I am developing a questionnaire and first performing an exploratory factor analysis. After I have the final factor structure, I plan on regressing the factor scores on some demographic covariates. Since I am anticipating missing item responses, I am thinking of imputing the item scores before combining them into factor scores (by average or sum).
I came across a paper that suggested using mice in stata and specifying the factor scores as passive variables. I am wondering if this is the best approach since I read somewhere that says passive variables may be problematic. Or, are there any alternative solutions? Thank you!
https://link.springer.com/article/10.1186/s13104-016-1853-5#Sec17
Here is a link to the paper, and the stata codes are included in the Appendix.
Dear Justine,
First I would advise that you should develop your questionnaire by adapting question items with optimally scale reliabilty coefficients and construct validity scores from questionnaires from past similar empirical studies. Secondly, you need to assess the pattern in which some responses are missing, if any. If u decide to apply multiple imputation, please note that the usual assumption is that the data should be missing at random (MAR), further anchored on assumptions of ignorability/exogeneity (a feature of an experiment design whereby a data collection method does not depend on missing data, nonconfounding, or absence of omitted variable bias which is standard in most analysis models. The MAR model is much safer than the more restrictive missing completely at random (MCAR) assumption which is required for listwise deletion.
Thank you Elvis Munyaradzi Ganyaupfu , Jos Feys , and Ganie Abdul Hamid .
I will make sure my factors are reliable, have adequate internal consistency, and construct validity before exploring missing mechanisms and performing MICE.
Kindly check https://www.theanalysisfactor.com/multiple-imputation-5-recent-findings-that-change-how-to-use-it/
Also check https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4701045/
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