Beneath the Tip of the 

Iceberg: A Human Factors 

Analysis of General Aviation 

Accidents in Alaska Versus 

the Rest of the United States

Cristy Detwiler, Carla Hackworth, Kali Holcomb1

Albert Boquet2

Elaine Pfleiderer1

Douglas Wiegmann3

Scott Shappell4

1

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

2

Embry-Riddle Aeronautical University

Daytona Beach, FL 32114

3

Mayo Clinic

Rochester, MN 55905

4

Clemson University 

Clemson, SC 29634

March 2006

Final Report

DOT/FAA/AM-06/7

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.

___________

This publication and all Office of Aerospace Medicine 

technical reports are available in full-text from the Civil 

Aerospace Medical Institute’s publications Web site: 

www.faa.gov/library/reports/medical/oamtechreports/index.cfm
i

Technical Report Documentation Page 

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

DOT/FAA/AM-06/7 

4. Title and Subtitle 5. Report Date 

Beneath the Tip of the Iceberg: A Human Factors Analysis of General March 2006 

Aviation Accidents in Alaska Versus the Rest of the United States 6. Performing Organization Code 

 

7. Author(s) 8. Performing Organization Report No. 

Detwiler CA, 1 Hackworth CA,1 Holcomb KA,1Boquet AJ,2 Pfleiderer E,1

Wiegmann DA,

3

 Shappell SA4

 

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

 11. Contract or Grant No. 

1

FAA Civil Aerospace Medical Institute 

P.O. Box 25082, Oklahoma City, OK 73125 

2

Embry-Riddle Aeronautical University

Daytona Beach, FL 32114 

3

Mayo Clinic 

Rochester, MN 55905 

4

Clemson University 

Clemson, SC 29634

 

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

Office of Aerospace Medicine 

Federal Aviation Administration 

800 Independence Ave., S.W. 

Washington, DC 20591 14. Sponsoring Agency Code 

15. Supplemental Notes 

Work was accomplished under approved task AM-B-05-HRR-521. 

16. Abstract 

Historically, general aviation (GA) accidents have been overlooked and their impact under-appreciated when 

compared with those in the commercial or military sector. Recently however, the Federal Aviation 

Administration and other governmental and civilian organizations have focused their attention on one piece 

of this proverbial “iceberg,” that being GA accidents occurring in Alaska. This study examines more than 

17,000 GA accidents using the Human Factors Analysis and Classification System. Comparisons of Alaska 

to the rest of the U.S. (RoUS) included traditional demographic and environmental variables, as well as the 

human errors committed by aircrews. Overall, categorical differences among unsafe acts (decision errors, 

skill-based errors, perceptual errors, and violations) committed by pilots involved in accidents in Alaska and 

those in the RoUS were minimal. However, a closer inspection of the data revealed notable variations in the 

specific forms these unsafe acts took within the accident record. Specifically, skill-based errors associated with 

loss of directional control were more likely to occur in Alaska than the rest of the U.S. Likewise, the decision 

to utilize unsuitable terrain was more likely to occur in Alaska. Additionally, accidents in Alaska were 

associated with violations concerning Visual Flight Rules into Instrument Meteorological Conditions. These 

data provide valuable information for those government and civilian programs tasked with improving GA 

safety in Alaska and the RoUS. 

 

17. Key Words 18. Distribution Statement 

 

 

General Aviation, Aviation Accidents, Human Error, 

HFACS

Document is available to the public through the 

Defense Technical Information Center, Ft. 

Belvior, VA 22060; and the National Technical 

Information Service, Springfield, VA 22161 

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

Unclassified Unclassified 14 

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

1

Beneath the Tip of The Iceberg: A Human Factors Analysis of General

Aviation Accidents in Alaska Versus the Rest of the United States

INTRODUCTION

Considerable effort has been expended over the last 

several decades to improve safety in both military and 

commercial aviation. Even though many people have 

died and millions of dollars in assets have been lost, the 

numbers pale in comparison to those suffered every year 

within general aviation (GA). For example, according to 

the National Transportation Safety Board (NTSB), there 

were 1,741 GA accidents in 2003 that resulted in 629 

fatalities (NTSB, 2005). While the numbers may not 

register with some, when considered within the context 

of commercial aviation, the losses suffered annually by 

GA are roughly equivalent to the complete loss of three 

commercial passenger Boeing 727s. 

Why, then, has GA historically received less attention? 

Perhaps it is because flying has become relatively common 

as literally millions of travelers board commercial aircraft 

daily. Not surprising then, 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.

Over the last couple of years, general aviation has 

deservedly received increasing attention from the Federal 

Aviation Administration (FAA Flight Plan 2004-2008) 

and other safety professionals. Indeed, several groups 

from the government (e.g., the FAA’s Civil Aerospace 

Medical Institute, National Institute of Occupational 

Safety and Health), private sector (e.g., the Medallion 

Foundation), and universities (e.g., University of Illinois, 

Johns Hopkins University) have conducted a number of 

studies examining GA accident causation.

Alaskan Aviation

It is of note that many of these efforts have focused on 

Alaska, where aviation is the primary mode of transportation. It has been said that people in Alaska fly private 

aircraft like those in the lower 48 take taxis. As can be 

seen in Figure 1, when taking into account the size of 

the state, it is no wonder that air travel is a must. In fact, 

some parts of Alaska are only accessible by air. 

Alaska is known for its varied and often unique landscape, including but not limited to, seemingly endless 

mountain ranges, glaciers, lakes, long coastlines, volcanoes, and fjords. When this veritable obstacle course 

is considered, along with temperamental weather and 

seasonal lighting conditions, even the most experienced 

pilot would have to agree that Alaskan aviation represents 

some of the most difficult flying in the U.S., if not the 

world. The combination of factors mentioned above, the 

number of GA accidents that are occurring in Alaska, 

and the FAA’s accident reduction goal (FAA Flight Plan 

2004-2008) were factors in our decision to implement 

this study.

Human Error and General Aviation

A variety of studies have been conducted in an attempt 

to understand the causes of GA accidents. Most have 

focused on contextual factors or pilot demographics, 

rather than the underlying causes of the accidents. Past 

research has shown factors like weather [e.g., Instrument 

Meteorological Conditions (IMC) versus Visual Meteorological Conditions (VMC)], lighting (e.g., day versus 

night), and terrain (e.g., mountainous versus featureless) 

play a part in these accidents; however, pilots have little 

control over them. Other studies have found that a pilot’s 

gender, age, occupation, or flight experience contribute 

to the accidents (Baker, Lamb, Grabowski, Rebok, & 

Li, 2001; Li, Baker, Grabowski, & Rebok, 2001; Urban, 

Figure 1. Relative size of Alaska to the 

continental United States. (Taken from a 

briefing from the FAA Alaska Region.) 
2

1984) and aid in the identification of target populations 

for the dissemination of safety information.

However, when the leading cause of accidents, human 

error, has been addressed, it is often only to report the 

percentage of accidents associated with aircrew error in 

general or to identify those in which alcohol or drug use 

occurred. What is needed is a thorough human error 

analysis. Previous attempts to do just that have been 

met with limited success (O’Hare, Wiggins, Batt, & 

Morrison, 1994; Wiegmann & Shappell, 1997). This is 

primarily because human error is influenced by a variety 

of factors that are usually not addressed by traditional 

classification schemes (Shappell & Wiegmann, 1997). 

Yet, with the development of the Human Factors Analysis 

and Classification System (HFACS) previously unknown 

patterns of human error in aviation accidents have been 

uncovered (Shappell & Wiegmann, 2001; Wiegmann 

& Shappell, 2001a). 

HFACS

Drawing upon Reason’s “Swiss-cheese” model of human error, Wiegmann and Shappell developed HFACS. 

The HFACS framework includes 19 causal categories 

within Reason’s (1990) four levels of human failure, of 

which the Unsafe Acts of Operators are most germane 

to this study (Figure 2). For a complete description of 

the HFACS framework, see Wiegmann and Shappell, 

2003.

In general, the unsafe acts of operators (in the case 

of aviation, the aircrew) can be classified as either errors 

or violations. Within HFACS, the category of errors 

was expanded to include three basic types (decision, 

skill-based, and perceptual errors) that, in simple terms, 

refer to errors of “thinking,” “doing,” and “perceiving.” 

To be more specific, decision errors represent conscious 

decisions/choices made by an individual that are carried 

out as intended but prove to be inadequate for the situation at hand. In contrast, skill-based behavior within the 

context of aviation is best described as “stick-and-rudder” 

and other basic flight skills that occur without significant 

conscious thought. As a result, these skill-based actions are 

particularly vulnerable to failures of attention, memory, 

or simply poor technique. Finally, perceptual errors occur 

when sensory input is degraded or “unusual,” as is often 

the case when flying at night, in weather, or in other 

visually impoverished conditions.

By definition, errors occur while aircrews are behaving within the rules and regulations implemented by an 

organization. In contrast, violations represent the willful 

disregard for the rules and regulations that govern safe 

flight. The key word is “willful” in this definition. That 

is, the individuals knew that what they were doing was 

unauthorized but elected to continue anyway.

While there are many ways to distinguish between 

types of violations, two distinct forms have been 

identified, based on their etiology. The first, routine 

violations, tend to be habitual by nature and are often 

tolerated by the governing authority. The second type, 

exceptional violations, appear as isolated departures 

from authority and are not necessarily characteristic 

of an individual’s behavior nor are they condoned by 

management. 

PURPOSE

The present study set out to uncover the types of human error, as identified by HFACS, that contributed to 

GA accidents in Alaska and compare those results with 

the rest of the United States. Both the human error findings and contextual factors are presented here to obtain 

a more complete picture.

METHODS

General aviation accident data from calendar years 

1990-2002 were obtained from databases maintained by 

the National Transportation Safety Board and the FAA’s 

National Aviation Safety Data Analysis Center (NASDAC). In total, 24,978 GA accidents were extracted for 

analysis. These so-called “GA” accidents actually included 

a variety of aircraft being flown under several different 

operating rules: 1) 14 CFR Part 91 – Civil aircraft other 

than moored balloons, kites, unmanned rockets, and 

unmanned free balloons; 2) 14 CFR Part 91F – Large 

and turbine-powered multiengine airplanes; 3) 14 CFR 

Part 103 – Ultralight vehicles; 4) 14 CFR Part 125 – Airplanes with seating capacity of 20 or more passengers or a 

maximum payload capacity of 6,000 pounds or more; 5) 

14 CFR Part 133 – Rotorcraft external-load operations; 

6) 14 CFR Part 137 – Agricultural aircraft operations. 

In addition, the database contained several accidents 

involving public use aircraft (i.e., law enforcement, state 

owned aircraft, etc.) and a few midair accidents involving 

military aircraft.

It is difficult to envision that large commercial aircraft 

being ferried from one airport to the next (operating 

under 14 CFR Part 91F) or aircraft being used to spread 

chemicals on a field (operating under 14 CFR Part 137) 

can be equated with small private aircraft being flown 

for personal or recreational purposes (operating under 

14 CFR Part 91). Therefore, we selected only 14 CFR 

Part 91 accidents for our analyses (22,987) to obtain a 

more discrete GA sample.
3

Errors

UNSAFE

ACTS

Perceptual

Errors

Skill-Based

Errors

Decision

Errors Routine Exceptional

Violations

Inadequate

Supervision

Planned

Inappropriate

Operations

Failed to

Correct

Problem

Supervisory

Violations

UNSAFE

SUPERVISION

Resource

Management

Organizational

Climate

Organizational

Process

ORGANIZATIONAL

INFLUENCES

PRECONDITIONS

FOR

UNSAFE ACTS

Condition of 

Operators

Physical/

Mental

Limitations

Adverse 

Mental 

States

Technological 

Environment

Physical 

Environment

Personal

Readiness

Crew Resource 

Management

Personnel 

Factors

Adverse 

Physiological 

States

Environmental 

Factors

Figure 2. The HFACS framework.
4

This analysis was primarily concerned with powered 

aircraft and thus the data were further restricted to include 

only accidents involving powered fixed-wing aircraft, helicopters, and gyrocopters. The remaining 22,248 accidents 

were then examined for aircrew-related causal factors. 

Since we were only interested in those accidents involving 

aircrew error, not those that were purely mechanical in 

nature or solely attributable to other human involvement, 

a final reduction of the data was conducted. Note, this 

does not mean that mechanical failures or other sources 

of human error did not exist in the final database, only 

that some form of aircrew error was also involved in each 

of the accidents included. Figure 3 depicts the frequency 

of GA accidents associated with human error from 1990 

to 2002. In the end, 17,808 accidents were included in 

the database that were associated with some form of human error and were submitted to further analyses using 

the HFACS framework.

Causal Factor Classification Using HFACS

Six GA pilots were recruited from the Oklahoma City 

area as subject matter experts and received roughly 16 

hours of training on the HFACS framework. All seven 

were certified flight instructors with a minimum of 1,000 

flight hours in GA aircraft (mean = 3,530 flight hours) 

when the study began. 

After training, the six GA pilot-raters were randomly 

assigned accidents, so at least two separate pilot-raters 

analyzed each accident independently. Using narrative 

and tabular data obtained from both the NTSB and the 

FAA NASDAC, the pilot-raters classified each human 

causal factor using the HFACS framework. Note, however, 

that only those causal factors identified by the NTSB 

were classified. That is, the pilot-raters were instructed 

not to introduce additional casual factors that were not 

identified by the original investigation. To do so would 

be presumptuous and only infuse additional opinion, 

conjecture, and guesswork into the analysis process.

After the pilot-raters made their initial classifications 

of the NTSB causal factors using HFACS (i.e., skill-based 

error, decision-error, etc.) the two ratings were compared. 

Where differences existed between the ratings, the two 

pilots were asked to reconcile their differences and an 

agreed-upon “consensus” classification was included in 

the database for further analysis. Overall, the independent 

pilot-raters agreed on the classification of human causal 

factors within the HFACS framework more than 85% 

of the time. More important, all human causal factors 

identified in the NTSB records were accommodated 

using the HFACS framework, and the data were ultimately submitted to a final quality assurance analysis 

by the authors.

RESULTS

When using HFACS to examine the GA accident 

data, the majority of the accidents are coded with either 

a precondition for unsafe acts or an unsafe act. This is 

due primarily to the fact that there is less of an organizational or supervisory influence on the majority of GA 

pilots, as compared with their counterparts conducting 

commercial or “for hire” operations. 

Indeed, with few exceptions (e.g., flight instructors 

and flight training institutions), the top two tiers of 

HFACS (unsafe supervision and organizational influences) remained sparsely populated when examining 

the GA accidents, leaving the majority of causal factors 

within the bottom two tiers of HFACS. Consequently, 

the balance of this report will focus only on the unsafe 

acts of the operator level of the HFACS framework.

Unsafe Acts of Operators (Aircrew)

An overall review of the GA accident data yielded 

the following results (see Figure 4). The most prevalent 

error noted in the accident data over the past decade 

was skill-based errors (73%), followed by decision errors 

(28%), violations (13%), and perceptual errors (7%). 

The relatively flat lines in the types of unsafe acts across 

the years suggest that past intervention strategies have 

had little differential impact on any particular category 

of error. 

To obtain a better sense of how human error differences 

between Alaska and the rest of the United States (RoUS) 

are represented in the data, the error types were broken 

out accordingly (Figure 5). The analysis of the unsafe 

acts revealed that there were slightly more decision errors, 

fewer skill-based errors, perceptual errors, and violations 

in Alaska than there were in the RoUS. 

Note, the following analyses did not distinguish between those pilots who were native to Alaska and were 

involved in an accident versus those who were less familiar 

with the state. Accordingly, the statistics for Alaska reflect 

the accidents that occurred within the physical boundaries of the state. 

Skill-Based Errors. Differences that existed between 

Alaska and the RoUS were fairly consistent across the 

years of study, with slightly more skill-based errors associated with accidents in the RoUS (see Figure 6). The 

only exception involved 1991, 1996, and again in 2002, 

where the percentages were nearly equal. 

Differences between Alaska and the RoUS were more 

distinct when the actual types of skill-based error were 

compared (Table 1). For instance, directional control 

was the most frequently cited skill-based error for both 

Alaska (19%) and for the rest of the U.S. (13%). Pilots in 
5

0

10

20

30

40

50

60

70

80

90

100

Skill-based

Error

Decision

Error

Perceptual

Error

Violation

Percentage of Accidents

Alaska

Rest of US

Figure 5. Percentage of accidents associated 

with each of the unsafe acts of the operator. 

0

10

20

30

40

50

60

70

80

90

100

'90 '91 '92 '93 '94 '95 '96 '97 '98 '99 '00 '01 '02

Percentage of Accidents

Year Percentages do not add up to 100%

Alaska

Rest of US

Figure 6. Skill-based errors broken out by 

Alaska versus the rest of the U.S. 

Table 1. Top 5 Skill-based errors occurring for 

Alaska and the rest of the U.S.

Alaska N (%) RoUS N (%) 

Directional 

Control 206 (18.6%) Directional Control 2139 (12.6%) 

Compensation 

for Wind 

Conditions 

170 (15.4%) Airspeed 1932 (11.3%) 

Stall 88 (8.0%) Stall 1312 (7.7%) 

Airspeed 76 (6.9%) Aircraft 

Control 1310 (7.7%) 

Ground 

Loop/Swerve 50 (4.5%) 

Compensation 

for Wind 

Conditions 

1009 (5.9%) 

0

250

500

750

1000

1250

1500

1750

2000

'90 '91 '92 '93 '94 '95 '96 '97 '98 '99 '00 '01 '02

Frequency of Accidents

Year

All Part 91 Fixed and 

Rotary-wing Accidents

Part 91 Fixed and Rotary-wing 

Accidents Associated with 

Human Error

Figure 3. All 14 CFR Part 91 fixed and rotary-wing 

(Helicopters & Gyrocopters) accidents and the 

influence of human error in those accidents.

0

10

20

30

40

50

60

70

80

90

100

'90 '91 '92 '93 '94 '95 '96 '97 '98 '99 '00 '01 '02

Skill-based

Errors

Perceptual

Errors

Decision

Errors

Violations

Percentage of Accidents

Year Percentages do not add up to 100%

Figure 4. Overall review of general aviation data for 

HFACS unsafe acts.
6

Alaska were more likely to experience a loss of directional 

control of their aircraft than those in the rest of the U.S. 

(odds ratio = 1.593, Χ2 = 33.400, p <.001). Additionally, inadequate compensation for wind conditions was 

almost three times more likely to occur in Alaska (odds 

ratio = 2.884, Χ2 = 150.893, p <.001). Conversely, pilots 

in the rest of the U.S. were almost two times more likely 

to commit airspeed errors than those in Alaska (odds ratio 

= 1.733, Χ2 = 20.652, p <.001). 

Decision Errors. To better understand the complexity 

of the decision errors that were occurring in the accidents 

for both Alaska and the rest of the U.S., a fine-grained 

analysis of the data was conducted. Figure 7 illustrates 

the decision error trends for Alaska and the rest of the 

U.S. across the 13-year period from 1990-2002. With 

the exception of 1990, 1991, and 2002, any difference 

that did exist was remarkably consistent across years of 

the study.

Upon closer examination, the largest proportion of 

decision errors in the rest of the U.S. involved in-flight 

planning/decision making, accounting for 19% of those 

observed. However, the top decision error for pilots flying in Alaska dealt with decisions to utilize unimproved 

landing, takeoff, taxi areas, or unsuitable terrain. As a 

matter of fact, those flying in Alaska were almost 15 

times more likely to take off from and land on unsuitable 

terrain than those in the rest of the U.S. (odds ratio = 

14.703, Χ2 = 829.461, p <.001). A break-out of the top 

five decision errors for Alaska and the rest of the U.S. are 

presented in Table 2. 

Perceptual Errors. Generally associated with less than 

10% of the accidents, perceptual errors in Alaska occurred 

with a similar frequency as those in the rest of the U.S. 

(see Figure 8). Moreover, there were few, if any, reliable 

differences between Alaska and the RoUS when the type 

of perceptual error was examined (Table 3). Indeed, given 

the very small cell size for specific types of perceptual 

errors occurring in Alaska, it was difficult to draw any 

defensible conclusions.

Violations. In general, violations were associated with 

less than 20% of GA accidents (Figure 9). For the entire 

U.S. population, nearly 50% of these accidents resulted 

0

10

20

30

40

50

60

70

80

90

100

'90 '91 '92 '93 '94 '95 '96 '97 '98 '99 '00 '01 '02

Percentage of Accidents

Year Percentages do not add up to 100%

Alaska

Rest of US

Figure 7. Decision errors broken out by 

Alaska versus the rest of the U.S. 

Table 3. Top 5 Perceptual errors occurring for 

Alaska and the rest of the U.S.

Alaska N (%) RoUS N (%) 

Flare 12 (21.1%) Flare 246 (20.1%) 

Aircraft Control 6 (10.5%) Aircraft 

Control 201 (16.4%) 

Altitude 5 (8.8%) Altitude 121 (9.9%) 

Clearance 5 (8.8%) Distance/ 

Speed 98 (8.0%) 

Proper 

Touchdown 

Point 

 5 (8.8%) Distance/ 

Altitude Figure 8. Perceptual errors broken out by Alaska 87 (7.1) 

versus the rest of the U.S.

0

10

20

30

40

50

60

70

80

90

100

'90 '91 '92 '93 '94 '95 '96 '97 '98 '99 '00 '01 '02

Percentage of Accidents

Year Percentages do not add up to 100%

Alaska

Rest of US

0

10

20

30

40

50

60

70

80

90

100

'90 '91 '92 '93 '94 '95 '96 '97 '98 '99 '00 '01 '02

Percentage of Accidents

Year Percentages do not add up to 100%

Alaska

Rest of US

Table 2. Top 5 Decision errors occurring for 

Alaska and the rest of the U.S. 

Alaska N (%) RoUS N (%) 

Unsuitable 

Terrain 193 (40.5%) 

In-flight 

Planning/ 

Decision 

1002 (18.7%) 

In-flight 

Planning/ 

Decision 

 59 (12.4) Planning/ 

Decision 374 (7.0%) 

Aborted 

Takeoff 28 (5.9%) Refueling 351 (6.5%) 

Planning/ 

Decision 19 (4.0%) Remedial Action 339 (6.3%) 

Go-around 18 (3.8%) Go-around 336 (6.3%) 
7

in a fatality. When examining accidents in Alaska separately from the rest of the U.S., differences were found. 

Accidents involving violations in Alaska were nine times 

more likely to result in a fatality (odds ratio = 9.248, Χ2

= 127.606, p <.001); whereas those that occurred in the 

rest of the U.S. were four times more likely to result in a 

fatality, (odds ratio = 4.410, Χ2 = 1054.059, p <.001).

A closer look at the types of violations revealed that 

the most frequently cited violation for all GA accidents 

was Visual Flight Rules (VFR) flight into Instrument 

Meteorological Conditions (IMC), (Table 4). VFR flight 

into IMC, alone, accounted for one-third of the violations in the Alaska data and was more than two and a 

half times more likely to occur than in the rest of the U.S. 

(odds ratio = 2.629, Χ2 = 22.467, p <.001). Furthermore, 

when the weather-related violations were combined (VFR 

into IMC, flight into known adverse weather, and flight 

into adverse weather), nearly half of the violations in the 

Alaska data were represented. 

Contextual Data

Phase of Flight. The majority of GA accidents for Alaska 

and the rest of the U.S. occurred during the landing and 

takeoff phases of flight (see Figure 10). Note, however, 

that the accidents in Alaska had a higher occurrence in 

both of those phases than those in the rest of the U.S., 

where cruise and approach were higher. Additionally, when 

takeoff and climb are compared against descent, approach, 

and landing, across the board, comparatively more accidents occurred during the latter phases of flight.

Fatal vs. Non-Fatal and Injury Level. Curiously, accidents occurring in the RoUS were more likely to include 

a fatality (23%) than those in Alaska (10%, see Figure 

11). Specifically, the accidents in the RoUS were 2.8 times 

more likely to result in a fatality (odds ratio =2.808, Χ2

= 125.090, p <.001). This pattern held across all levels 

of injury severity (Figure 12) as roughly three-fourths 

of the GA accidents occurring in Alaska involved no 

injuries at all. 

0

10

20

30

40

50

60

70

80

90

100

'90 '91 '92 '93 '94 '95 '96 '97 '98 '99 '00 '01 '02

Percentage of Accidents

Year Percentages do not add up to 100%

Alaska

Rest of US

Figure 9. Violations broken out by Alaska 

versus the rest of the U.S. 

Table 4. Top 5 Violations occurring for Alaska and 

the rest of the U.S. 

Alaska N (%) RoUS N (%) 

VFR Into IMC 38 (32.5%) VFR Into IMC 369 (15.5%) 

Aircraft Weight 

& Balance 13 (11.1%) 

Operation with 

Known 

Deficiencies 

261 (10.9%) 

Procedures/ 

Directives 12 (10.3%) Procedures/ Directives 248 (10.4%) 

Flight Into 

Known Adverse 

Weather 

 11 (9.4%) 

Flight Into 

Known 

Adverse 

Weather 

212 (8.9%) 

Operation With 

Known 

Deficiencies 

 8 (6.8%) Aircraft Weight 

& Balance 149 (6.2%) 

0

10

20

30

40

50

60

70

80

90

100

Non-Fatal Fatal

Percentage of Accidents

Alaska

Rest of US

Figure 11. Percentage of fatal versus non-fatal 

accidents in Alaska and the rest of the U.S. 

0

10

20

30

40

50

TaxiTakeoffClimb

Cruise

DescentApproachLanding

Alaska

Rest of US

Percentage of Accidents

Figure 10. Percentage of accidents occurring in each 

phase of flight for Alaska and the rest of the U.S.
8

Weather and Lighting Conditions. Very few differences 

between Alaska and the rest of the U.S. were noted with 

regard to either lighting conditions (day, twilight, and 

night) or weather (IMC vs. VMC). That is, the vast 

majority of accidents occurred during the daytime and 

in VMC conditions (Figures 13 & 14). However, when 

the two conditions were combined to create a measure 

of visibility (i.e., clear versus impoverished condition), 

some small but significant differences emerged (Figure 

15). Specifically, accidents were more likely to occur in 

visually impoverished (at night/twilight or IMC) conditions in the rest of the U.S. than in Alaska (odds ratio 

=2.160, Χ2 = 68.766, p <.001).

Discussion

On the surface, there were no major differences between 

Alaska and the rest of the U.S. with regard to the overall 

pattern of human error. If anything, there were slightly 

more decision errors associated with accidents occurring 

in Alaska and fewer skill-based errors, perceptual errors, 

and violations. This information is similar to research in 

other aviation operations, which identified skill-based 

errors as the most commonly occurring type of error 

(Shappell & Wiegmann, 2003; Wiegmann & Shappell, 

2001b; 2003). 

Upon closer examination, both Alaska and the rest 

of the U.S. exhibited similar problems with regards to 

the specific types of each HFACS causal category. When 

addressing skill-based errors, the accident data suggest 

that aircraft handling should be taken into account when 

determining where interventions should be applied. For 

instance, any training (both ab initio and recurrent) 

along these lines should include control of the aircraft 

on the ground (e.g., ground loops), crosswind landings, 

avoiding and recovering from stalls, and general control 

of the aircraft in flight. Given the inherent risk associated 

with some of these maneuvers, it makes sense to utilize 

modern simulators during this training. Unfortunately, it 

is unclear whether adequate transfer of training warrants 

this possibility. Therefore, before utilizing simulations to 

address these issues, research needs to be conducted to 

examine the role simulators might offer. In the meantime 

however, it appears to make sense to emphasize these 

topics during actual in-flight training. 

The only notable exception among the HFACS casual 

categories involved decision errors. Specifically, pilots in 

Alaska were more likely to utilize unsuitable terrain for 

landing, taxi, and takeoff. It would appear that educating aviators on the hazards of utilizing frozen rivers or 

gravel bars, for example, may reduce these types of errors. 

However, it may be that there are simply more “improved” 

0

10

20

30

40

50

60

70

80

90

100

None Minor Serious Fatal

Percentage of Accidents

Alaska

Rest of US

Figure 12. Percentage of accidents associated 

with the four levels of injury. 

0

10

20

30

40

50

60

70

80

90

100

Day Twilight

Percentage of Accidents

Night

Alaska

Rest of US

Figure 13. Lighting conditions for accidents 

occurring in Alaska and the rest of the U.S. 

0

10

20

30

40

50

60

70

80

90

100

VMC IMC

Percentage of Accidents

Alaska

Rest of US

Figure 14. Weather conditions associated with 

accidents in Alaska and the rest of the U.S. 

0

10

20

30

40

50

60

70

80

90

100

Clear Impoverished

Percentage of Accidents

Alaska

Rest of US

Figure 15. Accidents occurring in clear versus 

impoverished conditions for Alaska and the rest 

of the U.S. 
9

areas in the rest of the United States, providing pilots 

with more options in case of an emergency (i.e., alternate 

airports, highways, roads, etc.), in which case education 

in and of itself may not prove successful. Additionally, 

it is worth noting that “unsuitable terrain” was defined 

by the NTSB investigators after the fact; the moment to 

moment judgment of how suitable terrain may be during 

a flight may be influenced by factors not considered fully 

in post hoc analyses. 

Also of concern in both Alaska and the rest of the 

U.S. was in-flight planning/decision making. After all, 

decisions made during flight are often more critical than 

those occurring on the ground. Thus, when confronted 

with important decisions in-flight, pilots are often under 

pressure to be right the first time with limited information. Scenario-based training along these lines as 

provided within the FAA-Industry Training Standards 

(FITS) program may improve decision-making in the 

cockpit, particularly if examples are drawn from the 

accident record. 

Of the unsafe acts that aircrew commit, addressing 

violations may be the most difficult and complex. Recall 

that violations are the “willful” disregard for the rules 

and, as such, are not necessarily something that can be 

easily deterred or mitigated. Nevertheless, since nearly 

half of violations involved fatalities, such behaviors as 

VFR flight into IMC are of great concern to the FAA 

and other aviation safety professionals. 

Even though the percentage of accidents associated 

with violations did not differ markedly between Alaska 

and the rest of the U.S., the specific types of violations did 

differ in meaningful ways. In particular, when intentional 

VFR flight into IMC and other adverse weather conditions were combined, an alarming 47% of the violations 

occurring in Alaska were accounted for (27% for the rest 

of the U.S.). Exactly why a larger proportion was observed 

in Alaska remains unknown, but one reason may be the 

rapid climatic changes that often occur, especially around 

mountainous areas.

So why would a VFR-only pilot fly into such hazardous conditions? This has perplexed safety professionals 

and aviation psychologists alike. At least one study suggests that pilots' overconfidence in their personal ability 

and need for goal achievement (too much was already 

invested in the trip to turn around or deviate from course) 

may explain this behavior (Goh & Wiegmann, 2002). 

Other research proposed certain factors that influence 

the pilot’s decision to press into the weather, specifically 

in Alaska, could be due to the lack of relevant information, ambiguous cues, time pressure, and risk perception, 

among others (Holbrook, Orasanu, & McCoy, 2003). 

Batt and O’Hare (2005) have proposed that the decision 

to fly into degraded conditions could depend on the stage 

of flight. They hypothesize that in the early part of the 

flight, pilots will weigh the alternatives of continuing the 

flight or turning around. Later in flight, pilots debate on 

whether to perform a precautionary landing (considering the loss and potential damage that can result) or to 

continue into weather and hope conditions improve, 

avoiding the potential loss. Regardless of the reasons, 

it is imperative that pilots be adequately informed and 

trained on the real dangers that they encounter when 

they continue or attempt VFR flight into hazardous 

weather conditions.

Current interventions, like weather cameras in mountain passes and other locations, have proved useful by 

providing pilots with access to real-time weather information and therefore allowing them to make informed 

decisions. In addition, the Medallion Foundation has 

provided GA pilots training using high-resolution flight 

simulators capable of producing simulated weather and 

lighting conditions over the Alaskan terrain. With this 

technology, pilots are able to safely navigate through 

Alaska and see what flying through places such as Merrill 

Pass in adverse weather conditions could entail, a difficult 

task even for a highly experienced pilot to successfully 

perform in clear conditions.

Alaska, as perhaps the FAA’s largest aviation laboratory, 

has been the testbed for advanced avionics like those associated with the Capstone project. Enhanced weather 

radar, global positioning sensors, Automated Dependent 

Surveillance – Broadcast (ADS-B), and other cutting-edge 

technologies provide a more accurate picture of how the 

weather, terrain, and traffic situations actually look from 

inside the cockpit. These technologies have proven useful 

with 14 CFR Part 135 (commuter) operations (Williams, 

Yost, Holland, & Tyler, 2002). However, their efficacy 

within GA remains to be seen. 

Although technology has led to a reduction in aviation 

accidents in Alaska, we cannot rely solely on it as the 

panacea for GA safety. Being a successful pilot requires 

basic “stick and rudder” skills. These are particularly 

important during the critical phases of flight (i.e., takeoff 

and landing). Similar to previous reports (AOPA, 2005), 

we found the largest percentage of accidents occurred 

during takeoff and landing. A larger proportion of these 

accidents occurred in Alaska than in the rest of the U.S. 

This is consistent with the observation that in Alaska decisions concerning takeoff and landing from unimproved 

terrain account for a significant proportion of accidents. 

Importantly, unlike violations, these types of decision 

errors typically have not resulted in fatalities (Wiegmann 

et al., 2005). However, this does not mean that they did 

not involve significant damage to the aircraft or have a 

significant economic impact. 
10

Conclusions

In recent years, a growing concern has been directed 

toward GA accident rates. Indeed the FAA Administrator 

has set a goal of a 20% reduction in GA accidents by fiscal year 2008. If this goal is to be realized, interventions 

that target the underlying human causes as identified in 

this analysis need to be developed. 

The next step in this research effort will be the development of the Human Factors Intervention Matrix (HFIX), 

which pits the unsafe acts of operators (i.e., skill-based 

errors, decision errors, perceptual errors, and violations) 

against several putative intervention approaches (e.g., 

organizational, human-centered, technology, task, and 

environment; Figure 16). In addition, other features will 

be integrated into the model/matrix such as feasibility, 

efficacy, and acceptance. 

Once developed, HFIX will be validated and assessed 

using intervention programs currently in use and planned 

within the Small Airplane Directorate (ACE-100), the 

General Aviation & Commercial Division (AFS-800), 

Alaska Region (AAL), and other FAA offices.

Ultimately, the systematic application of HFACS, 

coupled with the methodical utilization of HFIX (once 

fully developed) to generate intervention solutions, should 

ensure that the aviation industry’s personnel and monetary 

resources are utilized wisely. This should occur because 

such efforts will be needs-based and data-driven. Together, 

these tools will allow the true effectiveness of intervention 

programs to be objectively and impartially evaluated so 

that they can be either modified or reinforced to improve 

system performance. Only then can any great strides in 

improving the GA accident rate be achieved.

References

Aircraft Owners and Pilots Association (AOPA). (2005). 

Ups and downs of takeoffs and landings. Retrieved 

July 29, 2005, from www.aopa.org/asf/publications/sa18.pdf. 

Baker, S., Lamb, M., Grabowski, J., Rebok, G., & Li, G. 

(2001). Characteristics of general aviation crashes 

involving mature male and female pilots. Aviation, Space, and Environmental Medicine, 72(5), 

447-52.

Batt, R. & O’Hare, D. (2005). Pilot behaviors in the face 

of adverse weather: A new look at an old problem. 

Aviation, Space, and Environmental Medicine, 76(6), 

552-9.

Federal Aviation Administration. (n.d.) FAA Flight Plan 

2004-2008. Retrieved August 24, 2004, from www.

faa.gov/AboutFAA/FlightPlan.cfm.

Federal Aviation Administration. (n.d.) FAA-Industry 

Training Standards (FITS). Retrieved July 29, 

2005, from www.faa.gov/education_research/training/fits/.

Goh, J. & Wiegmann, D. (2002). Human factors analysis 

of accidents involving visual flight rules flight into 

adverse weather. Aviation, Space, and Environmental 

Medicine, 73(8), 817-22.

Holbrook, J., Orasanu, J., & McCoy, C.E. (2003). 

Weather-related decision making by aviators in 

Alaska. Proceedings of the 12th International Symposium on Aviation Psychology, The Ohio State 

University, 576-81. 

Li, G., Baker, S., Grabowski, J., & Rebok, G. (2001). 

Factors associated with pilot error in aviation 

crashes. Aviation, Space, and Environmental Medicine, 72(1), 52-8.

National Transportation Safety Board (2005). Table 10. 

Accidents, Fatalities, and Rates, 1985 through 2004, 

U.S. General Aviation. Retrieved July 12, 2005, 

from www.ntsb.gov/aviation/Table10.htm.

O’Hare, D., Wiggins, M., Batt, R. & Morrison, D. 

(1994). Cognitive failure analysis for aircraft accident investigation. Ergonomics, 37(11), 1855-69.

Reason, J. (1990). Human error. New York: Cambridge 

University Press.

Decision

Errors

Skill-based

Errors

Perceptual

Errors

Violations

Organizational/

Administrative

Human/

Crew

Technology/ 

Engineering

Task/

Mission

Operational/

Physical 

Environment

Figure 16. The Human Factors Intervention 

Matrix (HFIX). 
11

Shappell, S. & Wiegmann, D. (1997). Why would an 

experienced aviator fly a perfectly good aircraft 

into the ground? Proceedings of the 9th International 

Symposium on Aviation Psychology, The Ohio State 

University, 26-32.

Shappell, S. & Wiegmann, D. (2001). Beyond Reason: 

Defining the holes in the Swiss Cheese. Human 

Factors in Aviation Safety, 1(1), 59-86.

Shappell, S. & Wiegmann, D. (2003). A human error 

analysis of general aviation controlled flight into terrain accidents occurring between 1990-1998. (DOT/

FAA/AM-03/4). Washington, DC: Federal Aviation 

Administration, Office of Aerospace Medicine.

Urban R. (1984). Comparative analysis of social, demographic, and flight-related attributes between 

accident and nonaccident general aviation pilots. 

Aviation, Space, and Environmental Medicine, 55(4), 

308-12.

Wiegmann, D., Shappell, S., Boquet, A., Detwiler, C., 

Holcomb, K., & Faaborg, T. (2005). Human error 

and general aviation accidents: A comprehensive, 

fine-grained analysis using HFACS. (DOT/FAA/

AM-05/24). Washington, DC: Federal Aviation 

Administration, Office of Aerospace Medicine.

Wiegmann, D.A. & Shappell, S.A. (1997). Human factors 

analysis of post-accident data: Applying theoretical taxonomies of human error. The International 

Journal of Aviation Psychology, 7(1), 67-81.

Wiegmann, D.A. & Shappell, S.A. (2001a). Human 

error perspectives in aviation. Proceedings of the 

9th International Journal of Aviation Psychology, 

11, 341-57.

Wiegmann, D.A. & Shappell, S.A. (2001b). A human 

error analysis of commercial aviation accidents using 

the human factors analysis and classification system 

(HFACS). (DOT/FAA/AM-01/3). Washington, 

DC: Federal Aviation Administration, Office of 

Aviation Medicine.

Wiegmann, D. & Shappell, S. (2003). A human error 

approach to aviation accident analysis: The human 

factors analysis and classification system. Ashgate 

Publishing Company: Aldershot, Great Britain.

Williams, K., Yost, A., Holland, J. & Tyler, R. (2002). 

Assessment of advanced cockpit displays for general aviation aircraft – The Capstone Program. (DOT/FAA/

AM-02/21). Washington, DC: Federal Aviation 

Administration, Office of Aerospace Medicine.