Missing values in exploratory factor analysis: A ‘best of all possible worlds’ approach to imputation for incomplete survey data

In the social sciences, multi-item scales and factor analyses are standard tools in survey research. In the social sciences, such tools are omnipresent, as are, unavoidably, nonresponses. The question is how to handle missing values when an exploratory factor analysis is intended. Deletion methods will result in — oftentimes substantial and damaging — reduction of power. The seemingly obvious alternative is to keep all respondents and apply imputation to missing values. However, with the true factor structure unknown, theoretically recommendable multiple imputation methods cannot simply be applied. Instead of declaring an entire method unsuitable for exploratory analysis, we propose an approach that keeps the relevant aspects of various methods and combines these by sacrificing less relevant aspects. Doing so, we keep understanding and ease of use in mind, aiming for an approach that is more rigorous and ‘correct’ than what is commonly used in practice, whilst still being straightforward enough to actually be used.