Multiple Imputation

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

Missing Data, and multiple imputation specifically, is one area of statistics that is changing rapidly. Research is still ongoing, and each year new findings on best practices and new techniques in software appear.

The downside for researchers is that some of the recommendations missing data statisticians were making even five years ago have changed.

Remember that there are three goals of multiple imputation, or any missing data technique: Unbiased parameter estimates in the final analysis [Read more…] about Multiple Imputation: 5 Recent Findings that Change How to Use It

Multiple Imputation of Categorical Variables

by Karen Grace-Martin 1 Comment

Most Multiple Imputation methods assume multivariate normality, so a common question is how to impute missing values from categorical variables.

Paul Allison, one of my favorite authors of statistical information for researchers, did a study that showed that the most common method actually gives worse results that listwise deletion. (Did I mention I’ve used it myself?) [Read more…] about Multiple Imputation of Categorical Variables

Missing Data: Criteria for Choosing an Effective Approach

by Karen Grace-Martin 3 Comments

In choosing an approach to missing data, there are a number of things to consider. But you need to keep in mind what you’re aiming for before you can even consider which approach to take.

There are three criteria we’re aiming for with any missing data technique:

1. Unbiased parameter estimates: Whether you’re estimating means, regressions, or odds ratios, you want your parameter estimates to be accurate representations of the actual population parameters. In statistical terms, that means the estimates should be unbiased. If all the [Read more…] about Missing Data: Criteria for Choosing an Effective Approach

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

by Karen Grace-Martin 31 Comments

I’m sure I don’t need to explain to you all the problems that occur as a result of missing data. Anyone who has dealt with missing data—that means everyone who has ever worked with real data—knows about the loss of power and sample size, and the potential bias in your data that comes with listwise deletion.

Listwise deletion is the default method for dealing with missing data in most statistical software packages. It simply means excluding from the analysis any cases with data missing on any variables involved in the analysis.

A very simple, and in many ways appealing, method devised to [Read more…] about EM Imputation and Missing Data: Is Mean Imputation Really so Terrible?

The Second Problem with Mean Imputation

by Karen Grace-Martin Leave a Comment

A previous post discussed the first reason to not use mean imputation as a way of dealing with missing data–it does not preserve the relationships among variables.

A second reason is that any type of single imputation underestimates error variation in any statistic that used the imputed data. Because the imputations are themselves estimates, there is some error associated with them. But your statistical software doesn’t know that. It treats it as real data.

Ultimately, because your standard errors are too low, so are your p-values. Now you’re making Type I errors without realizing it.

A better approach? Mulitple Imputation or Full Information Maximum Likelihood.

Multiple Imputation Resources

by Karen Grace-Martin Leave a Comment

Two excellent resources about multiple imputation and missing data:

Joe Schafer’s Multiple Imputation FAQ Page gives more detail about multiple imputation, including a list of references.

Paul Allison’s 2001 book Missing Data is the most readable book on the topic. It gives in-depth information on many good approaches to missing data, including multiple imputation. It is aimed at social science researchers, and best of all, it is very affordable (about $15).