Identifying MNAR is challenging because it requires understanding the relationship between the missing data and the variables of interest. One approach is to use sensitivity analysis to explore how different assumptions about the missing data mechanism affect the results. Another method is to collect auxiliary data that might help explain the missingness. For example, collecting additional information on why participants dropped out of a study can provide insights into whether the data are MNAR.
