Missing Data

Paul D. Allison - University of Pennsylvania; University of Pennsylvania, USA
Quantitative Applications in the Social Sciences
Missing Data
August 2001 | 104 pages | Sage US
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Description

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. 

Contents

Series Editor's Introduction

Series Editor's Introduction

1. Introduction

1. Introduction

2. Assumptions

  • Missing Completely at Random
  • Missing at Random
  • Ignorable
  • Nonignorable

3. Conventional Methods

  • Listwise Deletion
  • Pairwise Deletion
  • Dummy Variable Adjustment
  • Imputation
  • Summary

4. Maximum Likelihood

  • Review of Maximum Likelihood
  • ML With Missing Data
  • Contingency Table Data
  • Linear Models With Normally Distributed Data
  • The EM Algorithm
  • EM Example
  • Direct ML
  • Direct ML Example
  • Conclusion

5. Multiple Imputation: Bascis

  • Single Random Imputation
  • Multiple Random Imputation
  • Allowing for Random Variation in the Parameter Estimates
  • Multiple Imputation Under the Multivariate Normal Model
  • Data Augmentation for the Multivariate Normal Model
  • Convergence in Data Augmentation
  • Sequential Verses Parallel Chains of Data Augmentation
  • Using the Normal Model for Nonnormal or Categorical Data
  • Exploratory Analysis
  • MI Example 1

6. Multiple Imputation: Complications

  • Interactions and Nonlinearities in MI
  • Compatibility of the Imputation Model and the Analysis Model
  • Role of the Dependent Variable in Imputation
  • Using Additional Variables in the Imputation Process
  • Other Parametric Approaches to Multiple Imputation
  • Nonparametric and Partially Parametric Methods
  • Sequential Generalized Regression Models
  • Linear Hypothesis Tests and Likelihood Ratio Tests
  • MI Example 2
  • MI for Longitudinal and Other Clustered Data
  • MI Example 3

7. Nonignorable Missing Data

  • Two Classes of Models
  • Heckman's Model for Sample Selection Bias
  • ML Estimation With Pattern-Mixture Models
  • Multiple Imputation With Pattern-Mixture Models

8. Summary and Conclusion

8. Summary and Conclusion

Notes

Notes

References

References

About the Author

About the Author

Description

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. 

Contents

Series Editor's Introduction

Series Editor's Introduction

1. Introduction

1. Introduction

2. Assumptions

  • Missing Completely at Random
  • Missing at Random
  • Ignorable
  • Nonignorable

3. Conventional Methods

  • Listwise Deletion
  • Pairwise Deletion
  • Dummy Variable Adjustment
  • Imputation
  • Summary

4. Maximum Likelihood

  • Review of Maximum Likelihood
  • ML With Missing Data
  • Contingency Table Data
  • Linear Models With Normally Distributed Data
  • The EM Algorithm
  • EM Example
  • Direct ML
  • Direct ML Example
  • Conclusion

5. Multiple Imputation: Bascis

  • Single Random Imputation
  • Multiple Random Imputation
  • Allowing for Random Variation in the Parameter Estimates
  • Multiple Imputation Under the Multivariate Normal Model
  • Data Augmentation for the Multivariate Normal Model
  • Convergence in Data Augmentation
  • Sequential Verses Parallel Chains of Data Augmentation
  • Using the Normal Model for Nonnormal or Categorical Data
  • Exploratory Analysis
  • MI Example 1

6. Multiple Imputation: Complications

  • Interactions and Nonlinearities in MI
  • Compatibility of the Imputation Model and the Analysis Model
  • Role of the Dependent Variable in Imputation
  • Using Additional Variables in the Imputation Process
  • Other Parametric Approaches to Multiple Imputation
  • Nonparametric and Partially Parametric Methods
  • Sequential Generalized Regression Models
  • Linear Hypothesis Tests and Likelihood Ratio Tests
  • MI Example 2
  • MI for Longitudinal and Other Clustered Data
  • MI Example 3

7. Nonignorable Missing Data

  • Two Classes of Models
  • Heckman's Model for Sample Selection Bias
  • ML Estimation With Pattern-Mixture Models
  • Multiple Imputation With Pattern-Mixture Models

8. Summary and Conclusion

8. Summary and Conclusion

Notes

Notes

References

References

About the Author

About the Author

August 2001 | 104 pages | Sage US

Format Published Date ISBN Price

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. 


Table Of Contents:

  • Series Editor's Introduction
  • 1. Introduction
  • 2. Assumptions
  • Missing Completely at Random
  • Missing at Random
  • Ignorable
  • Nonignorable
  • 3. Conventional Methods
  • Listwise Deletion
  • Pairwise Deletion
  • Dummy Variable Adjustment
  • Imputation
  • Summary
  • 4. Maximum Likelihood
  • Review of Maximum Likelihood
  • ML With Missing Data
  • Contingency Table Data
  • Linear Models With Normally Distributed Data
  • The EM Algorithm
  • EM Example
  • Direct ML
  • Direct ML Example
  • Conclusion
  • 5. Multiple Imputation: Bascis
  • Single Random Imputation
  • Multiple Random Imputation
  • Allowing for Random Variation in the Parameter Estimates
  • Multiple Imputation Under the Multivariate Normal Model
  • Data Augmentation for the Multivariate Normal Model
  • Convergence in Data Augmentation
  • Sequential Verses Parallel Chains of Data Augmentation
  • Using the Normal Model for Nonnormal or Categorical Data
  • Exploratory Analysis
  • MI Example 1
  • 6. Multiple Imputation: Complications
  • Interactions and Nonlinearities in MI
  • Compatibility of the Imputation Model and the Analysis Model
  • Role of the Dependent Variable in Imputation
  • Using Additional Variables in the Imputation Process
  • Other Parametric Approaches to Multiple Imputation
  • Nonparametric and Partially Parametric Methods
  • Sequential Generalized Regression Models
  • Linear Hypothesis Tests and Likelihood Ratio Tests
  • MI Example 2
  • MI for Longitudinal and Other Clustered Data
  • MI Example 3
  • 7. Nonignorable Missing Data
  • Two Classes of Models
  • Heckman's Model for Sample Selection Bias
  • ML Estimation With Pattern-Mixture Models
  • Multiple Imputation With Pattern-Mixture Models
  • 8. Summary and Conclusion
  • Notes
  • References
  • About the Author

Recent Product Reviews:

"…an excellent resource for researchers who are conducting multivariate statistical studies."
Richard A. Chechile, Journal of Mathematical Psychology

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