A Mixed Method Approach to Collegiate Aviation Risk Assessment for Dual Cross-Country Flights at the John D. Odegard School of Aerospace Sciences

Risk is unavoidable within aviation. It is not possible to eliminate all risks, but rather to mitigate risks and the potential outcomes that a risk could cause to occur. The University of North Dakota utilizes a risk assessment form that is potentially inadequate in its ability to properly assess the risk of a flight as compared to the risk perceived by the pilots that will conduct the flight. The purpose of this study was to create a more accurate preflight risk assessment form to evaluate the potential risks that could occur on a cross-country training flight and to determine the effectiveness compared to the previous version. Another reason for this particular study was to examine the relationship of perceived risk assessment compared to pilot background characteristics. Overall, the findings of the study determined that the newer format of the preflight risk assessment is worthwhile in regards to time and an accurate representation of the actual risks perceived during training flights. The study also found that the role of participant, the flight instructor, placed more emphasis on risk assessment before a flight. The flight instructor also applied more mitigation techniques prior and during a flight compared to the student. Another significant finding was the private pilot placed more emphasis on risk assessment after the flight compared to commercial/ATP pilots. The results of this research bring about new areas to explore and examine. Numerous recommendations for further research are presented to help improve the overall safety towards the UND Aerospace program and for general aviation as well.

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Aviation

Year

2016

Author dc:creator

• Carlson, Paula Renee

Repository record dc:identifier

https://commons.und.edu/theses/396

OAI identifier oai:identifier

oai:commons.und.edu:theses-1395