Identifying the Important Contributory Factors From Maintenance Error Decision Aid (MEDA) Data

Abstract

Abstract. Aviation maintenance organizations that monitor frequencies of contributory factor taxonomy codes historically struggle to identify which contributory factors are most potent. This research used Boeing’s Maintenance Error Decision Aid (MEDA) to categorize 138 aviation maintenance accident, incident, and occurrence report narratives. Analyses of contingency tables using Pearson’s chi-square, lambda, and odds ratio statistics revealed that a modest frequency of communication was highly significantly associated with leadership and supervision, individual factors, and technical knowledge contributory factors. The results demonstrate that use of these analyses goes beyond frequency and singular associative methods to identify the presence and strength of associations between contributory factors.