I have data from a a simple 2(voice system, manual system) x 2(easy, hard) within subjects experiment. Multiple DVs were collected.
Due to issues with both the systems tested, data for one system condition or another is corrupt (so badly that it can be considered missing) for many subjects (ss). Out of data for 34ss, only 14 have data for both system conditions. The remaining 20 have data for the voice system (9) or the manual system (11).
To list it out:
- 14 x data for both
- 11 x data for manual system condition, but missing voice
- 09 x data for voice system condition, but missing manual
I am considering my options:
I can use a repeated measures analysis with listwise deletion of missing data and run my analysis on only the 14 with full data.
I can use multiple imputation to attempt to fill in for 20 missing sets of data, however, that is a lot of data to impute. I'm wondering if that is wise.
I can redesign the experiment into a between/within ss experiment, randomly discarding data from the 14 who have both systems dara and arriving at a balanced 22n in each newly coined 'between' group. The difficulty group would still be analyzed as within.
I can begin collecting again. Sadly, in repairing the facility it was also 'upgraded', meaning enough changes that I'll have to start from scratch.
Any opinions on which is the best path forward? Any paths I have not considered?