| Description |
1 online resource (228 pages) |
| Contents |
Intro; Halftitle Page; Title Page; Copyright; Table of Contents; Preface; 1 Basic Concepts; 1.1 Introduction; 1.2 Definition of a Missing Value; 1.3 Patterns of Missing Data; 1.4 Missing Data Mechanisms; 1.5 What is Imputation?; 1.6 General Framework for Imputation; 1.7 Sequential Regression Multivariate Imputation (SRMI); 1.8 How Many Iterations?; 1.9 A Technical Issue; 1.10 Three-variable Example; 1.10.1 SRMI Approach; 1.10.2 Joint Model Approach; 1.10.3 Comparison of Approaches; 1.10.4 Alternative Modeling Strategies; 1.11 Complex Sample Surveys; 1.12 Imputation Diagnostics |
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1.12.1 Propensity Based Comparison1.12.2 Synthetic Data Approach; 1.13 Should We Impute or Not?; 1.14 Is Imputation Making Up Data?; 1.15 Multiple Imputation Analysis; 1.15.1 Point and Interval Estimates; 1.15.2 Multivariate Hypothesis Tests; 1.15.3 Combining Test Statistics; 1.16 Multiple Imputation Theory; 1.17 Number of Imputations; 1.18 Additional Reading; 2 Descriptive Statistics; 2.1 Introduction; 2.2 Imputation Task; 2.2.1 Imputation of the NHANES 2011-2012 Data Set; 2.3 Descriptive Analysis; 2.3.1 Continuous Variable; 2.3.2 Binary Variable; 2.4 Practical Considerations |
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2.5 Additional Reading2.6 Exercises; 3 Linear Models; 3.1 Introduction; 3.2 Complete Data Inference; 3.2.1 Repeated Sampling; 3.2.2 Bayesian Analysis; 3.3 Comparing Blocks of Variables; 3.4 Model Diagnostics; 3.5 Multiple Imputation Analysis; 3.5.1 Combining Point Estimates; 3.5.2 Residual Variance; 3.6 Example; 3.6.1 Imputation; 3.6.2 Parameter Estimation; 3.6.3 Multivariate Hypothesis Testing; 3.6.4 Combining F-statistics; 3.6.5 Computation of R2 and Adjusted R2; 3.7 Additional Reading; 3.8 Exercises; 4 Generalized Linear Model; 4.1 Introduction; 4.2 Multiple Imputation Analysis |
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4.2.1 Logistic Model4.2.1.1 Imputation; 4.2.1.2 Parameter Estimates; 4.2.1.3 Testing for Block of Covariates; 4.2.1.4 Estimate command; 4.2.2 Poisson Model; 4.2.2.1 Full Code; 4.2.3 Multinomial Logit Model; 4.2.3.1 Full Code; 4.3 Additional Reading; 4.4 Exercises; 5 Categorical Data Analysis; 5.1 Contingency Table Analysis; 5.2 Log-linear Models; 5.3 Three-way Contingency Table; 5.4 Multiple Imputation; 5.5 Two-way Contingency Table; 5.5.1 Chi-square Analysis; 5.5.2 Log-linear Model Analysis; 5.6 Three-way Contingency Table; 5.6.1 Log-linear Model; 5.6.2 Weighted Least Squares |
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5.7 Additional Reading5.8 Exercises; 6 Survival Analysis; 6.1 Introduction; 6.2 Multiple Imputation Analysis; 6.2.1 Proportional Hazards Model; 6.2.1.1 Outcome Imputed (Method 1); 6.2.1.2 Outcome Not Imputed (Method 2); 6.2.2 Tobit Model; 6.3 Additional Reading; 6.4 Exercises; 7 Structural Equation Models; 7.1 Introduction; 7.2 Example; 7.3 Multiple Imputation Analysis; 7.4 Additional Reading; 7.5 Exercises; 8 Longitudinal Data Analysis; 8.1 Introduction; 8.2 Example 1: Binary Outcome; 8.3 Example 2: Continuous Outcome; 8.4 Example 3: A Case Study; 8.4.1 Code; 8.4.2 Analysis Results |
| Summary |
Multiple Imputation in Practice: With Examples Using IVEware provides practical guidance on multiple imputation analysis, from simple to complex problems using real and simulated data sets. Data sets from cross-sectional, retrospective, prospective and longitudinal studies, randomized clinical trials, complex sample surveys are used to illustrate both simple, and complex analyses. Version 0.3 of IVEware, the software developed by the University of Michigan, is used to illustrate analyses. IVEware can multiply impute missing values, analyze multiply imputed data sets, incorporate complex sample design features, and be used for other statistical analyses framed as missing data problems. IVEware can be used under Windows, Linux, and Mac, and with software packages like SAS, SPSS, Stata, and R, or as a stand-alone tool. This book will be helpful to researchers looking for guidance on the use of multiple imputation to address missing data problems, along with examples of correct analysis techniques |
| Notes |
8.5 Discussion |
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Print version record |
| Subject |
Missing observations
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Multivariate analysis.
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Multivariate analysis -- Data processing
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MATHEMATICS -- Applied.
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MATHEMATICS -- Probability & Statistics -- General.
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Multivariate analysis
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Multivariate analysis -- Data processing
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| Form |
Electronic book
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| Author |
Solenberger, Peter W
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Berglund, Patricia A
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| ISBN |
9781351650311 |
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1351650319 |
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