I am performing a cross-country regression analysis with a sample of 101 countries. Most of my variables are averages of annual data across a period of 7 years. Every one of my primary variables has data available in each of these 7 years. However, certain countries have data missing in certain years for variables used in my robustness checks.
How should I handle this missing data for each robustness variable? Here are a few ideas I have considered
A. Average data for each country, regardless of missing years
B. Exclude any country with any missing years from data for that respective variable
C. Exclude countries that are missing data up to a certain benchmark, perhaps removing countries that are missing more than 2 or 3 of the 7 years that are being averaged for that respective regressor
D. Only use robustness variables that have available data for every country in every year that is being averaged
Please offer the best solution and any other solutions that would be acceptable.
You either tell that the data are missing for this and that or you calculate the mean and use that as replacement for missing data. Then you tell in the method section that you did this. There may be better ways so somebody else may answer.
Using multiple imputation or full information maximum likelihood (FIML) would probably be your best options. Under the assumption of missing at random (MAR) data, these techniques allow you to include all available data points in your analyses. Most other techniques either lead to a loss of data (and therefore statistical power), make more restrictive assumptions about the missing data mechanism, or both. See
Enders, C. K. (2022). Applied missing data analysis (2nd ed.). Guilford Press.
Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7(2), 147–177. https://doi.org/10.1037/1082-989X.7.2.147
I agree with Christian that multiple imputation or FIML is always the best solution for missing data. See more publications to support this idea.
Article Missing Data in Clinical Research: A Tutorial on Multiple Imputation
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