We conducted a pre-post attitudinal survey measuring “Attitudes toward STEM” (28-items; α > .90) and “Multi-Ethnic Identity” (12-items; α > .90) among 50 middle schoolers. Students skipped items resulting in incomplete scale scores. Given the small sample size and nature of the data, we plan to use case mean substitution to account for missing data before computing a “mean difference” variable between pre- and post-survey scores. The dependent variable will be mean attitude change and the predictor will be exposure to culturally-responsive STEM curriculum (regression).
The literature is inconclusive in identifying clear cutoffs for the maximum number of items that can be missing per scale/measure and will still allow case mean substitution (values ranged from 20-60%). We are hoping to keep as much data as possible but want to select an appropriate approach.
Is there a better and/or more robust way to approach this problem?
Are there recommendations for peer-reviewed citations which utilize this approach and/or suggest cutoffs?