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Reference:
Garson, G. D. (2015). Missing Values Analysis and Data Imputation. Asheboro, NC: Statistical Associates Publishers.
 

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About the author
 
Overview
 
Table of Contents
 
ISBN-10: 1626380333
ISBN-13: 978-1-62638-033-2
ASIN number (e-book counterpart to ISBN): B00B0N2W34
 
@c 2015 by G. David Garson and Statistical Associates Publishers. worldwide rights reserved in all languages and on all media. Permission is not granted to copy, distribute, or post e-books or passwords.
 


MISSING VALUES ANALYSIS & DATA IMPUTATION

Overview

An illustrated tutorial and introduction to missing values analysis and data imputtion using SPSS, SAS, and Stata. Suitable for introductory graduate-level study.

The 2015 edition is a major update to the 2012 edition. Among the new features are these:

The full content is now available from Statistical Associates Publishers. Click here.

Below is the unformatted table of contents.

MISSING VALUES ANALYSIS AND DATA IMPUTATION
Overview	6
SPSS	6
SAS	7
Stata	8
Data examples in this volume	8
Key Concepts and Terms	9
Causes of non-response	9
Item non-response	9
Listwise deletion of cases with missing values	10
Types of Missingness	11
Missing completely at random (MCAR)	11
Missing at random (MAR)	15
Missing not at random (MNAR)	15
Multiple imputation	16
When imputation should not be used	16
Summary	17
Testing for missing at random (MAR)	18
Overview	18
Testing for MAR in SPSS	21
Testing for MAR in SAS	24
Testing for MAR in Stata	27
Single versus multiple imputation	30
MI estimation and monotonicity	31
The imputation model assumption	34
Number of imputations	35
Multiple imputation in SPSS	35
Overview	35
How MI works	36
What MI does	36
Pooled estimates	37
SPSS input	38
Overview	40
The Method tab	41
The Constraints tab	42
The Output tab	44
Checking for convergence	45
SPSS output	46
The "Imputation Models" table	46
The "Descriptive Statistics" tables	47
A logistic regression example	48
Pooling diagnostics	51
Multiple imputation SAS	52
Overview	52
SAS input	52
Checking for convergence	53
SAS output	54
Multiple imputation Stata	56
Overview	56
Stata input	57
Initial assessment of missingness	57
Preparing for data imputation	59
Data imputation	60
Checking for convergence	62
Running statistical procedures on imputed data	63
Stata output	64
Single imputation of missing values	66
Mean imputation	66
Other simple replacement methods	66
SPSS	66
SAS	67
Stata	69
The hot deck method of data imputation	69
SPSS	70
SAS	70
Stata	70
Missing Value Analysis (MVA) in SPSS	70
Overview	70
MVA set-up in SPSS	72
Types of estimation	73
The variables button	75
The patterns button	76
The descriptives button	82
Other MVA output	82
Default output	82
The percent mismatch table	83
Output for t tests	84
Crosstabulation	87
Expectation maximization (EM) estimates	88
Saving EM-imputed data	90
Assumptions	90
Multivariate normality	90
Frequently Asked Questions	91
Why not just delete cases with missing values rather than impute values at all?	91
Is it permitted to impute the dependent variable?	91
Do I need a large sample to do MI?	91
Should I round my MI estimates?	91
MI versus EM or FIML estimation	92
SPSS	95
SAS	95
Stata	95
Can MI be used with hierarchical data?	96
Should I use original data or imputed data when reporting results?	96
In SPSS, which procedures support pooling of MI estimates?	97
Can I use multiple imputation with complex survey data?	101
What is Heckman's correction for sample selection bias?	101
What is approximate Bayesian bootstrapping?	102
How can I identify missing value patterns in SAS?	103
How can I identify missing value patterns in Stata?	105
How can I restrict the bounds of imputed values in Stata?	107
Acknowledgments	107
Bibliography	107
Pagecount: 113