Data quality is a continual issue in almost all sciences. In EFA, as in most other quantitative analyses, data quality issues can bias or completely derail your analyses. Thus, we encourage you to closely examine all data prior to analysis for extreme cases, random, or other types of motivated misresponding, and to deal with missing data effectively.