The Analysis Factor
Statistical Consulting, Resources, and Statistics Workshops for Researchers
There are a number of simplistic methods available for tackling the problem of missing data. Unfortunately there is a very high likelihood that each of these simplistic methods introduces bias into our model results.
Multiple imputation is considered to be the superior method of working with missing data. It eliminates the bias introduced by the simplistic methods in many missing data situations.
[Read more…] about Member Training: Multiple Imputation for Missing Data
A data set can contain indicator (dummy) variables, categorical variables and/or both. Initially, it all depends upon how the data is coded as to which variable type it is.
For example, a categorical variable like marital status could be coded in the data set as a single variable with 5 values: [Read more…] about Multiple Imputation for Missing Data: Indicator Variables versus Categorical Variables
One important consideration in choosing a missing data approach is the missing data mechanism—different approaches have different assumptions about the mechanism.
Each of the three mechanisms describes one possible relationship between the propensity of data to be missing and values of the data, both missing and observed.
Missing Completely at Random, MCAR, means there is no relationship between [Read more…] about How to Diagnose the Missing Data Mechanism
Sure. One of the big advantages of multiple imputation is that you can use it for any analysis.
It’s one of the reasons big data libraries use it–no matter how researchers are using the data, the missing data is handled the same, and handled well.
I say this with two caveats. [Read more…] about Is Multiple Imputation Possible in the Context of Survival Analysis?
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: [Read more…] about Quiz Yourself about Missing Data
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