I have a dataset of around 15000 subscribers to a Software, and I also have some meta data collected for each of these subscribers based on which I need to do some analysis.
I need to decide whether there is a difference between larger subscribers and smaller subscribers on using a specific third party service. However all of the meta data that contains the 'size of the subscriber' and 'third party service use' is missing for a big portion of subscribers (more than 30%).
However the data on the behaviour of the subscriber within the software is complete.
I was wondering what is the best way to have an unbiased analysis. Most of the methods mentioned online such as imputation , KNN are for handling smaller number of missing data.
Considering that the proportion of missing data is high in our case, is it a viable way to assume the complete dataset as the population and it’s corresponding meta data with large missing records as the survey data and try to use survey weighting methods for removing the bias in estimation and the exploratory analysis?
In other words I am aiming to compare the behaviour of the subscribers with complete data with the ones with missing data in the software and conclude that the subscribers that have similar behaviour in the software (Population variables) have similar size and usage of the third party service (Meta/Survey data).