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Table of Contents:

• Cover
• Half Title Page
• Series Editor
• Title Page
• Copyright
• Series Editor's Note
• Preface
• Contents
• 1. Introduction to Missing Data
• 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

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Author/Creator:

Enders, Craig K.

Languages:

English

Language Notes:

Item content: English

Main Work:

Applied Missing Data Analysis [by Enders, C.K.]

Other Related Resources:

Print version: Applied Missing Data Analysis, Second Edition [by Enders, C.K.] (2nd ed.; New York : Guilford Publications, 2022 — ISBN 9781462550005)

Related Series:

Methodology in the Social Sciences Ser.

Subjects:

Social sciences — Statistical methods

Missing observations (Statistics)

Social sciences — Research — Methodology

Social sciences

General Notes:

Description based on: Print version of record.
Description based on: Print version record.

Physical Description:

1 online resource (564 pages).

Digital Characteristics:

text file

Call Numbers:

HA29 .E497 2022eb

ISBNs:

9781462550005 (electronic bk.)
1462550002

OCLC Numbers:

1334891006