Missing Data

I don’t need to tell you, missing data stinks. After getting stuck on a big problem with missing data many years ago, I started studying what to do about it in a big way.

Answers to Questions from the Missing Data Webinar

Do Top Journals Require Reporting on Missing Data Techniques?

What is the Difference between MAR and MCAR?

Is Multiple Imputation Possible in the Context of Survival Analysis?

These questions were originally asked in a live webinar. We didn’t get through all the questions, so I’m answering many of them in this series. If you want to listen to the full webinar, you can get the recording on this page. It’s free.

Online Workshops

Effectively Dealing with Missing Data without Biasing your Results (On Demand)

Approaches to Dealing with Missing Data

Multiple Imputation: 5 Recent Findings that Change How to Use It

When Listwise Deletion works for Missing Data

Missing Data Mechanisms: A Primer

Quiz Yourself about Missing Data

Answers to the Missing Data Quiz

3 Ad-hoc Missing Data Approaches that You Should Never Use

Multiple Imputation of Categorical Variables

Missing Data: Criteria for Choosing an Effective Approach

A great Article about modern approaches to missing data

EM Imputation and Missing Data: Is Mean Imputation Really so Terrible?

Seven Ways to Make up Data: Common Methods to Imputing Missing Data

Mean Imputation

The Second Problem with Mean Imputation

Multiple Imputation Resources

Multiple Imputation in a Nutshell

Two Recommended Solutions for Missing Data: Mulitple Imputation and Maximum Likelihood

Software

Computing Cronbach’s Alpha in SPSS with Missing Data

New version released of Amelia II: A Program for Missing Data

Multiple Imputation in SPSS 17.0

Averaging and Adding Variables with Missing Data in SPSS

Missing Data in the Context of Data Analysis

The 13 Steps for Statistical Modeling in any Regression or ANOVA

Five Advantages of Running Repeated Measures ANOVA as a Mixed Model

 

Books

Missing Data

by Paul Allison

Very reader-friendly.
One
of “the little green Sage books.” This is an excellent overview, covers
much of what a data analyst needs to know, and very accessible. This is
the book to start with. And
very reasonably priced.

Analysis of Incomplete Multivariate Data

by Joseph Schafer

This book is the basis
for Joe’s series of multiple imputation programs
in S-Plus. It is somewhat more readable than Little & Rubin (below).

Statistical Analysis with Missing Data, Second
Edition

by Roderick Little
& Donald Rubin

This is the Missing
Data Bible. It can get pretty technical at times, but can be worth
working through.