How to deal with missing data when analyzing research findings
Missing data is not a deal breaker! Use simple strategies to analyze your data even when you are missing some of the values.
The first thing you should do when dealing with missing data is to look at its distribution. If the missing values are randomly distributed, the impact on the final result will be marginal. On the contrary, if the unavailable values follow a pattern, we’ll show you five different strategies you can use to analyze your data.
Become a great clinician with our video courses and workshops
Don't let statistics scare you away from clinical research. We have designed this course for the clinician who is not a statistician to help you validate clinical findings and test hypotheses using chi-square, non-parametric, and t-test protocols.
Upon completion, you will also be able to compare different research methods, work with different datasets, and use R programming to deal with missing data.