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E-raamat: Multiple Imputation in Practice: With Examples Using IVEware

(University of Michigan, Ann Arbor, USA), (University of Michigan, Ann Arbor, USA), (University of Michigan, Institute of Social Research, Ann Arbor, USA)
  • Formaat: 264 pages
  • Ilmumisaeg: 20-Jul-2018
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781498770170
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Multiple Imputation in Practice: With Examples Using IVEwareSuurem pilt
  • Formaat: 264 pages
  • Ilmumisaeg: 20-Jul-2018
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781498770170
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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.

Arvustused

"This is a very useful book for applied researchers, especially those working with complex survey samples with stratification, clustering, and weighting. It contains detailed examples and programming codes that can be easily followed and carried out by users of all levels. It has a good balance of statistical methods and practical application of multiple imputation. In most chapters, the authors start by explaining the basic concepts in complete data analysis, then extending the topics to amultiple imputation setting. Relevant data examples appear throughout the text. Additional readings are listed at the end of each chapter to helpmore advanced readers gain a better understanding of the methods and theories underlying the topics presented in the text." - Qixuan Chen, The American Statistician, October 2020

Preface xi
1 Basic Concepts
1(32)
1.1 Introduction
1(1)
1.2 Definition of a Missing Value
1(1)
1.3 Patterns of Missing Data
2(1)
1.4 Missing Data Mechanisms
3(2)
1.5 What is Imputation?
5(4)
1.6 General Framework for Imputation
9(1)
1.7 Sequential Regression Multivariate Imputation (SRMI)
10(2)
1.8 How Many Iterations?
12(1)
1.9 A Technical Issue
13(1)
1.10 Three-variable Example
14(5)
1.10.1 SRMI Approach
14(1)
1.10.2 Joint Model Approach
14(4)
1.10.3 Comparison of Approaches
18(1)
1.10.4 Alternative Modeling Strategies
18(1)
1.11 Complex Sample Surveys
19(1)
1.12 Imputation Diagnostics
20(2)
1.12.1 Propensity Based Comparison
21(1)
1.12.2 Synthetic Data Approach
21(1)
1.13 Should We Impute or Not?
22(1)
1.14 Is Imputation Making Up Data?
23(1)
1.15 Multiple Imputation Analysis
24(2)
1.15.1 Point and Interval Estimates
24(1)
1.15.2 Multivariate Hypothesis Tests
24(1)
1.15.3 Combining Test Statistics
25(1)
1.16 Multiple Imputation Theory
26(3)
1.17 Number of Imputations
29(1)
1.18 Additional Reading
30(3)
2 Descriptive Statistics
33(12)
2.1 Introduction
33(2)
2.2 Imputation Task
35(3)
2.2.1 Imputation of the NHANES 2011-2012 Data Set
35(3)
2.3 Descriptive Analysis
38(3)
2.3.1 Continuous Variable
38(2)
2.3.2 Binary Variable
40(1)
2.4 Practical Considerations
41(1)
2.5 Additional Reading
42(1)
2.6 Exercises
42(3)
3 Linear Models
45(18)
3.1 Introduction
45(1)
3.2 Complete Data Inference
46(2)
3.2.1 Repeated Sampling
46(2)
3.2.2 Bayesian Analysis
48(1)
3.3 Comparing Blocks of Variables
48(1)
3.4 Model Diagnostics
49(1)
3.5 Multiple Imputation Analysis
50(2)
3.5.1 Combining Point Estimates
50(1)
3.5.2 Residual Variance
51(1)
3.6 Example
52(6)
3.6.1 Imputation
52(1)
3.6.2 Parameter Estimation
53(2)
3.6.3 Multivariate Hypothesis Testing
55(2)
3.6.4 Combining F-statistics
57(1)
3.6.5 Computation of R2 and Adjusted R2
57(1)
3.7 Additional Reading
58(1)
3.8 Exercises
59(4)
4 Generalized Linear Model
63(16)
4.1 Introduction
63(1)
4.2 Multiple Imputation Analysis
64(9)
4.2.1 Logistic Model
65(1)
4.2.1.1 Imputation
65(2)
4.2.1.2 Parameter Estimates
67(1)
4.2.1.3 Testing for Block of Covariates
68(1)
4.2.1.4 Estimate command
68(1)
4.2.2 Poisson Model
69(1)
4.2.2.1 Full Code
69(2)
4.2.3 Multinomial Logit Model
71(1)
4.2.3.1 Full Code
71(2)
4.3 Additional Reading
73(1)
4.4 Exercises
74(5)
5 Categorical Data Analysis
79(20)
5.1 Contingency Table Analysis
79(1)
5.2 Log-linear Models
80(2)
5.3 Three-way Contingency Table
82(1)
5.4 Multiple Imputation
83(1)
5.5 Two-way Contingency Table
83(5)
5.5.1 Chi-square Analysis
85(2)
5.5.2 Log-linear Model Analysis
87(1)
5.6 Three-way Contingency Table
88(7)
5.6.1 Log-linear Model
88(4)
5.6.2 Weighted Least Squares
92(3)
5.7 Additional Reading
95(1)
5.8 Exercises
96(3)
6 Survival Analysis
99(12)
6.1 Introduction
99(1)
6.2 Multiple Imputation Analysis
100(6)
6.2.1 Proportional Hazards Model
101(1)
6.2.1.1 Outcome Imputed (Method 1)
101(1)
6.2.1.2 Outcome Not Imputed (Method 2)
102(3)
6.2.2 Tobit Model
105(1)
6.3 Additional Reading
106(1)
6.4 Exercises
107(4)
7 Structural Equation Models
111(10)
7.1 Introduction
111(2)
7.2 Example
113(2)
7.3 Multiple Imputation Analysis
115(2)
7.4 Additional Reading
117(1)
7.5 Exercises
117(4)
8 Longitudinal Data Analysis
121(28)
8.1 Introduction
121(2)
8.2 Example 1: Binary Outcome
123(3)
8.3 Example 2: Continuous Outcome
126(5)
8.4 Example 3: A Case Study
131(13)
8.4.1 Code
132(8)
8.4.2 Analysis Results
140(4)
8.5 Discussion
144(1)
8.6 Additional Reading
145(1)
8.7 Exercises
146(3)
9 Complex Survey Data Analysis using BBDESIGN
149(14)
9.1 Introduction
149(2)
9.2 Example
151(10)
9.2.1 Code
151(9)
9.2.2 Results
160(1)
9.3 Additional Reading
161(1)
9.4 Exercises
161(2)
10 Sensitivity Analysis
163(18)
10.1 Introduction
163(1)
10.2 Pattern-Mixture Model
164(2)
10.3 Examples
166(10)
10.3.1 Bivariate Example: Continuous Variable
166(4)
10.3.2 Binary Example
170(3)
10.3.3 Complex Example
173(3)
10.4 Additional Reading
176(1)
10.5 Exercises
177(4)
11 Odds and Ends
181(26)
11.1 Imputing Scores
181(1)
11.2 Imputation and Analysis Models
182(2)
11.2.1 Example
183(1)
11.3 Running Simulations Using IV Eware
184(5)
11.4 Congeniality and Multiple Imputations
189(3)
11.4.1 Example of Impact of Uncongeniality
190(2)
11.5 Combining Bayesian Inferences
192(6)
11.5.1 Example of Combining Bayesian Inferences
195(3)
11.6 Imputing Interactions
198(5)
11.6.1 Simulation Study
198(1)
11.6.2 Code for Simulation Study
199(4)
11.7 Final Thoughts
203(1)
11.8 Additional Reading
204(1)
11.9 Exercises
205(2)
A Overview of Data Sets
207(20)
A.1 St. Louis Risk Research Project
207(1)
A.2 Primary Biliary Cirrhosis Data Set
208(3)
A.3 Opioid Detoxification Data Set
211(1)
A.4 American Changing Lives (ACL) Data Set
212(1)
A.5 National Comorbidity Survey Replication (NCS-R)
212(2)
A.6 National Health and Nutrition Examination Survey, 2011-2012 (NHANES 2011-2012)
214(5)
A.7 Health and Retirement Study, 2012 (HRS 2012)
219(1)
A.8 Case Control Data for Omega-3 Fatty Acids and Primary Cardiac Arrest
220(3)
A.9 National Merit Twin Study
223(1)
A.10 European Social Survey-Russian Federation
224(1)
A.11 Outline of Analysis Examples and Data Sets
224(3)
B IVEware
227(6)
B.1 What is IVEware?
227(1)
B.2 Download and Setup
228(2)
B.2.1 Windows
228(1)
B.2.2 Linux
229(1)
B.2.3 Mac OS
230(1)
B.3 Structure of IVEware
230(3)
Bibliography 233(14)
Index 247
Trivellore Raghunathan is the director of the Survey Research Center in the Institute for Social Research and professor of biostatistics in the School of Public Health at the University of Michigan. He has published numerous papers in a range of statistical and public health journals. His research interests include applied regression analysis, linear models, design of experiments, sample survey methods, and Bayesian inference.



Patricia A. Berglund is a senior research associate in the Youth and Social Indicators Program and Survey Methodology Program in the Survey Research Center at the University of Michigans Institute for Social Research.



 



 
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