Proper handling of missing values is important in all statistical analyses. Improper handling of missing values will distort analysis because, until proven otherwise, the researcher must assume that missing cases differ in analytically important ways from cases where values are present. That is, the problem with missing values is not so much reduced sample size as it is the possibility that the remaining data set is biased. The imputation of values where data are missing is an area of statistics which has developed much since the 1980s.

The SPSS add-on module "Missing Value Analysis" has long supported several imputation algorithms, the most popular being expectation maximization (EM). Since SPSS 17 a separate module, "Multiple Imputation," has supported the newer and increasingly preferred MI estimation method. Both are discussed below. Note that maximum likelihood data imputation, an EM method, can also be implemented in AMOS, the structural equation program supported by SPSS.

Statistical objections can be raised about any of the methods which might be used for data imputation. Missing data are a form of measurement error. As such missing data may both bias the sample and attenuate effect sizes. Data imputation may reduce bias but also may introduce systematic regularities in the data arising from the prediction method.

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Below is the unformatted table of contents.

Table of Contents

Overview	5
Key Concepts and Terms	5
Item non-response	5
Dropping cases with missing values	6
Types of Missingness	6
Missing completely at random (MCAR)	6
Missing at random (MAR)	8
Non-ignorable missingness	10
Multiple imputation (MI) in SPSS	11
Overview	11
MI vs. MVA	11
How MI works	11
What MI does	12
Using MI data in SPSS	13
SPSS multiple imputation set-up	15
The Method tab	17
The Constraints tab	18
The Output tab	19
Multiple imputation output in SPSS	20
The imputation models table	20
The descriptive statistics tables	20
Missing Value Analysis (MVA) in SPSS	21
What MVA does	21
MVA set-up in SPSS	22
Types of estimation	23
The variables button	25
The patterns button	26
The descriptives button	29
Other MVA output	30
Default output	30
The percent mismatch table	30
Output for t tests	31
Crosstabulation	34
Replacement of missing values	34
Mean substitution	35
Frequently Asked Questions	36
Why not just delete cases with missing values rather than impute values at all?	36
Should I use original data or imputed data when reporting results?	36
What is approximate Bayesian bootstrapping?	36
Bibliography	37