DOT/FAA/AM-02/17

David R. Hunter

Research and Special Projects Staff

Federal Aviation Administration

Office of Aerospace Medicine

Washington, DC 20591

September 2002

Final Report

This document is available to the public

through the National Technical Information

Service, Springfield, VA 22161.

Risk Perception and Risk

Tolerance in Aircraft Pilots Office of Aerospace Medicine

Washington, DC 20591
NOTICE

This document is disseminated under the sponsorship of

the U.S. Department of Transportation in the interest of

information exchange. The United States Government

assumes no liability for the contents thereof.
i

Technical Report Documentation Page

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No.

DOT/FAA/AM-02/17

4. Title and Subtitle 5. Report Date

Risk Perception and Risk Tolerance in Aircraft Pilots September 2002

6. Performing Organization Code

7. Author(s) 8. Performing Organization Report

No.

Hunter, D.R.

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS)

11. Contract or Grant No.

Office of Aerospace Medicine

Federal Aviation Administration

800 Independence Ave., S.W.

Washington, D.C. 20591

12. Sponsoring Agency name and Address 13. Type of Report and Period

Covered

14. Sponsoring Agency Code

Office of Aerospace Medicine

Federal Aviation Administration

800 Independence Ave., S.W.

Washington, D.C. 20591

15. Supplemental Notes

This work was performed under Task AM-A-01-HRR-189

16. Abstract:

Poor pilot decision-making has been identified as a factor in a large percentage of fatal aviation accidents.

Risk perception and risk tolerance are two factors that can significantly impact pilot decision-making.

Inaccurate risk perception can lead pilots to ignore or misinterpret external cues that demand immediate and

effective decisions to avoid hazards. High risk tolerance can lead pilots to choose courses of action that

unnecessarily expose them to hazards and increased likelihood of accident.

Risk perception and risk tolerance are related and often confounded constructs. This study sought to

separate these two constructs and to develop and evaluate measures that could be used to compare individual

pilots on the constructs. A large sample of pilots visiting a government web site completed two risk

perception, and three risk tolerance measures. They also completed a short scale assessing their involvement

in hazardous aviation events and provided demographic information.

Analysis of the data showed that the five new measures demonstrated acceptable internal consistency. The

measures of risk perception were only mildly related to risk tolerance, suggesting that these are separate

constructs. As hypothesized, pilot perception of risk was negatively related to risk tolerance. In addition, risk

perception demonstrated a small, but significant, correlation with self-reported involvement in hazardous

aviation events. However, contrary to expectations, risk tolerance was not significantly related to hazardous

events.

The results suggest that it is differences in cognitive skills required for accurate risk perception that place

pilots are greater likelihood of accident involvement, rather than differences in underlying personality traits

related to risk tolerance. The implications of the findings are discussed, along with limitations on the

generalizability of the results, and suggestions for future research to improve the measurement scales are

given.

17. Key Words 18. Distribution Statement

Document is available to the public through the

National Technical Information Service

Aircraft Pilots, Aviation Accidents, Safety, Risk,

Risk Perception, Risk Tolerance

Springfield, Virginia 22161

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price

Unclassified Unclassified 26

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
1

RISK PERCEPTION AND RISK TOLERANCE IN AIRCRAFT PILOTS

Risk is ubiquitous. There is no human state or

action that is without risk, short of death itself,

although there are clearly some states and actions that

carry substantially more risk than others. Although

flight in commercial aircraft is generally acknowledged to be the safest form of transportation, flying in

general aviation (GA) aircraft is arguably toward the

high end of the risk continuum, even though its

practitioners (pilots) are generally oblivious to the

magnitude of the risk (O’Hare, 1990).

Here, risk is being used in the commonly understood

sense. That is, risk is the possibility of loss of life or

injury, and it encompasses both the probability of an

encounter with a hazard and the severity of a hazard. In

that sense it is equivalent to what Sanders and McCormick

(1993) term danger. Sanders and McCormick argue that

the term risk should be used to denote the probability of

an adverse encounter with a hazard, independent of the

nature of the hazard. . However, the current usage

follows Slovic, Fischhoff, and Lichtenstein (1980), who

suggest that risk perception is determined by a combination of severity and likelihood of injury.

Risk assessment and management is one component

of the broader process of pilot decision-making. Poor

pilot decision-making has been implicated as a leading

factor in fatal general aviation accidents (Jensen & Benel,

1977), and poor risk assessment can contribute significantly to poor decision-making. O’Hare (1990) suggested that “...an unrealistic assessment of the risks

involved may be a factor in leading pilots to ‘press on’

into deteriorating weather.” (p. 599) He developed an

Aeronautical Risk Judgment Questionnaire to assess

pilots’ perceptions of the risks and hazards of general

aviation. Hazard awareness was assessed by (a) having

pilots estimate the percentage of accidents attributable to

six broad categories, (b) ranking the phases of flight by

hazard level, and (c) ranking detailed causes of fatal

accidents (e.g., spatial disorientation, misuse of flaps).

O’Hare found that pilots substantially underestimated

the risk of GA flying relative to other activities, and

similarly underestimated their likelihood of being in a

GA accident. They were fairly accurate in their appraisal

of the proportion of weather-related accidents, but estimated the rating for the pilot causal factors at 57%, when

the actual figure is approximately 80%.

There are three major theories which attempt to

explain behavior in the presence of risk. Risk homeostasis, as proposed by Wilde (Wilde, 1994; Trimpop

& Wilde, 1994) maintains that people in any given

activity have a target level of acceptable risk. People do

not attempt to minimize risk, rather they seek to

maintain an equilibrium by adjusting their behavior

to maintain their target (non-zero) level. McKenna

(1988) reviewed the evidence for and against risk

homeostasis theory, “which argues that the level of

risk people are willing to accept is the sole determining factor in overall accident involvement.” (p. 469)

He concludes that there is little evidence in favor of

the theory.

Zero risk theory (Naatanen & Summala, 1974;

Summala, 1988) was proposed in terms of driver

behavior, and holds that driving and similar behavior

is motivational in nature. The primary motive for

using the traffic system is the mobility provided by the

vehicle. According to zero risk theory, the perceived

risk in a situation is the product of the perceived

likelihood of a hazardous event and the importance

attached by the individual to the consequences of the

event (Ranney, 1994; Comsis Corp., 1995). According to this theory, as self-confidence increases (largely

as a function of increasing experience in the situation), perceived risk diminishes to the point of zero

perceived risk. That is, experienced drivers (and presumably, pilots) feel there is no real risk at all. It is

interesting to note that Lester & Bombaci (1984)

found that invulnerability was the preponderant response in a study of hazardous attitudes among pilots.

Out of 5 alternative explanations for why they might

engage in a risky aviation scenario, 43% of the pilots

chose the response associated with an attitude of

invulnerability, possibly indicating that they felt no

risk from the situation.

The threat avoidance model (Fuller, 1988; 1984) is

similar to the zero risk theory, in that it proposes that

driving is motivational in nature. However, it suggests that drivers learn to anticipate hazardous events

and avoid them, so that no negative consequences

occur. Thus the driver rarely experiences any perceived risk of a crash, since those situations are avoided.
2

Assessment

In addition to the study noted earlier by O’Hare

(1990) which examined pilot risk perceptions from

the perspective of accident involvement, other researchers have proposed and evaluated measures of

pilot risk-taking for selection purposes. A risk-taking

exercise described by Imhoff and Levine (1981) has

been used in several of these studies.

Shull and Dolgin (1988) investigated risk-taking as

a predictor of success in US Navy aviation training.

They administered the risk test described by Imhoff

and Levine (1981) to a sample of 440 Navy flight

students and tracked their performance in flight training. For a subgroup of 217 who had completed flight

training, one of six correlations between pass/fail in

training and risk test scores was significant (r = -.184),

and suggested that increased risk taking was associated with success in training. As Shull, et al, point out,

however, the result failed to replicate in the second

and third trials of the risk test for the same group, so

the finding is suspect.

In a later paper that seems to subsume their earlier

sample, Shull and Dolgin (1989) report significant

point-biserial correlations of .132 and -.184 between

pass/fail for a sample of 322 student naval aviators and

the risk task number right and reaction time, respectively, For 77 student naval flight officers, corresponding point biserial correlations of .277 and -.447

were obtained. Both these results indicate that the

students who responded faster, as well as those who

responded more often (that is, took more risks), were

more likely to pass flight training

Siem, Carretta, and Mercatante (1988) used the

Imhoff and Levine risk test in a study of U.S. Air Force

pilot selection, but failed to find a significant correlation between scores taken from the test and pass/fail

in undergraduate pilot training for a sample of 883

pilot trainees. Outside the area of pilot selection,

Edkins, Coakes, and O’Hare (2000) compared objective with subjective perceptions of risk in commuter

airline operations, and found that there was a strong

tendency to rate external factors outside the control of

the flight crew as more hazardous and more likely to

occur than is indicated by the objective accident data.

However, personal factors were rated as much less

significant than is reflected in the accident data.

Williams (1999) gave a paper-and-pencil test assessing the willingness to fly toward an approaching

storm under various conditions to 36 pilots. He found

that the propensity was moderated by environmental

aspects of the scenarios. He also found some indication that the pilots’ aversion or propensity for risk

were related to age and flight experience; younger or

more experienced pilots were less risk averse. However, only 18 pilots were used in those analyses and the

results were not statistically significant.

Training

Risk management is a central concept in much of

the current aviation training. Although the terminology differs radically, the basic principles of risk management are incorporated in training designed for

novice pilots (c.f., Kirkbride, Jensen, Chubb, &

Hunter, 1996) to airline captains in the civilian sector, while similar programs, usually termed Operational Risk Management, have been established in

each of the military services. All of these programs

focus upon an assessment of risks through the identification of hazards and their expected likelihood, and

the development of plans to counter those risk factors.

Regan, Triggs and Wallace (2000) described the

development of a CD-ROM based training product

for the development of risk perception skills in novice

automobile drivers. This effort was in response to

earlier studies that identified four specific skills as

critical in moderating the crash risk of novice drivers.

One of those skills was risk perception, which they

define as “the ability to detect, perceive and assess the

degree of risk associated with actual and emerging

traffic hazards.” (paragraph 1.1)

Risk Perception and Risk Tolerance

Risk perception and risk tolerance are related and

often confounded constructs. Oppe (1988) discusses

the concept of risk, and notes the difficulty of arriving

at a good definition of risk perception and risk tolerance (termed risk acceptance by Oppe) because of the

complexities of the situations in which these judgments are made. In particular, he suggests that risk

tolerance may be very dependent upon the specifics of

the situation and the transient utilities of a large

number of factors.

DeJoy (1992) notes that the various risk perception

formulations based on driver research suggest that

“risk-taking behavior is mediated by the level of

perceived risk in the outcome, suggesting that low

levels of perceived risk would be associated with

riskier driving.” (p. 237) From his study of gender

differences in risk perception by drivers, he concludes

that, “The problem is not that young males do not

consider driving to be a dangerous activity...The

problem is that this danger is not perceived as applying to them personally.” (p. 246) He suggests that

interventions should be developed that personalize

the risk to the driver, as opposed to making the risk an

abstract statistical concept.
3

Brown & Groeger (1988) provide an insightful

review and discussion of the relationship between risk

perception and decision making, from the viewpoint

of driver research. They note that, “Research ... demonstrates that acceptance and misperception of traffic

risks present a relatively serious problem for road

safety.” (p. 585) They suggest a definition of risk

which incorporates both the measure of the adverse

consequences of an event (severity), and the likelihood of experiencing the event. They also point out

that there are two elements to risk perception: information on potential hazards, and information of the

joint abilities of the operator and the vehicle to prevent the potential of the hazard from being realized.

They conclude, “Novice drivers...are insufficiently

experienced to evaluate hazards adequately and inclined to asses their abilities inaccurately.” (p. 592),

and propose that driving experience leads to the development of schemata which represent the spatio-temporal characteristics of vehicles and traffic. The

schemata are of central importance, because “...these

are the internal representations by which risk is perceived and opportunities for risky manoeuvres declined or accepted.” (p. 593)

Jonah (1986) notes that “risk-taking does not necessarily imply volition. Risks can be taken while

driving with or without awareness of what one is

doing.” (p. 258) He concludes that, “The weight of

the empirical evidence tends to support the view that

young drivers may take risks more often because they

are less likely to recognize risky situations when they

develop.” (p. 265)

Elander, West, and French (1993) provide an extensive review of research literature on individual

differences and the relationship to driving accidents.

With respect to hazard-perception ability, they concluded that “younger drivers rate potentially hazardous situations as less dangerous than do older drivers.”

(p 285). They also note, however, that there was “no

evidence to date about whether the perceived level of

hazardousness of situations is associated with crash

frequency.” (p. 285)

Risk perception is the recognition of the risk inherent in a situation. Risk perception may be mediated

both by the characteristics of the situation and the

characteristics of the viewer. Situations which present

a high level of risk for one person may present only low

risk for another. For example, the presence of clouds

and low visibility may present a very high risk for a

pilot qualified to fly only under visual meteorological

conditions (VMC), but the same conditions would

present very little risk for an experienced pilot qualified to fly in instrument meteorological conditions

(IMC) in an appropriately equipped aircraft.

The viewer must therefore perceive accurately not

only the external situation, but also their personal

capacities. Underestimation of the external situation

and overestimation of personal capacity leads to a

misperception of the risk and is frequently seen as a

factor in aircraft accidents. Risk perception may therefore be conceived as primarily a cognitive activity,

involving the accurate appraisal of external and internal states.

By contrast, risk tolerance is better conceptualized

as a personality trait. Risk tolerance may be defined as

the amount of risk that an individual is willing to

accept in the pursuit of some goal. Risk tolerance may

be mediated both by the general tendency to risk

aversion of the person and the personal value attached

to the goal of a particular situation. Some goals may

be judged as worthy of higher levels of risk exposure

than other goals. For example, in one survey, pilots

indicated that they would take more risks in order to

return home for the Thanksgiving and Christmas

holidays than they would for flying medicine to a

remote village. (Driskill, Weissmuller, Quebe, Hand,

& Hunter, 1998)

In an extensive review of research on young driver

risk, Jonah (1986) found that “a major factor underlying this [automotive] accident risk – indeed perhaps

the major factor – is risk-taking by youth.” (p. 255,

italics in original.)

Study Objectives and Hypotheses

The thesis of the present study is that both risk

perception and risk tolerance may independently affect pilot decision making, and that an understanding

of pilot actions requires a knowledge of both constructs as they exist in a particular pilot in a particular

situation. The specific objectives of the present study

were (a) to examine the relationship between risk

perception and risk tolerance among pilots, and (b) to

assess the degree to which these constructs were related to increased risk of an aviation accident.

To accomplish those objectives it was necessary to

develop measures of both risk perception and risk

tolerance. Previous studies have assessed pilots’ estimates of global risk levels. For example, O’Hare

(1990) asked pilots to estimate the percentages of

accidents attributable to six broad causal categories

(e.g., pilot, weather, etc.) and to rank causes of fatal

accidents (e.g., spatial disorientation, misuse of flaps,
4

etc.) and found that pilots generally underestimate

the level of risk of general aviation flight. He suggests

that training that emphasized the relationship between personal actions and risk might be useful.

However, to be effective in reducing accidents, that

training may need to be very specific in its focus. The

automobile driver research shows that objective risk

levels are not used in making tactical decisions (as

opposed to strategic decisions, like choosing one form

of transport over another). Therefore, for improving

operator safety, whether a person can judge risk at the

global level may be irrelevant. Once a person has

committed to a mode of transport, it may not matter,

from a practical point of view, whether they have a

good appreciation of the global risk of that mode. It

is much more important that they have a good appreciation for the elements (hazards) associated with that

mode that contribute to the global risk. Having a

good global risk assessment might alert the person to

the need to be vigilant and conservative in their

analyses of the environment, however zero risk theory

(Naatanen and Summala, 1974) suggests that is not

necessarily the case.

In contrast to measures of risk perception for broad

categories of activities, for the current study, risk perception measures were created for realistic, specific aviation

situations. It is believed that measures of risk perception

for specific aviation situations would be more useful in

predicting pilot behavior than global risk perceptions.

There is a critical distinction between rating the risk of

a specific situation and estimating the objective risk level

of a general set of activities (for example, flying, riding a

motorcycle, driving a car, etc.).

The present work attempts to clarify further some

of the relationships among the factors present in the

decision making process. It differs from previous

studies of pilot risk perception with regard to the

nature of the measurements. In addition, it separates

the measurement of risk perception from the measurement of risk tolerance, which are often confounded.

Specifically, this study seeks to:

1. Develop aviation-specific measures of risk perception and risk tolerance.

2. Assess the relationship between risk perception

and risk tolerance.

3. Estimate the degree to which these constructs

may relate to likelihood of accident involvement.

METHOD

Participants

Participants were recruited from visitors to an FAA

sponsored Web site. Visitors to the site are required to

create a personal identifier (call sign) to gain access to

the site, and are encouraged, though not required, to

provide an email address. The call sign can be any

name of their choosing, and hence could not be used

to identify any specific individual. In theory, the

email address could be used to identify participants,

however they were promised that the information

would not be used in that way. Other than that

potentially identifying information, the visitors and

study participants remain anonymous. Participants

who indicated they did not hold at least a student pilot

certificate were excluded from the analyses.

An inducement to participate was used – inclusion

in a series of drawings for aviation-related prizes. All

participants who completed all the study exercises

were entered into a drawing for one of three carbon

monoxide detectors, and additional prizes were available to participants who obtained high scores on the

risk tolerance exercises. In order to be eligible to

receive one of the five prizes, the participant must

have supplied an email address. The five winners were

notified using the email address and asked for their

mailing address. This was the only exception to the

promise of anonymity as indicated above, and was

clearly stated in the instructions provided to

participants.

There are approximately 4,500 registered users of

the Web site and approximately 2,400 visited the site

during the period of the study. Of that number, 642

completed at least one of the study exercises, and

slightly over 400 completed all the study requirements. Demographic characteristics of the participants are given in Tables 1 and 2. Characteristics of

the initial sample (N = 642) who completed at least

one exercise, and the final sample (N = 402) who

completed all the study exercises are provided. The

proportions of pilot certificate holders in the US pilot

population are also included for comparison. The

numbers of participants who completed each of the

sequential exercises are given in Table 3.
5

Table 1. Sample Demographics for Continuous Variables.

Initial Sample Final Sample

N M SD N M SD

TOTFLY1 667 1041.6 2641.5 411 839.3 1917.7

RECFLY1 664 84.6 117.8 409 82.6 109.1

XCFLY1 646 606.5 2137.4 408 464.7 1357.0

AGE1 654 45.3 12.4 411 45.4 12.4

YEAR1 654 1988 14 411 1989 13.4

Note 1: See Table 7 for variable definitions and abbreviations.

Table 2. Sample Demographics for Categorical Variables.

Initial Sample Final Sample

Certificate

Student (STU) 2 (16.4%)1 16.1% 14.1%

Private (PPL) 2 (41.6%)1 57.7% 60.5%

Commercial (COM) 2 (20.0%)1 19.4% 21.2%

Airline Transport (ATP) 2 (22.1%)1 5.3% 4.1%

Instrument Rating (IFR) 2

Yes 39.1% 40.9%

No 60.9% 59.1%

CFI Rating (CFI) 2

Yes 9.8% 9.2%

No 90.2% 90.8%

Military Experience (MIL) 2

Yes 5.5% 5.3%

No 94.5% 94.7%

Type of Flying (ACT) 2

Receiving Instruction 22.2% 22.0%

Personal Pleasure 53.7% 57.4%

Personal Business 4.3% 3.4%

Part-time Flight Instructor 3.7% 3.4%

Full-time Flight Instructor 1.8% 2.2%

Air-taxi or Charter 0.4% 0.2%

Corporate 0.6% 0.7%

Agriculture Aerial Application 0.1% 0.2%

Military 1.0% 1.0%

Scheduled Commuter (Part 135) 0.0% 0.0%

Air Carrier (Part 121) 0.9% 0.7%

Other 7.7% 8.0%

Note 1: Proportion of these certificate holders in the total US pilot population (Source: FAA 1999

Annual Report on Aviation System Indicators).

Note 2: See Table 7 for variable definitions and abbreviations.
6

Table 3. Numbers of participants in each exercise.

Exercise N

Risk Perception 1 642

Risk Perception 2 570

Risk Tolerance 1 441

Risk Tolerance 2 428

Risk Tolerance 3 411

Hazardous Events 402

Instrument Development

Only one previous measure (Imhoff & Levine,

1981) had been developed to address pilot risk tolerance. This study incorporates that measure, as well as

two new measures for the assessment of risk tolerance.

Additionally, because this study departs considerably

from the previous research on risk perception and

tolerance, it was necessary to develop new measures of

risk perception.

Risk Perception. Two instruments were developed

to assess pilots’ perceptions of risk. Each instrument

consisted of a series of brief scenario descriptions,

depicting situations and activities with what was believed to be a broad span of risk. The two instruments

differ with respect to the person involved in the risky

scenario.

In the first exercise (Risk Perception 1) the scenarios were presented as if a third party were involved

in the situation. (See Table 4 for the complete instrument.) There were 17 scenarios in the exercise.

In the second exercise (Risk Perception 2) the

scenarios were presented and the participant was asked

to rate the risk as if he or she were involved in that

situation tomorrow. (See Table 5 for the complete

instrument.). There were 26 scenarios in this exercise.

The specific scenarios presented in the two instruments were different, however some scenarios shared

a common theme of adverse weather encounters. The

scenarios were developed based upon information

taken from accident reports, descriptions of events

provided by participants in previous surveys of pilots,

and the personal experiences of the author. Each set of

scenarios was intended to represent a broad spectrum

of conditions, ranging from benign to exceptionally

hazardous. In addition to aviation scenarios, the second risk perception measure included some common

activities (such as driving a car on the freeway, crossing a street, climbing a ladder) in order to provide

reference points for possible cross-modal studies.

Each of the two exercises used a common rating

scale to assign a risk rating to the scenarios. This rating

scale ranged from 1 (low risk) to 100 (high risk), and

anchor descriptions were given for the two extremes

and for the mid-point (50).

Risk Tolerance Three instruments were developed

to assess pilots’ risk tolerance levels. Each of the three

instruments structured the risk tolerance decisions in

terms of aviation situations, so as to focus pilots’

attention on assessments of aviation risk. One instrument dealt, ostensibly, with aviation maintenance

issues, while the other instruments dealt with weatherrelated decisions.

Each participant entered the risk tolerance exercises with their own individual motivation for engaging in the exercises. Uncontrolled, some participants

might be content simply to take the easiest, safest,

alternative in each exercise; while others would strive

to maximize their score, in order to demonstrate

mastery of the situation or to fulfill some other need,

unrelated to the aim of this study. I attempted to

control for this extraneous effect by establishing a

common level of motivation which would encourage

those who would otherwise skip through the exercises

to attempt the tasks, while at the same time restraining

those participants who would take any risk, because it

was just a game and they had nothing to lose.

In real aviation settings, in order to get where one

wants to go, it is not possible to sit forever on the

ground (taking zero risk) or to fly headlong through

all obstacles on the most direct route (ignoring all

risks). Rather, it is necessary to consider the risks in

the context of the desired outcome, and to expose

oneself to the minimum risk necessary to accomplish

the goal. Clearly, the risks and rewards of actual flying

cannot be duplicated in what amount to computerbased games. However, to parallel reality as closely as

possible, some degree of risk must be present to both

encourage and constrain behavior.

To provide a common level of motivation to engage in the risk tolerance exercises, participants were

told that drawings would be held to award prizes to

study participants. Pilots who had a combined score

on the three risk tolerance exercises in the top 10%

were entered in a drawing for a global positioning

system (GPS) receiver – a very desirable item among

pilots. Pilots who had a combined score in the top

25% were entered in a drawing for a transceiver – also

a desirable aviation accessory, but only about onethird the cost of the GPS receiver. By limiting eligibility to those participants with the highest scores, they
7

(Continued)

Table 4. Risk Perception 1 Items Sorted by Mean Risk Rating.

Item M SD Scenario

4 96.8 5.8 Low ceilings obscure the tops of the mountains, but the pilot thinks

that he can see through the pass to clear sky on the other side of the

mountain ridges. He starts up the wide valley that gradually gets

narrower. As he approaches the pass he notices that he occasionally

loses sight of the blue sky on the other side. He drops down closer to

the road leading through the pass and presses on. As he goes through

the pass, the ceiling continues to drop and he finds himself suddenly in

the clouds. He holds his heading and altitude and hopes for the best.

3 89.4 10.5 A line of thunderstorms block the route of flight, but a pilot sees that

there is a space of about 10 miles between two of the cells. He can see

all the way to clear skies on the other side of the thunderstorm line, and

there does not seem to be any precipitation along the route, although it

does go under the extended anvil of one of the cells. As he tries to go

between the storms, he suddenly encounters severe turbulence and the

aircraft begins to be pelted with hail.

15 85.9 12.3 For the first part of this late night flight, the low-time VFR pilot has

enjoyed a spectacular view of the stars as he cruised at 8,500 feet with

over 25 miles visibility. As he nears his destination airport, which sits

on the far side of a large lake, he notices that the visibility is decreasing

because of haze nearer the surface. As he starts across the lake at about

2,500 feet he loses sight of the lights on the shore, and the dim lights

scattered far apart on the ground seem to be indistinguishable from the

stars.

2 85.1 14.2 The pilot is in a hurry to get going and does not carefully check his

seat, seat belt, and shoulder harness. When he rotates, the seat moves

backward on its tracks. As it slides backward, the pilot pulls back on

the control yoke, sending the nose of the aircraft upward. As the

airspeed begins to decay, he strains forward to push the yoke back to a

neutral position.

1 79.6 17.6 On short final, a pilot drops his microphone on the floor. He looks

down while bending over trying to reach it. He inadvertently moves the

control yoke and the aircraft banks sharply.

14 79.3 14.3 An instrument-rated pilot on an IFR flight plan has just climbed

through a 4000 foot thick layer of clouds. Although icing was not

forecast, he notices a trace of ice on the edges of the windscreen. The

aircraft is not equipped for flight into known or forecast icing

conditions. As he approaches his destination airport, air traffic control

issues a clearance that will require him to hold for approximately 15

minutes in the cloud layer.

6 74.9 16.3 During the planning for a 2 hour cross-country flight, a pilot makes a

mistake in computing the fuel consumption. He believes that he will

have over an hour of fuel remaining upon arrival, but he will really

only have about 15 minutes of fuel left.

9 73.9 15.6 When he took off about an hour earlier, there was a quartering

headwind of about 15 knots. He made it into the air, but it was a rocky

takeoff, and one he hoped none of the other pilots at the small airport

noticed. Now as he entered the downwind leg for landing, he noticed

that the windsock was indicating almost a direct crosswind of about the

same strength. On final he is holding a large crab to keep from drifting

away from the centerline, and as he starts the flare he begins to drift

toward the side of the runway.
8

Table 4 (Continued)

8 72.0 16.1 It is late afternoon and the VFR pilot is flying west into the setting sun.

For the last hour, the visibility has been steadily decreasing, however

his arrival airport remains VFR, with 4 miles visibility and haze. This

is a busy uncontrolled airfield with a single East-West runway. He

decides to do a straight-in approach.

7 69.4 17.7 After working a full day, a businesswoman drives out to the airport for

her three hour flight home. She is tired, and the sun is setting, but the

weather forecast is for clear sky and good visibility. About an hour

after takeoff, she begins to feel very tired and sleepy. She regrets not

bringing any coffee along, and opens the cockpit air vent to get some

fresh, cool air.

5 68.2 20.2 Just after takeoff a pilot hears a banging noise on the passenger side of

the aircraft. He looks over at the passenger seat and finds that he can't

locate one end of the seatbelt. He trims the aircraft for level flight,

releases the controls, and tries to open the door to retrieve the seatbelt.

17 66.1 16.8 While cruising at 4,500 feet AGL, the engine on the single-engine

aircraft sputters and quits. The pilot checks the fuel settings and tries to

restart the engine but is unsuccessful. He sees a level field within

gliding distance and turns toward it. He will be landing into the wind.

11 58.0 21.0 The instructor pilot had been suffering from a cold and when he arose

in the morning, he took an over-the-counter antihistamine to try and

control his runny nose. After a morning of giving instruction in the

flight simulator, he had a lesson scheduled after lunch with a pilot

working on his COM certificate. He felt a little drowsy, but the

weather was good and they were going to be working on short-field

landings, so he did not cancel the lesson.

16 45.0 27.5 It is time for an oil change and the pilot/owner decides to do it himself.

He consults with his local A&P mechanic and then follows his

instructions. He does not have the work inspected afterwards and

makes the appropriate log book notation himself.

10 32.1 22.3 While on a local sightseeing flight, the pilot notices that the weather is

deteriorating to the west. A line of clouds is moving in his direction,

but they are still over 20 miles away. He decides to cut his flight short

and turns to return to his home airfield about 25 miles east of his

present position.

13 27.1 21.2 An experienced pilot with a rated passenger are taxiing out for takeoff.

They are at a controlled airfield, on the ground-control radio

frequency. They have been cleared to "taxi to and hold short of

Runway 31" and are now approaching the hold-short line.

12 25.4 20.7 A pilot is cruising in good weather to a destination airport about an

hour away. It is midday, and there are three hours of fuel on board.

* Scale 1 (low) to 100 (high). Risk is for third party, low-time GA pilot (unless specified otherwise).
9

Table 5. Risk Perception 2 Items Sorted by Mean Risk Rating.

Item M SD Scenario

18 76.3 17.3 Start a light aircraft with a dead battery by hand-propping it.

26 76.1 17.2 At night, fly from your local airport to another airport about 150 miles

away, in a well-maintained aircraft, when the weather is marginal VFR

(3 miles visibility and 2,000 foot overcast.

21 75.0 14.7 At night, take a cross-country flight in which you land with 30 minutes

of fuel remaining.

19 73.4 18.3 Make a two-hour cross country flight with friends, without checking

your weight and balance.

7 72.2 15.9 Fly in clear air at 6,500 feet between two thunderstorms about 25 miles

apart.

22 68.5 16.9 Take a two-hour sightseeing flight over an area of wooded valleys and

hills, at 1,000 above ground level.

14 68.0 17.4 During the daytime, fly from your local airport to another airport about

150 miles away, in a well-maintained aircraft, when the weather is

marginal VFR (3 miles visibility and 2,000 foot overcast).

10 67.2 17.4 Make a traffic pattern so that you end up turning for final with about a

45 degree bank.

17 66.3 14.7 Drive your car on a freeway near your home, during the day, at 65

MPH in moderate traffic, during heavy rain.

4 65.6 19.2 Fly across a large lake or inlet at 500 feet above ground level.

9 62.4 18.2 During the daytime, take a cross-country flight in which you land with

30 minutes of fuel remaining.

2 55.5 23.4 Jaywalk (cross in the middle of the block) across a busy downtown

street.

15 55.4 20.1 Fly across a large lake or inlet at 1,500 feel above ground level.

11 54.5 15.2 Drive your car on a freeway near your home at night, at 65 MPH in

moderate traffic.

23 53.7 19.9 At night, fly from your local airport to another airport about 150 miles

away, in clear weather, in a well-maintained aircraft.

5 52.9 21.4 At night, take a cross-country flight in which you land with over an

hour of fuel remaining.

8 52.2 18.9 Take a two-hour sightseeing flight over an area of wooded valleys and

hills, at 3,000 above ground level.

24 49.1 18.9 Fly across a large lake or inlet at 3,500 feet above ground level.

20 47.5 13.5 Drive your car on a freeway near your home during the day, at 65

MPH in moderate traffic.

16 45.6 20.5 Make a traffic pattern so that you end up turning for final with about a

30 degree bank.

3 39.9 21.4 Make a two-hour cross-country flight with friends, after checking your

weight and balance.

13 38.8 20.1 During the daytime, take a cross-country flight in which you land with

over an hour of fuel remaining.

1 35.8 21.8 During the daytime, fly from your local airport to another airport about

150 miles away, in clear weather, in a well-maintained aircraft.

6 33.9 22.0 Climb up a 10-foot ladder to replace an outside light bulb.

12 30.9 19.9 Take a two-hour flight in a jet aircraft on a major US air carrier.

25 17.7 16.8 Ride an elevator from the ground floor to the 25th floor of an office

building.

Scale: 1(low) to 100 (high). Risk if you performed this tomorrow.
10

were encouraged to take some risks in order to gain

points, while at the same time avoiding wildly reckless

behavior which would cost them points.

In addition, simply as a reward for participation, all

pilots who completed all the exercises were entered

into a drawing for one of three carbon monoxide

detectors – a useful safety accessory.

Risk Tolerance 1. The first risk tolerance exercise

utilized the paradigm suggested by Imhoff and Levine

(1981) for assessing risk-taking in pilots. There were

10 trials in this exercise. During each trial pilots were

presented with a set of 10 aircraft, one of which is

defective. Pilots were allowed to pick one aircraft for

a flight, and, if they did not pick the defective aircraft,

their score for that trial was increased. After each

flight, the pilot chose another aircraft for the next

flight. This process continued until either the pilot

chose to end the trial, thus retaining the accumulated

points, or they chose the defective aircraft, in which

case they lost all the points accumulated for that trial.

Points, if any, accumulated in each trial were added to

the cumulative total. The objective, from the pilots’

perspective, was to maximize the cumulative total by

flying as many flights in each trial as possible.

Participants were told that the assignment of the

defective aircraft was random. However, in order to

control for exposure to success and failure, the actual

determination of the defective aircraft was not random, but was fixed. To the participants it seemed that

one particular aircraft out of the ten depicted was

defective. However, in actuality which particular aircraft they chose was irrelevant. Rather, the defective

aircraft was presented solely based upon the number

of flights taken by a pilot in any particular trial.

Specifically, for the 1st, 9th, and 10th trials, the pilot was

not given a defective aircraft until the 10th flight of

those trials. However, for the 2nd trial the pilot received a defective aircraft on the 2nd flight of that trial,

regardless of which of the 9 remaining aircraft they

chose. For the other trials, the defective aircraft was

presented between the 4th and 8th flights. This procedure ensured that each pilot was equally free to express

their risk-taking proclivity on the trials, without being prematurely terminated by a random encounter

with a defective aircraft.

Risk Tolerance 2. A scenario involving a thunderstorm was used in the second risk tolerance exercise.

In this exercise, pilots were presented with a scenario

involving a series of flights (each constituting a separate trial) between two airports. However, two thunderstorms blocked the most direct route between the

two airports, forcing a diversion around the storms. A

gap of varying width between the storms was presented

on each trial, and pilots could choose to either fly

through the gap, or to divert around the storms.

Because the score for this exercise was based on minimizing the distance flown, pilots had an incentive to

fly between the two storms, whenever they thought

they could do so. The pilots were instructed that if

they choose to fly between the two storms, they might

encounter severe turbulence, in which case their score

would be penalized. They were also advised that the

probability of an encounter with severe turbulence

increased as a function of their proximity to the

storms. Severe turbulence was (supposedly) random,

but was more likely if the gap between the storms was

small. Participants were given access to a government

publication which recommended a separation of 25

miles from thunderstorms.

As in the first risk tolerance exercise, the incidence

of turbulence was not, in fact, random, but was set to

occur on the first five trials, but not thereafter. As in

the first exercise, the purpose of setting (arbitrarily)

the turbulence to occur on those five trails was to

control exposure to the hazard, while not limiting the

potential for expression of the participants’ proclivity

to risk taking on the other trials. These first five trials

corresponded to gap sizes of 5 to 25 miles. There were

20 trials in this exercise.

Risk Tolerance 3. The third risk tolerance exercise

was similar to the second exercise. However, in this

exercise low clouds obscuring a mountain pass were

used as the seemingly random hazard. As before the

pilots were presented with 20 trials involving flights

between two airports. A weather briefing was provided that indicated a deterioration of conditions,

occurring with varying rapidity. In some instances the

weather was forecast to deteriorate before the pilot

could fly through the pass (80 minutes after departure), while in other cases the time to deteriorate was

either equal to or greater than 80 minutes. The pilots

were advised that, as when dealing with real weather,

the forecasts may not be accurate, and conditions may

change more rapidly or more slowly than predicted.

Pilots were motivated to take the most direct route

through the mountains to maximize their scores.

However, they could elect to take the longer route

around the mountains with a much lower risk of

losing all the points for the trial.

Proxy Measure for Accident Involvement

Although flying in general aviation is a relatively

risky activity (O’Hare, 1990), accidents are still infrequent events. Using accident occurrence as a criterion

for the evaluation of new scales, therefore, presents some

difficulties. Even in a relatively large (by psychological
11

Table 6. Questions Comprising the Hazardous Events Scale.

Item

Number Question1

1 How many aircraft accidents have you been in (as a flight crew member)?

2 How many times have you run so low on fuel that you were seriously concerned about making

it to an airport before you ran out?

3 How many times have you made a precautionary or forced landing at an airport other than

your original destination?

4 How many times have you made a precautionary or forced landing away from an airport?

5 How many times have you inadvertently stalled an aircraft?

6 How many times have you become so disoriented that you had to land or call ATC for

assistance in determining your location?

7 How many times have you had a mechanical failure which jeopardized the safety of your

flight?

8 How many times have you had an engine quit because of fuel starvation, either because you

ran out of fuel or because of an improper pump or fuel tank selection?

9 How many times have you flown into areas of instrument meteorological conditions, when

you were not on an instrument flight plan?

10 How many times have you turned back or diverted to another airport because of bad weather

while on a VFR flight?

Note 1: All questions referred to events during the last 24 months.

research standards) sample, the number of pilots who

have been in an accident is small. In the current

sample, of the approximately 400 pilots with complete data, only 4% reported having been in an aircraft

accident. This restricted distribution leads to severe

restrictions on the maximum point-biserial correlation that may be observed. (Nunnaly, 1967, pg 133)

In the present case, the maximum possible pointbiserial correlation would be approximately .40.

To address this problem, Hunter (1995) developed

a set of questions which assessed the numbers of times

that a pilot had been involved in potentially hazardous events. The advantage of assessing the frequency

of these hazardous events lies in the fact that they

occur much more frequently than do accidents. In

most instances, a hazardous event does not lead to an

accident or incident. It is only in the rare occasions

when a hazardous event occurs in combination with

other factors or hazardous events that an accident

ensues. The hazardous event scale was developed

based on the simple premise that being in a hazardous

event increases the likelihood of an accident, and that

the more frequently a person is placed at increased risk

by being in an hazardous event the more likely it is

that an accident will eventually occur. Certainly the

relationship is not completely deterministic, but in

the absence of a better criterion the measurement of

hazardous events is suggested as a proxy for accidents,

the criterion of ultimate interest.

O’Hare and Chalmers (1999) administered 12

questions from this scale to a nationwide sample of

pilots in New Zealand and obtained results that were

very close to those reported by Hunter (1995) for

pilots in the United States.

In the present study, ten questions from the original scale were used. One question asked whether the

pilot had been in an aircraft accident, while the

remaining nine questions measured involvement in

various hazardous aviation events during the preceding 24 months. The questions are given in Table 6. A

summary score (HAZ) was formed by summing the

responses to all ten items.

Procedure

Pilots who visited the Web site were invited to

participate in the study through a notice posted on the

home page of the site. This notice directed potential

participants to a page on which the aims of the

research, the rules for participation in the prize drawings, and the usual human research subject assurances

were displayed.

All exercises were completed in the same order.

Following a brief review of the demographic data

provided when they originally registered with the

Web site, participants were asked for some additional

demographic information. They then completed the

two risk perception exercises, the three risk tolerance
12

exercises, a scale which assessed their encounters with

hazardous aviation events, and an aviation attitude

scale (which will not be addressed in this paper).

Participants were free to complete all the exercises

at one time, or to complete any portion they wished,

and then come back at a later time to complete the

remainder.

The study was activated on February 1 and data

collection ceased on May 8, 2001.

RESULTS

The exercises were administered in a fixed order,

and the numbers of participants who completed each

succeeding exercise declined steadily as participants

withdrew from the study for unspecified reasons.

Indeed, some participants (N = 34) did not even

complete the first exercise after having registered. To

best use the available data, each analysis used the

maximum number of participants for whom appropriate data were available. For example, the reliability

analysis of Risk Perception 1 used 621 participants for

whom complete item response data were available.

This differs from the 642 participants who completed

this exercise, because some of their item responses

were missing (i.e., blank) and reliability analysis requires complete item responses for all items. However, later analyses of Risk Perception 1 and Hazardous

Events used a much smaller number of participants

because, of the 642 participants who completed Risk

Perception 1, only 402 also completed Hazardous

Events.

Using this approach means that the numbers of

participants reported in each of the analyses will

differ. However, to restrict the analyses only to those

participants for whom complete data are available on

all measures would needlessly and arbitrarily discard

useful information. It is recognized that the constantly shifting sample sizes may contribute to some

confusion; however, the value of the added data is

worth the cost.

To help the reader follow the analyses, the major

variables used in the analyses and the abbreviations

that will be used to conserve space and improve

legibility in the tables are listed in Table 7. Except for

the computation of coefficient alpha, SPSSTM (Version 9.0) was used for all statistical computations.

Risk Perception 1 (Third-party Risk)

Reliability. A coefficient alpha was computed using the STATISTICATM reliability procedure. For a

sample of 621, the obtained internal consistency equals

.845. The average inter-item correlation was .258.

Item and Scale Scores. The means and standard

deviations for each of the 17 items are given in Table

3. An overall composite risk rating score (P1MEAN)

was computed for each individual by taking the mean

of the 17 item ratings. A baseline risk rating score

(P1BASE) for each individual was computed by taking the mean of items 10 and 12 (see Table 3 for item

content). I chose those two items because inspection

of the scenarios for these items suggested that they

describe normal general aviation activities without

any particular elements that would elevate risk. This

score, therefore, represents the individual’s baseline

evaluation of risk in nominal general aviation activities. Similarly, a weather risk rating score (P1WX) was

computed for each individual by taking the mean of

items 3, 4, 14, and 15. These items all deal with

various aspects of weather. Therefore, this score represents the individual’s evaluation of risk in weatherrelated GA activities. Table 8 contains the means and

standard deviations for these composite scores.

Risk Perception 2 (Personal Risk)

Reliability. A coefficient alpha was computed using the STATISTICATM reliability procedure. For a

sample of 514, the obtained internal consistency equals

.937. The average inter-item correlation was .376.

Item and Scale Scores. The means and standard

deviations for each of the 26 items are given in Table

4. An overall composite risk rating score (P2FLY) was

computed for each individual by taking the mean of

the 20 items dealing with aviation. A composite risk

rating score (P2LIFE) was computed by taking the

mean of the 6 items which dealt with non-aviation

scenarios (e.g., driving a car, crossing a street, etc.). A

baseline risk rating score (P2BASE) was computed for

each individual by taking the mean of items 1, 13, and

23 (see Table 4 for item content). The scenarios for

these three items describe normal general aviation

activities without any particular elements that would

elevate risk. This score, therefore, represents the

individual’s baseline evaluation of risk in nominal

general aviation activities. Similarly, a weather risk

rating score (P2WX) was computed for each individual by taking the mean of items 7, 14, and 26.

These items all deal with various aspects of weather.

Therefore, this score represents the individual’s evaluation of risk in weather-related GA activities. Table 8

contains the means and standard deviations for these

composite scores.

Risk Tolerance 1 (Broken Airplane)

Reliability. A coefficient alpha was computed using the STATISTICATM reliability procedure. For a
13

Table 7. Variable Definitions and Abbreviations.

Full Name Abbreviation

P1MEAN (Mean of all 17 items) P1MEAN

P1BASE (Mean of items 10, 12) P1BASE

P1WX (Mean of items 3, 4, 14, 15) P1WX

P2FLY (Mean of 20 aviation items) P2FLY

P2LIFE (Mean of 6 non-aviation items) P2LIFE

P2BASE (Mean of items 1, 13, 23) P2BASE

P2WX (Mean of items 7, 14, 26) P2WX

Tolerance 1 Summary (Mean of items 9 and 10) T1SUM

Tolerance 2 Set 1 T2_SET1

Tolerance 2 Set 2 T2_SET2

Tolerance 2 Summary (Mean of Set1 and Set2) T2SUM

Tolerance 3 Summary (Total Weighted Proportions) T3SUM

Hazardous Events (for previous 24 months) – Sum of All Items HAZ

Pilot Certificate – STU STU

Pilot Certificate – PPL PPL

Pilot Certificate - COM COM

Pilot Certificate – ATP ATP

Instrument Rating (Coded 0=No, 1=Yes) IFR

Certified Flight Instructor (Coded 0=No, 1=Yes) CFI

Military Flying Experience (Coded 0=No, 1=Yes) MIL

Main Type of Flying Activity (See Table 2 for list) MAINFLY

Total Flying Time in Career TOTFLY

Recent Flying Time (during last 12 months) RECFLY

Cross country flight time XCFLY

Age in Years AGE

Table 8 Means and Standard Deviations of Computed Variables.

M SD N

Risk Perception 1

P1MEAN 66.5 9.9 643

P1BASE 28.8 19.3 643

P1WX 87.9 7.5 643

Risk Perception 2

P2FLY 57.9 12.7 524

P2LIFE 45.2 12.2 553

P2BASE 41.7 18.8 600

P2WX 71.4 14.8 589
14

Table 9. Risk Tolerance 1 (Broken Airplane) Mean Scores

for Each Trial.

Trial Limit1 M SD

1 10 5.3 1.7

2 2 2.0 0.4

3 8 5.2 1.5

4 3 3.0 0.5

5 6 4.9 1.3

6 8 5.0 1.4

7 4 3.8 0.6

8 5 4.4 0.9

9 10 4.8 1.4

10 10 4.9 1.5

Note 1: If this number of flights within the trial is reached, the

subject loses the accumulated points for that trial.

Table 10. Frequency Distribution of Tolerance 2

Scores (N=426).

Set 1 Set 2

5 miles 22.5% 5.4%

10 miles 13.4% 4.7%

15 miles 10.8% 8.7%

20 miles 14.3% 18.7%

25 miles 8.7% 20.1%

30 miles 8.2% 18.4%

40 miles 12.0% 16.1%

50 miles 4.0% 3.5%

75 miles 1.2% 1.2%

100 miles .2% .5%

Over 100 4.7% 2.6%

Note: First 5 trials (5-25 miles) in Set 1 result in crash

on Route A.

sample of 377, the obtained internal consistency equals

.913. The average inter-item correlation was .539.

These coefficients are based upon only those participants who responded to all 10 items in this scale.

Excluding those participants who did not complete all

the items yields the most conservative estimate of the

internal consistency.

Item and Scale Scores. The means and standard

deviations for each of the 10 trials are given in Table

9. An overall composite risk rating score (T1SUM)

was computed for each individual by taking the mean

of trials 9 and 10. On these two trials the subject did

not encounter a defective aircraft until the 10th flight

of the trials. Therefore, these two trials offer an

opportunity to observe the participants’ level of risk

tolerance, with the least constraints placed upon their

potential responses. The first trial is also interesting in

this regard, since it allows for observation of the risk

tolerance of the subject before they encounter any of

the contrived disasters of the subsequent trials. The

mean of the composite score (T1SUM) was 4.84, with

a standard deviation of 1.37 (N=409).

Risk Tolerance 2 (Thunder Storm Avoidance)

Reliability. A coefficient alpha was computed using the STATISTICATM reliability procedure. For a

sample of 364, the obtained internal consistency equals

.864. The average inter-item correlation was .273.

Item and Scale Scores. This exercise consists of two

approximately equivalent sets of 10 trials each. For

each set of trials (1-10 and 11-20) the trial with the

lowest storm separation chosen by each subject was

identified. These are identified as “T2_SET1” and

“T2_SET2”. The frequency distribution for these

two variables are given in Table 10. The correlation

between T2_SET1 and T2_SET2 equals .520 (N=421;

p < .01), which might be interpreted as a measure of

parallel forms reliability. Remember, however that

these two sets differ in that during the first set the

participant is exposed to five trials in which turbulence is present. Further, there is undoubtedly some

learning effects that occur during those trials, which

are not present during the second set. Therefore, the

correlation of .520 may more reasonably be taken as a

lower bound of their relationship.

Risk Tolerance 3 (Mountain Weather)

Reliability. A coefficient alpha was computed using the STATISTICATM reliability procedure. For a

sample of 375, the obtained internal consistency equals

.901. The average inter-item correlation was .308.

Item and Scale Scores. Four scores were computed

which represent the numbers of times the pilot selected Route A (through the mountains) for each of

the predicted times for weather passage. The computed variables are:

1. Variable T3T60 — Instances with predicted time

< 80 minutes

2. Variable T3T80 — Instances with predicted time

= 80 minutes

3. Variable T3T100 — Instances with predicted

time = 100 minutes

4. Variable T3T120 — Instances with predicted

time = 120 minutes
15

Each of these numbers was then converted into a

proportion, by dividing by the number of opportunities the pilot had to fly under each of the specified

predicted times. An overall score (T3SUM) was computed by summing the four proportions, using an a

priori weighting scheme of:

T3SUM = 4 x (T3T60) + 3 x (T3T80) + 2 x

(T3T100) + 1 x (T3T120).

Thus, higher scores would indicate riskier decisions by the pilot. The means and standard deviations

of these variables are given in Table 11.

Relationships Among the Risk Perception, Risk

Tolerance, and Demographic Variables

Intercorrelations of risk perception and risk tolerance measures and the continuous (or binary) demographic variables are given in Table 12. One-way

ANOVAs were conducted to examine effects of pilot

certificate level on scores, with post hoc comparisons

of means using the Bonferroni technique.

Results of the ANOVAs are given in Table 13.

Overall significant differences among the pilot certificate levels (e.g., Private, Commercial, Airline Transport) were found only for the measures from Risk

Perception 2. Results of the post hoc comparisons for

the Risk Perception 2 measures are given in Table 14.

Hazardous Events and the Perception and

Tolerance Measures

 Frequency counts for the 10 hazardous event items

are given in Table 15. Correlations between the total

hazardous event scores (HAZ) and the risk perception

and risk tolerance measures are given as the last row in

Table 12. Significant correlations with the hazard

scores were found for the scores from Risk Perception

2. The negative correlations indicate that those pilots

who rated the weather scenarios as low in risk experienced larger numbers of hazardous events than those

pilots who rated the weather scenarios as high in risk.

None of the risk tolerance measures correlated significantly with the hazard criteria, and generally had

values close to zero.

DISCUSSION

Evaluating the Measures

As the first step in the evaluation of pilot risk

perception and risk tolerance, it was necessary to

create measures of those constructs, targeted at specific, concrete aviation situations. The five instruments developed for that purpose exhibit acceptable

psychometric properties in terms of internal consistency.

The two risk perception instruments exhibit moderate correlations of .501, and .445 between the corresponding baseline and weather scores, respectively.

This suggests that they are measuring somewhat the

same constructs. Remember, however that the risk

perceptions addressed by the two instruments are

oriented differently. Perception 1 is oriented towards

risk experienced by others, while Perception 2 is

oriented towards personal risk. It may be, as suggested

by these results, that these are distinct constructs. As

no other instruments have been developed which

assess risk perception in quite this manner, it was not

possible to obtain external construct validation.

The three risk tolerance instruments also exhibited

good internal consistency. However, examination of

their intercorrelations suggests that they may not be

functioning as intended. Even though the two instruments that assessed risk tolerance regarding weather

(Tolerance 2 and Tolerance 3) were very similar in

their design and the types of decisions required of the

participants, the correlation between the summary

scores was only moderate (-.485). Further, both of

these instruments had low correlations (approximately

0.2) with Risk Tolerance 1 – the only instrument that

had been used in previous studies of risk-taking by pilots.

From Table 12 it is apparent that many of the risk

perception measures are significantly correlated with

pilot demographic characteristics, although the magnitude of those correlations are small. The preponderance of negative values clearly shows that higher levels

of experience and qualification are associated with

lower levels of risk perception. This might be interpreted as support for the zero risk theory (Summala,

1988; Naatanen et al., 1974) which suggests that

higher levels of experience lead to lower levels of

perceived risk. It is also interesting to note that

perceived personal risk (from Perception 2) shows

Table 11. Descriptive Statistics for Risk Tolerance

3 – Mountain Weather Scores.

N M SD

T3T60 413 .8668 1.2264

T3T80 412 1.8398 1.8766

T3T100 411 3.2847 1.9252

T3T120 411 2.2871 1.0956

T3SUM 411 17.8297 12.4270
16

Table 12. Correlations of Demographic, Perception, and Tolerance measures, and Hazard Score.

(N varies from approximately 400 to 550).

AGE IFR TOTFLY RECFLY XCFLY MIL CFI

AGE 1.000 .127* .150* -.025 .108* .039 .017

IFR .127* 1.000 .374* .387* .305* .194* .367*

TOTFLY .150* .374* 1.000 .428* .938* .436* .289*

RECFLY -.025 .387* .428* 1.000 .309* .176* .468*

XCFLY .108* .305* .938* .309* 1.000 .390* .146*

MIL .039 .194* .436* .176* .390* 1.000 .052

CFI .017 .367* .289* .468* .146* .052 1.000

P1MEAN -.081* -.036 -.093* .045 -.124* -.035 .039

P1BASE -.106* -.061 -.129* .007 -.119* -.038 -.037

P1WX .040 -.090* -.117* -.068 -.149* -.045 .029

P2FLY -.102* -.202* -.244* -.179* -.237* -.080 -.133*

P2LIFE -.025 -.030 -.010 -.044 -.027 .020 .032

P2BASE -.090* -.177* -.221* -.178* -.200* -.076 -.155*

P2WX -.025 -.268* -.303* -.309* -.293* -.094* -.148*

T1SUM .021 .039 -.052 -.056 -.068 .025 -.021

T2SUM .024 -.024 -.094 -.071 -.077 -.053 .019

T3SUM -.118* -.002 -.004 -.004 -.012 -.003 -.061

HAZ .198* .253* .368* .315* .297* .093 .189*

P1MEAN P1BASE P1WX P2FLY P2LIFE P2BASE P2WX T1SUM T2SUM T3SUM

AGE -.081* -.106* .040 -.102* -.025 -.090* -.025 .021 .024 -.118*

IFR -.036 -.061 -.090* -.202* -.030 -.177* -.268* .039 -.024 -.002

TOTFLY -.093* -.129* -.117* -.244* -.010 -.221* -.303* -.052 -.094 -.004

RECFLY .045 .007 -.068 -.179* -.044 -.178* -.309* -.056 -.071 -.004

XCFLY -.124* -.119* -.149* -.237* -.027 -.200* -.293* -.068 -.077 -.012

MIL -.035 -.038 -.045 -.080 .020 -.076 -.094* .025 -.053 -.003

CFI .039 -.037 .029 -.133* .032 -.155* -.148* -.021 .019 -.061

P1MEAN 1.000 .589* .615* .568* .460* .443* .444* -.092 .258* -.169*

P1BASE .589* 1.000 .077* .458* .251* .501* .246* -.035 .207* -.156*

P1WX .615* .077* 1.000 .365* .322* .177* .445* -.065 .189* -.125*

P2FLY .568* .458* .365* 1.000 .560* .864* .775* -.109* .221* -.137*

P2LIFE .460* .251* .322* .560* 1.000 .436* .451* -.060 .100* -.097

P2BASE .443* .501* .177* .864* .436* 1.000 .557* -.133* .205* -.139*

P2WX .444* .246* .445* .775* .451* .557* 1.000 -.029 .204* -.147*

T1SUM -.092 -.035 -.065 -.109* -.060 -.133* -.029 1.000 -.198* .215*

T2SUM .258* .207* .189* .221* .100* .205* .204* -.198* 1.000 -.485*

T3SUM -.169* -.156* -.125* -.137* -.097 -.139* -.147* .215* -.485* 1.000

HAZ -.049 -.049 -.061 -.125* .067 -.112* -.203* -.039 -.021 -.039

* Correlation is significant at the 0.05 level (2-tailed).
17

Table 13. ANOVA of Perception and Tolerance Measures by Pilot Certificate Level.

SS df MS F Sig.

P1MEAN Between 223.300 3 74.433 .766 .514

Within 61348.352 631 97.224

Total 61571.652 634

P1BASE Between 2788.209 3 929.403 2.507 .058

Within 233951.234 631 370.763

Total 236739.443 634

P1WX Between 558.801 3 186.267 3.273 .021

Within 35907.937 631 56.906

Total 36466.738 634

P2FLY Between 4707.214 3 1569.071 10.184 .000

Within 79499.313 516 154.068

Total 84206.527 519

P2LIFE Between 960.136 3 320.045 2.162 .092

Within 80688.183 545 148.052

Total 81648.319 548

P2BASE Between 13091.939 3 4363.980 13.130 .000

Within 196424.239 591 332.359

Total 209516.178 594

P2WX Between 11891.747 3 3963.916 19.775 .000

Within 116262.048 580 200.452

Total 128153.795 583

T1SUM Between 1.637 3 .546 .285 .836

Within 769.274 402 1.914

Total 770.911 405

T2SUM Between 38.405 3 12.802 2.753 .042

Within 1957.940 421 4.651

Total 1996.345 424

T3SUM Between 56.471 3 18.824 3.246 .022

Within 2342.998 404 5.799

Total 2399.469 407

HAZ Between 492.995 3 164.332 14.698 .000

Within 4326.959 387 11.181

Total 4819.954 390

*Note: Application of Bonferroni technique would result in failure to reject null hypothesis for comparisons

where significance >.005.
18

Table 14. Summary of Post-hoc Mean Comparisons for Pilot Certificate Levels.

Mean Difference (Row – Column)

STU PPL COM ATP

P2FLY

STU 6.85 11.33

PPL 5.63 10.12

P2BASE

STU 10.62 20.20

PPL 7.08 16.65

P2WX

STU 7.50 18.10

PPL 7.41 18.02

COM -7.50 -7.41 10.60

Note: Bonferroni technique applied. All differences shown significant, p < .05

Table 15. Frequency Counts of the Hazardous Event Items.

Number of Reported Events

0 1 2 3 4 5 6+

Accident 96% 4% 0% 0% 0% 0% 0%

Low fuel 81% 17% 2% 0% 0% 0% 0%

On-airport

precautionary

landing

60% 22% 9% 4% 5% 0% 0%

Off-airport

landing

96% 3% 0% 1% 1% 0% 0%

Inadvertent stall 93% 5% 1% 0% 1% 0% 0%

Lost 91% 8% 1% 0% 0% 0% 0%

Mechanical

failure

70% 20% 7% 2% 1% 0% 0%

Engine quit due

to fuel

96% 3% 0% 1% 0% 0% 0%

Flown VFR into

IMC

82% 14% 2% 1% 1% 0% 0%

Turn back or

divert for weather

43% 24% 17% 4% 12% 0% 0%
19

consistently higher correlations with the demographic

variables than does the perceived risk for others (Perception 1).

Also from Table 12, it is evident that the risk

tolerance measures generally had almost no relationship to pilot demographic variables. Except for the

correlation between age and Risk Tolerance 3, none of

the other correlations were significant, and were generally close to zero.

The Relationship of Risk Perception to Risk

Ttolerance

One question of interest was whether pilots who

rate weather hazards as being relatively lower in risk

also tend to get higher weather risk scores—that is, do

they tolerate more weather risks? To address this

question, I examined the correlations of the weather

risk perception measures with the weather risk tolerance scores. From Table 12, the correlations of P1WX

(weather risk perception) with T2SUM and T3SUM

(weather risk tolerance measures) are 0.189 and -

0.125, respectively. Both of these correlations are

statistically significant (p<.05). High scores on both

Perception 1 and Perception 2 indicate perception of

weather scenarios as higher in risk. High scores on

Tolerance 1 and Tolerance 3 indicate that the subject

is more risk tolerant – that is, takes higher risks.

However, the scoring system is reversed for Tolerance

2, such that low scores indicate higher risk tolerance.

Therefore, the correlation of 0.189 between P1WX

and T2SUM indicates that high risk perception was

associated with lower risk tolerance. Pilots who perceived the weather scenarios as higher risk tended to have

a lower tolerance for the weather risk decisions. Because

high scores on Tolerance 3 indicate high levels of risk

taken, the negative correlation of –0.125 between P1WX

and T3SUM means that pilots who rated the weather

scenarios as high in risk, tended to make less risky

decisions on the tolerance exercise. Thus the two correlations lead to a consistent interpretation.

Similar results were obtained when comparing Perception 2 with the tolerance scores. The correlation of

P2WX with weather tolerance measures were 0.204

and -0.147, for T2SUM and T3SUM, respectively.

Both these correlations are also statistically significant

(p<.05). Results from all the correlations thus demonstrate that pilots who rate weather as risky tend to be

less tolerant of weather risk.

A similar but considerably more modest result is

obtained when the more general measure of risktaking (Tolerance 1) is compared with the risk

perception measures. Although all the correlations

between the Tolerance 1 measure (T1SUM) and the

risk perception measures are negative, consistent with

the previous results, only two of the six correlations

are significant, and all are considerably smaller than

those obtained for the other risk tolerance measures.

Relationships to Hazardous Activities

To assess the degree to which the measures from

these instruments might be associated with accident

involvement, they were correlated with an index of

involvement in hazardous activities. As shown in

Table 12, significant correlations were obtained between measures computed from Perception 2 and the

hazardous activity score. Specifically, correlations of -

0.125, –0.112, and –0.203 were obtained for the

overall aviation (P2FLY), baseline (P2BASE), and

weather (P2WX) risk perception scores, respectively.

The negative correlations indicate that higher levels of

perceived risk for the scenarios were associated with

lower numbers of hazardous events. Note that in

Table 12 there is a correlation of 0.560 between the

general non-aviation risk level (P2LIFE) and the level

of risk perception for flying (P2FLY). If general risk

perception, as measured by the items dealing with

non-aviation scenarios (P2LIFE), is held constant,

then partial correlations of –0.202, -0.169, and –

0.263 between the hazardous activity score and P2FLY,

P2BASE, and P2WX, respectively, are obtained. However, Table 12 also shows that there were significant

correlations between the risk perception measures and

pilot demographic variables – specifically, recent and

total flight time. If these two variables are also held

constant, then considerably attenuated partial correlations of -.0923, -.0762, and -.1158 between the

hazardous activity score and P2FLY, P2BASE, and

P2WX, respectively are obtained. Only the correlation for P2WX remains statistically significant (p <

.05; N = 345) when these other effects are included.

None of the correlations between the risk tolerance

measures and the hazardous events index were statistically significant, and all were close to zero.

Study Limitations and Threats to Generalizability

There are several aspects of this study which may

limit the generalizability of the results. Primarily,

these aspects are related to the sample of participants

and the data collection methodology.

First, recall that this sample was obtained from

pilots who had, for unspecified reasons, visited a Web

site that provides self-evaluation exercises and training aimed at general aviation pilots. They are, therefore, self-selected from a pool of pilots who (1) had

Internet access and, (2) knew about and chose to visit

this Web site during the period of the data collection.
20

The demographic information presented in Table 2

clearly indicates that this sample differed from the

population of U.S. pilots in terms of pilot certificate.

The results certainly should not be taken as representing pilots holding airline transport certificates, since

they are clearly underrepresented in this sample.

Second, the demographic data and reports of involvement in hazardous events are based upon selfreport, and are therefore subject to inaccuracies due to

respondent forgetfulness, bias, or misinterpretation

of the questions. Because the data collection was

anonymous, external validation of the participants’

responses was not possible. The questions dealing

with involvement in hazardous events are probably

the most susceptible to these errors.

Third, to simplify both the programming burden and

the instructions to the participants, the order of administration of the data collection was fixed, not random.

This means that performance on the later tasks may have

been affected by exposure to the earlier tasks. Because of

the attrition from the study noted earlier, this also means

that the participants who completed the later tasks are a

subset, with potentially different characteristics, of those

who began the study. Examination of the characteristics

of the initial and final samples given in Tables 1 and 2

suggest that this did not substantially change the composition of the sample, however it is possible that some

other factor (for example, gender) not assessed in the

study, was related to the decision to withdraw before

completion of all the tasks.

Fourth, the attempt to establish a significant motivating force in the risk tolerance tasks may not have been

equally effective with all pilots. Recall that in these tasks

pilots are motivated to obtain a high score in order to be

entered into a drawing for two highly desirable aviation

accessories. Without a motivation, participants have no

reason to attempt to maximize their scores by engaging

in higher risk activities in the exercises. However, some

pilots reported that they were indifferent to the prizes,

and simply completed the tasks for altruistic or other

personal reasons. Lack of a strong motivator may have

led those pilots to limit their choices in the exercises to

the low risk alternatives, while in a motivated state they

might have chosen high risk alternatives. This effect

would tend to attenuate the obtained results.

Fifth, the synthetic risk tolerance tasks may not be

indicative of behavior in the real world. Clearly, the risks

inherent in flight are not present in these computerbased exercises. Rather, it is assumed, as it is in much

of psychological research, that the personal attributes

which moderate risk tolerance in the laboratory also

act in approximately the same way to moderate risk

tolerance elsewhere.

Sixth, the data collection medium may have influenced the outcomes. Using the Web as a psychological

laboratory has advantages and disadvantages too numerous to discuss here, and readers are directed to

Birnbaum (2000) and Dillman (2000) for further

information. However, as a new activity, much needs

to be done to work out how to perform many tasks

that are easy in the university laboratory, but difficult

when the experimental cubicle is in someone’s home

a thousand miles away. Questions of subject recruitment and attrition, and generalizability of the results

will remain for some time. It is important to note that

this and similar studies are not statistical probability

surveys. Rather, they are identical in most respects to

the usual psychology experiment conducted with a

sample of convenience. It might be argued that drawing a more-or-less random sample from a national

population results in a more representative sample

and, hence, more generalizable results than are obtained from students in the freshman psychology class

or a small group of experts chosen solely because they

were the only participants available. However, the

data to address that argument are not available here.

CONCLUSIONS

In this study, new measures were developed to assess

pilot risk perception and risk tolerance. The data from

those measures demonstrated that, for weather, pilot

perception of risk is negatively related to tolerance for

risk. This finding is important because it means that

pilots who do not perceive the risks associated with

adverse weather are more likely to engage in higher risk

activities when dealing with weather.

The measures of risk tolerance were only mildly

related to the measures of risk perception, suggesting

that these are quite distinct constructs. Interestingly,

none of the measures of risk tolerance were related to

involvement in hazardous aviation activities. Measures of risk perception, however, were related to

hazardous event activities – pilots with a low perception of risk tended to be involved in more hazardous

events. This seems to agree with the finding that pilots

with low risk perception are more tolerant of risks.

However it must be noted that although statistically

significant, the effect size obtained for the relationship between risk perception (specifically, perception

of weather risks) and hazardous events is very small,

accounting for around 4% (from Table 12) of the

variance. Additional work would certainly be required

to improve the reliability and validity of these scales

before they could be used, even as a simple selfawareness exercise for pilots.
21

Consistent with the zero risk theory (Naatanen and

Summala, 1974), higher levels of experience and qualifications were associated with lower levels of perceived

risk, except for student pilots who tended to have a low

estimation of the risks in aviation. Apparently they have

not learned enough to estimate these risks.

It is clear that additional research is needed beyond

this first, exploratory study. In particular, the measures of risk tolerance need to be refined. It may be

that the weak intercorrelations among these measures

and the present failure to find significant relationships between those measures and hazardous activities

can be attributed to poor measurement of that construct. A next step might be to attempt to measure the

construct in a more realistic setting, possibly in a

flight simulator.

Additional efforts are also needed to explore the

construct validity of these measures. Assessments of

their relationships with constructs such as locus of

control and adventure-seeking would be worthwhile.

An assessment of the relationship of gender to performance on the perception and tolerance measures would

also be interesting.

The results of the present study suggest that it is

risk misperception, not high risk tolerance, that is

associated with exposure to hazardous aviation events.

This conclusion is in agreement with previous research (O’Hare and Smitheram, 1995; Goh and

Wiegmann, 2001) that shows that pilots who continue flight into adverse weather conditions have a

poor perception of the risks. If the hazardous events

index is a reasonable and valid proxy for actual accident involvement, then this suggests that risk

misperception may place these pilots at increased risk

of accident involvement. However, the link to accidents

must remain a tentative conclusion until the specific

relationship between the proxy measure and actual accident involvement is established empirically.

Although it is somewhat surprising to find that risk

tolerance was unrelated to the hazardous event criterion, from the perspective of a regulatory agency

charged with improving aviation safety, these are

actually very encouraging results. It is far better to

have a problem caused by pilot skill deficiencies than

to have a problem caused by pilot personality traits,

because the former are far easier to change than the

latter. Deficiencies in pilot skill may be addressed

through a variety of training interventions, and the

mechanisms for developing and delivering these interventions are well established and understood. Further, pilots are generally receptive to initiatives aimed

at improving their skill levels. Thus, although this is

by no means a trivial undertaking, changes in pilot

perception may be accomplished with a reasonable

expectation of success. On the other hand, making

fundamental changes to the levels of risk that pilots

are willing to tolerate would be a considerable challenge. If, as is proposed here, risk tolerance reflects a

basic personality trait of the individual, then it would

be exceptionally difficult to substantially change that

trait within any reasonable and acceptable intervention program. Fortunately, the data indicate that the

aviation community is not faced with that challenge.

Changing risk perception is not a straightforward

undertaking, however, since the factors that influence

risk perception are complex and confounded. It is

probably not appropriate simply to raise everyone’s

perception of risk. Rather, risk perceptions must be

made more valid. The results of this study show that

experience changes risk perception. More experience

leads to lower perceptions of risk. Since higher levels

of experience are generally considered better, in the

sense that pilots with more experience are more competent, then it may well be that their assessments are

correct. Since they are more competent, then the

scenarios presented to them represented truly lower

levels of risk. However, more work is needed to

identify those factors that result in inaccurate perception of risk. In that regard, there are three broad

avenues of approach. First, researchers may investigate how pilots arrive at their assessment of their

personal levels of competency and what causes those

assessments to be inaccurate. Second, they may investigate how pilots gather information from the environment and identify the cues associated with

conditions of high risk. Third, they may investigate

the interaction between the pilots perception of self and

their perception of the world and the resulting decision

– “Yes, I can do this,” or “No, I cannot do this.”

Research by Wiggins and his associates (Wiggins and

O’Hare, 1993; Wiggins, O’Hare, and Lods, 2000) suggests that pilots can be trained to look for and recognize

the weather cues indicative of increased weather risk. In

addition, O’Hare at the University of Otago and

Wiegmann at the University of Illinois are conducting

research on problem framing and other factors that may

influence continuation error – continuing flight into

adverse weather conditions. Additional research is also

needed to assess how environmental cues are combined

to form the global risk perception for a situation. Better,

and more specific measures of risk perception are also

needed. Research into the mental processes by which

perceptions of external risk and perceptions of internal

capability, presumably in two different metrics, are

combined to arrive at the go/no-go decision would also

be very illuminating.
22

The present effort, and the suggested follow-on

research, serve two purposes. First, they advance our

scientific understanding of the processes and attributes

of people and situations that contribute to decisionmaking, and potentially to accidents. Second, their

results shape the interventions that will reduce accidents. The present results show that attempts to

change risk tolerance (a potentially massive undertaking) would be far less effective in reducing accidents

than training pilots in risk recognition skills. This

supports the current approaches to training hazard

identification and control and suggests that these

programs need to be broadened and strengthened. It

also shows that interventions aimed at changing risk

tolerance would likely be ineffective, thus avoiding

expenditure of resources in an area unlikely to reduce

accidents. Risk may be ubiquitous, but with effective,

research-based intervention programs, accidents may

be prevented by improved pilot education regarding

risk identification and management.

REFERENCES

Birnbaum, M.H. (Ed.) (2000). Psychological experiments

on the Internet. New York: Academic Press.

Brown,I.D., & Groeger,J.A. (1988). Risk perception

and decision taking during the transition between

novice and experienced driver status. Ergonomics,

31(4), 585-97.

Comsis Corp. (1995). Understanding youthful risk

taking and driving. Washington, DC: National

Highway Traffic Safety Administration. DOT

HS 808 18.

DeJoy,D.M. (1992). An examination of gender differences in traffic accident risk perception. Accident Analysis and Prevention, 24(3), 237-46.

Dillman, D. A. (2000). Mail and Internet surveys: The

tailored design method. (2nd ed.). New York:

John Wiley.

Driskill,W.E., Weissmuller,J.J., Quebe,J., Hand,D.K.,

& Hunter,D.R. (1998). Evaluating the decisionmaking skills of general aviation pilots. Washington,

DC: Federal Aviation Administration. DOT/FAA/

AM-98/7.

Edkins,G., Coakes,S., & O’Hare,D. (2000). Risk perception in commuter airline operations: Objective

versus subjective perceptions of risk. Unpublished

manuscript.

Elander,J., West,R., & French,D. (1993). Behavioral

correlates of individual differences in road-traffic

crash risk: An examination of methods and findings. Psychological Bulletin, 113(2), 279-94.

Federal Aviation Administration (1999). Aviation System Indicators: 1999 Annual Report. Washington,

DC: Assistant Administrator for System Safety.

Fuller,R. (1984). A conceptualization of driving

behaviour as threat avoidance. Ergonomics, 27(11),

1139-55.

Fuller,R. (1988). On learning to make risky decisions.

Ergonomics, 31(4), 519-26.

Goh, J., & Wiegmann, D. (2001). Visual flight rules

(VFR) flight into instrument meteorological conditions (IMC): A review of the accident data. In

R.S. Jensen (Ed.) Proceedings of the 11th International Symposium on Aviation Psychology. Columbus, OH: The Ohio State University.

Hunter,D.R. (1995). Airman Research Questionnaire:

Methodology and Overall Results. Washington,

DC: Federal Aviation Administration. DOT/

FAA/AM-95/27.

Imhoff,D.L., & Levine,J.M. (1981). Perceptual-Motor

And Cognitive Performance Task Battery Pilot Selection. Brooks AFB, TX: Air Force Human Resources Laboratory. AFHRL-TR 80-27

Jensen,R.R., & Benel,R.A. (1977). Judgment evaluation and instruction in civil pilot training. Washington, DC: Federal Aviation Administration.

FAA-RD-78-24.

Jonah,B.A. (1986). Accident risk and risk-taking behavior among young drivers. Accident Analysis and

Prevention, 18(4), 255-71.

Kirkbride,L.A., Jensen,R.S., Chubb,G.P., & Hunter,D.R. (1996). Developing the personal minimums

tool for managing risk during preflight go/no-go decisions. Washington, DC: Federal Aviation Administration. DOT/FAA/AM-96/19.

Lester,L.F., & Bombaci,D.H. (1984). The relationship

between personality and irrational judgment in

civil pilots. Human Factors, 26, 565-72.

McKenna,F.P. (1988). What role should the concept of

risk play in theories of accident involvement? Ergonomics, 31(4), 469-84.
23

Naatanen,R., & Summala,H. (1974). A model for

the role of motivational factors in drivers’ decision-making. Accident Analysis and Prevention,

6, 243-61.

Nunnally, J.C. (1967). Psychometric Theory. New York:

McGraw-Hill.

O’Hare,D. (1990). Pilots’ perception of risks and hazards in general aviation. Aviation, Space, and Environmental Medicine, 61(7), 599-603.

O’Hare,D., & Chalmers,D.J. (1999). The incidence of

incidents: A nationwide study of flight experience

and exposure to accidents and incidents. International Journal of Aviation Psychology, 9 (1), 1-18.

O’Hare, D., & Smitheram, T. (1995). “Pressing on”

into deteriorating conditions: An application of

behavioral decision theory to pilot decision making. International Journal of Aviation Psychology, 5,

351-70.

Oppe,S. (1988). The concept of risk: A decision theoretic approach. Ergonomics, 31(4), 435-40.

Ranney,T. (1994). Models of driving behavior: A review of their evolution. Accident Analysis and Prevention, 26 (6), 733-50.

Regan,M.A., Triggs,T.J., & Wallace,P.R. (2000). A

CD ROM product for enhancing perceptual and

cognitive skills in novice car drivers. (unpublished

manuscript)

Sanders, M.S., & McCormick, E.J. (1993). Human

Factors in Engineering and Design. New York:

McGraw-Hill.

 Shull,R.N., & Dolgin,D.L. (1988). The Relationship

Between Flight Training Performance, A Risk

Assessment Test, and The Jenkins Activity Survey.

. Pensacola, FL: Naval Aerospace Medical Research Laboratory. NAMRL 1339

 Shull,R.N., & Dolgin,D.L. (1989). Personality And Flight

Training Performance. In Proceedings of the Human Factors Society—33rd Annual Meeting.

 Siem,F.M., Carretta,T.R., & Mercatante,T.A.

(1988). Personality, attitudes, and pilot training

performance: preliminary analysis. Brooks AFB,

TX: Manpower & Personnel Division, AFHRL.

AFHRL-TP-87-62.

 Slovic,P., Fischhoff,B., & Lichtenstein,S. (1980). Facts

and fears: Understanding perceived risk. In R.

Schwing & W. Albers (Eds.), Societal risk assessment. New York: Plenum.

 Summala,H. (1988). Risk control is not risk adjustment: The zero-risk theory of driver behaviour

and its implications. Ergonomics, 31(4), 491-506.

 Trimpop,R.M., & Wilde,G.J.S. (1994). Challenges to

accident prevention: The issue of risk compensation

behaviour. Groningen, The Netherlands: Styx Publications.

Wiggins, M, & O’Hare, D. (1993) A skills-based approach to training aeronautical decision-making.

In R. A. Telfer (Ed.) Aviation instruction and

training. Brookfield, VT: Ashgate.

Wiggins, M., O’Hare, D, & Lods, O. (2000)

Weatherwise: A computer-based training program

for weather-related pilot decision-making. University of Western Sydney. Unpublished manuscript.

 Wilde,G.J.S. (1994). Target Risk. Toronto: PDE Publications.

 Williams,K. (1999). Environmental factors influencing

pilot risk-taking. Oklahoma City, OK: Civil Aeromedical Institute. Unpublished manuscript.