I have some missing data for gender in a study (between 10-20%) and am wondering how to address it. There are methods that assign gender based on first name. However, this came up in discussions of them:
I'm interested in:
- thoughts on the above link
- if one is not going to assign gender based on first name, what is the next least bad option? just dropping the missings?
UPDATE with further information: The gender for those with ‘known’ gender were provided by the respondents themselves, and I trust this as valid. There are too many subjects to ‘google’, and I fear that could lead to the same criticism as the name imputation: assigning a gender to someone without their consent. The results I get are that one gender has a higher rate of success than the other overall, and the Unknown as the lowest rate, which perhaps I might interpret as less conscientious in terms of proving complete demographic information, less conscientious overall :) Currently, in the descriptive analysis I am: 1) showing applicants/selections data with the unknown category included in the totals and 2) also dropped (so not included in the totals). The problem is determining to what extent those in the unknown could skew the overall results.