Applied Missing Data Analysis Craig K. Enders
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April 23, 2010
ISBN 9781606236390
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377 Pages
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March 1, 2011
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Read the Series Editor's Note by Todd D. Little
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 Impu

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