Assessing the benefits of imputing ERP projects with missing data

Incomplete, or missing, data is likely to be encountered in empirical softw are engineering data sets. fn this paper we evaluate some methods for handl ing missing data. The methods are presented and discussed in general and th ereafter applied to effort estimation of ERP projects. We found that two sa mpling-based methods, mean imputation (MI) and similar response pattern imp utation (SRPI), waste less information than listwise deletion (LD). However , MI may introduce more bias than the SRPI method. Compared to sampling-bas ed methods, likelihood-based imputation methods require too large data sets to be realistic to use in empirical software engineering. None of the samp ling-based methods, such as MI and SRPI, seem able to correct bias. So, tho ugh imputation is an attractive idea, the available methods still have seve re limitations.