How does one measure human factors, and how does one measure the attribution of human factors to aviation accidents?

MUHAMMAD F.

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How does one measure human factors, and how does one measure the attribution of human factors to aviation accidents?

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Themes

Human factors, aviation accidents, tests, research, statistics, hypotheses, air station, safety, climate, data, safety culture, US Coast Guard Aviation

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Abstract

Lieutenant Adam Bryant, a U.S. Coast Guard officer, was a recent graduate of flight school and strong believer in the Coast Guard's primary mission of Search & Rescue. On October 29, 2009, Lieutenant Bryant found himself launched in a C-130 airplane in search of a person in a skiff that were overdue for two days. While the Coast Guard was searching off southern California, the U.S. Marine Corps was also flying nearby. At 7:09PM, a Marine Corps Cobra helicopter collided with the Coast Guard airplane, ending the lives of 9 servicemen in an instant (United States Coast Guard, 2010). This tragic event is the catalyst for this researcher's interest in aviation safety, starting officially in 2014, and culminating with the work codified in this dissertation. Most aviation accidents are not mechanical in nature, but instead have a human factors component like fatigue, leadership, and decision-making. The involvement of humans in any system, no matter how highly automated, makes certain ingredients of accidents inevitable.

Introduction
1. Lieutenant Adam Bryant, a U.S. Coast Guard Officer Was a Recent Graduate of Flight School
2. Coast Guard Air Stations
3. What is Known
4. What is Unknown
5. Purpose of the Study
6. Problem Statement
Literature Review
1. U.S. Coast Guard Aviation
2. Philosophies of Safety Culture
3. Leadership Effect
4. Safety Culture Subcomponents
5. Measuring Safety Climate
6. Statistics
7. Research Questions
8. Hypotheses
Methodology
1. Purpose of the Study
2. Research Questions
3. Research Design
4. The Survey
5. Survey Development
6. Statistical Tests
7. Data Collection
8. Institutional Review Board (IRB)
9. Timeline
Results
1. Survey Version 1 - Descriptive Statistics
2. Exploratory Factor Analysis
3. Edited Items
4. Demographic t-Tests
5. ANOVA
6. Construct Correlations
7. Confirmatory Factor Analysis
8. MANOVA
9. Survey Regression
10. Research Question Answers
11. Defining and Determining the Safety Climate in the USCG
12. Predictive Power
Discussion
1. Summary
2. Interpret Hypotheses
3. Practical Implications
4. Generalizability
5. Limitations
6. Future Research
Conclusion

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[...] While safety culture is an invisible reflection of priorities and values pertaining to SMS, safety climate is "the temporal state measure of safety culture. [Safety climate] refers to the perceived state of safety at a particular place at a particular time, is relatively unstable, and subject to change depending on the features of the current environment or prevailing conditions" (Vu & Cieri p. 6). As mentioned above, safety climates affect behaviors and behaviors affect safety climates. Cigularov et al. ( 2010) found that nurses' perceptions pertaining to just culture exacerbate nursing unit personnel's fear of reprisal and willingness to report safety information. [...]

[...] Post-CFA, an output of this research is the suggested list of edits to the survey for the USCG to operationalize. What is the safety climate in USCG aviation? After initially exploring the data during the factor analysis, the question arose if there were any differences in the demographics at all? One of the benefits of a valid survey is that leadership can discover where resources are needed, and subsequently allocate resources according to the need. Further, best practices may be imparted from demographics with high survey results to those with comparatively low survey results. [...]

[...] The aforementioned items were subtracted from each construct's dataframe (using RStudio) and reliability tests were performed a second time. All constructs had reliability figures between .85 and .90 except RM and SF, which were between .80 and .85. PAF 3 Based on previous factor analyses, the following constructs were combined in ver. 2 (Appendix JC and RC; RM with SO, SF, and and GC with SL. Given the combination of constructs coupled with the scree plot outputs, PAF 3 retained five factors. These five factors are the factors that comprise survey ver. [...]

[...] Therefore, the results suggest that both rm and JRC are significant predictors of the number of HF mishaps. For reference, Figure 18 illustrates the relationship between the RM construct and HF-related mishap counts. Figure 18 RM Construct (Survey ver. and Human Factors Mishaps OPHAZREPs. The final stepwise multiple regression was conducted to examine the relationship between the count of OPHAZREPs and the average scores of the same 5 constructs at the unit level. Similar to the previous findings, the final model included RM and JRC constructs, finding significance in predicting the count of OPHAZREPs, 22) = 3.665, p [...]

[...] 0.60 I am able to train to the extent of feeling proficient at my aviation tasks. 0.62 Safety equipment (flight safety & hangar deck PPE) is readily available. 0.68 Safety equipment adequately serves its intended purpose. 0.64 I have sufficient time to prepare for flight duties. 0.65 The unit is willing to invest money and effort to improve safety. 0.55 Non-aviation factors do not contribute to my aviation safety (collateral duty stress, non-USCG influences, etc.) 0.64 Coast Guard computers/IT supports efficient administration of aviation- related tasks (ALMIS, training minimums, flight data, etc.) 0.66 Official guidance (e.g., Flight Manual, Maintenance Procedures, SOP) is incorporated into day-to-day operational decisions at my unit. [...]

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