Multiple Imputation

Quiz Yourself about Missing Data

Do you find quizzes irresistible? I do.

Here’s a little quiz about working with missing data:

True or False?

1. Imputation is really just making up data to artificially inflate results. It’s better to just drop cases with missing data than to impute.

2. I can just impute the mean for any missing data. It won’t affect results, and improves power.

3. Multiple Imputation is fine for the predictor variables in a statistical model, but not for the response variable.

4. Multiple Imputation is always the best way to deal with missing data.

5. When imputing, it’s important that the imputations be plausible data points.

6. Missing data isn’t really a problem if I’m just doing simple statistics, like chi-squares and t-tests.

7. The worst thing that missing data does is lower sample size and reduce power.

Answers to the Missing Data Quiz

by Karen Grace-Martin 3 Comments

In my last post, I gave a little quiz about missing data. This post has the answers.

If you want to try it yourself before you see the answers, go here. (It’s a short quiz, but if you’re like me, you find testing yourself irresistible).

True or False?

1. Imputation is really just making up data to artificially inflate results. It’s better to just drop cases with missing data than to impute. [Read more…] about Answers to the Missing Data Quiz

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

by Karen Grace-Martin 33 Comments

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?