
"This is a well-written book that will be particularly useful for analysts who are not PhD statisticians. Enders provides a much-needed overview and explication of the current technical literature on missing data. The book should become a popular text for applied methodologists."—Bengt Muthén, PhD, Professor Emeritus, University of California, Los Angeles
 

"Many applied researchers are not trained in statistics to the level that would make the classic sources on missing data accessible. Enders makes a concerted—and successful—attempt to convey the statistical concepts and models that define missing data methods in a way that does not assume high statistical literacy. He writes in a conceptually clear manner, often using a simple example or simulation to show how an equation or procedure works. 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, Department of Sociology, The Pennsylvania State University
 

"A useful overview of missing data issues, with practical guidelines for making decisions about real-world data. This book is all about an issue that is usually ignored in work on OLS regression—but that most of us spend significant time dealing with. The writing is clear and accessible, a great success for a challenging topic. Enders provides useful reminders of what we need to know and why. I appreciated the interpretation of formulas, terms, and output. This book provides comprehensive and vital information in an easy-to-consume style. I learned a great deal reading it."—Julia McQuillan, PhD, Director, Bureau of Sociological Research, and Department of Sociology, University of Nebraska-Lincoln
 

"I would certainly recommend this book to anybody who deals with missing data at any level. I have no doubt that this book will serve as a solid reference for quantitative social and behavioral scientists."—Hakan Demirtas, PhD, Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago
 
"The chapter on MNAR provides a good overview of the current state of the art. I would recommend it to anyone working with missing data, as well as to developers of multilevel and structural equation modeling software who are interested in adding new features, such as pattern mixture models. The focus is on the 'how-tos' of working with MNAR data. The author illustrates the many pitfalls and how different model assumptions could lead to different parameter estimates and standard error estimates, and hence to different conclusions."—Stephen du Toit, PhD, Senior VP of Technical Operations, Scientific Software International, Inc.
 
"I would highly recommend this book to colleagues and will require it in my advanced graduate courses on longitudinal data analysis."—Scott M. Hofer, PhD, Professor and Mohr Chair in Adult Development and Aging, Department of Psychology, University of Victoria, Canada
 
"The book contains very accessible material on missing data. I would recommend it to colleagues and students, especially those who do not have formal training in mathematical statistics."—Ke-Hai Yuan, PhD, Department of Psychology, University of Notre Dame
 
"A needed and valuable addition to the literature on missing data. The simulations are excellent and are a clear strength of the book."—Alan C. Acock, PhD, Distinguished Professor and Knudson Chair in Family Research, Department of Human Development and Family Sciences, Oregon State University

American Statistician
"The book is well written, and successfully achieves the goal, stated in the Preface, of 'translat[ing] the technical missing data literature into an accessible reference text' (p. vii) for the social sciences. 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 software implementations are available on the book's website http://www.appliedmissingdata.com, which is invaluable."—American Statistician
American Statistician

"The book is well written, and successfully achieves the goal, stated in the Preface, of 'translat[ing] the technical missing data literature into an accessible reference text' (p. vii) for the social sciences. 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 software implementations are available on the book's website http://www.appliedmissingdata.com, which is invaluable."--American Statistician
From the Publisher

"This is a well-written book that will be particularly useful for analysts who are not PhD statisticians. Enders provides a much-needed overview and explication of the current technical literature on missing data. The book should become a popular text for applied methodologists."--Bengt Muthén, PhD, Professor Emeritus, University of California, Los Angeles
 

"Many applied researchers are not trained in statistics to the level that would make the classic sources on missing data accessible. Enders makes a concerted--and successful--attempt to convey the statistical concepts and models that define missing data methods in a way that does not assume high statistical literacy. He writes in a conceptually clear manner, often using a simple example or simulation to show how an equation or procedure works. 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, Department of Sociology, The Pennsylvania State University
 

"A useful overview of missing data issues, with practical guidelines for making decisions about real-world data. This book is all about an issue that is usually ignored in work on OLS regression--but that most of us spend significant time dealing with. The writing is clear and accessible, a great success for a challenging topic. Enders provides useful reminders of what we need to know and why. I appreciated the interpretation of formulas, terms, and output. This book provides comprehensive and vital information in an easy-to-consume style. I learned a great deal reading it."--Julia McQuillan, PhD, Director, Bureau of Sociological Research, and Department of Sociology, University of Nebraska-Lincoln
 

"I would certainly recommend this book to anybody who deals with missing data at any level. I have no doubt that this book will serve as a solid reference for quantitative social and behavioral scientists."--Hakan Demirtas, PhD, Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago
 
"The chapter on MNAR provides a good overview of the current state of the art. I would recommend it to anyone working with missing data, as well as to developers of multilevel and structural equation modeling software who are interested in adding new features, such as pattern mixture models. The focus is on the 'how-tos' of working with MNAR data. The author illustrates the many pitfalls and how different model assumptions could lead to different parameter estimates and standard error estimates, and hence to different conclusions."--Stephen du Toit, PhD, Senior VP of Technical Operations, Scientific Software International, Inc.
 
"I would highly recommend this book to colleagues and will require it in my advanced graduate courses on longitudinal data analysis."--Scott M. Hofer, PhD, Professor and Mohr Chair in Adult Development and Aging, Department of Psychology, University of Victoria, Canada
 
"The book contains very accessible material on missing data. I would recommend it to colleagues and students, especially those who do not have formal training in mathematical statistics."--Ke-Hai Yuan, PhD, Department of Psychology, University of Notre Dame
 
"A needed and valuable addition to the literature on missing data. The simulations are excellent and are a clear strength of the book."--Alan C. Acock, PhD, Distinguished Professor and Knudson Chair in Family Research, Department of Human Development and Family Sciences, Oregon State University

Read More Show Less

Product Details

  • ISBN-13: 9781606236390
  • Publisher: Guilford Publications, Inc.
  • Publication date: 5/17/2010
  • Series: Methodology In The Social Sciences Series
  • Pages: 377
  • Sales rank: 1,328,078
  • Product dimensions: 7.20 (w) x 10.10 (h) x 1.00 (d)

Meet the Author

Craig K. Enders is Associate Professor in the Quantitative Psychology concentration in the Department of Psychology at Arizona State University. The majority of his research focuses on analytic issues related to missing data analyses. He also does research in the area of structural equation modeling and multilevel modeling. Dr. Enders is a member of the American Psychological Association and is also active in the American Educational Research Association.

Read More Show Less

Table of Contents

1. An Introduction to Missing Data

1.1 Introduction

1.2 Chapter Overview

1.3 Missing Data Patterns

1.4 A Conceptual Overview of Missing Data Theory

1.5 A More Formal Description of Missing Data Theory

1.6 Why Is the Missing Data Mechanism Important?

1.7 How Plausible Is the Missing at Random Mechanism?

1.8 An Inclusive Analysis Strategy

1.9 Testing the Missing Completely at Random Mechanism

1.10 Planned Missing Data Designs

1.11 The Three-Form Design

1.12 Planned Missing Data for Longitudinal Designs

1.13 Conducting Power Analyses for Planned Missing Data Designs

1.14 Data Analysis Example

1.15 Summary

1.16 Recommended Readings

2. Traditional Methods for Dealing with Missing Data

2.1 Chapter Overview

2.2 An Overview of Deletion Methods

2.3 Listwise Deletion

2.4 Pairwise Deletion

2.5 An Overview of Single Imputation Techniques

2.6 Arithmetic Mean Imputation

2.7 Regression Imputation

2.8 Stochastic Regression Imputation

2.9 Hot-Deck Imputation

2.10 Similar Response Pattern Imputation

2.11 Averaging the Available Items

2.12 Last Observation Carried Forward

2.13 An Illustrative Simulation Study

2.14 Summary

2.15 Recommended Readings

3. An Introduction to Maximum Likelihood Estimation

3.1 Chapter Overview

3.2 The Univariate Normal Distribution

3.3 The Sample Likelihood

3.4 The Log-Likelihood

3.5 Estimating Unknown Parameters

3.6 The Role of First Derivatives

3.7 Estimating Standard Errors

3.8 Maximum Likelihood Estimation with Multivariate Normal Data

3.9 A Bivariate Analysis Example

3.10 Iterative Optimization Algorithms

3.11 Significance Testing Using the Wald Statistic

3.12 The Likelihood Ratio Test Statistic

3.13 Should I Use the Wald Test or the Likelihood Ratio Statistic?

3.14 Data Analysis Example 1

3.15 Data Analysis Example 2

3.16 Summary

3.17 Recommended Readings

4. Maximum Likelihood Missing Data Handling 

4.1 Chapter Overview

4.2 The Missing Data Log-Likelihood

4.3 How Do the Incomplete Data Records Improve Estimation?

4.4 An Illustrative Computer Simulation Study

4.5 Estimating Standard Errors with Missing Data

4.6 Observed Versus Expected Information

4.7 A Bivariate Analysis Example

4.8 An Illustrative Computer Simulation Study

4.9 An Overview of the EM Algorithm

4.10 A Detailed Description of the EM Algorithm

4.11 A Bivariate Analysis Example

4.12 Extending EM to Multivariate Data

4.13 Maximum Likelihood Software Options

4.14 Data Analysis Example 1

4.15 Data Analysis Example 2

4.16 Data Analysis Example 3

4.17 Data Analysis Example 4

4.18 Data Analysis Example 5

4.19 Summary

4.20 Recommended Readings

5. Improving the Accuracy of Maximum Likelihood Analyses

5.1 Chapter Overview

5.2 The Rationale for an Inclusive Analysis Strategy

5.3 An Illustrative Computer Simulation Study

5.4 Identifying a Set of Auxiliary Variables

5.5 Incorporating Auxiliary Variables Into a Maximum Likelihood Analysis

5.6 The Saturated Correlates Model

5.7 The Impact of Non-Normal Data

5.8 Robust Standard Errors

5.9 Bootstrap Standard Errors

5.10 The Rescaled Likelihood Ratio Test

5.11 Bootstrapping the Likelihood Ratio Statistic

5.12 Data Analysis Example 1

5.13 Data Analysis Example 2

5.14 Data Analysis Example 3

5.15 Summary

5.16 Recommended Readings

6. An Introduction to Bayesian Estimation

6.1 Chapter Overview

6.2 What Makes Bayesian Statistics Different?

6.3 A Conceptual Overview of Bayesian Estimation

6.4 Bayes’ Theorem

6.5 An Analysis Example

6.6 How Does Bayesian Estimation Apply to Multiple Imputation?

6.7 The Posterior Distribution of the Mean

6.8 The Posterior Distribution of the Variance

6.9 The Posterior Distribution of a Covariance Matrix

6.10 Summary

6.11 Recommended Readings

7. The Imputation Phase of Multiple Imputation

7.1 Chapter Overview

7.2 A Conceptual Description of the Imputation Phase

7.3 A Bayesian Description of the Imputation Phase

7.4 A Bivariate Analysis Example

7.5 Data Augmentation with Multivariate Data

7.6 Selecting Variables for Imputation

7.7 The Meaning of Convergence

7.8 Convergence Diagnostics

7.9 Time-Series Plots

7.10 Autocorrelation Function Plots

7.11 Assessing Convergence from Alternate Starting Values

7.12 Convergence Problems

7.13 Generating the Final Set of Imputations

7.14 How Many Data Sets Are Needed?

7.15 Summary

7.16 Recommended Readings

8. The Analysis and Pooling Phases of Multiple Imputation

8.1 Chapter Overview

8.2 The Analysis Phase

8.3 Combining Parameter Estimates in the Pooling Phase

8.4 Transforming Parameter Estimates Prior to Combining

8.5 Pooling Standard Errors

8.6 The Fraction of Missing Information and the Relative Increase in Variance

8.7 When Is Multiple Imputation Comparable to Maximum Likelihood?

8.8 An Illustrative Computer Simulation Study

8.9 Significance Testing Using the t Statistic

8.10 An Overview of Multiparameter Significance Tests

8.11 Testing Multiple Parameters Using the D1 Statistic

8.12 Testing Multiple Parameters by Combining Wald Tests

8.13 Testing Multiple Parameters by Combining Likelihood Ratio Statistics

8.14 Data Analysis Example 1

8.15 Data Analysis Example 2

8.16 Data Analysis Example 3

8.17 Summary

8.18 Recommended Readings

9. Practical Issues in Multiple Imputation

9.1 Chapter Overview

9.2 Dealing with Convergence Problems

9.3 Dealing with Non-Normal Data

9.4 To Round or Not to Round?

9.5 Preserving Interaction Effects

9.6 Imputing Multiple-Item Questionnaires

9.7 Alternate Imputation Algorithms

9.8 Multiple Imputation Software Options

9.9 Data Analysis Example 1

9.10 Data Analysis Example 2

9.11 Summary

9.12 Recommended Readings

10. Models for Missing Not at Random Data

10.1 Chapter Overview

10.2 An Ad Hoc Approach to Dealing with MNAR Data

10.3 The Theoretical Rationale for MNAR Models

10.4 The Classic Selection Model

10.5 Estimating the Selection Model

10.6 Limitations of the Selection Model

10.7 An Illustrative Analysis

10.8 The Pattern Mixture Model

10.9 Limitations of the Pattern Mixture Model

10.10 An Overview of the Longitudinal Growth Model

10.11 A Longitudinal Selection Model

10.12 Random Coefficient Selection Models

10.13 Pattern Mixture Models for Longitudinal Analyses

10.14 Identification Strategies for Longitudinal Pattern Mixture Models

10.15 Delta Method Standard Errors

10.16 Overview of the Data Analysis Examples

10.17 Data Analysis Example 1

10.18 Data Analysis Example 2

10.19 Data Analysis Example 3

10.20 Data Analysis Example 4

10.21 Summary

10.22 Recommended Readings

11. Wrapping Things Up: Some Final Practical Considerations

11.1 Chapter Overview

11.2 Maximum Likelihood Software Options

11.3 Multiple Imputation Software Options

11.4 Choosing between Maximum Likelihood and Multiple Imputation

11.5 Reporting the Results from a Missing Data Analysis

11.6 Final Thoughts

11.7 Recommended Readings

Read More Show Less

Customer Reviews

Be the first to write a review
( 0 )
Rating Distribution

5 Star

(0)

4 Star

(0)

3 Star

(0)

2 Star

(0)

1 Star

(0)

Your Rating:

Your Name: Create a Pen Name or

Barnes & Noble.com Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & Noble.com that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & Noble.com does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at BN.com or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation

Reminder:

  • - By submitting a review, you grant to Barnes & Noble.com and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Noble.com Terms of Use.
  • - Barnes & Noble.com reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & Noble.com also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on BN.com. It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

 
Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously

    If you find inappropriate content, please report it to Barnes & Noble
    Why is this product inappropriate?
    Comments (optional)