Relationship between Recent Flight Experience and Pilot Error General Aviation Accidents

Dissertation
Submitted to Northcentral University
Graduate Faculty of the School of Business and Technology Management
in Partial Fulfillment of the 
Requirements for the Degree of 

DOCTOR OF PHILOSOPHY

by
SARAH J. NILSSON

Prescott Valley, Arizona
May 2011

Copyright 2011

Sarah J. Nilsson

APPROVAL PAGE

Effect of Recent Flight Experience on Pilot Error General Aviation Accidents
by

Sarah J. Nilsson

Approved by:

_______________________________________________	________________
Chair: Bos Philip, Ph.D.							Date

Member: Ison David, Ph.D.  

Member: Cross David Scott, Ed.D.

Certified by:

_______________________________________________	________________
School Dean: A. Lee Smith, Ph.D.					Date

Abstract

Aviation insurance agents and fixed-base operation (FBO) owners use recent flight experience, as implied by the 90-day rule, to measure pilot proficiency in physical airplane skills, and to assess the likelihood of a pilot error accident.  The generally accepted premise is that more experience in a recent timeframe predicts less of a propensity for an accident, all other factors excluded.  Some of these aviation industry stakeholders measure pilot proficiency solely by using time flown within the past 90, 60, or even 30 days, not accounting for extensive research showing aeronautical decision-making and situational awareness training decrease the likelihood of a pilot error accident.  In an effort to reduce the pilot error accident rate, the Federal Aviation Administration (FAA) has seen the need to shift pilot training emphasis from proficiency in physical airplane skills to aeronautical decision-making and situational awareness skills.  However, current pilot training standards still focus more on the former than on the latter.  The relationship between pilot error accidents and recent flight experience implied by the FAA’s 90-day rule has not been rigorously assessed using empirical data.  The intent of this research was to relate recent flight experience, in terms of time flown in the past 90 days, to pilot error accidents.  A quantitative ex post facto approach, focusing on private pilots of single-engine general aviation (GA) fixed-wing aircraft, was used to analyze National Transportation Safety Board (NTSB) accident investigation archival data.  The data were analyzed using t-tests and binary logistic regression.  T-tests between the mean number of hours of recent flight experience of tricycle gear pilots involved in pilot error accidents (TPE) and non-pilot error accidents (TNPE), t(202) = -.200, p = .842, and conventional gear pilots involved in pilot error accidents (CPE) and non-pilot error accidents (CNPE), t(111) = -.271, p = .787, indicate there is no statistically significant relationship between groups.  Binary logistic regression indicate that recent flight experience does not reliably distinguish between pilot error and non-pilot error accidents for TPE/TNPE, χ2 = 0.040 (df =1, p = .841) and CPE/CNPE, χ2 = 0.074 (df =1, p = .786).  Future research could focus on different pilot populations, and to broaden the scope, analyze several years of data.

Acknowledgements
I am eternally indebted to my husband Willem Borst who believed in my journey to PhD-dom even when I did not.  Willem, you are my foundation, my rock, my love.  Words cannot express how blessed I am to have you in my life.  
I extend my heartfelt gratitude to Dr. Phil Bos, my Committee Chair, for always being helpful and prompt, even when the process was not.  Assuredly, I would not have made it this far without your guidance and encouragement.  I will always owe my initial inspiration to Dr. Michael Wetmore, a former committee member.  Thank you for getting me started along this journey.  I owe Dr. David Cross, one of my committee members, a definite appreciation for your eye to detail.  Thanks to you I have learnt to read with one brain cell focused on APA style!  I owe Dr. David Ison, my other committee member, a ton of statistically significant thank-yous for helping me with my data analysis.  
Finally, I owe huge hugs to Lin Saunders, my Academic Advisor, who put up with me throughout the difficult and frustrating ordeal, otherwise known as the dissertation process.  Lin, without your patient and kind words on the phone, your shoulder to cry on, and your prompt e-mail responses I would have given up at least a dozen times.  Thank you!

Table of Contents
List of Tables
List of Figures
Chapter 1: Introduction
Background
Problem Statement
Purpose
Theoretical Framework
Research Questions
Hypotheses
Nature of the Study
Significance of the Study
Definitions
Summary
Chapter 2: Literature Review
Pilot Error and the Industry
Pilot Error and Scenario-Based Training
Scenario-Based Training and Simulation
Pilot Error and Transitions
The Reasons Behind Pilot Error
Aviation Insurance and Fixed-Based Operators
Pilot Error and FAA Law
Pilot Error and Aeronautical Decision-Making
Recent Flight Experience and NTSB Recommendations
Summary
Chapter 3: Research Method
Research Method and Design
Participants
Materials/Instruments
Operational Definition of Variables
Data Collection, Processing, and Analysis
Methodological Assumptions, Limitations, and Delimitations
Ethical Assurances
Summary
Chapter 4: Findings
Results
Evaluation of Findings
Summary
Chapter 5: Implications, Recommendations, and Conclusions
Implications
Recommendations
Conclusions
References

List of Tables

Table 1 Grouping Classifications of Pilots
Table 2 Grouping Classifications of Pilots
Table 3 Mean and Standard Deviation – Tricycle Gear Pilots
Table 4 Mean and Standard Deviation – Conventional Gear Pilots
Table 5 Independent Samples t-Test - Tricycle Gear Pilots
Table 6 Independent Samples t-Test - Conventional Gear Pilots
Table 7 Classification of Data
Table 8 Variables in the Equation
Table 9 Variables Not in the Equation
Table 10 Omnibus Test of Model Coefficients
Table 11 Model Summary
Table 12 Contingency Table for Hosmer-Lemeshow Test – Tricycle Gear Pilots
Table 13 Contingency Table for Hosmer-Lemeshow Test – Conventional Gear Pilots
Table 14 Hosmer-Lemeshow Test
Table 15 Classification of Data
Table 16 Variables in the Equation – Tricycle Gear Pilots
Table 17 Variables in the Equation – Conventional Gear Pilots

List of Figures

Figure 1. Pilot certificates in fixed-wing aircraft.
Figure 2. Accidents - Tricycle landing gear.
Figure 3. Accidents - Conventional landing gear.
Figure 4. Comparison of recent flight experience.

Chapter 1: Introduction
Aviation insurance agents and fixed-base operation (FBO) owners use recent flight experience as a measure of pilot proficiency in physical airplane skills (Avemco, n.d.; Aviation Insurance Resources, n.d.; CS&A Insurance, n.d.; Mid Island Air Service, Inc., n.d.).  These aviation industry stakeholders, following the requirements listed in the Code of Federal Regulations (CFRs), use the 90-day rule of recent flight experience to assess the likelihood of a pilot error accident.  The 90-day rule, put in place by the Federal Aviation Administration (FAA) in Title 14 of the CFRs, titled Recent Flight Experience: Pilot in Command, states “no person may act as a pilot in command of an aircraft carrying passengers…unless that person has made at least three takeoffs and three landings within the preceding 90 days” (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010], para. 1).  
For the past seven years, between 71.9% and 75.8% of all general aviation (GA) fixed-wing aircraft accidents have been attributed to pilot error (Aircraft Owners and Pilots Association Air Safety Foundation [AOPA ASF], 2003, 2004, 2005, 2006, 2007, 2008, 2009).  These statistics caused the FAA, the government agency charged with the provision of safety and efficiency, to look into the root causes of pilot error accidents (FAA, n.d., 2003a, 2006c).  The two main causes were found to be a lack of situational awareness and poor aeronautical decision-making skills (FAA, 2003a).  In 2002, researchers on the Joint Safety Analysis Team (JSAT) discovered these main causes were linked to 51.6% of fatal GA accidents.  Despite these findings, revealed in the FAA Industry Training Standards (FITS) initiative, current pilot training standards focus less on these causes and more on the development of physical airplane skills (also known as stick and rudder skills) that, while important to safe flight, are not the reason for most fatal accidents (FAA, 2003a).  
The generally accepted premise held by aviation insurance agents is that more experience in a recent timeframe predicts less of a propensity for an accident, all other factors excluded (Avemco, n.d.; Aviation Insurance Resources, n.d.; CS&A Insurance, n.d.).  Some aviation insurance agents measure pilot proficiency solely by using time flown within the past 90, 60, or even 30 days (Avemco, n.d.; Phoenix Aviation Managers, Inc., n.d.).  They do not take into account the extensive research showing aeronautical decision-making and situational awareness training decrease the likelihood of a pilot error accident (FAA, 2003a; Guilkey, 1997; Hannigan, Blickensderfer, Greenfield, & Bartosiewicz, 2008; Hendrickson, 2009).  The relationship between pilot error accidents and recent flight experience implied by the FAA’s 90-day rule has not been rigorously assessed using empirical data (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]; Goh & Wiegmann, 2002; Guohua, Baker, Grabowski, Qiang, McCarthy, & Rebok, 2002).  
The first portions of this chapter will contain a brief discussion on the background and nature of recent flight experience as it affects GA pilot error accidents.  This is followed by an outline of the problem addressed by this study and the purpose.  After this brief discussion, the researcher will present the theoretical framework in more detail and questions that this research addressed.
Background
Numerous studies corroborate the FAA findings that the two root causes of pilot error accidents are a lack of situational awareness and poor aeronautical decision-making skills (FAA, 2003a; Guilkey, 1997; Hannigan, Blickensderfer, Greenfield, & Bartosiewicz, 2008; Hendrickson, 2009).  Guilkey (1997) showed that while reliance on flight hours as a predictor of pilot proficiency might be accurate when considering stick and rudder skills used on every flight, it may not be a sufficient predictor of complex problem-solving tasks that form the majority of aeronautical decision-making skills.  More recent research by Hendrickson (2009) showed the need for improved training programs using contextually-based training scenarios that would improve a pilot’s situational awareness.  
While the FAA has only recently seen the need to shift training emphasis from physical airplane skills to aeronautical decision-making and situational awareness skills, within the aviation insurance industry some awareness has been manifest in the form of different, albeit sometimes contradictory, standards for measuring pilot proficiency (Avemco, n.d.; FAA, 2003a; Phoenix Aviation Managers, Inc., n.d.).  For example, some aviation insurance agents prefer the pilot undergo additional training beyond the minimum FAA requirement of 14 C.F.R. § 61.57 that may include aeronautical decision-making and situational awareness skills (Avemco, n.d.; FAA, 2003a; FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]).  Some agents, like those working at Avemco, make attempts to reward pilots with discount programs for participating in additional training that can improve their aeronautical decision-making and situational awareness skills (Avemco, n.d.).  On the other hand, some require that the pilot fly more frequently, such as every 60 or even 30 days, emphasizing the importance of the practice of physical airplane skills (FAA, 2003a; FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]; Phoenix Aviation Managers, Inc., n.d.).  
Despite using these standards of pilot proficiency to mitigate risk, 907, or 72%, of all accidents occurring in GA fixed-wing aircraft in 2008 were still the result of pilot error (AOPA ASF, 2009).  The National Transportation Safety Board (NTSB) data for the years 2002 to 2007 were similar.  Between 71.9% and 75.8% of all GA fixed-wing aircraft accidents were attributed to pilot error (AOPA ASF, 2003, 2004, 2005, 2006, 2007, 2008).  Whereas extensive research exists demonstrating that aeronautical decision-making and situational awareness training decrease the likelihood of pilot error accidents, the relationship between pilot error accidents and recent flight experience implied by the FAA’s 90-day rule has not been rigorously assessed using empirical data (Goh & Wiegmann, 2002; Guohua, et al., 2002).  To improve aviation safety, there should be clear, consistent, or standardized guidelines concerning the significance of the recent flight experience requirement of a pilot.
Problem Statement
Following the requirements listed in the CFRs, aviation insurance agents use the 90-day rule to assess the likelihood of a pilot error accident (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]).  The generally accepted premise by these agents is that more experience in a recent timeframe predicts less of a propensity for an accident, all other factors excluded (Avemco, n.d.; Phoenix Aviation Managers, Inc., n.d.).  To this end, some aviation insurance agents and FBO owners only measure pilot proficiency using time flown within the past 90, 60, or even 30 days (Avemco, n.d.; Aviation Insurance Resources, n.d.; CS&A Insurance, n.d.).  They do not account for the extensive research demonstrating that aeronautical decision-making and situational awareness training decrease the likelihood of a pilot error accident (Cunningham, 2006; Hendrickson, 2009; Summers, Ayers, Connolly, & Robertson, 2007).  Guilkey (1997) showed that while reliance on flight hours as a predictor of pilot performance might be accurate when considering skills used on every flight, it may not be a sufficient predictor of complex problem-solving tasks that form the majority of aeronautical decision-making skills.  More recent research by Hendrickson (2009) showed the need for improved training programs using contextually-based training scenarios that would improve a pilot’s situational awareness.  However, the relationship between pilot error accidents and recent flight experience implied by the FAA’s 90-day rule has not been rigorously assessed using empirical data (Goh & Wiegmann, 2002; Guohua, et al., 2002).  Findings from this study examining the relationship between recent flight experience and pilot error accidents may assist aviation insurance agents, the FAA, and FBO owners in the development of clear, consistent, or standardized guidelines concerning recent flight experience and its significance to pilot error.  
Purpose
The purpose of this quantitative ex post facto study on NTSB accident investigation archival data was to examine the relationship between recent flight experience, in terms of hours flown in the past 90 days, and type of accident from 2008, namely, pilot error versus non-pilot error.  The relationship between pilot error accidents and recent flight experience implied by the FAA’s 90-day rule has not been rigorously assessed using empirical data (Goh & Wiegmann, 2002; Guohua, et al., 2002).  This experience requirement would indicate that as pilots move through that 90-day period they will have a greater propensity for accidents.  In other words, the accident rate should show an increase as the time from the recent experience grows longer.  The population selected was of private pilots of single-engine GA fixed-wing aircraft having conventional and tricycle landing gear configurations.  The significance of the recent flight experience of a sample of private pilots involved in accidents attributed to pilot error was analyzed alongside the recent flight experience of a sample of private pilots involved in accidents caused by factors other than pilot error, using t-tests and binary logistic regression.  The independent variable (X1), recent flight experience of the accident pilots, was defined as hours flown within the last 90 days prior to the accident.  The dependent variable (Y1) was defined as the cause of the accident, either pilot error or some factor other than pilot error.  The results could provide guidance to the aviation insurance agents, the FAA, and FBO owners who currently still use recent flight experience as a direct predictor of pilot proficiency and therefore as an indirect predictor of GA safety (Avemco, n.d.; Mid Island Air Service, Inc, n.d.; Phoenix Aviation Managers, Inc., n.d.). 
Theoretical Framework
An aircraft accident, according to NTSB accident investigators, is an occurrence associated with the operation of an aircraft that takes place “between the time any person boards the aircraft with the intention of flight and all such persons have disembarked, and in which any person suffers death or serious injury, or in which the aircraft receives substantial damage” (NTSB Notification and Reporting of Aircraft Accidents or Incidents and Overdue Aircraft, and Preservation of Aircraft Wreckage, Mail, Cargo, and Records, 49 C.F.R. § 830.2 [2010], para. 2).  The term, general aviation, is defined as “all other uses of aviation in the United States other than military and scheduled aviation” (Bednarek, n.d., para. 1).  These other uses “include, but are not limited to, private and sport flying, aerial photography and surveying, crop-dusting, business flying, medical evacuation, flight training, and the police and fire fighting uses of aircraft” (Bednarek, n.d., para. 1).  For the purpose of this research, general aviation applied to flights conducted in accordance with 14 C.F.R. § 91 titled General Operating and Flight Rules (FAA General Operating and Flight Rules, 2010).  A fixed-wing aircraft is “an airplane or glider whose wing is rigidly attached to the structure” (Crane, 2006, p. 256).  This is to differentiate these aircraft from “rotary-wing aircraft, such as helicopters and autogiros” (Crane, 2006, p. 256).  Furthermore, a fixed-wing aircraft is an aircraft weighing 12,500 pounds or less as defined in the Nall Report (AOPA ASF, 2009). 
Accidents involving pilot error include those concerning fuel management, weather, preflight and taxi, takeoff and climb, cruise, maneuvering, descent and approach, go-around, landing, collisions, and pilot incapacitation (AOPA ASF, 2008).  The FAA deemed the two root causes of pilot error to be a lack of situational awareness and poor aeronautical decision-making skills (FAA, 2003a).  Situational awareness is the “accurate perception and understanding of all the factors and conditions within the four fundamental risk elements that affect safety before, during, and after the flight” (FAA, 1991a, p. iii).  These four fundamental risk elements are noted as the pilot, aircraft, environment, and external pressures (FAA, 2008a).  Aeronautical decision-making is a “systematic approach to the mental process used by aircraft pilots to consistently determine the best course of action in response to a given set of circumstances” (FAA, 1991a, p. ii).  
Despite the findings, revealed in the 2003 FITS initiative, current pilot training standards focus less on these two root causes and more on the development of physical airplane skills (also known as stick and rudder skills) that, while important to safe flight, are not the cause of most fatal accidents (FAA, 2003a).  These physical airplane skills are psychomotor and perceptual skills that a pilot uses to control a specific aircraft.  They are perfected through training and become almost automatic through experience (FAA, 2008b).  To ensure that pilots practice these physical airplane skills, the FAA Administrator has a rule in place under 14 C.F.R. § 61.57 titled Recent Flight Experience: Pilot in Command.  This rule states “no pilot may act as a pilot in command of an aircraft carrying passengers…unless that person has made at least three takeoffs and three landings within the preceding 90 days” (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010], para. 1).  Furthermore, in an airplane equipped with conventional, or tail-wheel, landing gear, this rule states “the takeoffs and landings must have been made to a full stop” (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010], para. 3).  It should be noted that the practice of other skills required during pilot training that could help with aeronautical decision-making and situational awareness is not part of this rule (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]).  A GA pilot need only practice these types of skills once every two years in completion of a flight review (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.56 [2010]).  Finally, 14 C.F.R. § 61.57 only applies to a pilot in command of an aircraft carrying passengers, and not to a pilot flying alone or carrying cargo.  
The 1,254 GA fixed-wing aircraft accidents that occurred in 2008 resulted from 19.78 million flight hours, creating an average accident rate of 6.34 per 100,000 hours (AOPA ASF, 2009).  Eight percent of the accidents were caused by student pilots.  Only one percent was caused by sport pilots.  This was consistent with the relatively small number of sport pilot certificates issued which, as of 2008, was less than 0.5% of the pilot population.  However, half of the accidents were caused by private pilots.  Commercial pilots caused another 27% and ATPs were involved in another 10%.  The remaining 4% was unverified or unknown data (AOPA ASF, 2009).  In 18% of the GA fixed-wing aircraft accidents in 2008, there was a CFI on board.  To avoid this confounding variable of having an instructor on board, who may or may not have contributed to the pilot error, these accidents were not included in this study.  Furthermore, 91% of these accidents happened in single-engine aircraft and 9% in multi-engine aircraft (AOPA ASF, 2009).  This is indicative of the higher number of single-engine aircraft operations in GA.  This breakdown of data directly relates to the research questions outlined later in this chapter.
Some of the accident aircraft were equipped with conventional, or tail-wheel, landing gear whereas the rest were equipped with tricycle landing gear (AOPA ASF, 2008).  Conventional, or tail-wheel, landing gear, is a type of aircraft landing gear “with the main wheels ahead of the center of gravity and a small wheel supporting the tail when the airplane is on the ground” (Crane, 2006, p. 157).  Tricycle landing gear is a type of aircraft landing gear that “uses two main wheels located behind the center of gravity and a nose wheel well ahead of the center of gravity” (Crane, 2006, p. 645).  As a result of their design and structure, tail-wheel landing gear airplanes “exhibit operational and handling characteristics that are different from those of tricycle gear airplanes” (FAA, 2007, p. 13-1).  This explains why a pilot is required to perform all landings to a full stop when flying a tail-wheel landing gear airplane (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]).  Also for these reasons, aviation insurance agents assess risk differently depending on the landing gear configuration (Avemco, n.d.; Phoenix Aviation Managers, Inc., n.d.).  
The topic of this study is the relationship between recent flight experience, or hours flown in the last 90 days prior to the accident, and the number of GA pilot error accidents in 2008 (AOPA ASF, 2009; FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]).  The following two examples of GA pilot error accidents that occurred in 2008 exhibit the typically-found disparities between pilots’ recent flight experience.  The first example is a GA pilot error accident that took place on August 30, 2008, at about 12:15 Eastern Daylight Time.
The pilot reported that the engine ‘stumbled and quit’ about 3 miles from the destination airport.  He was able to momentarily restart the engine before the engine lost complete power.  The pilot performed a forced landing into a cornfield and the nose gear collapsed when it encountered rough terrain.  A post accident inspection did not reveal any anomalies consistent with a loss of engine power.  The left and right fuel tanks contained approximately 3.9 gallons and 2.6 gallons, respectively.  The temperature and dew point in the vicinity of the accident site were 24 degrees and 14 degrees Celsius, respectively.  Data indicated the possibility of moderate carburetor icing at cruise power and serious icing at descent power under those conditions.  (NTSB, n.d.c, para. 1)
The second example is a GA pilot error accident that took place on October 5, 2008, at about 11:15 Eastern Daylight Time.
The pilot was performing slow flight over his property at 60 knots and 1,000 feet above ground level.  He noticed that the airplane was descending and applied full throttle.  The engine did not respond as expected, so he looked for a place to land.  He did not recall if the engine stopped running.  While attempting a forced landing, the airplane collided with trees and terrain in a wooded area.  Examination of the airframe did not reveal evidence of a pre-impact malfunction or failure.  Examination of the propeller at the accident site did not reveal evidence of rotation at impact.  The carburetor heat control was found in the closed (cold) position.  Following the recovery of the wreckage, the engine was tested on the airframe and attained 3,300 rpms with no misfiring or hesitation.  The airplane had accumulated 88 hours since new.  Review of a Federal Aviation Administration Carburetor Icing Probability Chart indicated ‘Serious icing (glide power)’ for the reported temperature and dew point conditions.  (NTSB, n.d.d, para. 1)
NTSB accident investigators deemed the cause of both accidents to have been loss of engine power due to carburetor icing because of the pilots’ failure to apply carburetor heat (NTSB, n.d.c, n.d.d).  The pilots of these two accidents had similar lengths of total flight experience. The first pilot had logged 200 hours as pilot in command, whereas the second pilot had logged 165 hours as pilot in command (NTSB, n.d.c, n.d.d).  Both pilots held a private pilot certificate in single-engine airplanes (NTSB, n.d.c, n.d.d).  The only glaring disparity between these two accident pilots was in their recent flight experience, or time flown in the last 90 days prior to the accident.  The pilot in the first accident had logged only one hour in the past 90 days (NTSB, n.d.c).  Although one hour was a small amount of time, it was still enough to fulfill the FAA requirement (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]; NTSB, n.d.c).  The pilot in the second accident had logged 47 hours in the past 90 days (NTSB, n.d.d).  This disparity in recent flight experience prompts the question, “What effect does recent flight experience have on GA pilot error accidents?”  
In the Aviation Instructors Handbook, it is stated that in the early 20th century Thorndike formulated three laws of learning: the law of readiness, the law of exercise, and the law of effect (FAA, 2008a).  FAA curriculum designers incorporated these laws of educational psychology into their training philosophies in addition to three more: the law of primacy, the law of intensity, and the law of recency (FAA, 2008a).  These training philosophies are routinely taught to CFIs who are then expected to implement them with their students in the course of their day-to-day flight instruction.  The researcher took into account this last law, the law of recency, when investigating the significance of recent flight experience on the number of GA pilot error accidents.  The law of recency states that items most recently learned are best remembered (FAA, 2008a).  The longer time passes, the less will be remembered.  In the Aviation Instructors Handbook, it is also stated “the closer the training or learning time is to the time of the actual scenario, the more apt the learner is to perform successfully” (FAA, 2008a, p. 2-12).  Given this background information on the law of recency, would it not seem prudent for the FAA 90-day rule to also include a review of situational awareness and aeronautical decision-making skills?  However, the intent of this research was to examine the relationship between recent flight experience, in terms of hours flown in the past 90 days, and the likelihood of an accident being the result of pilot error (AOPA ASF, 2009; FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]).  
Despite studies performed by several groups of researchers (FAA, 2003a; Guilkey, 1997; Hannigan, Blickensderfer, Greenfield, & Bartosiewicz, 2008; Hendrickson, 2009), recent flight experience, in terms of hours flown in the past 90 days, continues to serve as a primary predictor of a pilot error accident to some aviation insurance agents and FBO owners (Avemco, n.d.; Mid Island Air Service, Inc, n.d.; Phoenix Aviation Managers, Inc., n.d.).  To date, the relationship between pilot error accidents and recent flight experience implied by the FAA’s 90-day rule has not been rigorously assessed using empirical data (Goh & Wiegmann, 2002; Guohua, et al., 2002).  The number of all GA accidents caused by pilot error has been between 71.9% and 75.8% of all accidents for the past seven years (AOPA ASF, 2003, 2004, 2005, 2006, 2007, 2008, 2009).  
Research Questions
The problem that was analyzed was the significance of recent flight experience, or hours flown in the last 90 days prior to the accident, on the number of GA pilot error accidents that occurred in 2008 (AOPA ASF, 2009; FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  For the last seven years, FAA curriculum specialists have advocated a transition from focus on physical airplane skills to focus on the root causes of pilot error, namely, aeronautical decision-making and situational awareness skills (FAA, 2003a).  The population chosen for this study was determined using three different classifications to mirror those used by aviation industry stakeholders (Avemco, n.d.; FAA, 2003a; FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010; Phoenix Aviation Managers, Inc., n.d.).  The first of these classifications is based upon pilot certification.  Only private pilots were selected for this study from among all other certifications, namely, student, sport, recreational, commercial, airline transport pilot (ATP), and CFI.  Private pilots were responsible for 50% of GA accidents in 2008 (AOPA ASF, 2009).  The second of these classifications is based upon number of aircraft engines, in terms of single-engine and multi-engine.  Only single-engine aircraft were selected.  Single-engine aircraft were involved in 91% of GA accidents in 2008 (AOPA ASF, 2009).  The third of these classifications is based upon aircraft landing gear configuration, in terms of tail-wheel and tricycle.  For this study, the researcher selected both landing gear configurations yet analyzed each group individually as shown below.  Research, therefore, focused on a population of 589 private pilots of single-engine aircraft of both landing gear configurations.  This population represented the largest group in terms of the GA accident rate for the past seven years (AOPA ASF, 2003, 2004, 2005, 2006, 2007, 2008, 2009).
The research questions were formulated, as suggested by Trochim (2006), to look at the relationship between two variables: the independent variable, recent flight experience, in terms of time flown in the past 90 days, and the dependent variable, the cause of accident, pilot error and some factor other than pilot error.  There were two groups of tricycle landing gear pilots, representing 423 accidents in 2008, as shown in Table 1.  The two groups were based on whether the private pilots of single-engine aircraft were involved in an accident caused by pilot error or some other cause unrelated to pilot error.  There were two groups of conventional, or tail-wheel, landing gear pilots, representing 166 accidents in 2008, as shown in Table 1.  Similarly, the two groups were based on whether or not the private pilots of single-engine aircraft were involved in an accident caused by pilot error.

Table 1
Grouping Classifications of Pilots
				Pilot Error (PE)		Non-Pilot Error (NPE)

Tricycle gear (T)			TPE				TNPE
Conventional (tail-wheel) (C)		CPE				CNPE

The research questions that addressed the problem statement in the research study are listed below.  
Q1.	Is there a relationship between the recent flight experience of TPE pilots and TNPE pilots?  
Q2.	Is there a relationship between the recent flight experience of CPE pilots and CNPE pilots?  
Q3.	How does recent flight experience predict the likelihood of a tricycle gear pilot being involved in a pilot error accident?  
Q4.	How does recent flight experience predict the likelihood of a conventional gear pilot being involved in a pilot error accident?  
Hypotheses
The first research question, Q1, was addressed with the formulation of the following hypotheses.  
H10.	 There is no statistically significant relationship between the recent flight experience of TPE pilots and TNPE pilots.  
H1a.	 There is a statistically significant relationship between the recent flight experience of TPE pilots and TNPE pilots.  
The second research question, Q2, was addressed with the formulation of the following hypotheses.  
H20.	 There is no statistically significant relationship between the recent flight experience of CPE pilots and CNPE pilots.  
H2a.	 There is a statistically significant relationship between the recent flight experience of CPE pilots and CNPE pilots. 
The third research question, Q3, was addressed with the formulation of the following hypotheses.  
H30.	 Recent flight experience does not predict the likelihood of a tricycle gear pilot being involved in a pilot error accident. 
H3a.	 Recent flight experience predicts the likelihood of a tricycle gear pilot being involved in a pilot error accident.
The fourth research question, Q4, was addressed with the formulation of the following hypotheses.  
H40.	 Recent flight experience does not predict the likelihood of a conventional gear pilot being involved in a pilot error accident.
H4a.	 Recent flight experience predicts the likelihood of a conventional gear pilot being involved in a pilot error accident.  
Nature of the Study
A quantitative ex post facto approach, as outlined by Vogt (2005), was used on NTSB accident investigation archival data.  As required by Vogt (2005), the participants selected had already been exposed to a particular condition.  In this case the condition was involvement in an accident.  This method, also described by Black (1999), was chosen over others because the accidents were factual, and the experiments, the accidents, had already occurred.  The data on these accidents were collected from pre-existing archival data from the NTSB accident investigation files.   
A power analysis was performed on each population, tricycle landing gear pilots and conventional, or tail-wheel, landing gear pilots, to determine the required sample sizes.  The researcher used G*Power version 3.1.2., power analysis software, to calculate the a priori sample sizes.  A power of 80% was used to calculate the values for alpha value, expected effect size, and statistical test type so the program would generate the minimum sample size needed (Cohen, 1988).  A medium effect size of 0.3 was chosen as this is the recommended value unless a different effect size is otherwise necessary or known (Cohen, 1988).  The significance level or alpha, α = .05 was chosen so the researcher would have 95% confidence the results would not be due to chance.  The calculation for the tricycle landing gear pilot population of 423 rendered a sample size of 202, whereas the calculation for the conventional landing gear pilot population of 166 rendered a sample size of 117 based on a power of 80% (Cohen, 1988).   
The samples of 202 and 117 accidents were randomly selected from among these populations of 423 tricycle landing gear pilots and 166 conventional landing gear pilots, respectively.  Details of the accidents relevant to this study were gathered, to include type of aircraft landing gear configuration and cause of accident.  Data compilation involved investigating and analyzing pages of NTSB final, or probable cause, reports to research the following data for entry into Statistical Procedures for the Social Sciences (SPSS®) software: NTSB accident identification number, number of hours flown in the past 90 days, and cause of accident.  Data processing involved running two separate t-tests and two separate binary logistic regression analyses, one for the tricycle landing gear pilots and another for the conventional, or tail-wheel, landing gear pilots.  
Significance of the Study
The number of all GA accidents that were caused by pilot error has been between 71.9% and 75.8% of all accidents for the past seven years (AOPA ASF, 2003, 2004, 2005, 2006, 2007, 2008, 2009).  Following the requirements listed in the CFRs, aviation insurance agents use the 90-day rule to assess the likelihood of a pilot error accident (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]).  The generally accepted premise by these agents is that more experience in a recent timeframe predicts less of a propensity for an accident, all other factors excluded (Avemco, n.d.; Phoenix Aviation Managers, Inc., n.d.).  To this end, some aviation insurance agents and FBO owners measure pilot proficiency solely by using time flown within the past 90, 60, or even 30 days (Avemco, n.d.; Aviation Insurance Resources, n.d.; CS&A Insurance, n.d.).  They do not take into account the extensive research demonstrating aeronautical decision-making and situational awareness training decrease the likelihood of a pilot error accident (Cunningham, 2006; Hendrickson, 2009; Summers, Ayers, Connolly, & Robertson, 2007).  The relationship between pilot error accidents and recent flight experience implied by the FAA’s 90-day rule has not been rigorously assessed using empirical data (Goh & Wiegmann, 2002; Guohua, et al., 2002).  Findings from this study could assist aviation insurance agents, the FAA, and FBO owners.  These findings on the significance of recent flight experience could lead to the revision of regulations, and insurance or rental policies in an attempt to lower the high accident rate in GA fixed-wing aircraft.  
Definitions
Aeronautical decision-making.  Aeronautical decision-making is a “systematic approach to the mental process used by aircraft pilots to consistently determine the best course of action in response to a given set of circumstances” (FAA, 1991a, p. ii).  
Aircraft accident.  An aircraft accident, as defined by the NTSB, is an occurrence associated with the operation of an aircraft that “takes place between the time any person boards the aircraft with the intention of flight and all such persons have disembarked, and in which any person suffers death or serious injury, or in which the aircraft receives substantial damage” (NTSB Notification and Reporting of Aircraft Accidents or Incidents and Overdue Aircraft, and Preservation of Aircraft Wreckage, Mail, Cargo, and Records, 49 C.F.R. § 830.2 [2010], para. 2).  
Aircraft incident.  An aircraft incident, as defined by the NTSB, is an “occurrence other than an accident, associated with the operation of an aircraft, which affects or could affect the safety of operations” (NTSB Notification and Reporting of Aircraft Accidents or Incidents and Overdue Aircraft, and Preservation of Aircraft Wreckage, Mail, Cargo, and Records, 49 C.F.R. § 830.2 [2010], para. 5). 
Aircraft insurance.  Aircraft insurance, as defined in Plane Sense (FAA, 2008c) is an important consideration for an aircraft owner, renter pilot and CFI.  Furthermore, “responsible aircraft owners always carry sufficient insurance on their aircraft” (FAA, 2008c, p. 1-2).
Aircraft operator.  An aircraft operator is someone who uses, causes to use or authorizes to use aircraft, for the purpose of air navigation including the piloting of aircraft, with or without the right of legal control, as owner, lessee, or otherwise (FAA Definitions and Abbreviations 14 C.F.R. § 1.1 [2010]).
Automation management.  Automation management is the “ability to control and navigate an aircraft by correctly managing its automated systems” (FAA, 2008, p. F-1).
Complex aircraft.  A complex aircraft, as defined by the federal aviation regulations, is an “airplane that has retractable landing gear, flaps, and a controllable pitch propeller, or, in the case of a seaplane, flaps and a controllable pitch propeller” (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.31 [2010], para. 5).
Controlled flight into terrain.  Controlled flight into terrain (CFIT), as defined by the FAA (2003b), is an event that “occurs when an airworthy aircraft is flown, under the control of a qualified pilot, into terrain (water or obstacles) with inadequate awareness on the part of the pilot of the impending collision” (p. 3).
Cross-country time.  Cross-country time is time acquired during flight conducted by a person who holds a pilot certificate, conducted in an aircraft, including a landing at a point other than the point of departure, and involving the use of dead reckoning, pilotage, electronic navigation aids, radio aids, or other navigation systems to navigate to the landing airport (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.1 [2010]).  For the purpose of meeting the aeronautical experience requirements for a private, recreational, or ATP, the cross-country time is time acquired during a flight that includes a landing that is at least a straight-line distance of more than 50 nautical miles from the original point of departure (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.1 [2010]).  For the purpose of meeting the aeronautical experience requirements for a sport pilot, the cross-country time is time acquired during a flight that includes a landing that is at least a straight-line distance of more than 25 nautical miles from the original point of departure (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.1 [2010]).
Fatal injury.  Fatal injury, as defined by the NTSB, means any injury “which results in death within 30 days of the accident” (NTSB Notification and Reporting of Aircraft Accidents or Incidents and Overdue Aircraft, and Preservation of Aircraft Wreckage, Mail, Cargo, and Records, 49 C.F.R. § 830.2 [2010], para. 4). 
Federal Aviation Administration Safety Team (FAASTeam).  FAASTeam managers and program managers within the FAA are on a mission “to improve the nation’s aviation safety record by conveying safety principles and practices through training, outreach, and education” (FAASTeam, n.d.a, para. 1).
Federal Aviation Administration Safety Team (FAASTeam) Wings Program. The Wings program was designed to help individual pilots “construct an educational curriculum suitable for their unique flight requirements” (FAASTeam, n.d.b, para. 2). The Wings program “encourages pilots to continue their aviation educational pursuits and requires education, review, and flight proficiency…that correspond with the leading accident causal factors” (para. 2).  In an Advisory Circular to pilots, AC 61-91H, FAA researchers reported that “regular proficiency training is essential to the safety of all pilots and their passengers” (FAA, 1996, p. 1).
Fixed-base operation.  A fixed-base operation (FBO), as defined by the FAA (2006d), is a “commercial business granted the right by the airport sponsor to operate on an airport and provide aeronautical services such as fueling, hangaring, tie-down and parking, aircraft rental, aircraft maintenance, flight instruction, etc.” (p. 13).   
Fixed-wing aircraft.  A fixed-wing aircraft, as defined in the Dictionary of Aeronautical Terms, is “an airplane or glider whose wing is rigidly attached to the structure” (Crane, 2006, p. 256).  This is to differentiate these aircraft from “rotary-wing aircraft, such as helicopters and autogiros” (p. 256).  For the purpose of this study, a fixed-wing aircraft is an aircraft weighing less than 12,500 pounds or less as defined in the Nall Report (AOPA ASF, 2008).
Flight plan.  A flight plan contains specific information related to the intended route of flight of an aircraft.  Prior to flight, a pilot will typically file a flight plan either with an agent at an FAA flight service station or an air traffic controller (Crane, 2006).
Flight training.  Flight training, as defined by the FAA, means that “training, other than ground training, received from an authorized instructor in flight in an aircraft” (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.1 [2010], para. 6). 
General aviation.  The term, general aviation, was defined by Bednarek (n.d.) during a speech at the United States Centennial of Flight Commission, as “all other uses of aviation in the United States other than military and scheduled aviation” (para. 1).  These uses “include, but are not limited to, private and sport flying, aerial photography and surveying, crop-dusting, business flying, medical evacuation, flight training, and the police and fire fighting uses of aircraft” (para. 1).  The term “came into use during the 1950s” (Bednarek, n.d., para. 1).  For the purpose of this study, general aviation applied to flights conducted under 14 C.F.R. § 91 titled General Operating and Flight Rules (FAA General Operating and Flight Rules 14 C.F.R. § 91 [2010]).
Ground training.  Ground training, according to the FAA Administrator, means that “training, other than flight training, received from an authorized instructor” (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.1 [2010], para. 8). 
High performance airplane.  The term, high performance airplane, is defined as an “airplane with an engine of more than 200 horsepower” (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.31 [2010], para. 6).
Human factors.  The term, human factors, as defined by researchers from the human factors research and engineering division of the FAA (2003c), is 
a multidisciplinary effort to generate and compile information about human capabilities and limitations and apply that information to: equipment, systems, software, facilities, procedures, jobs, environments, training, staffing, and personnel management to produce safe, comfortable, effective human performance.  Thus there are two components to human factors: a) human factors research (acquiring the information), and b) human factors engineering (applying the information). (p. 1-2)
Judgment.  In aviation, judgment is the “mental process of recognizing and analyzing all pertinent information in a particular fashion, a rational evaluation of alternative actions in response to it, and a timely decision on which action to take” (FAA, 1991a, p. iii). 
Mishap.  The term, mishap, is used by military personnel in much the same manner as the term “accident” is used in the civilian world.  According to the United States Air Force, a mishap is “an unplanned or unsought event, or series of events, resulting in death, injury, occupational illness or damage to, or loss of, equipment or property” (Department of the Air Force, 2010, p. 58). 
Multi-engine airplane.  A multi-engine airplane has more than one engine but typically two.  For the purpose of this study, the term, multi-engine airplane, is a “reciprocating or turbo propeller-powered airplane with a maximum certificated takeoff weight of 12,500 pounds or less” (FAA, 2007, p. 12-1).  
Nall Report.  The Nall Report is dedicated to the memory of  J. T. Nall, an NTSB board member who died as a passenger in an airplane accident in Caracas, Venezuela, in 1989 (AOPA ASF, 2008).  This report is AOPA ASFs annual review of GA aircraft accidents that occurred during the previous year (AOPA ASF, 2008).
Physical airplane skills.  Physical airplane skills, as defined by the FAA (2006b), means pilot control of the aircraft during the performance of maneuvers.  The Practical Test Standards (PTS) published by the FAA define the parameters of satisfactory performance based on the pilot certificate that the pilot holds.  These physical airplane skills are psychomotor and perceptual skills that a pilot uses to control a specific aircraft.  They are perfected through training and become almost automatic through experience (FAA, 2008b).  In contrast, mental airplane skills refer to the aeronautical knowledge that the pilot must possess as specified also in the PTS based on the pilot certificate.
Pilot in command.  A pilot in command, as defined by the FAA, means the person who can satisfy these three conditions (FAA Definitions and Abbreviations 14 C.F.R. § 1.1 [2010]).  First, he or she has “final authority and responsibility for the operation and safety of the flight” (para. 1).  Second, he or she has “been designated as pilot in command before or during the flight” (para. 1).  Third, he or she “holds the appropriate category, class, and type rating, if appropriate, for the conduct of the flight” (para. 1). 
Pilot error.  Pilot error, as defined by the FAA (2008b), means that “an action or decision made by the pilot was the cause, or a contributing factor, that led to the accident” (p. 16-1).  This definition also includes “the pilot’s failure to make a decision or take action” (p. 16-1).  Pilot error, as used in the Nall Report (AOPA ASF, 2008), represents an accident cause brought about by a pilot’s decisions concerning fuel management, weather, preflight and taxi, takeoff and climb, cruise, maneuvering, descent and approach, go-around, landing, collisions, and pilot incapacitation.
Risk management.  Risk management is a decision-making process “designed to systematically identify hazards, assess the degree of risk, and determine the best course of action” (FAA, 2008a, p. F-1).  
Simulator.  Simulator is a term “sometimes used loosely to describe any mechanism that can provide a pilot in training with hands-on practice operating the airplane in an on-the-ground environment” (Larson, 2006, p. 44).  
Situational awareness.  Situational awareness is the “accurate perception and understanding of all the factors and conditions within the four fundamental risk elements that affect safety before, during, and after the flight” (FAA, 1991a, p. iii).  These four fundamental risk elements are noted as pilot, aircraft, environment, and external pressures (FAA, 2008a).
Substantial damage.  Substantial damage, as defined by the NTSB, means damage “or failure which adversely affects the structural strength, performance, or flight characteristics of the aircraft, and which would normally require major repair or replacement of the affected component” (NTSB Notification and Reporting of Aircraft Accidents or Incidents and Overdue Aircraft, and Preservation of Aircraft Wreckage, Mail, Cargo, and Records, 49 C.F.R. § 830.2 [2010], para. 9).
Tail-wheel landing gear.  Tail-wheel landing gear, also known as conventional landing gear, is a type of aircraft landing gear “with the main wheels ahead of the center of gravity and a small wheel supporting the tail when the airplane is on the ground” (Crane, 2006, p. 157).
Technically advanced aircraft.  A technically advanced aircraft (TAA) is an aircraft that contains global positioning system (GPS) navigator with a moving map, together with any other systems, such as an autopilot (FAA, 2006a).  Many new TAAs have “highly integrated systems, including advanced engine management, integrated cockpit systems, and ‘glass cockpit’ avionics (i.e. primary flight displays and multifunctional displays)” (FAA, 2006a, p. 1).
Traffic pattern.  The traffic pattern, as defined by the FAA, is “the traffic flow that is prescribed for aircraft landing at, taxiing on, or taking off from, an airport” (FAA Definitions and Abbreviations 14 C.F.R. § 1.1 [2010], para. 1).
Transition training.  Transition training for a pilot is deemed necessary by the FAA whenever a pilot moves up from a simple to a more sophisticated aircraft (FAA, 1974).  According to the FAA Administrator (1974), “greater knowledge and skills are needed for the efficient and safe operation of today’s more powerful aircraft” (p. 1).
Tricycle landing gear.  Tricycle landing gear is a type of aircraft landing gear that “uses two main wheels located behind the center of gravity and a nose wheel well ahead of the center of gravity” (Crane, 2006, p. 645).
Visual Flight Rules.  Visual flight rules (VFR) are adopted by the FAA to “govern aircraft flight when the pilot has visual reference to the ground at all times” (Crane, 2006, p. 672).
Summary
Aviation insurance agents and FBO owners use recent flight experience to predict the likelihood of a pilot error accident (Avemco, n.d.; Aviation Insurance Resources, n.d.; CS&A Insurance, n.d.; Mid Island Air Service, Inc., n.d.).  The generally accepted premise of aviation insurance agents is that more experience in a recent timeframe predicts less of a propensity for an accident, all other factors excluded (Avemco, n.d.; Aviation Insurance Resources, n.d.; CS&A Insurance, n.d.).  Some stakeholders do not take into account the extensive research showing aeronautical decision-making and situational awareness training decrease the likelihood of a pilot error accident (FAA, 2003a; Guilkey, 1997; Hannigan et al., 2008; Hendrickson, 2009).  To date, the relationship between pilot error accidents and recent flight experience implied by the FAA’s 90-day rule has not been rigorously assessed using empirical data (Goh & Wiegmann, 2002; Guohua, et al., 2002).  The topic of this study is the relationship between recent flight experience, or time flown in the last 90 days prior to the accident, and the number of GA pilot error accidents in 2008 (AOPA ASF, 2009; FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]).  

Chapter 2: Literature Review
This study addressed the problem that the relationship between pilot error accidents and recent flight experience implied by the FAA’s 90-day rule has not been rigorously assessed using empirical data (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]; Goh & Wiegmann, 2002; Guohua, et al., 2002).  This experience requirement would indicate that as pilots move through that 90-day period they will have a greater propensity for accidents.  In other words, the accident rate should show an increase as the time from the recent experience grows longer.  Despite extensive research demonstrating aeronautical decision-making and situational awareness training decrease the likelihood of a pilot error accident, some aviation insurance agents use the 90-day rule to assess the likelihood of a pilot error accident (Avemco, n.d.; Aviation Insurance Resources, n.d.; Cunningham, 2006; Hendrickson, 2009; Summers, Ayers, Connolly, & Robertson, 2007).  The majority of GA accidents are associated with “pilot performance rather than mechanical or structural failure” (Pauley, O’Hare, & Wiggins, 2009, p. 201).  Of the 1,254 accidents that occurred in 2008 in GA fixed-wing aircraft, and subsequently investigated by the NTSB, 907, or 72%, were attributed to pilot error (AOPA ASF, 2009).  
Pilot error means that “an action or decision made by the pilot was the cause, or a contributing factor, that led to the accident” (FAA, 2008b, p. 16-1).  This definition also includes “the pilot’s failure to make a decision or take action” (FAA, 2008b, p. 16-1).  Pilot error, as used in the Nall Report and in this study, represents an accident cause brought about by a pilot’s decisions concerning fuel management, weather, preflight and taxi, takeoff and climb, cruise, maneuvering, descent and approach, go-around, landing, collisions, and pilot incapacitation (AOPA ASF, 2008).  GA pilot performance plays a “prominent role in overall public safety and consequently, it is of the utmost importance that the industry maintains the highest standards to ensure safety and the lowest possible accident rate” (Bolstad, Endsley, Costello, & Howell, 2010, p. 269-270).  Yet, according to Bolstad et al. (2010), GA pilots receive “little direct instruction on how to carry out preflight planning and how to use this process to manage workload and SA [situation awareness] when in flight” (p. 272).  
This literature review will familiarize the reader with aviation human factors research, most of which was performed in the past five years although some key research dating to earlier years had to be included for clarity.  This aviation human factors research was performed by investigators working at government agencies, such as the NTSB and the FAA; researchers working for military organizations, such as the United States Air Force; and researchers at numerous universities such as Embry-Riddle Aeronautical University.  This research is intended to provide the reader with a clear picture of the current GA situation relating to pilot error.  To this end, evidence will be provided directly as seen through the eyes of agents working in the aviation insurance industry and FBO owners.  
First, the current GA situation relating to pilot error will be explained in the context of the aviation industry as a whole.  Next, pilot error will be placed within the context of scenario-based training, as a method to reduce pilot error.  The issues surrounding pilot transition training and its effect on pilot error will be discussed.  A brief discussion on the reasons behind pilot error will follow.  Next, the researcher will provide evidence directly from agents working within the aviation insurance industry and FBO owners.  Pilot error will be discussed in relation to the CFRs.  The review will then include a section relating pilot error to aeronautical decision-making.  Finally, the review will conclude with recommendations from NTSB accident investigators dating as far back as 30 years showing the need for changes to the CFRs pertaining to recent flight experience.
Pilot Error and the Industry
It is necessary, before delving into the body of knowledge, to become familiar with the hierarchy attached to pilot certificates to appreciate the classification system used in the aviation industry and in the following study.  This classification system of pilot certificates is directly related to the research questions in this study.  Pilot certificates in fixed-wing aircraft, as demonstrated in Figure 1, consist of student, sport, recreational, private, commercial, airline transport pilot (ATP), and certificated flight instructor (CFI) (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61 [2010]).  
A person may apply for a student pilot certificate/FAA medical certificate through a designated aviation medical examiner (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.85 [2010]).  Once the person has attained at least a third-class medical certificate/student pilot certificate, and has undergone required ground and flight training, he or she will conduct solo flights only under the strict supervision and by direct authorization of his or her CFI as stated in 14 C.F.R. § 61.87, 61.89, 61.93, and 61.95 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  This authorization, or endorsement, is written both on the student pilot certificate and on the student pilot’s logbook, and expires after 90 days in accordance with 14 C.F.R. § 61.87 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  

Figure 1. Pilot certificates in fixed-wing aircraft. 

A person may be eligible for a student pilot certificate in a fixed-wing aircraft as early as 16 years of age in accordance with 14 C.F.R. § 61.83 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  Among several limitations, a student pilot is not allowed to carry passengers or transport property for compensation or hire as stated in 14 C.F.R. § 61.89 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  According to 14 C.F.R. § 61.19, a student pilot certificate expires 60 calendar months after the month of the date of the examination shown on the medical certificate if the student has not yet reached his or her 40th birthday (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  The 60 calendar months rule changes to 24 calendar months for a student pilot who has reached his or her 40th birthday (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  
The sport pilot certificate is an option for persons of at least 17 years of age as stated in 14 C.F.R. § 61.305 and 61.307 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  The requirement for this application in a fixed-wing single-engine aircraft is at least 20 hours of flight time.  At least 15 of these 20 flight hours are to be performed with an authorized instructor.  At least five of these 20 hours are to be performed in solo flight, i.e. as the sole occupant of the aircraft in accordance with 14 C.F.R. § 61.313 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  
A student pilot in a fixed-wing aircraft may apply for a recreational pilot certificate as early as 17 years of age so long as he or she has completed required ground and flight training and possesses logbook endorsements to that effect from his or her CFI as outlined in 14 C.F.R. § 61.96 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  The recreational pilot applicant must have logged at least 30 hours of flight training, of which at least 15 hours were performed with a CFI in accordance with 14 C.F.R. § 61.99 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  Among several limitations, a recreational pilot is only allowed to carry one passenger and typically is restricted to a 50 nautical mile radius of the departure airport unless additional training is completed as required by 14 C.F.R. § 61.101 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  
On the other hand, a student pilot in a fixed-wing aircraft may choose to apply for a private pilot certificate rather than a recreational one, also as early as 17 years of age so long as he or she has completed the required ground and flight training and possesses logbook endorsements to that effect from his or her CFI as required by 14 C.F.R. § 61.103 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  The private pilot applicant must have logged at least 40 hours of flight training, of which at least 20 hours were performed with a CFI, and at least 10 hours were performed solo, i.e. as the sole occupant of the aircraft in accordance with 14 C.F.R. § 61.109 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  One of the privileges of a private pilot is that he or she may now carry any number of passengers the aircraft will permit, given weight and balance limits and available seats, as well as property as stated in 14 C.F.R. § 61.113 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  One of the limitations of 14 C.F.R. § 61.113, however, is that a private pilot may not carry passengers or property for compensation or hire (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  That privilege, according to 14 C.F.R. § 61.133, is reserved for the commercial pilot and will be discussed in the next paragraph (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  With an increase in flight experience, the private pilot’s privileges also increase.  For example, after amassing 100 hours of logged flight time, and receiving additional training, the private pilot may then tow a glider or unpowered ultra light vehicle.  After amassing 200 hours of logged flight time the private pilot may act as an aircraft salesman and demonstrate an aircraft to a prospective buyer.  Finally, after amassing 500 hours of logged flight time the private pilot may then act as pilot in command of a charitable, nonprofit, or community event as stated in 14 C.F.R. § 61.113 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  
After a private pilot has logged 250 hours of flight time, and meets other requirements as to the type of flight time flown, he or she may choose to apply for a commercial pilot certificate as outlined in 14 C.F.R. § 61.129 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  The pilot must be at least 18 years of age as required by 14 C.F.R. § 61.123 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  Holding a commercial pilot certificate allows a pilot to carry passengers or property for compensation or hire in accordance with 14 C.F.R. § 61.133 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  Usually, sometime before obtaining a commercial pilot certificate, a private pilot may have applied for an instrument rating. The additional training for an instrument rating involves 40 hours of actual or simulated instrument time, of which 15 hours must have been received from an authorized instrument instructor as stated in 14 C.F.R. § 61.89 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  If a commercial pilot does not hold an instrument rating, he or she is prohibited from carrying passengers at night or outside a 50 nautical mile radius from the departure airport in accordance with 14 C.F.R. § 61.133 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010). 
After a commercial pilot with an instrument rating has reached 23 years of age and amassed at least 1,500 flight hours, he or she may apply for an ATP certificate as stated in 14 C.F.R. § 61.153 and 61.159 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  In addition to enjoying the same privileges as a commercial pilot with an instrument rating, the ATP can now also instruct other ATPs in the aircraft or in a simulator in accordance with 14 C.F.R. § 61.167 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  The ATP certificate is the pinnacle of fixed-wing pilot certificates, with one exception, the CFI certificate(s).  
To apply for a CFI certificate, a person must first hold either a commercial pilot or an ATP certificate, and be at least 18 years of age as required by 14 C.F.R. § 61.183 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  CFIs are allowed to train and endorse other pilots “within the limitations of that person’s flight instructor certificate and ratings” (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.193 [2010], para. 1).  Furthermore in accordance with 14 C.F.R. § 61.195, a CFI must be additionally certified as an instrument instructor (CFII) to train pilots in preparation for an instrument rating, and a CFI must be additionally certified as a multi-engine instructor (MEI) to train pilots to fly multi-engine aircraft (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  The CFI certificate expires after 24 calendar months “from the month in which it was issued, renewed, or reinstated, as appropriate” (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.19 [2010], para. 6).
Federal aviation regulations state “no pilot may act as a pilot in command of an aircraft carrying passengers…unless that person has made at least three takeoffs and three landings within the preceding 90 days” (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010], para. 1).  In addition, the rule states that in an airplane equipped with tail-wheel landing gear, “the takeoffs and landings must have been made to a full stop” (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010], para. 3).  For years agents working in the aviation insurance industry have used this indicator as one of the standards by which to measure pilot proficiency by requiring the pilot to provide proof of time flown in the past 90 days (Avemco, n.d.; FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010; Phoenix Aviation Managers, Inc., n.d.).  
The “fundamental principle of all forms of insurance is that the premiums of the many will pay the losses of the few” (Flouris, Hayes, Pukthuanthong-Le, Thiengtham, & Walker, 2009, p. 228).  Aviation insurance is no different.  Aviation insurance owners must cover the cost of running their businesses, pay their expenses, share commissions among their agents, as well as provide their stakeholders with a reasonable profit and return on invested capital.  Nevertheless, several features set aviation insurance apart from all other forms of insurance.  Three major differences are “the limited number of risks available to insure, the comparatively small size of the insurance class, and the industry’s exposure to catastrophic events” (Flouris et al., 2009, p. 229).  It is in the interest of aviation insurance underwriters that catastrophic events are kept to a minimum.  
Mackey (2010a) explained the reasons why pilots should purchase their own renters or non-owned aircraft insurance coverage.  First, pilots should purchase liability insurance protection that, in the unlikely event of an accident, would cover any damage the pilot may cause to the non-owned airplane (Mackey, 2010a).  Secondly, during the time a student pilot is receiving flight training from a flight school, the pilot could, in the event of an accident, be held responsible for any damages he or she may cause.  These damages could include damages to the airplane and loss of income incurred by the flight school while the airplane is being repaired (Mackey, 2010a).
NTSB accident investigators report that pilot error is the cause of anywhere between 71.9% and 75.8% of all GA accidents (AOPA ASF, 2003, 2004, 2005, 2006, 2007, 2008).  However, according to some researchers (FAA, 2000), the problem is actually larger than this.  These accidents represent only the tip of the iceberg or the 10% that shows on the surface.  There remains submerged, the other 90% representing all incidents that were not reported together with some incidents that were reported (FAA, 2000).  Heinrich, as quoted in the FAA System Safety Handbook, conducted a statistical study of accidents and determined that “out of 300 incidents, one fatal accident may occur” (FAA, 2000, p. 16-2).  Byrd, also quoted in the FAA System Safety Handbook, later conducted a similar study and found that out of 600 incidents one fatal accident occurred (FAA, 2000).  It would, therefore, seem prudent to research ways to minimize GA pilot error.  
Aviation insurance agents and FBO owners currently still use recent flight experience as a direct predictor of pilot performance and therefore as an indirect predictor of GA safety (Avemco, n.d.; Mid Island Air Service, Inc, n.d.; Phoenix Aviation Managers, Inc., n.d.).  According to Chappell (2010), aviation insurance underwriters look more favorably on the flying skills of professional pilots than those of GA pilots because of the commitment to training and safety of the former group.  This commitment to training and safety is what makes professional pilots a safer risk (Chappell, 2010).  For similar reasons, the type of flying the pilot performs also influences the insurance rates.  Underwriters, according to Chappell (2010), offer the lowest rate structure to “professionally flown, corporate/industrial aid aircraft” (p. 19).  Underwriters offer the next best set of rates to “professionally managed aircraft and charter aircraft” (Chappell, 2010, p. 19).  Next, underwriters offer the third best set of rates to pilots flying aircraft for business and pleasure (Chappell, 2010).  Lastly, according to Chappell (2010), underwriters offer the most expensive rates to flight schools and “special usage aircraft such as powerline and pipeline patrol, or aerial application” (p. 19).  
Chappell (2008) contended that in the aviation insurance underwriting world, pilot experience in specific make and model of aircraft is more a measure of competency than total flight time.  To exemplify, Chappell (2008) watched “an underwriter compare a 1,000-hour total time pilot with 500 hours in the make and model equally with a 10,000-hour pilot who has no make and model experience” (p. 10).  Continual practice and instruction by qualified CFIs have proven to be instrumental to good pilot performance (Chappell, 2008).  This is the reason why underwriters require annual training in sophisticated aircraft (Aircraft Owners and Pilots Association Insurance Agency Inc., n.d.) in lieu of the 24 calendar month flight review requirement under 14 C.F.R. § 61.56 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  Mackey (2010b) contended that if a pilot does not fly often enough, the aviation insurance underwriter would claim he or she is not staying proficient.  However, what constitutes ‘often enough’ is debatable, so a pilot should strive to maintain his or her pilot skills by taking an annual flight review, instead of the recommended flight review every 24 calendar months (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  Additionally, a pilot should strive to engage in extra training or participate in recurrent training programs (Mackey, 2010b).  
Cunningham (2006) was of the opinion that accident rates bear a direct relationship to training frequency.  Therefore, a pilot “MUST train at least two times per year” (Cunningham, 2006, p. 15).  This frequency is double the annual training that was recommended by Mackey (2010b), and four times the biennial training that is currently recommended by the FAA Administrator (2006c).  Cunningham (2006) believed that this twice-a-year training regime is the reason for the safety record of corporate flight departments.  GA pilots could reduce their accident rate by modeling the actions of corporate and airline pilots and investing in frequent training, incorporating standard operating procedures (SOPs), and utilizing aeronautical decision-making skills (Cunningham, 2006).
FAA researchers found two root causes of GA accidents: a pilot’s lack of situational awareness and poor aeronautical decision-making skills (FAA, 2003a).  Situational awareness is the “accurate perception and understanding of all the factors and conditions within the four fundamental risk elements that affect safety before, during, and after the flight” (FAA, 1991a, p. iii).  These four fundamental risk elements are noted as pilot, aircraft, environment, and external pressures (FAA, 2008a).  Aeronautical decision-making is a “systematic approach to the mental process used by aircraft pilots to consistently determine the best course of action in response to a given set of circumstances” (FAA, 1991a, p. ii).  Pauley et al. (2009), concurring with this FAA research, additionally contended that fatal accidents were more likely attributed to poor decision-making than nonfatal ones.  Similarly, military accident investigators, analyzing 208 military accidents, found three major types of pilot error lead to the mishaps: decision-making, mission analysis, and situational awareness (Lu, Wetmore, & Przetak, 2006).  The term, mishap, is used by military personnel in much the same manner as the term “accident” is used in the civilian world.  According to the United States Air Force, a mishap is “an unplanned or unsought event, or series of events, resulting in death, injury, occupational illness or damage to, or loss of, equipment or property” (Department of the Air Force, 2010, p. 58). 
However, current pilot training standards focus less on these two root causes and more on the development of physical airplane skills, also known as stick and rudder skills, that, while important to flight safety, are not the cause of most fatal accidents (FAA, 2003a).  These physical airplane skills are psychomotor and perceptual skills that a pilot uses to control a specific aircraft.  They are perfected through training and become almost automatic through experience (FAA, 2008b).  Guilkey (1997) showed that while reliance on flight hours as a predictor of pilot performance might be accurate when considering physical airplane skills used on every flight, it may not be a sufficient predictor of complex problem-solving tasks that form the majority of aeronautical decision-making skills.  
Soares (2002) studied the role of total flight experience in the determination of accident outcomes in GA and found that the pilots most at risk for an accident were either those pilots who had logged their first thousand hours of flight time or those pilots who had logged between three to four thousand hours of flight time.  This research suggested that policy measures should focus on “young and inexperienced pilots” (p. 130).  One thousand hour, or “young”, pilots were still relatively new to flying and 3,000- to 4,000-hour pilots were typically transitioning to jets or more sophisticated aircraft and consequently were still relatively “inexperienced”.  Soares (2002) based the study upon NTSB accident investigation archival data.  Likewise, this study on recent flight experience is also based upon NTSB accident investigation archival data collected from the NTSB website.  
For the same reasons as this researcher, Soares (2002) also focused upon GA pilots because “the private or ‘general aviation’ group of flights has long been ignored in the safety and aviation literature” (p. 92).  Detwiler et al. (2006) focused the study on GA accidents in Alaska because “historically, general aviation (GA) accidents have been overlooked and their impact under-appreciated when compared with those in the commercial or military sector” (p. i).  According to Detwiler et al. (2006), the reason for the historical neglect of the GA sector is the following:
When a commercial airliner crashes, it instantly becomes headline news, shaking the confidence of the flying public.  In contrast, GA accidents happen virtually every day, yet they receive little attention and seldom appear on the front page of USA Today.  Perhaps this is because they happen in isolated places, involving only a couple of unfortunate souls at a time.  In fact, unless the plane crashed into a school, church, or some other public venue, it is unlikely that anyone outside the local media, government, or those intimately involved with the accident even knew it happened. (p. 1) 
Pilot Error and Scenario-Based Training
Summers, Ayers, Connolly, and Robertson (2007) and Hendrickson (2009) indicated need for improved training programs using contextually-based training scenarios that would improve a pilot’s situational awareness.  Scenario-based training is also the focus of FAA curriculum designers (FAA, 2006a).  Scenario-based training is a “training system that uses a highly structured script of real-world experiences to address flight training objectives in an operational environment” (FAA, 2006a, p. 6).  This type of training can be applied to initial flight training of student, recreational, and private pilots; transition training for pilots moving into more complex and technologically advanced aircraft (TAA); upgrade training for pilots moving from a second in command role to pilot in command role; ‘upset’ training whereby pilots learn to recover from unusual aircraft attitudes; and recurrent training for maintaining pilot proficiency.  Scenario-based flight training “represents a non-traditional approach to GA pilot training” where the “shift is away from meaningless drill in the practice area toward meaningful application as a part of a normal flight activity” (FAA, 2006a, p. 11).  
The FAA/Industry Training Standards (FITS) program utilizes “scenario-based, learner-focused training materials that encourage practical application of knowledge and skills” (FAA, 2006a, p. 1).  FITS is a partnership between the FAA, the aviation industry, and academia (FAA, 2006a).  To name a few members in this partnership, FITS members include the University of North Dakota Aerospace, Embry-Riddle Aeronautical University, Eclipse Aviation, Adam Aircraft, Cessna, Cirrus Design, Airshares Elite, Garmin, Avidyne Corporation, Avemco Insurance Company, and the FAA Air Transportation Center of Excellence for General Aviation Research (FAA, 2006a).  In addition to partnering with FITS, the leadership at Eclipse Aviation has become the first GA aircraft manufacturer to create a safety management system utilizing “key elements used by major airlines, military aviation organizations and large corporate flight departments” (George, 2007, p. 144).  These key elements include using “comprehensive SOPs, rigorous pilot training and QAR [Quick Access Recorders] feedback” (George, 2007, p. 144).
The FITS program was created to “help pilots of technically advanced aircraft (TAAs)” (FAA, 2006a, p. 1).  TAA have more automation and typically achieve greater performance than their earlier counterparts.  A TAA is an aircraft that contains a global positioning system (GPS) navigator with a moving map, together with any other systems, such as an autopilot (FAA, 2006a).  Many new TAA have “highly integrated systems, including advanced engine management, integrated cockpit systems, and ‘glass cockpit’ avionics (i.e. primary flight displays and multifunctional displays)” (FAA, 2006a, p. 1).  The pilot, therefore, must “develop the risk management skills and in-depth systems knowledge needed to safely operate and maximize the capability of these aircraft in the National Airspace System (NAS)” (FAA, 2006a, p. 1).  
The initial movement of pilots into TAA caused underwriters within the aviation insurance industry to struggle with the decision concerning which training guidelines to establish (Davis, 2006).  Finally, the underwriters agreed that to obtain insurance, in addition to the minimum FAA requirements, the pilots of TAA would need to show proof of completion “of initial TAA-specific training from an FAA Industry Training Standards (FITS) certified facility” (Davis, 2006, p. 12).  Furthermore, these aviation insurance underwriters began to favor CFIs who had been through an initial training program for the TAA system at a FITS-certified training center (Davis, 2006).  The reason was because the CFI would have been schooled in the TAA panel as well as the make and model of the aircraft (Davis, 2006).  Cessna Aircraft Company began to offer a training program for CFIs at their factory, after which the CFI was awarded a Cessna FITS Accepted Instructor (CFAI) designation (Davis, 2006).  Likewise, Diamond Aircraft began to offer a training program and awarded the Diamond Aircraft Factory Authorized Training (DAFAT) designation (Davis, 2006).  Other manufacturers, such as Mooney, Beechcraft, and Cirrus Aircraft also began to do the same.  In addition to providing training to CFIs, these aircraft manufacturers also provide training to new owners of TAA.  These courses often meet or exceed the requirements of aviation insurance underwriters (Davis, 2006).  The importance of TAA-specific training then filtered down to FBO owners who now choose to purchase TAA simulators, hire more CFIs who possess TAA-certifications, or both (Davis, 2006). 
One of the training goals of FITS is the advancement of higher order thinking as it relates to “risk management, aeronautical decision-making, situational awareness, pattern recognition (emergency procedure) and decision-making” (FAA, 2006a, p. 5).  In the Aviation Instructors Handbook, FAA curriculum writers advocated the use of higher order thinking skills from Bloom’s taxonomy which are analysis, synthesis, and evaluation skills, in addition to training based on problems or scenarios (FAA, 2008a).  This school of thought is based upon constructivism, a derivative of cognitive theory that holds “that learners do not acquire knowledge and skills passively but actively build or construct them based on their experiences” (FAA, 2008a, p. 2-4).  Higher order cognitive skills training includes teaching skills “to support attention sharing and contingency planning, such as information seeking and filtering, task management and prioritization, self-checking, and other meta-skills identified to be important for developing and maintaining SA [situation awareness]” (Bolstad et al., 2010, p. 272).
Dornan, Beckman, Gossett, and Craig (2007) studied a FITS scenario-based training program designed “to increase the knowledge and safety of pilots using this technology by focusing on GPS mode awareness, situational assessment, risk and time management, and situational awareness” (p. 1).  This study by Dornan et al. (2007) revealed that utilizing a GPS FITS scenario-based training program for GPS training “significantly reduced omission errors and incorrect or inappropriate use of the GPS when compared to controls” (p. 1).  These findings could help reduce the number of GA accidents caused by controlled flight into terrain (CFIT). 
Pilots commonly call higher order thinking skills aeronautical decision-making skills (FAA, 2008a).  However in addition to aeronautical decision-making skills, risk management, automation management, situational awareness, and controlled flight into terrain awareness are also skills encompassed by higher order thinking skills (FAA, 2008a).  Risk management is a decision-making process “designed to systematically identify hazards, assess the degree of risk, and determine the best course of action” (FAA, 2008a, p. F-1).  Automation management is the “ability to control and navigate an aircraft by correctly managing its automated systems” (FAA, 2008, p. F-1).  Situational awareness is the “accurate perception and understanding of all the factors and conditions within the four fundamental risk elements that affect safety before, during, and after the flight” (FAA, 1991a, p. iii).  These four fundamental risk elements are noted as pilot, aircraft, environment, and external pressures (FAA, 2008a).  CFIT is the term used to describe what “occurs when an airworthy aircraft is flown, under the control of a qualified pilot, into terrain (water or obstacles) with inadequate awareness on the part of the pilot of the impending collision” (FAA, 2003b, p. 3).  CFIT accidents account for 17% of all GA accidents (FAA, 2003b).  In the Aviation Instructors Handbook, FAA curriculum writers also advocate the use of combining discussion with lecture to provide active student participation as well as to allow students to develop higher order thinking skills because discussion “helps students learn to evaluate ideas, concepts, and principles” (FAA, 2008a, p. 4-13).  
As far back as 1966, researchers at the McMaster University School of Medicine in Canada pioneered a new approach to teaching and curriculum design called problem-based learning.  Problem-based learning shifts the focus of learning from an instructor-centered approach to a student-centered approach (FAA, 2008a).  In this type of learning environment, lessons are structured “in such a way as to confront students with problems encountered in real life that force them to reach real world solutions” (FAA, 2008a, p. 4-16).  In the Aviation Instructors Handbook, FAA curriculum writers advocated the use of three types of problem-based instruction: scenario-based, collaborative problem-solving, and case study (FAA, 2008a).
Scenario-Based Training and Simulation
Scenario-based training can be performed in a simulator, a “mechanism that can provide a pilot in training with hands-on practice operating the airplane in an on-the-ground environment” (Larson, 2006, p. 44).  The FAA Administrator categorizes simulators depending on the level of realism they are capable of delivering to the user.  Starting with the least realistic, flight training devices are “designated by the FAA on a numerical scale of one though seven” (Larson, 2006, p. 44).  The FAA Administrator considers a flight training device as:
A device that is a full-size replica of the instruments, equipment, panels, and controls of an aircraft, or set of aircraft, in an open flight deck area or in an enclosed cockpit, including the hardware and software for the systems installed, that is necessary to simulate the aircraft in ground and flight operations; need not have a force (motion) cueing or visual system; and has been evaluated, qualified, and approved by the Administrator. (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.1 [2010], para. 7)
More realistic, and usually employing satellite imagery, and hydraulics or electronic motor drives for motion, simulators are rated by the FAA Administrator with letters, “A for the simplest through D for the most complex” (Larson, 2006, p. 44).  The FAA Administrator considers a flight simulator as:
A device that is a full-size aircraft cockpit replica of a specific type of aircraft, or make, model, and series of aircraft; includes the hardware and software necessary to represent the aircraft in ground operations and flight operations; uses a force cuing system that provides cues at least equivalent to those cues provided by a 3 degree freedom of motion system; uses a visual system that provides at least a 45 degree horizontal field of view and a 30 degree vertical field of view simultaneously for each pilot; and has been evaluated, qualified, and approved by the Administrator. (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.1 [2010], para. 5)
Aviation insurance underwriters are highly in favor of pilots using simulators for training purposes, especially when the simulators are highly specific to the aircraft they are meant to represent and also true to life (Larson, 2006).  For example, FlightSafety engineers design simulators with such a high degree of reality that the visual systems show individual leaves in the trees moving as a helicopter descends closer to the ground (Larson, 2006).  GA pilots and instructors will be able to utilize simulators more frequently in the future as the price of technology decreases.  The price of technology for simulators is closely tied with that of the personal computer world.  Currently, members of the aviation community have easy access to graphics cards, and satellite imagery; even “data in general, from aircraft data to geographical data” (Larson, 2006, p. 46).  Furthermore, with the growth in home theater design and availability, image projectors now utilize liquid crystal on silicon technology (Larson, 2006).  According to Larson (2006), liquid crystal on silicon technology “delivers high brightness, higher resolution …makes more business sense and, from a performance perspective, meets the requirements” of flight simulators (p. 47).
Eighty five percent of all helicopter accidents can be traced to pilot error (Phillips, 2007).  In an effort to reduce this high number of helicopter accidents, Phillips (2007) suggested a “massive investment in mission- and pilot-specific simulator-based training and the adoption of safety management and terrain avoidance systems” (p. 57).  Phillips (2007) is in total agreement with the FITS philosophy (FAA, 2003a).  According to Phillips (2007), a “recurring element in helicopter accidents is a pilot’s proficiency in the aeronautical decision-making process” (p. 57).  Failures in training, SOPs, maintenance, and other factors can all unite to expose a pilot to unexpected and critical decision-making scenarios (Phillips, 2007).  
Currently, aviation insurance underwriters are demanding that helicopter pilots undergo mission-specific training that replicates as closely as possible the environments that these pilots will encounter during the course of their operations (Phillips, 2007).  This training should consist of scenarios flown in simulators “equipped with accurate visual databases applicable to the mission being flown, and will be integrated with ADM [aeronautical decision-making] skills” (Phillips, 2007, p. 57).  Researchers from the Joint Helicopter Safety Analysis Team are optimistic that by increasing the emphasis on aeronautical decision-making training for helicopter pilots by using simulator-based training scenarios the accident rate could be reduced by anywhere from 36% to 54% (Phillips, 2007).  
During the course of practicing scenario-based training scripts, pilots should also strive to incorporate and reinforce the practice of SOPs.  Deviations from SOPs “erode safety margins and potentially result in violations, then incidents and finally fatal accidents” (George, 2007, p. 138).  George (2007) contended that flight operations having well-defined procedures together with well-disciplined pilots who adhere to them have the highest safety margins and safety records.  Examples of these types of flight operations include United States military aviation divisions, major airlines, and most large corporate flight departments (George, 2007).  
The importance of defining comprehensive SOPs for each phase of flight is recognized by members of the International Civil Aviation Organization (ICAO) (George, 2007).  NTSB researchers noted that the nonexistence of SOPs was a causal factor in accidents caused by pilot error (George, 2007).  Furthermore, FAA researchers and National Aeronautics and Space Administration (NASA) researchers all agreed that “almost half of all Controlled Flight into Terrain accidents are attributable to deficiencies in SOPs or SOP compliance by aircrews” (George, 2007, p. 141).  According to George (2007), “smaller flight departments and single-pilot operators are less likely to be aware of the need for SOP structure and discipline” (p. 142).  Additionally, an increasing number of aviation insurance agents have started to recognize the safety benefits of following SOPs and are making them an essential component for pilots qualifying for insurance (George, 2007).
Curriculum designers at the Medallion Foundation have put weather cameras, in mountain passes and other locations, to good use by accessing real-time weather and coupling it to flight simulators (Detwiler et al., 2006).  With this technology, pilots may hone their navigation skills in the safety of a simulator while “flying through places such as Merrill Pass in adverse weather” (p. 9).  The intent is to improve aeronautical decision-making because an accurate weather picture together with an accurate picture of perilous Alaskan terrain would create a realistic scenario for pilot training (Detwiler et al., 2006).
In 2010, Bolstad, Endsley, Costello, and Howell evaluated six different computer-based training modules created to teach skills “that underlie the development and maintenance of situation awareness, a competency shown to be critical in pilot decision-making performance” (p. 269).  These modules targeted basic skills training, such as checklist completion, air traffic control comprehension, and psychomotor skills, for low flight time pilots.  The modules also targeted training on higher order cognitive skills, such as attention sharing and contingency planning, and intensive preflight planning.  The results of this study showed that the training modules improved the participants’ performance on these targeted skills (Bolstad et al., 2010).  
Pilot Error and Transitions
Veillette (2007b) discovered an underlying theme in a study of 251 business jet accidents that occurred over a 12-year period and were caused by errors in the pilots’ decision-making.  The theme was inadequate transition training (Veillette, 2007b).  Transition training for a pilot is deemed necessary by the FAA Administrator whenever a pilot moves up from a simple to a more sophisticated aircraft (FAA, 1974).  As far back as 1974, FAA researchers agreed that “greater knowledge and skills are needed for the efficient and safe operation of today’s more powerful aircraft” (p. 1).  
Transition to a complex airplane, or a high performance airplane, can be demanding for most pilots without prior experience.  According to the FAA Administrator, “increased performance and increased complexity both require additional planning, judgment, and piloting skills” (FAA, 2007, p. 11-1).  A complex aircraft is defined in the federal aviation regulations as an “airplane that has retractable landing gear, flaps, and a controllable pitch propeller, or, in the case of a seaplane, flaps and a controllable pitch propeller” (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.31 [2010], para. 1).  A high performance airplane is defined as an airplane equipped with an engine of more than 200 horsepower (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  According to FAA training curriculum designers, transition to these types of airplanes should be accomplished in a “systematic manner through a structured course of training administered by a qualified instructor” (FAA, 2007, p. 11-1).  The training times should be based on the capabilities of the pilot.  Pilots with higher qualifications may require shorter training periods than pilots who do not meet the current certification requirements or who have little recent flight experience (FAA, 2007).
Transition to a multi-engine airplane brings with it the potential problem involving an engine failure that leads to a loss of performance and a loss of control (FAA, 2007).  For the purpose of this study, a multi-engine airplane is a “reciprocating or turbo propeller-powered airplane with a maximum certificated takeoff weight of 12,500 pounds or less” (FAA, 2007, p. 12-1).  Multi-engine airplanes possess several unique characteristics.  Knowledge of these characteristics and proficient flight skills is crucial to safe flight in these airplanes.  FAA researchers believe that the performance and systems redundancy of a multi-engine airplane “is a safety advantage only to a trained and proficient pilot” (FAA, 2007, p. 12-1).  
Transition to tail-wheel landing gear aircraft brings with it several unique issues.  Tail-wheel, also known as conventional, landing gear is a type of aircraft landing gear “with the main wheels ahead of the center of gravity and a small wheel supporting the tail when the airplane is on the ground” (Crane, 2006, p. 157).  Tricycle landing gear is a type of aircraft landing gear that “uses two main wheels located behind the center of gravity and a nose wheel well ahead of the center of gravity” (Crane, 2006, p. 645).  As a result of their design and structure, tail-wheel landing gear airplanes “exhibit operational and handling characteristics that are different from those of tricycle gear airplanes” (FAA, 2007, p. 13-1).  Therefore, tail-wheel landing gear aircraft require different skills from the pilots who fly them (Davis, 2007).  The need for different skills is a result of the location of the center of gravity with respect to the main landing gear (Davis, 2007).  
According to Davis (2007), aviation insurance underwriters lack an accurate understanding of these unique characteristics of tail-wheel landing aircraft.  The flight characteristics of tail-wheel landing gear aircraft are quite similar to those of tricycle landing gear aircraft.  However, the landing and ground handling characteristics of tail-wheel landing gear aircraft are quite different from those of tricycle landing gear aircraft (Davis, 2007).  One example of this lack of understanding occurs with some underwriters who require a pilot transitioning to a tail-wheel landing aircraft to complete a certain number of flight hours with a CFI prior to flying solo (Davis, 2007).  In a tail-wheel landing gear aircraft it is much more beneficial for the pilot to practice takeoff and landings rather than spend a certain number of hours in flight because the flight characteristics do not differ much from those of tricycle landing gear aircraft; however the landing and ground handling characteristics do (Davis, 2007).  Davis (2007) contended that typically, the underwriters place a 10-hour flight requirement with a qualified CFI upon a pilot transitioning to a tail-wheel landing gear aircraft.  What typically happens, according to Davis (2007), is that a pilot will plan a cross-country flight with the CFI and in the course of the 10 hours perhaps practice no more than 4 landings.  A far better transition plan would be for underwriters to require the pilot to accomplish a certain number of landings with the qualified CFI so that the pilot can become proficient in handling the aircraft in “various wind and runway conditions” (Davis, 2007, p. 21).  This finding by Davis (2007), that more practice in landing tail-wheel landing gear aircraft in various wind and runway conditions makes a safer pilot, does not seem to coincide with the FAA rule according to 14 C.F.R. § 61.57 requiring only three takeoffs and landings every 90 days (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).
Another example of this lack of understanding occurs with some underwriters who require a pilot transitioning to a tail-wheel landing gear aircraft to operate from “airstrips that are paved and at least 3,000 feet or greater” (Davis, 2007, p. 21).  Tail-wheel landing gear aircraft, by virtue of their design “sit at a higher angle of attack allowing for relatively short takeoff distances” compared to tricycle landing gear aircraft (Davis, 2007, p. 21).  According to Davis (2007), most common tail-wheel landing gear aircraft are safely off the ground in about 1,000 feet.  Furthermore, as for the paved runway requirement, most pilots of tail-wheel landing gear aircraft would rather enjoy the thrill of landing on a grass or dirt strip if given the opportunity.  Davis (2007) contended that it is actually safer for a pilot to land on grass or dirt, rather than a paved surface, because the friction involved on touchdown would decrease the likelihood of a ground loop.  A ground loop is the negative side effect of a pilot landing a tail-wheel landing gear aircraft with side load on the main gear.  This side load occurs if the pilot neglects to line up the nose of the aircraft with the direction of travel on touchdown (Davis, 2007).  Some underwriters (Davis, 2007) would argue that damage to tail-wheel landing gear components increases on unpaved strips.  This is not the case because tail-wheel landing gear aircraft are designed for unpaved strips by virtue of their wheel-barrow-like behavior when encountering potholes (Davis, 2007). 
Davis (2007) believed the reason for the high insurance premiums that pilots of tail-wheel landing gear aircraft are forced to pay is tied to the high number of claims placed for “repairs for landing accidents, lack of and cost of available parts, lack of experienced pilots, lack of available training, pilot attitude, and the off airport uses” (p. 22) to which many tail-wheel landing gear pilots submit their aircraft.  In addition, the high number of claims is caused by student pilots who choose to learn how to fly in a tail-wheel landing gear aircraft, and by experienced professional or military pilots who think they can fly anything (Davis, 2007).  This problem, according to Davis (2007), is precipitated by the lack of truly qualified tail-wheel landing gear CFIs.  
Transition to more sophisticated and faster aircraft brings with it another slew of issues.  In a study of pilots, most of whom had logged in excess of 10,000 flight hours, Veillette (2007b) demonstrated that although these pilots may have “sufficiently thick logbooks indicative of their overall flying experience, there are some vital gaps in knowledge about the equipment they’re flying” (p. 28).  This was a direct result of pilots transitioning between “aircraft of substantial performance and handling differences” (Veillette, 2007b, p. 28).  One glaring example in the study by Veillette (2007b) involved a crash that occurred in 1982 on a flight from Washington, DC to Fort Lauderdale, Florida.
The runway was closed over one hour during moderate to heavy snowfall for snow removal, thus delaying departures.  The aircraft was deiced with a heated ethylene glycol and water solution without engine inlet plugs or pitot static covers installed.  Contrary to procedures, reverse thrust was used to help a tug during pushback from the gate and blew snow.  After pushback, the flight was delayed 49 minutes while snow continued in subfreezing conditions.  While waiting, the aircraft was positioned near the exhaust of the aircraft ahead.  During takeoff, the EPRs [engine pressure ratios] were set for 2.04, but an anomaly was noted in engine instrument reading.  The captain elected to continue takeoff.  The aircraft took off approximately 2000 feet and 15 seconds past the normal takeoff point.  After liftoff, it initially climbed but failed to accelerate.  The stall warning stick shaker activated shortly after takeoff and continued till the aircraft settled, hit a bridge and several vehicles, and then plunged into a frozen river.  Investigation revealed engine inlet pressure probes became blocked with ice, resulting in high EPR indication, possible pitch up with snow/ice frozen on wings, no runway distance markers were available, and crew had limited cloud weather operational experience.  (NTSB, n.d.e, para. 1)
In this 1982 crash the pilot in command had logged 221 hours in the past 90 days in the same type of aircraft as the accident aircraft, yet still demonstrated improper planning and decision-making which directly led to the crash and subsequent demise of 78 people (NTSB, n.d.e).
On the other hand, Soares (2002) discovered that pilots with more flight time were less likely to be involved in an accident or incident.  However, it is conceivable to assume that “these pilots would also be able to better manage an accident, mitigating the extent of accident damage or severity” (Soares, 2002, p. 105).  Much like Veillette (2007b), Soares (2002) presupposed the effect of flight time to be “plane-specific” (p. 105) and that the proportion of time spent as pilot in command may have a separate impact on outcomes than total time.  Soares (2002) included in the study, much like the researcher in this one, recent flight experience, or time flown within the last 90 days prior to the accident.  The conclusion by Soares (2002) was that “more experienced and better trained pilots are able to substantially decrease their chances of having an accident” (p. 127).  Another finding by Soares (2002) was that pilots who filed a flight plan “may in fact be more cautious pilots” (p. 120) which would explain why this group of flight-plan-filing pilots “are almost five percentage points less likely to be at fault in an accident” (p. 120). 
The Reasons Behind Pilot Error  
Masys (2008) tried to reveal the reasons for pilot error by studying accidents among major air carriers.  The findings concurred with those of FAA researchers (FAA, 2003a).  Eighty eight percent of those accidents among major air carriers involving pilot error were attributed to problems with situational awareness (Masys, 2008).  Similarly, situational awareness problems were also the leading cause of military aviation mishaps (Masys, 2008).  In the military and civilian worlds, “operating complex systems such as that associated with aviation requires a high level of decision-making and performance that is predicated on achieving and maintaining a certain level of situation awareness” (Masys, 2008, p. 221).  It is, therefore, essential that CFIs tailor classroom, simulator, and in-flight instruction toward developing within the pilot a “working knowledge of the proper procedures and inflight decision-making” (Veillette, 2007b, p. 30).  
Findings by Veillette (2007b) that short discussions in ground school are “woefully insufficient” (p. 30) for pilots transitioning from one type of aircraft to another concur with those findings made by FAA researchers (FAA, 2003a).  It is also the contention of Bolstad et al. (2010) that developing and maintaining situational awareness is a complex process and “training needs to target not only exposure to knowledge of appropriate SA [situation awareness] behaviors, but also cultivating the range of cognitive and psychomotor skills that support SA formation” (p. 271).  O’Hare, Mullen, and Arnold (2010) investigated the effects of case-based training on decision-making in a simulated flight.  In the study, half of the participants read cases where “the aircraft encountered deteriorating weather and crashed, whereas the other half read cases where the pilot successfully dealt with the conditions and landed safely” (p. 48).  Following each case, half of the participants engaged in a reflective thinking exercise.  O’Hare et al. (2010) found that the participants who engaged in this case-based reflection exercise “made more appropriate and timely decisions” (p. 48).
In 2003, 17% of all GA accidents were caused by controlled flight into terrain (CFIT) (FAA, 2003b).  Even though, in 2000, in response to the high number of GA accidents caused by CFIT, several FAA researchers had formed the Joint Safety Analysis Team (JSAT) to review and analyze 195 GA CFIT accidents (FAA, 2003b).  As a result, the JSAT recommended 10 intervention strategies.  Some of the interventions included an increased pilot awareness on accident causes; improved pilot training such as decision-making and human factors training; and enhanced flight reviews in accordance with 14 C.F.R. § 61.56 (FAA, 2003b; FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010). 
In 2008, the percentage of accidents involving CFIT was still 17% (AOPA ASF, 2009; Katz, n.d.).  This prompted NTSB accident investigators to issue a safety alert aimed at GA pilots, noting that some of the CFIT accidents investigated in recent years “could have been avoided if the pilots had maintained better altitude and geographic position awareness” (para. 1).  In this safety alert, NTSB accident investigators claimed that “CFIT accidents are best avoided through proper preflight planning”, “terrain familiarization is critical to safe visual operations at night”, and pilots should “use sectional charts or other topographic references” (para. 3).  NTSB accident investigators reminded pilots not to depend on controllers to warn them of terrain hazards (Katz, n.d.). 
Aviation Insurance and Fixed-Based Operators
Noting the significance of the 2003 FAA findings of the two root causes of pilot error accidents, some aircraft insurance agents now require pilots to have completed additional training by taking FAA Safety Team (FAASTeam) Wings Knowledge Courses, or completing additional certificates, ratings or recurrent flight training within the last 12 months of their insurance quote (Avemco, n.d.).  On the other hand, some FBO owners, focusing only on the practice of physical airplane skills, require renter pilots to fly more frequently than the FAA recent flight experience rule under 14 C.F.R. § 61.57 requires, by requiring the pilot to have completed three takeoffs and landings every 60 days versus every 90 days (Mid Island Air Service, Inc., n.d.). 
This dichotomy between increased situational awareness and aeronautical decision-making training versus increased practice of physical airplane skills is highly evident within the aviation insurance industry where there are different standards for measuring pilot proficiency.  For example, for aircraft owners insurance quotes, Avemco agents factor in pilot certification level, total hours flown, hours flown in the last 12 months, in addition to proof of completion of FAASTeam Wings knowledge courses and the completion of additional certificates, ratings or recurrent flight training in the last 12 months (Avemco, n.d.).  For renter pilot’s insurance quotes, Avemco agents only require from a pilot evidence of completion of FAASTeam Wings knowledge courses in the past 12 months in order to secure 5% off the annual premium (Avemco, n.d.).  
For obtaining renter pilot’s insurance at another large aviation insurance agency, Aircraft Owners and Pilots Association Insurance Agency Inc., pilots need to provide proof of pilot certification, total hours, hours flown in the last 12 months, as well as total hours flown in the type of aircraft to be rented (Aircraft Owners and Pilots Association Insurance Agency Inc., n.d.).  At yet another agency, Aviation Insurance Resources, pilots are required to provide proof of an additional item which is time flown in the last 6 months (Aviation Insurance Resources, n.d.).  At CS&A Insurance, pilots are required to provide proof of total time flown in the last 90 days in addition to the last 12 months (CS&A Insurance, n.d.).  However, at Phoenix Aviation Managers, Inc., a member of Old Republic Insurance Company, insured FBO owners are required to check if the rental pilot is current in make and model in the last 30 days (Phoenix Aviation Managers, Inc., n.d.), thereby reinforcing the practice of physical airplane skills more frequently than the FAA requirement of 90 days (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).
The owners of the FBO, Mid Island Air Service, Inc., have opted to implement both options: additional training as well as more frequent practice of physical airplane skills. The owners at this FBO have implemented mandatory pilot training programs prior to making an aircraft available for rent by a pilot (Mid Island Air Service, Inc., n.d.).  As listed under the aircraft rental agreement, a portion of the insurance premium (i.e., $2.50 per flight hour) is then added to the cost of the aircraft rental (Mid Island Air Service, Inc., n.d.).  In addition, the owners of Mid Island Air Service, Inc. have restricted the recent flight experience rule from 90 days to 60 days when renting complex aircraft (Mid Island Air Service, Inc., n.d.).  When renting multi-engine aircraft, pilots are required to have “made three full stop landings within the previous 60 days” (Mid Island Air Service, Inc., n.d., para. 4) in the aircraft the pilot wishes to rent.  Finally, to rent a tail-wheel landing gear aircraft, if the pilot has less than 50 hours of tail-wheel landing gear aircraft experience, the owners of Mid Island Air Service, Inc. require the pilot to have completed “three full stop landings within the past 60 days” (Mid Island Air Service, Inc., n.d., para. 5).  By comparison, the FAA requirement is every 90 days (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010). 
Meanwhile the owners of other FBOs have elected to just reinforce the practice of physical airplane skills.  According to established rental policies and procedures, the owners of another FBO, Chandler Air Service, Inc., have restricted the recent flight experience rule to 60 days for the entire aircraft fleet rental.  “Renter pilot must have flown with C.A.S. [Chandler Air Service] within the last 60 days” (A. Owens, personal communication, September 11, 2009). 
In closing this section of the review it is necessary to emphasize the importance of the FAASTeam managers, who came into existence in October, 2006.  FAASTeam managers and program managers within the FAA are on a mission “to improve the nation’s aviation safety record by conveying safety principles and practices through training, outreach, and education” (FAASTeam, n.d.a, para. 1).  The team represents the FAA Administrator’s response to the need for a safety culture change within the aviation industry (Laws, 2006).  FAA aviation safety inspectors hope this new approach will help “aircraft owners, pilots, and mechanics avoid mistakes that lead to accidents” (Laws, 2006, p. 4).  The new approach behind the work of the FAASTeam managers deals primarily with “data mining, input from local FAA inspectors, and a change in how the agency analyzes accident data” (Laws, 2006, p. 4). 
The Wings program was designed by the FAASTeam managers to help individual pilots “construct an educational curriculum suitable for their unique flight requirements” (FAASTeam, n.d.b, para. 2).  The program “encourages pilots to continue their aviation educational pursuits and requires education, review, and flight proficiency…that correspond with the leading accident causal factors” (para. 2).  In an Advisory Circular to pilots, AC 61-91H, FAA researchers reported that “regular proficiency training is essential to the safety of all pilots and their passengers” (FAA, 1996, p. 1).  The Wings pilot proficiency program is “based on the premise that pilots who maintain currency and proficiency in the basics of flight will enjoy a safer and more stress-free flying experience” (FAASTeam, n.d.b, para. 2). 
Pilot Error and FAA Law
Recent flight experience as defined by the FAA Administrator in14 C.F.R. § 61.57 is one of two rules placed upon certificated pilots to ensure safety and proficiency (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  The other is 14 C.F.R. § 61.56 (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  This rule states that 
no person may act as pilot in command of an aircraft unless, since the beginning of the 24th calendar month before the month in which that pilot acts as pilot in command, that person has accomplished a flight review given in an aircraft for which that pilot is rated by an authorized instructor. (para. 1)
Furthermore, the flight review “consists of a minimum of 1 hour of flight training and 1 hour of ground training” (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.56 [2010], para. 1).  Once again, like was the case with 14 C.F.R. § 61.57, some FBO owners have stepped in to further tighten the restrictions of 14 C.F.R. § 61.56 (Chandler Air Service, September 11, 2009; Mid Island Air Service, Inc, n.d.).  For example, the owners of Mid Island Air Service, Inc. have included the following clause in the renter pilot agreement that further restricts this 24 calendar month rule to a 12 calendar month one: “All pilots must satisfy a Mid Island Annual Flight review in order to rent” (Mid Island Air Service, Inc, n.d., para.1).  Similarly, agents at Phoenix Aviation Managers, Inc., a member of Old Republic Insurance Company, require its insured FBO owners to check whether the rental pilot has had an annual checkout documented for each make and model aircraft flown prior to renting the aircraft (Phoenix Aviation Managers, Inc., n.d.).
FAA curriculum writers also encourage CFIs to focus on the two root causes of pilot error, namely, aeronautical decision-making and situational awareness, during the conduct of their flight training.  In a guide to pilots published by FAA researchers, Conducting an Effective Flight Review, the purpose of the flight review required by 14 C.F.R. § 61.56 is stated as being “to provide for a regular evaluation of pilot skills and aeronautical knowledge” (FAA, 2006b, p. 1).  In an Advisory Circular to pilots, AC 61-98A, the “flight review is also intended to offer pilots the opportunity to design a personal currency and proficiency program in consultation with a certificated flight instructor (CFI)” (FAA, 1991b, p. 2).  For this reason, included within the guide titled Conducting an Effective Flight Review, is a personal proficiency practice plan for a visual flight rules (VFR) flight profile “to be conducted by the pilot every 4 to 6 weeks” (FAA, 2006b, p. 25).  Within the guide is a specific list of items that the pilot is to practice, some for the purpose of improving physical airplane skills and others for improving aeronautical decision-making and situational awareness skills (FAA, 2006b; FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  The list includes preflight review of the risk management process.  The in-flight maneuvers include steep turns, power-off stalls and recovery, power-on stalls and recovery, ground reference maneuvers, traffic pattern practice (to include three takeoffs and landings according to 14 C.F.R. § 61.57), and soft-field and short-field takeoff and landing practice (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).
Pilot Error and Aeronautical Decision-Making
Aeronautical decision-making is one of the most crucial aspects of a pilot’s education.  Lee, Fanjoy, and Dillman (2005) suggested that “a safety information system based on information from incidents and near misses is an important tool to improve the intelligence and readiness of pilots” (p. 3).  Researchers (Lee, Fanjoy, & Dillman, 2005) have found that having regular access to a safety information program has improved the aeronautical decision-making of student pilots.  When student pilots receive structured decision-making training in their initial lessons, they should be better equipped to make competent decisions in the later stages of training and eventually in their careers as professional pilots.  This decision-making training need be no more complex than reading case studies from the NTSB accident investigation archives and learning from the experience of other pilots (Lee et al., 2005).  
However, despite this knowledge of the importance of decision-making training, most flight instructors utilize little or no structured judgment training during the course of their teaching (FAA, 2003a; Lee et al., 2005).  A person typically learns to recognize and interpret incoming messages based upon previous experience, expectations, and knowledge of the current situation.  Lee et al. (2005) found that flight students who periodically reviewed safety information made faster assessments of abnormal aircraft conditions than those students who had not.  Furthermore, the flight students who periodically reviewed safety information made a more accurate interpretation of incoming messages and were more likely to choose the correct actions for solving the problems (Lee et al., 2005).
It is a widely accepted fact that aircraft accidents can rarely be attributed to a single cause (Bergeon & Hensley, 2009).  Typically, a chain of unfortunate and unforeseen events lead to an accident that could have been prevented had any one of these events not taken place.  It is important to remember that “no single failure triggers most aircraft accidents, but…a chain of events usually leads to them” (Bergeon & Hensley, 2009, p. 49).  This is the Swiss cheese model of accident causation that FAA researchers have adopted.  Based upon the ‘cumulative act effect’ observed by Reason (Bergeon & Hensley, 2009), the Swiss cheese model attempts to explain major air disasters by “comparing the safety systems and defense mechanisms to slices of Swiss cheese” (p. 49).  These holes in the Swiss cheese “are said to correspond to weaknesses that contributed to the occurrence and were present in each of the layers” (Bergeon & Hensley, 2009, p. 49).  
Bergeon and Hensley (2009) recommended reducing the probability of an event occurring by either strengthening each layer, also known as “plugging the holes” (p. 53) in the Swiss cheese, or by implementing additional protection layers, also known as “adding more slices of Swiss cheese so as to block the ‘view’ of the holes” (p. 53).  Based upon this reasoning, Bergeon and Hensley (2009) proposed a predictive risk mitigation analysis (PRiMA) approach that consisted of “an assessment of the existing safety layers followed by a proposal for strengthening them” (p. 53).  Users of the PRiMA model should first consider whether new layers could be added to the system, and then should compare their benefits to the cost of implementing and maintaining those procedures (Bergeon & Hensley, 2009).
Dismukes, Berman, and Loukopoulos (2007) challenged the common understanding of accidents and error.  Through their detailed analysis of accidents, Dismukes et al. (2007) provided an understanding of why highly skilled professional pilots make errors, as well as provided guidance for the prevention of future accidents.  Dismukes et al. (2007) contended that errors will continue to occur as a result of systemic weaknesses unless aircraft designers support reliability of human performance and build into the system resilience against the human failures that will inevitably occur (Dismukes, Berman, & Loukopoulos, 2007).  Dismukes et al. (2007) believed that accidents stem from characteristics of the system as a whole and not individual shortcoming of the pilots.  Finally, human error became the beginning and not the end, or a starting point for identifying systemic change not a trigger for blaming the pilots (Dismukes et al., 2007).
Masys (2008), concurring with the findings of Dismukes et al. (2007), discussed the actor network theory (ANT), a concept in social theory that emerged from post structuralism and sociological studies of science and technology.  The network space of ANT, according to Masys (2008), provides insights into the causes of aviation accidents using the relationships and interactions between technology, human performance, and organizational context.  ANT is a relatively new and emerging social theory that “attempts to demonstrate that nature and society are ‘outcomes’ rather than ‘causes” (Masys, 2008, p. 224).  The core processes within ANT are inscription, or the way users and systems operate in their assigned role, and translation, or the way actors in a network manipulate other actors to their own benefit (Masys, 2008).  
Prior to the ANT perspective, researchers (FAA, 2003a; Lee et al., 2005) viewed human error as a “consequence, as a result of failing and problems deeper inside the systems in which people work” (Masys, 2008, p. 225).  Researchers today can use ANT as an alternative to the traditional view, one where human error is no longer the cause.  ANT offers researchers tools for explaining why people did what they did.  These tools make human error a starting point not a conclusion.  Ultimately pilot error becomes something to be explained versus the explanation itself (Masys, 2008), a conclusion that concurs with the findings of Dismukes at al. (2007).
Another problem with aeronautical decision-making is known as confirmation bias, which is a pilot’s tendency to ignore evidence that proves his or her decision wrong or worse yet, see what he or she expects to see.  The following example is a clear illustration of this problem.  
The flight crew was instructed to take off from runway 22 but instead lined up the airplane on runway 26 and began the takeoff roll.  The airplane ran off the end of the runway and impacted the airport perimeter fence, trees, and terrain.  The captain, flight attendant, and 47 passengers were killed, and the first officer received serious injuries.  The airplane was destroyed by impact forces and postcrash fire.  The flight was enroute to Hartsfield-Jackson Atlanta International Airport, Atlanta, Georgia.  Night visual meteorological condition prevailed at the time of the accident. (NTSB, n.d.f, para. 1)
The accident occurred on August 27, 2006, at Lexington, Kentucky Blue Grass airport and killed 49 of the 50 people on board (Fiorino, 2007; NTSB, n.d.f).  Both the pilot in command and the second in command “failed to heed all available cues in the cockpit and on the ground” (Fiorino, 2007, p. 44).  These cues would have helped them identify the aircraft’s position during taxi.  Instead they were both convinced that they were in the correct place.  The result was they taxied to and started to take off from the wrong runway that turned out to be insufficiently long enough for the aircraft they were flying, and they crashed into the fence and trees at the end of the runway (Fiorino, 2007).  Veillette (2007a) called this phenomenon expectation error, “we ‘hear’ and ‘see’ what we expect, despite clear evidence to the contrary” (p. 61).  NASA Ames researchers examined more than 50,000 NASA Aviation Safety Reporting Systems (ASRS) reports in 1986 and discovered about 70% of them involved some kind of communication problem of which expectation errors were among the majority of the causes (Veillette, 2007a).  Researchers from the human factors division of NASA Ames concurred with these findings of Veillette (2007a) and elaborated on the types of communications errors.  These included radio communications between air traffic controllers and pilots in which parts of the transmission was “clipped out by static, noise, or are ‘stepped on’ by another radio transmission” (Veillette, 2007a, p. 63).  Interestingly, about two-thirds of these radio transmissions involved a misunderstanding of the assigned altitude that could potentially have led the aircraft too close to another aircraft or terrain (Veillette, 2007a).  
According to Veillette (2007a), the best way to avoid an encounter with expectation error is for the pilot to reduce the level of stress.  Furthermore, it is crucial for the pilot to allow sufficient time to accomplish a given task.  Stress and time limitations create tunnel vision (Veillette, 2007a).  Finally, it is imperative that the pilot reduce distractions.  Unless a pilot is flying alone, he or she may also recruit the help of another set of eyes, depending on the situation, because the likelihood of another person “having the same expectation are slender” (p. 65). 
No discussion on pilot error would be complete without a brief discussion on error taxonomy.  NASA researchers laid the foundation with the human factors analysis and classification system (HFACS) primarily for use by United States Navy and Marine Corp accident investigators (Rantanen, Palmer, Wiegmann, & Musiorski, 2006).  Later, FAA researchers began exploring the use of HFACS to analyze underlying human factors causes of both commercial and GA accidents (Detwiler et al., 2006; Rantanen et al., 2006; Shappell et al., 2006).  “Within HFACS, the unsafe acts committed by pilots generally are of one of two forms, errors or violations” (Rantanen et al., 2006, p. 1223).  Errors, in this case, are “mental or physical activities that do not achieve their intended outcome” (Rantanen et al., 2006, p. 1223).  Violations, in this case, are a “willful disregard for rules and regulations” (Rantanen et al., 2006, p. 1223).  
Rantanen, Palmer, Wiegmann, and Musiorski (2006) defined three error types that are “skill-based errors, decision errors, and perceptual errors” (p. 1223).  Detwiler et al. (2006) referred to these three error types as errors of “thinking, doing, and perceiving” (p. 2).  Skill-based behavior is better known as ‘stick and rudder skills’, or actions “that occur without significant conscious thought” (Rantanen et al., 2006, p. 1223).  Skill-based errors occur as a result of inattention and/or memory failures.  Decision errors are manifest in “intentional behavior that proceeds as intended, yet the plan proves inadequate or inappropriate for the situation” (Rantanen et al., 2006, p. 1223).  Detwiler et al. (2006) also contended that decision errors “represent conscious decision/choices made by an individual that are carried out as intended but prove to be inadequate for the situation at hand” (p. 2).  However, Rantanen et al. (2006) defined a third error.  Perceptual errors occur “when sensory input is degraded or ‘unusual’ and can result in misjudged distances, and descent rates, as well as myriad visual illusions” (p. 1224).  Detwiler et al. (2006) contended that perceptual errors were more likely to occur “when flying at night, in weather, or in visually impoverished conditions” (p. 2).  The researcher in this study is interested in the significance of recent flight experience on all three of these types of pilot error.  
In a human factors study of GA accidents in Alaska, Detwiler et al. (2006) examined 17,000 GA accidents using HFACS.  The review of data over a span of 10 years revealed that 73% of accidents attributed to pilot error were caused by skill-based errors; 28% were caused by decision errors; and the remaining 7% were caused by perceptual errors (Detwiler et al., 2006).  Skill-based errors could be reduced by increasing recurrent training in ground handling and crosswind techniques.  Decision errors could be reduced by improving the education pilots received on the ground that would help with decision-making later on in the aircraft.
According to Shappell et al. (2006), HFACS describes human error at each of four levels: the unsafe acts of operators, preconditions for unsafe acts, unsafe supervision, and organizational influences.  The operator is considered to be the pilot, mechanic, or air traffic controller.  The supervision is considered to be that provided by middle-management.  Shappell et al. (2006) believed that focusing on the unsafe acts in isolation “is like focusing on a patient’s symptoms without understanding the underlying disease that caused it” (p. 3).  
Human memory is far from perfect.  Pilots are only human.  There exists no “simple-to-measure determinants of what aeronautical knowledge will be remembered and forgotten” (Casner, Heraldez, & Jones, 2006, p. 71).  The experience that pilots glean from everyday flying, or teaching in the case of CFIs, together with recent flight experience in accordance with 14 C.F.R. § 61.57, and flight review requirements in accordance with 14 C.F.R. § 61.56, does not appear to eliminate the need for ongoing study or rehearsal of aeronautical knowledge (Casner et al., 2006; FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  Memory research suggested that “the ability to retrieve any particular item from memory seems to be largely determined by how many times that knowledge has been used in the past, and how recently the knowledge has been used” (Casner et al., 2006, p. 72).  
In a study on the retention of aeronautical knowledge, Casner, Heraldez, and Jones (2006) found that pilot certificates and ratings had little influence on how well those pilots retained what they had learned during training.  Furthermore, the current role played by the CFI or the student pilot had a marginally significant effect (Casner et al., 2006).  In the study on retention of aeronautical knowledge, it was found that total flight experience had little effect on knowledge retention whereas recent flight experience was associated with better retention, especially for flight instructors (Casner et al., 2006).  This researcher is interested in the statement, “Proximity in time to past flight reviews and upcoming practical tests was associated with better retention” (Casner et al., 2006, p. 93).  It showed the need for additional training in aeronautical knowledge.  An interesting discovery in this study on aeronautical knowledge retention was “better retention was found among applicants who reported frequently reading more aviation-related materials” (Casner et al., 2006, p. 93).  This statement corroborates the findings of Lee et al. (2005) that flight students who periodically reviewed safety information by reading case studies from the NTSB accident investigation archives and learning from the experience of others made faster assessments of abnormal aircraft conditions than those students who had not.  
The results of the research by Casner et al. (2006) cast serious doubts on the assumption that everyday flying or teaching experience, together with the current recent flight experience rule, and the 24 calendar month flight review requirements, would automatically offer pilots the practice necessary to remember all the aeronautical knowledge they learned, and must retain, in order to be safe and avoid pilot error leading to accidents (FAA Certification: Pilots, Flight Instructors, and Ground Instructors,  2010).  The results of the research by Casner et al. (2006) indicated the need for regular study and perhaps even the need for “more explicit standards for ongoing aeronautical knowledge proficiency” (p. 94).  Casner et al. (2006) suggested a system of self-certification similar to that used for recent flight experience requirements of 14 C.F.R. § 61.57 and a more detailed and frequent evaluation of aeronautical knowledge beyond the one-hour requirement every 24 calendar months (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).
Researchers investigating the role of flight experience in the performance of “flight-related tasks have used and shown different measures to be important” (Goh & Wiegmann, 2002, p. 1).  Some of these measures included total flight hours, cross-country flight experience, and recent flight experience.  Other measures included pilot certifications and ratings (Goh & Wiegmann, 2002).  Cross-country flight experience, in accordance with 14 C.F.R. § 61.1, is time acquired during flight conducted by a person who holds a pilot certificate, conducted in an aircraft, including a landing at a point other than the point of departure; and involving the use of dead reckoning, pilotage, electronic navigation aids, radio aids, or other navigation systems to navigate to the landing airport (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  For the purpose of meeting the aeronautical experience requirements for a private, recreational, or ATP certificate, the cross-country flight experience is time acquired during a flight that includes a landing that is at least a straight-line distance of more than 50 nautical miles from the original point of departure (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).  For the purpose of meeting the aeronautical experience requirements for a sport pilot, the cross-country flight experience is time acquired during a flight that includes a landing that is at least a straight-line distance of more than 25 nautical miles from the original point of departure (FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010).
Goh and Wiegmann (2002) suggested there is no one measure of flight experience “that best captures the notion of ‘domain specific experience’ deemed to be important by various researchers in the fields of problem-solving” (p. 1) and decision-making.  It is possible that certain measures of flight experience could be more pertinent for certain flight tasks than others.  An interesting finding by Goh and Wiegmann (2002) is that while experience may produce better decision-making skills it may also have the undesirable effect of reducing a pilot’s perception of risk.  What this means is that more experienced pilots may choose to divert a flight into adverse weather or conversely they may choose to continue, making their behavioral outcomes produce “equivocal results” (Goh & Wiegmann, 2002, p. 2).  
Researchers have shown that “domain specific experience is particularly important in the diagnostic stage of decision-making” (Goh & Wiegmann, 2002, p. 3).  Furthermore, pilots with more flight experience were generally more confident of their ability “to recognize problems and to generate and implement solutions” (p. 3).  However, Goh and Wiegmann (2002) suggested pilots with more experience do not necessarily feel more confident in their ability to diagnose flight-related problems.  This may be a result of training.  Pilots are typically trained to detect problems, but then rely on checklists and SOPs to diagnose and solve the problems (Goh & Wiegmann, 2002).
Hunter (2006) studied risk perception among GA pilots.  Risk perception and risk tolerance are constructs that may explain the behaviors of pilots that result in incidents and accidents (Hunter, 2006).  A possible reason for a pilot’s behavior that leads to an accident could be that he or she did not perceive the risk inherent in the situation, and therefore did not take actions to avoid it or reduce the risk of it happening in the first place (Hunter, 2006).  Another possible reason for a pilot’s behavior that leads to an accident could be that he or she correctly perceives the risks but chooses to continue because the risk is not considered a huge enough threat (Hunter, 2006).  
In the study conducted by Hunter (2006) there was a strong tendency for the “more experienced and qualified pilots who held advanced certificates (commercial and airline transport) to view the situations depicted in both risk perception measures as involving less risk than the less advanced certificate holders (student and private)” (p. 138).  Participants in the study by Hunter (2006) who exhibited greater self-confidence and risk awareness rated the situation as less risky.  Conversely, participants who “exhibited greater safety orientation tended to rate the situations as higher in risk” (Hunter, 2006, p. 142).  Furthermore, Hunter (2006) found that those participants who had been in more hazardous aviation events tended to rate the scenarios as lower in risk, and had a more “inaccurate estimate of the safety of general aviation” (p. 142).  Pauley, O’Hare, Mullen, and Wiggins (2008) also discovered a relationship between a pilot’s “implicit perceptions and previous involvement in hazardous aeronautical events” (p. 723).  Finally, Hunter (2006) contended that for private pilots, the risk perception accuracy score changed “with age, with younger pilots having a more accurate view of flight risk than older pilots” (p. 143).  
While investigating aviation safety and risk assessment, Shyur (2007) proposed a new analytic method.  This model used data on both accident and safety indicators to quantify the aviation risk caused by human error.  According to Shyur (2007), it is expected that “when today’s accident rate is applied to the traffic forecast for 2015, the result would be the crashing of an airliner somewhere in the world almost every week” (p. 34).  What the aviation community needs is a shift in emphasis to “proactive safety” (Shyur, 2007, p. 34).  To achieve this proactive safety, an “idea risk assessment tool should be developed enabling an analyst to examine a wide variety of accidents quickly, systematically, and probabilistically and assisting a risk manager in priority setting and policy decision-making” (Shyur, 2007, p. 35).  It has been estimated by some researchers (Shyur, 2007) that all accidents have some form of human error attached to their causes.
Veillette (2007c) contended that behind every accident attributed to pilot error was a management accident.  Block, Sabin, and Patankar (2007) concurred with this contention in a study on the safety climate for accident free flight crews.  In 2006, Li and Harris analyzed 523 accidents and also concurred with this contention.  Findings from researchers at the Ohio State Aviation Psychology Laboratory lend evidence to support the hypothesis that the “organizational environment may be even more responsible for these ‘pilot errors’ than the pilot” (Veillette, 2007c, p. 54).  Veillette (2007c) also found that “pilot testing and certifying procedures were inadequate to meet the intent and needs of this pilot for proficiency” (p. 55).  The pilot in question here had no previous training in flying multi-engine aircraft in mountainous terrain yet, after just three flights, had been assigned to transport people, perform low-altitude reconnaissance, operate into remote mountain airstrips, and perform fire reconnaissance (Veillette, 2007c).  These assignments all involved “highly specialized and highly risky operations” (p. 55).
Reason uncovered numerous traits possessed by members of aviation organizations that could potentially increase the likelihood of a pilot being involved in an accident caused by pilot error (Veillette, 2007c).  Among these traits were a lack of organizational safety culture, poor supervision, attitudes of people who accept violations of rules as the norm, misperception of hazards, macho attitude culture, perceived permission to bend the rules, and ambiguous or meaningless rules (Veillette, 2007c).  According to Veillette (2007c), Helmreich found that organizational culture influences communications and trust between management and pilots as well as “adherence to regulations and SOPs” (p. 57).  Researchers at the Dutch National Aerospace Laboratory confirmed that organizational issues lead to non-adherence to SOPs by pilots, and the reasons were primarily caused by insufficient training and company culture (Veillette, 2007c).  Additionally, Jensen (Veillette, 2007c) concluded that “individual pilot judgment is influenced by the way management is seen to practice its own standard operating procedures” (p. 58).  Good SOPs handed to pilots by aviation safety-savvy managers would ease the decision-making burden placed upon pilots.  In conclusion, Jensen, as quoted in Veillette (2007c), recommended that owners of high performance and turbine aircraft take additional training on subjects like stress, pilot judgment and decision-making.
Recent Flight Experience and NTSB Recommendations
Following transportation accidents, such as GA accidents, or other safety problems, safety recommendations are issued by the NTSB accident investigators.  Managers at the Office of Safety Recommendations and Advocacy are responsible for “designing and coordinating strategies to implement safety recommendations” (NTSB, n.d.b, para. 1).  These recommendations usually address a “specific issue uncovered during an investigation or study and specify how to correct the situation” (NTSB, n.d.b, para. 1).  Several of these recommendations pertaining to the recent flight experience rule of 14 C.F.R. § 61.57 titled Recent Flight Experience: Pilot in Command date beyond 30 years.
On September 25, 1978, a Boeing 727 and a Cessna 172 collided in midair over San Diego, California (NTSB, n.d.b).  NTSB accident investigators revealed there might have been a lack of understanding on the part of the pilots as to the air traffic control (ATC) procedures concerning maintaining visual separation (NTSB, n.d.b).  On October 10, 1979, the NTSB accident investigators in charge of this case made the following recommendation to the Administrator of the FAA, Bond.  “A way to address this issue might be for the requirements of 14 C.F.R. 61.57, ‘Recent flight experience: Pilot in Command,’ to be expanded expressly to include a review of ATC procedures…” (NTSB, n.d.b, para. 3).
On April 21, 1979, a Piper Model PA-18 Super Cub crashed at Lebanon Regional Airport, Lebanon, New Hampshire (NTSB, n.d.b).  NTSB accident investigators revealed that the “severe bouncing observed during the landing attempt clearly indicates that the pilot did not perform the landing flare maneuver properly” (NTSB, n.d.b, para. 3).  On March 27, 1980, the NTSB accident investigators in charge of this case made the following recommendation to the Administrator of the FAA, Bond.  “Amend FAR 61.57: ‘Recent flight experience: Pilot in Command,’ to make more stringent the currency requirements for the pilot in command of a tail-wheel configured airplane carrying passengers” (NTSB, n.d.b, para. 4). 
Summary
The root causes of GA accidents according to FAA researchers were found to be a pilot’s lack of situational awareness and poor aeronautical decision-making skills (FAA, 2003a).  Despite these facts, current pilot training standards focus less on these two root causes and more on the development of physical airplane skills (also known as stick and rudder skills) that, while important, are not the cause of most fatal accidents (FAA, 2003a).  Some aviation insurance agents and FBO owners exhibit a similar emphasis on the practice of physical airplane skills despite the facts contained within the guide Conducting an Effective Flight Review (FAA, 2006b; Mid Island Air Service, Inc., n.d.; Phoenix Aviation Managers, Inc., n.d.).  Within this guide is a personal proficiency practice plan for a VFR flight profile “to be conducted by the pilot every 4 to 6 weeks” (p. 25).  There is also a specific list of items the pilot is to practice, some for the purpose of improving physical airplane skills and others for improving aeronautical decision-making and situational awareness skills.  For these reasons, together with the actions being taken by aviation insurance agents and some FBO owners to ensure that pilots who have access to an aircraft are adequately trained and proficient, there appears to exist a need for further investigation.  This need was also suggested by Goh and Wiegmann (2002) and Guohua, et al. (2002).  This investigation would analyze the relationship between recent flight experience, in terms of time flown in the past 90 days, and the number of GA accidents attributed to pilot error. 

Chapter 3: Research Method
Aviation insurance agents, following the requirements listed in the CFRs, use the 90-day rule to assess the likelihood of a pilot error accident (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]).  The generally accepted premise by these agents is that more experience in a recent timeframe predicts less of a propensity for an accident, all other factors excluded (Avemco, n.d.; Phoenix Aviation Managers, Inc., n.d.).  To this end, some aviation insurance agents and FBO owners measure pilot proficiency solely using time flown within the past 90, 60, or even 30 days (Avemco, n.d.; Aviation Insurance Resources, n.d.; CS&A Insurance, n.d.).  They do not take into account the extensive research demonstrating aeronautical decision-making and situational awareness training decrease the likelihood of a pilot error accident (Cunningham, 2006; Hendrickson, 2009; Summers, Ayers, Connolly, & Robertson, 2007).  Guilkey (1997) showed that while reliance on flight hours as a predictor of pilot performance might be accurate when considering skills used on every flight, it may not be a sufficient predictor of complex problem-solving tasks that form the majority of aeronautical decision-making skills.  However, the relationship between pilot error accidents and recent flight experience implied by the FAA’s 90-day rule has not been rigorously assessed using empirical data (Goh & Wiegmann, 2002; Guohua, et al., 2002).  This experience requirement would indicate that as pilots move through that 90-day period they will have a greater propensity for accidents.  In other words, the accident rate should show an increase as the time from the recent experience grows longer.  The purpose of this quantitative ex post facto study on NTSB accident investigation archival data was to examine the relationship between recent flight experience, in terms of time flown in the past 90 days, and type of accident from 2008, namely pilot error versus non-pilot error.  The population selected was of private pilots of single-engine GA fixed-wing aircraft having conventional and tricycle landing gear configurations.  The significance of the recent flight experience of a sample of private pilots involved in accidents attributed to pilot error was analyzed alongside the recent flight experience of a sample of private pilots involved in accidents caused by factors other than pilot error, using t-tests and binary logistic regression.  The independent variable (X1), recent flight experience of the accident pilots was defined as hours flown within the last 90 days prior to the accident.  The dependent variable (Y1) was defined as the cause of the accident, either pilot error or some factor other than pilot error.  
This chapter will detail the research method and design, provide a description of the participants, and present a brief overview of the materials.  The operational definition of variables will then be explained.  The chapter will continue with a description of the data collection, processing, and analyses.  Finally, there will be a discussion of methodological assumptions, limitations, and delimitations about the population, and a description of ethical assurances.  
The research questions were designed to examine the relationship between the independent variable, recent flight experience in terms of time flown in the past 90 days, and the dependent variable, the cause of accident, pilot error and some factor other than pilot error.  There were two groups of tricycle landing gear pilots, representing 423 accidents in 2008, as shown in Table 2.  The two groups were chosen based on whether the private pilots of single-engine aircraft were involved in an accident attributed to pilot error or some other cause unrelated to pilot error.  There were two groups of conventional, or tail-wheel, landing gear pilots, representing 166 accidents in 2008, as shown in Table 2.  Similarly, the two groups were chosen based on whether or not the private pilots of single-engine aircraft were involved in an accident attributed to pilot error. 

Table 2
Grouping Classifications of Pilots
				Pilot Error (PE)		Non-Pilot Error (NPE)

Tricycle gear (T)			TPE				TNPE
Conventional (tail-wheel) (C)		CPE				CNPE

The research questions together with the accompanying null and alternate hypotheses that are listed below addressed the problem statement in this research study.  
Q1.	Is there a relationship between the recent flight experience of TPE pilots and TNPE pilots?  
H10.	 There is no statistically significant relationship between the recent flight experience of TPE pilots and TNPE pilots.  
H1a.	 There is a statistically significant relationship between the recent flight experience of TPE pilots and TNPE pilots.  
Q2.	Is there a relationship between the recent flight experience of CPE pilots and CNPE pilots?  
H20.	 There is no statistically significant relationship between the recent flight experience of CPE pilots and CNPE pilots.  
H2a.	 There is a statistically significant relationship between the recent flight experience of CPE pilots and CNPE pilots.  
Q3.	How does recent flight experience predict the likelihood of a tricycle gear pilot being involved in a pilot error accident?  
H30.	 Recent flight experience does not predict the likelihood of a tricycle gear pilot being involved in a pilot error accident. 
H3a.	 Recent flight experience predicts the likelihood of a tricycle gear pilot being involved in a pilot error accident.
Q4.	How does recent flight experience predict the likelihood of a conventional gear pilot being involved in a pilot error accident?  
H40.	 Recent flight experience does not predict the likelihood of a conventional gear pilot being involved in a pilot error accident.
H4a.	 Recent flight experience predicts the likelihood of a conventional gear pilot being involved in a pilot error accident.  
Research Method and Design
The intent of business research is to produce information to reduce uncertainty (Zikmund, Babin, Carr, & Griffin, 2009).  This research is business research because it is related to the aviation insurance and airplane rental businesses.  The uncertainty this study aims to uncover is the relationship between recent flight experience and GA pilot error accidents.  There is evidence that the FAA rule, 14 C.F.R. § 61.57, is by itself inadequate to ensure safety as shown by the diversity of measures taken by FBO owners, and insurance agents as well as findings by FAA researchers in the last seven years (Avemco, n.d.; FAA, 2003a; Mid Island Air Inc., n.d.).  Whereas extensive research exists demonstrating that aeronautical decision-making and situational awareness training decrease the likelihood of a pilot error accident, the relationship between pilot error accidents and recent flight experience implied by the FAA’s 90-day rule has not been rigorously assessed using empirical data (Goh & Wiegmann, 2002; Guohua, et al., 2002).
Since it is accepted that total flight experience matters in all aircraft accidents (Cunningham, 2006; Hendrickson, 2009; Summers, Ayers, Connolly, & Robertson, 2007), the focus of this research was specifically recent flight experience that occurred in the last 90 days prior to the accident.  If recent flight experience were a factor, a trend toward more pilot error accidents would occur as the pilot experience shifted from more recent experience to less recent experience.  Also, the trend in pilot error accidents should diverge upward from the non-pilot error accidents as the experience time becomes greater.  To this end, GA fixed-wing aircraft accidents, regardless of cause, that had already occurred, were used in this study.  The reason why this study did not include non-accident related data is that no such data exists on non-accident pilots as they have not yet been involved in an accident.  To enable a more accurate analysis, a comparison was made between all accident pilots in a 1-year timeframe: those pilots who had an accident attributed to pilot error and those pilots who had an accident caused by some other factor.  This way, all other factors being equal, the cause of the accident would be the only major difference between groups. 
The purpose of this quantitative ex post facto study on NTSB accident investigation archival data was to analyze the relationship between recent flight experience, in terms of time flown in the past 90 days, and type of accident, namely pilot error versus non-pilot error.  The 90-day time frame was chosen for two reasons.  First, it coincides with the CFR describing this requirement (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]).  Second, most aviation industry stakeholders use this as a measure of pilot proficiency (Avemco, n.d.; Mid Island Air Service, Inc, n.d.; Phoenix Aviation Managers, Inc., n.d.). 
A non-experimental, quantitative ex post facto approach, as outlined by Vogt (2005), was used on archival data from NTSB final, or probable cause, reports of real events that occurred in 2008 and involved GA fixed-wing aircraft accidents, some fatal and some non-fatal (AOPA, 2009; NTSB, n.d.a).  Guilkey (1997), Detwiler et al. (2006), Soares (2002), and Veillette (2009b) used this same approach with archival data.  The year 2008 was chosen because NTSB accident investigators had already completed and published all final reports for these accidents.  These final reports were needed to identify and select those accidents that would be used in this study.  As required by Vogt (2005), the participants selected had already been exposed to a particular condition.  In this case the condition was involvement in an accident.  This method, also described by Black (1999), was chosen over others because the accidents were factual, and the experiments, the accidents, had already occurred.  
Data collection began after choosing the population.  The choice of population was determinant upon three different classifications that mirror those used by aviation industry stakeholders (Avemco, n.d.; FAA, 2003a; FAA Certification: Pilots, Flight Instructors, and Ground Instructors, 2010; Phoenix Aviation Managers, Inc., n.d.).  The first of these classifications is based upon pilot certification.  Only private pilots were selected for this study from among all other certifications, namely, student, sport, recreational, commercial, ATP, and CFI.  Private pilots were responsible for 50% of GA accidents in 2008 (AOPA ASF, 2009).  The second of these classifications is based upon number of aircraft engines, in terms of single-engine and multi-engine.  Only single-engine aircraft were selected.  Single-engine aircraft were involved in 91% of GA accidents in 2008 (AOPA ASF, 2009).  The third of these classifications is based upon aircraft landing gear configuration, in terms of tail-wheel and tricycle.  For this study, the researcher selected both landing gear configurations yet analyzed each group individually as shown below.  Research, therefore, focused on a population of 589 private pilots of single-engine aircraft of both landing gear configurations.  This population represented the largest group in terms of GA accident rate for the past seven years (AOPA ASF, 2003, 2004, 2005, 2006, 2007, 2008, 2009).
The significance of the recent flight experience of a sample of private pilots involved in accidents attributed to pilot error was analyzed alongside the recent flight experience of a sample of private pilots involved in accidents caused by factors other than pilot error, using t-tests and binary logistic regression.  Binary logistic regression analyses, outlined by Hilbe (2009), allowed the prediction of a discrete outcome from the set of variables that were a mix of continuous and dichotomous.  In this study, the dichotomous variable was the dependent variable (Y1) defined as the cause of the accident, either pilot error or some factor unrelated to pilot error.  The dependent variable was assigned one of two values, PE and NPE, as follows: PE for the accidents attributed to pilot error and NPE for the accidents caused by some factor other than pilot error.  The continuous variable was the independent variable (X1), recent flight experience of the accident pilots, defined as time flown within the last 90 days prior to the accident.  This design accomplished the goal of examining the significance of recent flight experience in relation to pilot error accidents.  This research could provide the necessary guidance to aviation insurance agents, the FAA, and FBO owners who currently still use recent flight experience as a direct predictor of pilot proficiency and therefore as an indirect predictor of GA safety (Avemco, n.d.; Mid Island Air Service, Inc, n.d.; Phoenix Aviation Managers, Inc., n.d.). 
Participants
The selected populations were private pilots involved in 423 accidents in tricycle landing gear aircraft together with 166 in conventional, or tail-wheel, landing gear aircraft.  These represented 589 private pilots who were involved in GA accidents in single-engine fixed-wing aircraft in 2008 (NTSB, n.d.a).  A power analysis was performed on each of these two populations to determine the sample sizes required for this study.  The researcher used G*Power version 3.1.2., power analysis software, to calculate the a priori sample sizes.  A power of 80% was used to calculate the values for alpha value, expected effect size, and statistical test type so the program would generate the minimum sample size needed (Cohen, 1988).  A medium effect size of 0.3 was chosen as this is the recommended value unless a different effect size is otherwise necessary or known (Cohen, 1988).  The significance level or alpha, α = .05 was chosen so the researcher would have 95% confidence the results would not be due to chance.  This level is accepted in most social science fields as statistically significant (Trochim, 2008).  The calculation for the tricycle landing gear pilot population of 423 rendered a sample size of 202, whereas the calculation for the conventional landing gear pilot population of 166 rendered a sample size of 117 based on a power of 80% (Cohen, 1988).   
Materials/Instruments
Data compilation involved the download of all final, or probable cause, reports compiled by NTSB accident investigators from the NTSB website (NTSB, n.d.a).  This was the source of the archival data.  No published instruments were used to gather data.  Details of the accidents relevant to this study were gathered from the archival data, to include aircraft landing gear configuration and cause of accident.  The researcher selected only private pilots of single-engine aircraft having both tricycle and conventional landing gear configurations.  The following data for each sample, gathered directly from the pages of NTSB final reports, were entered into Statistical Procedures for the Social Sciences (SPSS®) software: NTSB accident identification number, number of hours flown in the past 90 days, and cause of accident.  
Operational Definition of Variables
Identified below are the primary constructs, or variables, associated with this research together with a brief overview of how they were operationally defined.  Trochim and Donnelly (2008) defined a variable as “any entity that can take on different values” (p. 8).  These variables are not always numerical. 
Recent flight experience.  The independent variable (X1), recent flight experience of the pilots involved in all accidents, was defined as time flown within the last 90 days prior to the accidents (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]).  In terms of levels of measurement, the data are interval and consist of flight hours.  A t-test and a binary logistic regression analysis were conducted between this variable (X1) and the dependent variable, accident cause (Y1). 
Accident cause.  The dependent variable (Y1), accident cause, was a yes/no dichotomous variable that denoted whether or not the private pilot was involved in an accident caused by pilot error or some other factor unrelated to pilot error. The data are nominal.  To Y was assigned one of two values, PE and NPE, as follows: PE for the accidents caused by pilot error and NPE for the accidents caused by some factor other than pilot error.  A t-test and a binary logistic regression analysis were conducted between this variable (Y1) and the independent variable, recent flight experience (X1).  
Data Collection, Processing, and Analysis
Data collection and processing involved the following stages.  Final, or probable cause, reports produced by the NTSB accident investigators were downloaded from the NTSB website.  This was the source of the archival data.  Selection of cases involved separating the 589 accident pilots who served as the population for this study from among the 1,254 accident pilots in GA fixed-wing aircraft in 2008.  Next, those accidents in which the single-engine pilots were flying a tricycle landing gear aircraft at the time of the accident (T) were separated from those accidents in which the single-engine pilots were flying a conventional landing gear aircraft at the time of the accident (C).  Finally, the accidents were separated by cause of error, pilot error coded as PE, or some factor other than pilot error coded as NPE.  
The next stage involved randomly sampling the two pilot populations.  The power analysis calculation for the tricycle landing gear pilot population of 423 rendered a sample size of 202.  The power analysis calculation for the conventional landing gear pilot population of 166 rendered a sample size of 117 based on a power of 80% (Cohen, 1988).  However, only 111 cases were included in the analysis, as opposed to 117, as a result of insufficient data in 55 of the 166 NTSB accident reports yielding a power of 79.8% (Cohen, 1988).  The final stage involved entering the following data for the two samples, T and C, into SPSS® software: NTSB accident identification number, number of hours flown in the past 90 days, and cause of accident, in terms of PE, or NPE.
Data analysis involved running two separate t-tests and two separate binary logistic regression analyses, one for the tricycle landing gear pilots and another for the conventional, or tail-wheel, landing gear pilots.  T-tests allowed a statistical comparison of the mean number of hours of recent flight experience of pilots based upon involvement in an accident attributed to pilot error.  The intent of the t-tests was to examine whether there is a statistically significant relationship between the mean numbers of hours of recent flight experience of two groups of pilots based on whether or not they were involved in an accident caused by pilot error.  Binary logistic regression analyses were performed to determine whether recent flight experience is a predictor that a private pilot of single-engine aircraft would have an accident attributed to pilot error.  
Methodological Assumptions, Limitations, and Delimitations
Since it is accepted that total flight experience matters in all aircraft accidents (Cunningham, 2006; Hendrickson, 2009; Summers, Ayers, Connolly, & Robertson, 2007), the focus of this research was specifically recent flight experience that occurred in the last 90 days prior to the accident.  GA fixed-wing aircraft accidents, regardless of cause, that had already occurred, were used in this study. The reason why this study did not include non-accident related data is that no such data exists on non-accident pilots as they have not yet been involved in an accident.  To enable a more accurate analysis, a comparison was made between all accident pilots in a 1-year timeframe: those pilots who had an accident attributed to pilot error and those pilots who had an accident caused by some other factor.  This way, all other factors being equal, the cause of the accident would be the only major difference between groups. 
 The main limitation was that the data collected from the NTSB database was dependent on the thoroughness of the reports made by the original NTSB accident investigators.  Quite often these reports did not contain complete information, namely recent flight experience, which was needed for this study.  Therefore, the NTSB accident investigators’ thoroughness of investigation resulted in some of the accidents not being included in the study.  To be specific, in the case of the 423 accidents involving aircraft having tricycle landing gear, there were 127 accidents missing data on the past 90 days flight time.  This represented 30% of all accidents.  These accidents missing relevant data came from many different locations around the nation so there should be no threat to the representativeness of the sample.  In the case of the 166 accidents involving aircraft having conventional landing gear, there were 55 accidents missing data on the past 90 days flight time.  This represented 33% of all accidents.  Likewise, these accidents missing relevant data came from many different locations around the nation so there should be no threat to the representativeness of the sample.  The main assumption in this study was that the sample used was representative of the population.  
A number of confounding variables existed that may have been statistically related to, or correlated to, the independent variable, recent flight experience. Among these confounding variables were: sex of pilot, age of pilot, total flight experience of pilot, time, day, and year of accident, location of accident, weather at the time of accident, make and model of accident aircraft, presence of air traffic control, and date of last flight review. These confounding variables were not considered a threat to the internal validity of the study because they were present in both groups of accident pilots: those involved in a pilot error accident and those involved in an accident attributed to some factor other than pilot error.
However, the following confounding variable could have been significant to the internal validity.  This confounding variable dealt with pilots who had an accident caused by mechanical failure of the aircraft.  This created a limitation because it was possible that careless pilots may have neglected their aircraft maintenance duties and therefore have been prone to mechanical failures.  Furthermore, it is also possible that mechanical failures could be more prevalent in certain makes and models of aircraft causing risk-averse pilots to select aircraft that are inherently safer to fly.  Lastly, not all mechanical failures result in an accident, so this population of pilots who had mechanical failures was comprised of those pilots who were unable to avoid an accident.  There was no way for this researcher to compensate for this limitation.
There was one limitation that was completely eliminated in this study.  In 18% of the GA fixed-wing aircraft accidents in 2008, a CFI was on board the accident aircraft.  Since there was no way to determine whether the CFI could or could not have contributed to the pilot error, the researcher chose to completely discard those accidents from this study.  This ensured that the pilot error was solely that of the private pilot.    
To narrow the scope of the study, the largest population was chosen, that of private pilots, who caused 50% of all accidents in 2008 (AOPA ASF, 2009).  Additionally, the focus was on pilots of single-engine aircraft as this class of aircraft represented 91% of all accident aircraft in 2008 (AOPA ASF, 2009).  After those accidents involving a flight instructor on board the aircraft were discarded, together with those accidents involving aircraft having floats or amphibian landing gear, 23 accidents in total or 3.7%, there remained a population of 589 accident pilots, of whom 423 flew tricycle landing gear aircraft, and 166 flew conventional landing gear aircraft at the time of the accident.  
Ethical Assurances
The researcher complied with the standards for conducting research with human participants.  The use of informed consent procedures was not necessary as the data were available on a public website belonging to the NTSB.  Furthermore, confidentially was maintained because the NTSB archival database used did not contain individual pilot names.  In those rare instances where names were reported by the NTSB these names were completely omitted from the data collection to maintain the pilots’ anonymity.  No conflicts of interest were seen.  The researcher was unaffiliated with any aviation insurance agencies, FBO, or agencies of the FAA and NTSB.  IRB approval was sought prior to any data collection.
Summary
The problem addressed by this study is that the relationship between pilot error accidents and recent flight experience implied by the FAA’s 90-day rule has not been rigorously assessed using empirical data (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]; Goh & Wiegmann, 2002; Guohua, et al., 2002).  This research examined the relationship between a pilot’s recent flight experience, in terms of time flown within the past 90 days, and the number of GA accidents attributed to pilot error (NTSB, n.d.a).  A quantitative ex post facto approach was used on NTSB accident investigation archival data.  The focus population was the private pilots involved in all GA fixed-wing, single-engine aircraft accidents that occurred in 2008 (NTSB, n.d.a).  This chapter detailed the research method and design, provided a description of the participants, presented a brief overview of the materials, and an explanation of the operational definition of variables.  Finally, there was a discussion of methodological assumptions, limitations, and delimitations about the population, and a description of ethical assurances.  

Chapter 4: Findings
The purpose of this quantitative ex post facto study on NTSB accident investigation archival data was to examine the relationship between recent flight experience, in terms of time flown in the past 90 days, and type of accident from 2008, namely pilot error versus non-pilot error.  Extensive research demonstrates the relationship between situational awareness and aeronautical decision-making skills and pilot error (Cunningham, 2006; Hendrickson, 2009; Summers, Ayers, Connolly, & Robertson, 2007).  Guilkey (1997) showed that while reliance on flight hours as a predictor of pilot performance might be accurate when considering skills used on every flight, it may not be a sufficient predictor of complex problem-solving tasks that form the majority of aeronautical decision-making skills.  However, the relationship between pilot error accidents and recent flight experience implied by the FAA’s 90-day rule has not been rigorously assessed using empirical data (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]; Goh & Wiegmann, 2002; Guohua, et al., 2002).  The population selected was of private pilots of single-engine GA fixed-wing aircraft having conventional and tricycle landing gear configurations.  
The significance of the recent flight experience of a sample of private pilots involved in accidents attributed to pilot error was analyzed alongside the recent flight experience of a sample of private pilots involved in accidents caused by factors other than pilot error, using t-tests and binary logistic regression.  The results could provide guidance to the aviation insurance agents, the FAA, and FBO owners who currently still use recent flight experience as a direct predictor of pilot proficiency and therefore as an indirect predictor of GA safety (Avemco, n.d.; Mid Island Air Service, Inc, n.d.; Phoenix Aviation Managers, Inc., n.d.).  This chapter will be divided into two major sections.  In the first section the results of the two t-tests and the two binary logistic regression analyses are presented.  In the second section the findings are evaluated.
Results
The intent of the study was to examine the relationship between recent flight experience, in terms of time flown in the past 90 days, and type of accident, namely pilot error versus non-pilot error.  Accidentcause is the dependent variable.  Accidentcause was assigned either a PE for accidents attributed to pilot error or an NPE for the accidents caused by some factor other than pilot error.  Flighthours is the continuous independent variable in terms of hours flown in the last 90 days prior to the accident.  
The research questions together with the accompanying null and alternate hypotheses that are listed below addressed the problem statement in this research study.  
Q1.	Is there a relationship between the recent flight experience of TPE pilots and TNPE pilots?  
H10.	 There is no statistically significant relationship between the recent flight experience of TPE pilots and TNPE pilots.  
H1a.	 There is a statistically significant relationship between the recent flight experience of TPE pilots and TNPE pilots.  
Q2.	Is there a relationship between the recent flight experience of CPE pilots and CNPE pilots?  
H20.	 There is no statistically significant relationship between the recent flight experience of CPE pilots and CNPE pilots.  
H2a.	 There is a statistically significant relationship between the recent flight experience of CPE pilots and CNPE pilots.  
Q3.	How does recent flight experience predict the likelihood of a tricycle gear pilot being involved in a pilot error accident?  
H30.	 Recent flight experience does not predict the likelihood of a tricycle gear pilot being involved in a pilot error accident. 
H3a.	 Recent flight experience predicts the likelihood of a tricycle gear pilot being involved in a pilot error accident.
Q4.	How does recent flight experience predict the likelihood of a conventional gear pilot being involved in a pilot error accident?  
H40.	 Recent flight experience does not predict the likelihood of a conventional gear pilot being involved in a pilot error accident.
H4a.	 Recent flight experience predicts the likelihood of a conventional gear pilot being involved in a pilot error accident.  
The mean and standard deviation for the tricycle landing gear pilots (TGP) is outlined in Table 3.  All 202 cases were included in the analysis.  There were no missing data.  The means were compared to present an overview of the type of data collected.  TGP who had an accident attributed to pilot error (TPE) amassed more hours of recent flight experience than those pilots who had an accident attributed to some factor other than pilot error (TNPE).  If recent flight experience was a significant factor contributing to pilot error then it should follow that the pilots who had a pilot error accident would have had less hours of recent flight experience, not more.  Data for recent flight experience, in terms of mean number of hours flown in the past 90 days, for the 202 TGP is displayed in Figure 2.    

Table 3
Mean and Standard Deviation – Tricycle Gear Pilots
Error		n	Mean 		Standard Deviation	Standard Error Mean

TPE 		123	23.24		23.025			2.076
TNPE		79	22.59		20.969			2.359

Total 		202	22.99		22.193

Figure 2. Accidents - Tricycle landing gear.

The mean and standard deviation for the conventional landing gear pilots (CGP) is outlined in Table 4.  Only 111 cases were included in the analysis, as opposed to 117, as previously calculated in the power analysis.  This was a result of insufficient data in 55 of the 166 NTSB accident reports.  CGP who had an accident attributed to pilot error (CPE) amassed less hours of recent flight experience than those pilots who had an accident attributed to some factor other than pilot error (CNPE).  If recent flight experience was a significant factor contributing to pilot error then it should follow that the pilots who had a pilot error accident would have had significantly less hours of recent flight experience.   

Table 4
Mean and Standard Deviation – Conventional Gear Pilots
Error		n	Mean 		Standard Deviation	Standard Error Mean

CPE 		91	23.08		19.661			2.061
CNPE		20	24.35		15.732			3.518

Total 		111	23.31		18.954

Figure 3. Accidents - Conventional landing gear.
Data for recent flight experience, in terms of mean number of hours flown in the past 90 days, for the 111 CGP is shown in Figure 3.  A graphical representation displaying the relationship between the recent flight experience for TPE and CPE and that for TNPE and CNPE is shown in Figure 4.  In this illustration, TNPE and CNPE were combined into one group, named non-pilot error.  To provide a true and accurate comparison of the data sets, the three groups were compared as percentages of total accidents based on 5-hour groupings of recent flight experience.  The pilot error accidents and the non-pilot error accidents are consistent across the time scale.  If pilot error accidents were attributed to lack of recent flight time, the number of pilot error accidents would increase in relation to the non-pilot error accidents as time increased.  

Figure 4. Comparison of recent flight experience.
The independent samples t-test for TGP is outlined in Table 5.  This test revealed no statistically significant difference between the mean number of hours of recent flight experience of TPE and TNPE, t(202) =  -.200, p = .842.  The independent samples t-test for CGP is outlined in Table 6.  This test revealed no statistically significant difference between the mean number of hours of recent flight experience of CPE and CNPE, t(111) = -.271, p = .787.   

Table 5
Independent Samples t-Test - Tricycle Gear Pilots
			Levene’s test for 		T-test for 
Equality of Variances		Equality of Means
				F	p 		t	df        p(2-tail)   Mean Diff.

Hours – Equal variances 
assumed			.129	.720		-.200	200	.842	   -.641
Hours – Equal variances
not assumed						-.204	177.5	.839	   -.641

Table 6
Independent Samples t-Test - Conventional Gear Pilots
 			Levene’s test for 		T-test for 
Equality of Variances		Equality of Means
				F	p 		t	df       p(2-tail)   Mean Diff.

Hours – Equal variances 
assumed			1.021	.314		-.271	109	.787	  -1.273
Hours – Equal variances 
not assumed						-.312	33.4	.757	  -1.273

In the following binary logistic regression analyses the goal was to predict that a pilot would have a pilot error accident using recent flight experience as a predictor.  Before the data could be classified, a threshold of 0.500 was set so that cases with probabilities ≥ 0.500 would be predicted as having an accident attributable to pilot error whereas other cases would be predicted as having an accident attributable to some factor other than pilot error.  To consider this analysis valid, the model had to satisfy the following five assumptions of logistic regression.  First, the independent variables were not linear combinations of each other (Field, 2009).  This was not an issue as only one independent variable, flighthours, was used.  Second, final reports from the NTSB accident investigation archival data showed that they were independent of each other (Field, 2009).  Third, to satisfy the assumption that the research was measured without error, this research was dependent on the accuracy of the NTSB accident investigations (Osborne & Waters, 2002).  Fourth, final reports from the NTSB accident investigation archival data showed that they were mutually exclusive of each other (Burns & Burns, 2008).  Fifth and finally, multicollinearity was not an issue in this model as there was only one independent variable used, namely flighthours (Field, 2009).  
The classification of data for TGP and CGP, shown in Table 7, suggests that knowing nothing about our variable, and guessing that a pilot would be involved in a pilot error accident, the analysis would yield a correct prediction 60.9% of the time, in the case of TGP, and 82.0% of the time, in the case of CGP.  The reason for this is because the beginning block, Step 0, presents the results with only the constant, errorcause, included before the coefficient, namely, flighthours, is entered into the equation. Binary logistic regression compares this model with a model including the predictor, flighthours, to determine whether the latter model is more appropriate, shown later in Table 15.

Table 7
Classification of Data
     	Observed				       Predicted
						Error Cause		Percentage Correct
						PE	NPE		
TGP	Step 0     errorcause	PE		123	0		100.0
				NPE		79	0		0.0
		Overall Percentage					60.9

CGP	Step 0	  errorcause	PE		91	0		100.0
NPE		20	0		0.0
		Overall Percentage					82.0

Table 8
Variables in the Equation
				B	     SE	            Wald	           df	   p	Exp(B)

TGP	Step 0	Constant	-0.443	     0.144	9.429		1	.002	.642

CGP	Step 0	Constant	-1.515	     0.247	37.640		1	.000	.220

The variables in the equation for TGP and CGP are shown in Table 8.  The results suggest that knowing nothing about our variable, and guessing that a pilot would be involved in a pilot error accident, the analysis would yield predicted odds of .642 in the case of TGP, and .220, in the case of CGP.  Binary logistic regression compares this model with a model including the predictor, flighthours, to determine whether the latter model is more appropriate, shown later in Tables 16 and 17.  The calculation for variables not in the equation, shown in Table 9, is used to demonstrate whether the independent variable, flighthours, would improve the model.  Since p = .841 for TGP and p = .785 for CGP, the variable, flighthours, is not considered significant and when included would not add to the predictive power of the model. 

Table 9
Variables Not in the Equation
							Score		df		  p

TGP	Step 0		Variables	flighthours	0.040		1		.841
		Overall Statistics			0.040		1		.841

CGP	Step 0		Variables	flighthours	0.075		1		.785
		Overall Statistics			0.075		1		.785

The statistical tests, performed to analyze model fit and significance, are outlined in Tables 10 through 14.  These tables also determine whether the independent variable included, flighthours, makes a significant contribution to the model.  The omnibus test of model coefficients, in Table 10, demonstrates the overall significance tested using the model chi-square, χ2.  The resultant χ2 = 0.040 (df =1, p = .841) for TGP and χ2 = 0.074 (df =1, p = .786) for CGP contain significance levels that are greater than .05.  These results indicate that adding the variable, flighthours, does not make a significant contribution to the model.  It should be noted that Table 10 includes Step 1 for both TGP and CGP.  In stepwise binary logistic regression, steps are listed in the table as each variable is added or removed, creating different models. The step is a measure of the improvement in the predictive power of the model since the previous step.  The results, in Table 10, show that adding the independent variable, flighthours, does not make a significant contribution to the model.

Table 10
Omnibus Test of Model Coefficients
					χ2 			df		  p

TGP	Step 1		Step		0.040			1		.841
			Block		0.040			1		.841
			Model		0.040			1		.841

CGP	Step 1		Step		0.074			1		.786
			Block		0.074			1		.786
			Model		0.074			1		.786

Although there is no close analogous statistic in binary logistic regression to the measure of the strength of association R2, the model summary, in Table 11, provides some approximations.  Cox and Snell’s R2 attempts to imitate multiple R2 based on likelihood, but its maximum can be, and usually is, less than 1.0.  The results for TGP indicate that 0, or none, of the variation in the dependent variable, errorcause, is explained by the logistic model, whereas the result for CGP indicate that 0.1% of the variation in the dependent variable, errorcause, is explained by the logistic model.  The Nagelkerke R2 estimate is calculated in such a way as to range from 0 to 1, with a better model displaying a value closer to 1.  In this analysis, however, Nagelkerke’s R2 for both TGP and CGP are identical to the Cox and Snell measures.  The -2 log likelihood, -2LL, typically used to find out if adding one or more predictors to the model would make it a better fit, is not relevant as only one predictor was measured.  

Table 11
Model Summary
Step 		-2 Log Likelihood	Cox and Snell R2	Nagelkerke R2

TGP	1		270.330		0.000			0.000

CGP	1		104.636		0.001			0.001

The results of the Hosmer-Lemeshow test, that is used as an alternative to the model chi-square test, are outlined in Tables 12, 13, and 14.  The Hosmer-Lemeshow test first divides participants into 10 ordered groups and then compares the observed number in each group to the number expected by the logistic regression model.  The 10 ordered groups are created based on their estimated probability.  Those with estimated probability below .1 form one group, and so on, up to those with probability .9 to 1.0.  Each of these categories is further divided into two groups based on the actual observed outcome variable, PE or NPE.  The expected frequencies for each of the cells are obtained from the Tables 12 and 13 and a probability value is computed from the chi-square distribution with 8 degrees of freedom to test the fit of the logistic model.  This is shown for both TGP and CGP in Table 14.  Since the Hosmer-Lemeshow goodness-of-fit test statistics are greater than .05 for both TGP (p = .529) and CGP (p = .608) it is implied that the model predicts values significantly similar to those that are observed.  
Table 12
Contingency Table for Hosmer-Lemeshow Test – Tricycle Gear Pilots
			Errorcause = PE		Errorcause = NPE	

		Observed 	Expected	Observed	Expected	Total

Step 1	1	13		13.119		8		7.881		21	
	2	12		12.914		9		8.086		21
	3	14		12.225		6		7.775		20
	4	12		14.605		12		9.395		24
	5	11		11.538		8		7.462		19
	6	14		12.124		6		7.876		20
	7	13		12.699		8		8.301		21
	8	17		13.288		5		8.712		22
	9	10		11.457		9		7.543		19
	10	7		9.033		8		5.967		15

Table 13
Contingency Table for Hosmer-Lemeshow Test – Conventional Gear Pilots
			Errorcause = PE		Errorcause = NPE	

		Observed 	Expected	Observed	Expected	Total

Step 1	1	10		9.970		2		2.030		12	
	2	11		9.120		0		1.880		11
	3	9		9.096		2		1.904		11
	4	11		10.729		2		2.271		13
	5	10		10.704		3		2.296		13
	6	9		9.036		2		1.964		11
	7	8		9.800		4		2.200		12
	8	9		8.925		2		2.075		11
	9	8		8.852		3		2.148		11
	10	6		4.769		0		1.231		6

Table 14
Hosmer-Lemeshow Test
Step 		χ2  			df		  p

TGP	1		7.076			8		.529

CGP	1		6.352			8		.608

Once the predictive power of the model was deemed acceptable, the classification was recalculated with the independent variable, flighthours, added to the model.  This was done to show the proportion of cases the model correctly predicted.  Since the independent variable, flighthours, was not expected to be significant for neither TGP (p = .841) nor for CGP (p = .785), when added to the model it was not expected to alter the predictive power of the model.  For this reason, Table 15 looks identical to Table 7. 

Table 15
Classification of Data
 		Observed					       Predicted
							Error Cause		Percentage
							PE	NPE		Correct

TGP	Step 1     errorcause	PE			123	0		100.0
				NPE			79	0		0.0
			Overall percentage					60.9

CGP	Step 1    errorcause	PE			91	0		100.0
				NPE			20	0		0.0
			Overall percentage					82.0

	
The results of the variables in the equation, together with several important elements, are displayed in Tables 16 and 17.  The Wald statistic and associated probabilities provide an index of the significance of the predictor, flighthours, in the equation.  The Wald statistics for TGP (p =.841) and for CGP (p = .785) were more than .05 so the conclusion is that the variable, flighthours, does not make a significant contribution to the model.  	

Table 16
Variables in the Equation – Tricycle Gear Pilots
			B	    SE	   Wald	   df        p	Exp(B)    	95% CI
									    Lower         Upper

Step 1	flighthours	-0.001	   0.007    0.040   1     .841	0.999	   0.986            1.012
	Constant	-0.413	   0.208    3.936   1     .047	0.662

Note: CI = confidence interval

Table 17
Variables in the Equation – Conventional Gear Pilots
			B	     SE	    Wald       df	     p	Exp(B)	           95% CI
									    Lower          Upper

Step 1	flighthours	0.004	     0.013    0.075      1	  .785	1.004        0.979           1.029
	Constant	-1.598	     0.396    16.316    1	  .000	0.202

Note: CI = confidence interval

The models for the binary logistic regression analyses for both TGP and CGP appear to predict what actually happens, as indicated by the Cox and Snell’s R2 values and the Hosmer-Lemeshow goodness-of-fit tests.  However, what it predicts is not significantly better than what the model would have predicted without the predictor, flighthours.  
Evaluation of Findings
An independent samples t-test for tricycle gear pilots revealed no statistically significant difference between the mean number of hours of recent flight experience of TPE and TNPE, t(202) = -.200, p = .842.  An independent samples t-test for conventional gear pilots revealed no statistically significant difference between the mean number of hours of recent flight experience of CPE and CNPE, t(111) = -.271, p = .787.  This means that based upon a pilot’s recent flight experience alone an accident is as likely to have been the cause of pilot error as any other factor.
Binary logistic regression analyses were conducted to predict that a tricycle or conventional gear pilot would have a pilot error accident using recent flight experience as a predictor.  A test of the full model against a constant only model was not statistically significant, indicating that the predictor, recent flight experience, does not reliably distinguish between pilot error and non-pilot error accidents for TGP, χ2 = 0.040 (df = 1, p = .841) and for CGP, χ2 = 0.074 (df = 1, p = .786).  Cox and Snell’s R2 for TGP indicate that none of the variation in the dependent variable, errorcause, is explained by the logistic model.  Cox and Snell’s R2 for CGP indicate that 0.1% of the variation in the dependent variable, errorcause, is explained by the logistic model.  Prediction success overall was 60.9% for TGP and 82.0% for CGP.  The Wald criterion demonstrated that recent flight experience does not make a significant contribution to prediction, TGP (p =.841) and CGP (p = .785).  The models for the binary logistic regression analyses for both TGP and CGP appear to predict what actually happens, as indicated by the Cox and Snell’s R2 values and the Hosmer-Lemeshow goodness-of-fit tests.  However, what it predicts is not significantly better than what the model would have predicted without the predictor, flighthours.  So, it can be concluded that recent flight experience, in terms of hours flown in the past 90 days, is not a statistically significant predictor of whether or not a pilot will have an accident attributable to pilot error versus one attributable to some other factor.
Recent flight experience, in terms of time flown in the past 90 days, continues to serve as a predictor of a pilot error accident to some aviation insurance agents and FBO owners (Avemco, n.d.; Mid Island Air Service, Inc, n.d.; Phoenix Aviation Managers, Inc., n.d.).  The findings from this study show that recent flight experience is not a statistically significant predictor of whether or not a pilot will have an accident attributable to pilot error versus some other factor.  This study elaborates on the findings of Guilkey (1997).   Guilkey (1997) showed that while reliance on flight hours as a predictor of pilot performance might be accurate when considering stick and rudder skills, or physical airplane skills, used on every flight, it may not be a sufficient predictor of complex problem-solving tasks that form the majority of aeronautical decision-making skills. 
The intent of the study was to examine the relationship between recent flight experience, in terms of time flown in the past 90 days, and type of accident, namely pilot error versus non-pilot error.  In response to the first research question: Is there a relationship between the recent flight experience of TPE pilots and TNPE pilots?  Based upon an independent samples t-test between the mean number of hours of recent flight experience of TPE and TNPE, t(202) = -.200, p = .842, the researcher accepts that there is no statistically significant relationship between the recent flight experience of tricycle gear pilots based upon whether or not they were involved in a pilot error accident. 
In response to the second research question: Is there a relationship between the recent flight experience of CPE pilots and CNPE pilots?  Based upon an independent samples t-test between the mean number of hours of recent flight experience of CPE and CNPE, t(111) = -.271, p = .787, the researcher accepts that there is no statistically significant relationship between the recent flight experience of conventional gear pilots based upon whether or not they were involved in a pilot error accident. 
In response to the third research question:  How does recent flight experience predict the likelihood of a tricycle gear pilot being involved in a pilot error accident?  In a binary logistic regression analysis, a test of the full model against a constant only model was not statistically significant, indicating that the predictor, recent flight experience, does not reliably distinguish between pilot error and non-pilot error accidents for TGP, χ2= 0.040 (df = 1, p = .841).  
In response to the fourth research question:  How does recent flight experience predict the likelihood of a conventional gear pilot being involved in a pilot error accident?  In a binary logistic regression analysis, a test of the full model against a constant only model was not statistically significant, indicating that the predictor, recent flight experience, does not reliably distinguish between pilot error and non-pilot error accidents for CGP, χ2 = 0.074 (df = 1, p = .786).  
Summary
Based upon an independent samples t-test between the mean number of hours of recent flight experience of TPE and TNPE, t(202) = -.200, p = .842, and CPE and CNPE, t(111) = -.271, p = .787, the researcher accepts that there is no statistically significant relationship between the mean recent flight experience of tricycle or conventional gear pilots based upon whether or not they are involved in a pilot error accident.  In a binary logistic regression analysis, a test of the full model against a constant only model was not statistically significant, indicating that the predictor, recent flight experience, does not reliably distinguish between pilot error and non-pilot error for TGP, χ2= 0.040 (df = 1, p = .841) and CGP, χ2 = 0.074 (df = 1, p = .786).  

Chapter 5: Implications, Recommendations, and Conclusions
Aviation insurance agents, following the requirements listed in the CFRs, use the 90-day rule to assess the likelihood of a pilot error accident (FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]).  The generally accepted premise by these agents is that more experience in a recent timeframe predicts less of a propensity for an accident, all other factors excluded (Avemco, n.d.; Phoenix Aviation Managers, Inc., n.d.).  To this end, some aviation insurance agents and FBO owners measure pilot proficiency solely by using time flown within the past 90, 60, or even 30 days (Avemco, n.d.; Aviation Insurance Resources, n.d.; CS&A Insurance, n.d.).  They do not take into account the extensive research demonstrating aeronautical decision-making and situational awareness training decrease the likelihood of a pilot error accident (Cunningham, 2006; Hendrickson, 2009; Summers, Ayers, Connolly, & Robertson, 2007).
The purpose of this quantitative ex post facto study on NTSB accident investigation archival data was to examine the relationship between recent flight experience, in terms of time flown in the past 90 days, and type of accident, namely pilot error versus non-pilot error (FAA, 2003a; FAA Certification: Pilots, Flight Instructors, and Ground Instructors 14 C.F.R. § 61.57 [2010]).  This experience requirement would indicate that as pilots move through that 90-day period they will have a greater propensity for accidents.  In other words, the accident rate should show an increase as the time from the recent experience grows longer.  The study utilized data from NTSB final, or probable cause, reports of real events that occurred in 2008 and involved GA fixed-wing aircraft accidents, some fatal and some non-fatal (AOPA, 2009; NTSB, n.d.a).  Guilkey (1997), Detwiler et al. (2006), Soares (2002), and Veillette (2009b) used this same approach.  The year 2008 was chosen because NTSB accident investigators had already completed and published all final reports for these accidents.  These final, or probable cause, reports were needed to identify and select those accidents that would be used in this study.  This method was chosen over others because the accidents were factual, and the experiments, the accidents, had already occurred (Black, 1999; Vogt, 2005).  
The focus population was of 589 private pilots of single-engine aircraft having both tricycle and conventional landing gear configurations.  This group of pilots was the largest in terms of the GA accident rate (AOPA ASF, 2009).  Data processing involved running two separate t-tests and two separate binary logistic regression analyses: one for the tricycle landing gear pilots and another for the conventional, or tail-wheel, landing gear pilots.  
The main limitation was that the data collected from the NTSB database was dependent on the thoroughness of the reports made by the original NTSB accident investigators.  Quite often these reports did not contain complete information, namely recent flight experience, needed for this study.  Therefore, the NTSB accident investigators’ thoroughness of investigation resulted in 182 of the 589, or 31% of the accidents not being included in the study.  
The standards for conducting research with human participants were complied with.  The use of informed consent procedures was not necessary as the data were available on a public website belonging to the NTSB.  Furthermore, confidentially was maintained because the NTSB archival database used did not contain individual pilot names.  In those rare instances where names were reported, they were completely omitted from the data collection to maintain the pilots’ anonymity.  No conflicts of interest were seen.  The researcher was unaffiliated with any aviation insurance agencies, FBO, or agencies of the FAA and NTSB.  IRB approval was sought prior to any data collection.  This chapter will be divided into two major sections.  In the first section the implications of this study will be presented as they relate to the research questions.  In the second section recommendations will be provided for practical applications of the study together with recommendations for future research.  
Implications
The research questions together with the accompanying null and alternate hypotheses that are listed below addressed the problem statement in this research study.  
Q1.	Is there a relationship between the recent flight experience of TPE pilots and TNPE pilots?  
H10.	 There is no statistically significant relationship between the recent flight experience of TPE pilots and TNPE pilots.  
H1a.	 There is a statistically significant relationship between the recent flight experience of TPE pilots and TNPE pilots.  
Based upon an independent samples t-test between the mean number of hours of recent flight experience of TPE and TNPE, t(202) = -.200, p = .842, the researcher accepts there is no statistically significant relationship between the recent flight experience of tricycle gear pilots based upon whether or not they are involved in a pilot error accident.  This means that based upon a pilot’s recent flight experience alone an accident is as likely to have been the cause of pilot error as any other factor.
Q2.	Is there a relationship between the recent flight experience of CPE pilots and CNPE pilots?  
H20.	 There is no statistically significant relationship between the recent flight experience of CPE pilots and CNPE pilots.  
H2a.	 There is a statistically significant relationship between the recent flight experience of CPE pilots and CNPE pilots.  
Based upon an independent samples t-test between the mean number of hours of recent flight experience of CPE and CNPE, t(111) = -.271, p = .787, the researcher accepts there is no statistically significant relationship between the recent flight experience of conventional gear pilots based upon whether or not they were involved in a pilot error accident.  This means that based upon a pilot’s recent flight experience alone an accident is as likely to have been the cause of pilot error as any other factor.
Q3.	How does recent flight experience predict the likelihood of a tricycle gear pilot being involved in a pilot error accident?  
H30.	 Recent flight experience does not predict the likelihood of a tricycle gear pilot being involved in a pilot error accident. 
H3a.	 Recent flight experience predicts the likelihood of a tricycle gear pilot being involved in a pilot error accident.
A binary logistic regression analysis was conducted to predict that a TGP would have a pilot error accident using recent flight experience as a predictor.  A test of the full model against a constant only model was not statistically significant, indicating that the predictor, recent flight experience, does not reliably distinguish between pilot error and non-pilot error accidents for TGP, χ2= 0.040 (df = 1, p = .841).  Prediction success overall was 60.9%.  The Wald criterion (p =.841) demonstrated that recent flight experience does not make a significant contribution to prediction.  
Q4.	How does recent flight experience predict the likelihood of a conventional gear pilot being involved in a pilot error accident?  
H40.	 Recent flight experience does not predict the likelihood of a conventional gear pilot being involved in a pilot error accident.
H4a.	 Recent flight experience predicts the likelihood of a conventional gear pilot being involved in a pilot error accident.  
A binary logistic regression analysis was conducted to predict that a CGP would have a pilot error accident using recent flight experience as a predictor.  A test of the full model against a constant only model was not statistically significant, indicating that the predictor, recent flight experience, does not reliably distinguish between pilot error and non-pilot error accidents for CGP, χ2 = 0.074 (df = 1, p = .786).  Prediction success overall was 82.0%.  The Wald criterion (p = .785) demonstrated that recent flight experience does not make a significant contribution to prediction.  
The findings from this study of the recent flight experience of private pilots of single-engine aircraft involved in accidents in 2008 revealed no significant difference in the mean number of hours of recent flight experience between the two groups of pilots: those who had an accident attributable to pilot error and those who had an accident caused by some other event, say, mechanical failure of the aircraft.  Furthermore based upon this study of accidents in 2008, binary logistic regression analyses indicated that the predictor, recent flight experience, does not reliably distinguish between pilot error and non-pilot error accidents in either tricycle or conventional single-engine aircraft.  The accident rate remains relatively constant over the 90-day period so the FAA requirement does not in itself constitute an accident prevention requirement.
This study elaborates on findings by Guilkey (1997).  Guilkey (1997) demonstrated that while reliance on flight hours as a predictor of pilot proficiency might be accurate when considering skills used on every flight, it may not be a sufficient predictor of complex problem-solving tasks that form the majority of aeronautical decision-making skills.  According to Bolstad et al. (2010), GA pilots receive insufficient direct instruction on how to carry out preflight planning and how to use this process to manage workload and situation awareness when in flight.  Cunningham (2006) was of the opinion that accident rates bear a direct relationship to training frequency.  Cunningham (2006) believed that a twice-a-year training regime is the reason for the safety record of corporate flight departments.  GA pilots could reduce their accident rate by modeling the actions of corporate and airline pilots and investing in frequent training, incorporating standard operating procedures (SOPs), and utilizing aeronautical decision-making skills (Cunningham, 2006).
Findings by Veillette (2007b) that short discussions in ground school are insufficient for pilots transitioning from one type of aircraft to another concur with those findings made by FAA researchers (FAA, 2003a).  It is also the contention of Bolstad et al. (2010) that developing and maintaining situational awareness is a complex process and training needs to target exposure to knowledge of appropriate situation awareness behaviors.  However, despite this knowledge of the importance of decision-making training, most flight instructors utilize little or no structured judgment training during the course of their teaching (FAA, 2003a; Lee et al., 2005).  
Recommendations
The generally accepted premise by aviation insurance agents is that more experience in a recent timeframe predicts less of a propensity for a pilot error accident, all other factors excluded (Avemco, n.d.; Phoenix Aviation Managers, Inc., n.d.).  Based upon the findings of this study, comparing only the recent flight experience of accident pilots from 2008, it was evident that recent flight experience did not differ significantly based upon whether the cause of the accident was pilot error or non-pilot error, for example mechanical failure of the aircraft.  When comparing only the recent flight experience of accident pilots from 2008, binary logistic regression analyses indicate that recent flight experience does not reliably distinguish between pilot error and non-pilot error accidents for TGP, χ2= 0.040 (df = 1, p = .841) and CGP, χ2 = 0.074 (df = 1, p = .786).  
An aircraft accident, as defined by the NTSB, is an occurrence associated with the operation of an aircraft that “takes place between the time any person boards the aircraft with the intention of flight and all such persons have disembarked, and in which any person suffers death or serious injury, or in which the aircraft receives substantial damage” (NTSB Notification and Reporting of Aircraft Accidents or Incidents and Overdue Aircraft, and Preservation of Aircraft Wreckage, Mail, Cargo, and Records, 49 C.F.R. § 830.2 [2010], para. 2).  In some cases, the event is deemed an accident strictly because the aircraft sustained substantial damage even though no one suffered death or serious injury.  If it is accepted that an aircraft may suffer mechanical failure irrespective of how experienced or inexperienced, the pilot and sometimes, the mechanical failure is so catastrophic that it will cause an accident to occur, then a comparison between pilot error accidents and non-pilot error accidents is sufficient to show the relationship between recent flight experience and pilot error.  However, if there is even the slightest chance that mechanical failure of the aircraft, and the ensuing accident, are somehow related to the experience of a pilot, then this comparison becomes insufficient and one would need to compare recent flight experience of accident pilots to non-accident pilots, a topic for another study.
In this comparison of accident pilots, there is no statistically significant relationship between the mean number of hours of recent flight experience of TPE and TNPE, t(202) = -.200, p = .842.  There is also no statistically significant difference between the mean number of hours of recent flight experience of CPE and CNPE, t(111) = -.271, p = .787.  Binary logistic regression analyses of accident pilots revealed a test of the full model against a constant only model was not statistically significant, indicating that the predictor, recent flight experience, does not reliably distinguish between pilot error and non-pilot error accidents for TGP, χ2= 0.040 (df = 1, p = .841) and CGP, χ2 = 0.074 (df = 1, p = .786).  
Future research could focus on different pilot populations namely, CFIs, ATPs, and commercial pilots.  This could prove useful because there are experience and training differences between these groups and private pilots.  These differences may turn out to have been caused by the additional training these groups of pilots had been exposed to, or the higher total flight experience of these groups of pilots, or a combination of both.  To broaden the scope, several years of data could be incorporated into the study.  Given sufficient time and resources together with participants willing to divulge the contents of their logbooks, one could collect data to compare recent flight experience of accident pilots to non-accident pilots.  
Summers, Ayers, Connolly, and Robertson (2007) and Hendrickson (2009) indicated need for improved training programs using contextually-based training scenarios that would improve a pilot’s situational awareness.  Scenario-based training is also the focus of FAA curriculum designers (FAA, 2006a; Larson, 2006).  Aviation insurance underwriters are highly in favor of pilots using simulators for training purposes, especially when the simulators are highly specific to the aircraft they are meant to represent and also true to life (Larson, 2006).  Future research could focus on revealing what specific aeronautical decision-making and situational awareness knowledge a pilot could review in addition to recent flight experience within the last 90 days.  This additional research could be performed by placing test subjects in a scenario-based training situation.  Also, future research could analyze accidents attributed to pilot error using simulation whereby a pilot could re-create the environment surrounding the moments leading up to the accident and then try different decisions to uncover which ones would have led to a favorable outcome.  
Conclusions
The intent of the study was to examine the relationship between recent flight experience, in terms of time flown in the past 90 days, and type of accident, namely pilot error versus non-pilot error.  This study was performed to address the need for a rigorous assessment of this relationship using empirical data (Goh & Wiegmann, 2002; Guohua, et al., 2002).  In this comparison of accident pilots, there is no statistically significant relationship between the mean number of hours of recent flight experience of TPE and TNPE, t(202) = -.200, p = .842.  There is also no statistically significant difference between the mean number of hours of recent flight experience of CPE and CNPE, t(111) = -.271, p = .787.  Based upon the findings of this research, it is concluded that in a comparison of only accident pilots for 2008, recent flight experience does not reliably distinguish between pilot error and non-pilot error accidents for TGP, χ2= 0.040 (df = 1, p = .841) and CGP, χ2 = 0.074 (df = 1, p = .786).  This research could be replicated using different populations, namely, CFIs, ATPs, and commercial pilots.  To broaden the scope, several years of data could be incorporated into the study.  

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