You’ve probably heard of different approaches to dealing with missing data: imputation, last observation carried forward, listwise deletion. Some of them sound a bit sketchy, and yet, sometimes the approaches that sound the worst actually do the best.

Covered in this webinar:

1. The three types of missing data, and how they affect the approach to take
2. The common approach that is generally worse than any other
3. The easy, common, seemingly bad approach that often isn’t so bad, and the situations when it doesn’t work
4. The two approaches that give unbiased results: one that is very easy to implement, but only works in limited situations and one that is harder to implement well, but works with any statistical analysis