  Alongside consistency, completeness of information is one of the key factors
influencing data quality. The objective of this paper is to define ways of
treating missing entries in pairwise comparisons (PC) method with respect to
inconsistency and sensitivity. Two important factors related to the
incompleteness of PC matrices have been identified, namely the number of
missing pairwise comparisons and their arrangements. Accordingly, four
incompleteness indices have been developed, simple to calculate, each of them
take into account both: the total number of missing data and their distribution
in the PC matrix. A numerical study of the properties of these indices has been
also conducted using a series of Montecarlo experiments. It demonstrated that
both incompleteness and inconsistency of data equally contribute to the
sensitivity of the PC matrix. Although incompleteness is only just one of the
factors influencing sensitivity, a relative simplicity of the proposed indices
may help decision makers to quickly estimate the impact of missing comparisons
on the quality of final result.
