Richard Leland1

Rodney O. Rogers2

Albert Boquet2

Scott Glaser1

1

Environmental Tectonics Corporation

Southampton, PA 18966

2

Embry-Riddle Aeronautical University

Daytona Beach, FL 32114

September 2009

Final Report

An Experiment to Evaluate 

Transfer of Upset-Recovery Training 

Conducted Using Two Different 

Flight Simulation Devices

DOT/FAA/AM-09/17

Office of Aerospace Medicine

Washington, DC 20591

OK-09-0434-JAH

Federal Aviation

Administration
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
i

Technical Report Documentation Page 

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

DOT/FAA/AM-09/17 

4. Title and Subtitle 5. Report Date 

An Experiment to Evaluate Transfer of Upset-Recovery Training September 2009 

Conducted Using Two Different Flight Simulation Devices 6. Performing Organization Code 

 

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

Leland R,1 Rogers RO,2 Boquet A,2 Glaser S1 

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

 

11. Contract or Grant No. 

1

Environmental Tectonics Corporation 

Southampton, PA 18966 

2

Embry-Riddle Aeronautical 

University 

Daytona Beach, FL 32114 

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 

 

16. Abstract 

Air transport training programs provide simulator-based upset-recovery instruction for company pilots. 

However, no prior research demonstrates that such training transfers to an airplane in flight. We report on an 

FAA-funded research experiment to evaluate upset-recovery training transfer. Two groups of participants were 

given simulator-based training in upset-recovery, one in a high-end centrifuge-based device, the other using 

Microsoft Flight Simulator running on desktop computers. A third control group received no upset-recovery 

training at all. All three groups were then subjected to serious in-flight upsets in an aerobatic airplane. Pilots from 

both trained groups significantly outperformed control group pilots in upset-recovery maneuvering. However, 

performance differences between pilots from the two trained groups were less distinct. Moreover, pilot 

performance in both trained groups fell well short of the performance exhibited by pilots experienced in all 

attitude flight. Although we conducted flight testing in a general aviation airplane, our research has important 

implications for heavy aircraft upset-recovery trainers. 

 

17. Key Words 18. Distribution Statement 

 

 

Airplane Upset-Recovery Training; Flight Simulation; 

Training Transfer; Centrifuge-Based Flight Simulator; 

Microsoft Flight Simulator

Document is available to the public through the 

Defense Technical Information Center, Ft. Belvoir, 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 18 

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

iii

Contents

1. INTRODUCTION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2. Prior Research. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

 2.1 Calspan-Related Research. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

 2.2 Centrifuge-Based Flight Simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

 2.3 Human Factors Considerations in Upset-Recovery Training. . . . . . . . . . . . . . . . . . . . . . . . . . . 2

 2.4 Low-Cost Simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

3. The Research Experiment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

 3.1 The ERAU Research Experiment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

 3.2 The ETC Expanded Research Experiment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

4. Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

 4.1 Data Collected. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

 4.2 Data Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

 4.2.1 A Priori Analyses on Altitude Loss. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

 4.2.2 Multivariate Analyses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

 4.3 Implications of Univariate and Multivariate Analyses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

5. Concluding Remarks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

 5.1 Proposed Improvements to Training Syllabus. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

 5.2 Limitations of Ground-Based Simulators for Upset-Recovery Training. . . . . . . . . . . . . . . . . . 11

6. Endnotes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

7. References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1

An Experiment to Evaluate Transfer of Upset-Recovery Training

Conducted Using Two Different Flight Simulation Devices

1. INTRODUCTION

An upset occurs when an airplane enters an unexpected attitude that threatens loss of control (LOC) and 

subsequent ground impact. For the years 1998 - 2007 

inclusive, LOC was the leading cause of hull losses and 

passenger fatalities in worldwide air transport operations, 

causing almost 25% of all crashes and nearly 40% of all 

fatalities.1

 During the years 1991 - 2000, statistics for 

general aviation (GA) accidents in the United States are 

similar, while in Australia, LOC accounted for an even 

greater proportion of GA accidents and fatalities.2

Since LOC threatens passengers and flight crews as well 

as potential victims on the ground, air transport training 

programs typically contain a module instructing pilots 

how to recover an airplane from an upset. However, the 

effectiveness of such simulator-based training remains 

uncertain. Rogers et al. (2009) report significant training 

transfer using low-cost simulation to teach upset-recovery 

maneuvering to general aviation pilots with no prior 

aerobatic experience. Participant pilots were trained using 

Microsoft Flight Simulator running on low-cost desktop 

computers, then tested in an aerobatic Super Decathlon 

airplane.3

 Our research addresses the question of whether 

use of a more sophisticated fight-simulation device—the 

Environmental Tectonics Corporation GyroLab-2000 

(GL2000) centrifugal flight simulator—might significantly improve training transfer. In what follows, we:

•	Report on relevant prior research.

•	Describe our research experiment.

•	Present and interpret experimental results.

•	Provide concluding remarks.

2. Prior Research4

We have found only a few research articles related to 

the transfer of simulator-based upset-recovery training. 

Several reports result from research at the Calspan In-Flight 

Upset-Recovery Training Program in Roswell, NM.

5 A

second set of articles focuses on centrifuge-based flight 

simulators manufactured by the Environmental Tectonics Corporation. A third group discusses human factors 

considerations in upset-recovery training. Finally, we are 

aware of just one article related to training transfer when 

upset maneuvering is taught using low-cost simulation.

2.1 Calspan-Related Research

Calspan provides in-flight simulator-based upsetrecovery training in a variable stability Learjet 25 modified 

to simulate the control characteristics of an air transport 

airplane. The Calspan Lear can simulate various accident 

scenarios that, in the past, have resulted in air transport 

upsets leading to uncontrolled crashes.

Gawron used Calspan’s Learjet to test five groups 

of airline pilots with varying degrees of upset-recovery 

training and/or aerobatics experience on a series of eight 

upsets, hypothesizing that pilots with more training 

and/or experience would outperform those with less. 

However, she found no significant difference among the 

performances of the five groups.6

Kochan used the Calspan Lear to examine the roles 

of domain knowledge and judgment in upset-recovery 

proficiency. Domain knowledge is specific knowledge 

about upset-recovery procedures. Judgment is the ability to analyze and learn from an in-flight upset-recovery 

experience. She tested four groups of participants on a 

series of three in-flight upsets. Statistical analyses revealed 

that judgment was a significant factor in successful 

upset-recovery, especially when a pilot has low domain 

knowledge, i.e., when a pilot is not trained to proficiency 

in upset-recovery.7

Kochan and Priest studied the effect of upset-recovery 

training in the Lear. They measured pre- and post-training 

pilot performance in recovering from a series of upsets. 

Statistical analysis indicated “a strong positive influence 

of the [Calspan program] on a pilot’s ability to respond 

to an in-flight upset.”8

Kochan, Breiter, Hilscher, and Priest surveyed retention of knowledge in Calspan trained pilots. Although 

participants in retrospect “rated their ability to recover 

from loss-of-control situations as being greatly improved 

by the training,” most were unable to recall various specific 

details about upset-recovery maneuvering taught during 

their training.9

2.2 Centrifuge-Based Flight Simulation

The Calspan Learjet in-flight simulator allows pilots 

to experience upset maneuvering G forces that very few 

ground-based flight simulators can replicate. The Environmental Tectonics Corporation (ETC) of Southampton, Pennsylvania manufactures centrifuge-based flight 

simulators capable of generating continuous G forces. 
2

Such simulators bring to ground-based upset-recovery 

training a degree of realism unachievable even in Level 

D simulators. Three ETC proprietary technical reports 

(available from Dick Leland at dletc@aol.com) detail 

the capabilities of the company’s current generation of 

centrifugal simulators.10,11,12 One drawback of such simulators, however, is that “if a pilot moves his head while 

under G in a centrifuge, strong feelings of disorientation 

(the Coriolis illusion) result because of the small rotation 

radius needed to create the G forces artificially.”13,14 In a 

related article on motion-based flight simulation, Szczepanski and Leland argue that “simulator data analysis 

suggests that motion cueing is necessary when training 

ab initio pilots or pilots who have limited or no experience in the particular flying task that is being trained.”15

2.3 Human Factors Considerations in UpsetRecovery Training

A number of papers examine the “surprise” or “startle” 

factor in aviation, an effect that can hinder a pilot’s ability 

to respond appropriately to an emergency situation such 

as an upset. Kochan, Breiter, and Jentsch (2004) found 

pilots often miss cues that might lead to avoiding an 

emergency that later arrives as a surprise.16 In a follow-on 

paper, the same researchers develop “a conceptual framework for the study of unexpected events in aviation.”17

Kochan, Priest, and Moskal use a model for the “cognitive process of surprise”18 to study “how an unexpected 

event can escalate to a loss-of-control situation.” They 

conclude that in-flight [as opposed to ground-based] 

simulator training may be necessary to teach pilots to 

deal adequately with their perceptual biases in processing 

information during a surprise upset.19,20 In a related paper, 

Kochan argues that a pilot’s response to unexpected events 

can be improved through cognitive flexibility training (to 

discourage formulaic and encourage flexible responses to 

surprise events), adaptive expertise training (to reinforce 

modified or new responses to surprise based on responses 

learned in previous expert training), and metacognitive 

training (to teach pilots how to evaluate their mental 

processes in responding to surprise).21

2.4 Low-Cost Simulation

Roessingh studied training transfer from low-fidelity 

ground-based flight simulators to control of an actual 

airplane during aerobatic flight. Two experimental 

groups received ground-based instruction in aerobatic 

maneuvering using desktop flight simulators. The simulator syllabus was the same for both groups, but one 

experimental group’s simulator training was enhanced 

with a more “realistic layout of stick, rudder pedals, and 

throttle.” Then the two trained groups and a control 

group received five hours of in-flight aerobatic training. 

Data collected during subsequent testing revealed no 

significant difference in the aerobatic maneuvering of 

trained and control group pilots. 22

3. The Research Experiment

Our research experiment involves a partnership 

between Environmental Tectonics and Embry Riddle 

Aeronautical University (ERAU) in Daytona Beach, 

Florida. It continues an upset-recovery training transfer 

experiment performed at ERAU in Spring 2008. In 

this section we summarize the ERAU experiment, then 

describe our own research experiment and explain how 

its design expands the results of the original experiment.

3.1 The ERAU Research Experiment

Rogers et al. (2009) report the results of an experiment to test the hypothesis that simulator-trained pilots 

will outperform untrained pilots in recovering an actual 

airplane from a serious upset. The researchers trained a 

group of participant pilots in upset-recovery maneuvering 

using Microsoft Flight Simulator (MFS). Participants in 

the experiment were student pilots at ERAU Daytona 

Beach. All held a current instrument rating and had 

completed an academic course in basic aerodynamics 

for pilots. None had prior aerobatic experience or upsetrecovery training beyond that required for FAA flight 

certificates and ratings.

As reflected in Table 1, the ERAU experiment is a 2 

x 4 repeated measures factorial. The degree of training 

Table 1. The 2 x 4 Factorial Design 

Upset Attitude (Repeated Measure) 

2 x 4 Factorial Nose-High 

Upright 

Nose-Low 

Upright 

Nose-High 

Inverted 

Nose-Low 

Inverted 

10 Hours Classroom / Simulator 

(Trained Group) Trained pilots Trained pilots Trained pilots Trained pilots 

Training 

None (Control Group) Untrained pilots Untrained pilots Untrained pilots Untrained pilots
3

independent variable has two levels—trained and 

untrained. Trained participants received ten hours of 

classroom and ten hours of MFS upset-recovery training. 

Untrained participants—control group pilots—received 

no classroom or simulator training. The upset attitude 

independent variable has four levels corresponding to the 

four upsets each participant is subjected to during flight 

testing. Upset attitudes were categorized as nose-high or 

nose-low and as upright or inverted. An inverted attitude 

is one where the bank angle exceeds 90o

. Table 2 presents 

the upset attitudes used in flight testing.

Research participants trained in aerobatic and upsetrecovery maneuvering on MFS were flight tested in an 

Embry-Riddle aerobatic Super Decathlon airplane, as 

were control group participants. Participant pilots closed 

their eyes while the Decathlon safety pilot induced an 

upset, then—when instructed to do so—opened their 

eyes and attempted to bring the airplane under control. 

If a participant pilot returned the aircraft to straight and 

level flight without verbal or physical assistance from 

the safety pilot, the recovery was deemed successful; 

otherwise it was unsuccessful. A good recovery was one 

where a pilot respected aircraft operating limitations while 

returning the aircraft to straight and level flight with the 

minimum possible loss of altitude. Minimum altitude 

loss results from promptly applying full thrust in nose 

high and idle thrust in nose-low upsets; rolling without 

delay toward a wings-level upright attitude; unloading 

during inverted rolls or when airspeed is below VS

; using 

high G forces in dive pullouts; and recovering quickly. 

Dependent variables—shown in Table 3—were chosen 

to measure these factors.

Data were collected using a battery-operated video 

camera focused on the Decathlon’s instrument panel. A

high resolution palm-size video recorder captured cockpit 

voice communications and the camera’s output (Figure 

1). An installed battery operated Appareo GAU-1000 

flight data recording system proved erratic in aerobatic 

attitudes. Although the unit records aircraft position, 

altitude, airspeed, attitude (pitch and bank), G forces (x, 

y, and z), and yaw angle (ß) at 3-4 Hz, only the G force 

reading was reliable.

During Decathlon flight testing, data were collected for 

25 trained and 26 control participants. Six trained pilots 

and eight control group pilots experienced unsuccessful 

recoveries during the nose-low inverted upset as a result of 

threatening the Decathlon’s redline speed. Data for these 

recoveries were discarded. One-way MANOVAs were used 

to compare trained and control group participant performance for each of the four upsets.23 The difference between 

trained and control group participants was significant in 

all four cases. Table 4 reports samples sizes and Wilks’ 

Lambda values for each of the four MANOVAs. For each 

of the dependent variables, subsequent univariate t-tests 

with the Bonferroni adjustment were conducted. In Table 

5, an X indicates where univariate analysis revealed that 

trained pilots significantly outperformed control group 

pilots on the dependent measure and upset indicated.24

Statistical analysis confirmed that low-cost simulatorbased upset-recovery training improves pilot performance 

in recovering an airplane from a serious upset. Trained 

pilot performance exceeded untrained pilot performance 

in 16 of 23 dependent measure categories, or 69.6% 

of the time. Trained participants lost less altitude than 

control group pilots in all four upsets, and two of the 

four altitude differences were statistically significant. In 

general, trained pilots initiated rolls toward a wings level 

upright attitude sooner and applied more Gs in dive 

pullouts than untrained pilots, both critical factors in 

minimizing altitude loss. In addition, trained pilots also 

applied throttle more promptly than untrained pilots. 

These differences, in turn, resulted in a quicker return 

to straight-and-level flight.

At the same time, research results suggest the limitations of low-cost ground-based simulator training in 

Table 3. Dependent Variables 

Dependent Measure

Recovery Altitude Loss in Feet

Time to First Throttle Response in Seconds

Time to First Roll Response in Seconds

Minimum G Force Unloading During Rolls † 

Maximum G Force in Dive Pullout

Time to Recover in Seconds

† Not applicable to the nose-low upright upset, since trained pilots 

were taught to use rolling pullouts during dive recovery. 

Table 2. Levels of the Upset Attitude Independent Variable † 

Upset Pitch Bank Airspeed Thrust

Nose-high Upright 60o Nose-high 45o Left Wing Down 65 MPH Idle 

Nose-low Upright 45o Nose-low 70o Right Wing Down 130 MPH Full 

Nose-high Inverted 60o Nose-high 180o (Inverted, Wings Level) 65 MPH Idle 

Nose-low Inverted 20o Nose-low 180o (Inverted, Wings Level) 110 MPH Full 

† Nose-high airspeeds were set 12 mph above the Decathlon 1G stall speed (Vs=53mph); nose-low airspeeds were 

set at a maximum safe value considering pitch and roll angles and the Decathlon redline speed (Vne=200mph). 
4

Table 4. Multivariate Wilks’ Lambda Values and Group Sizes for Each Upset (Bolding

= Significant Difference) 

Upset Nose-Low 

Upright 

Nose-High 

Upright 

Nose-Low 

Inverted 

Nose-High 

Inverted 

Trained Group Size n=25 n=25 n=19 n=24 

Control Group Size n=26 n=26 n=17 n=26 

Combined Group Size n=51 n=51 n=36 n=50 

Wilks Lambda Value 

F (5,45) =9.59

p = .0001 

η2 = .0.52 

F(6,44) = 4.47

p = .001 

η2 = 0.38 

F (6,29) =9.11

p = .0001 

η2 = 0.653 

F (6,43) =10.26 

p = .0001 

η2 = 0.60 

Figure 1. Sample Decathlon Video Recorder Output 

Table 5. Significant Performance Differences (X) between MFS-Trained and Control Group 

Participants as Determined by 2-Group Multivariate Analyses with Follow-Up Protected 

Univariate Tests 

Upset Dependent 

Measure Nose-Low 

Upright 

Nose-High 

Upright 

Nose-Low 

Inverted 

Nose-High 

Inverted 

Altitude Loss X X 

Minimum Unload G in Rolls Not Applicable

Maximum G Load in Dive Pullout X X X X 

Seconds to First Throttle X X X 

Seconds to First Roll X X X X 

Seconds to Recover X X X 
5

developing upset-recovery maneuvering skills. There 

was no significant difference in altitude loss between 

trained and control group pilots in both nose-high 

upsets. Moreover, even when trained pilots significantly 

lost less altitude than control group pilots, there was a 

large disparity between research participant altitude losses 

and the far smaller altitude losses achievable by pilots 

experienced in all-attitude maneuvering. We return to 

this subject in Section 5.2 below.

3.2 The ETC Expanded Research Experiment

Our experiment expands the original research design 

described above by adding one level to the Degree of 

Training independent variable. It is designed to determine the added value of upset training in a motion 

based flight simulator capable of generating continuous 

G-forces.

Twenty-five Embry-Riddle flight students received 

the same ten hours of classroom training that trained 

participants received in the original ERAU experiment. 

They also received five hours of MFS training (aerobatic maneuvering only), then traveled in small groups 

to Pennsylvania to receive five hours of upset-recovery 

training in Environmental Tectonics’ proprietary GL2000 

flight simulator. The GL2000 was modified to give it the 

flight characteristics of a Super Decathlon airplane and to 

make its basic flight instruments replicate the layout on a 

Decathlon’s instrument panel. Figure 2 presents external 

and interior views of the GL2000 simulator with the 

cockpit modified to replicate a Super Decathlon cockpit.

Upon completion of their classroom and simulator 

training, the 25 research participants were subjected to 

Super Decathlon flight testing identical to that received 

earlier by MFS-trained and control group participants. 

Figure 2. Exterior and Interior Views of the GL2000 Simulator 
6

Performances of the three groups were then compared. We 

hypothesized that pilots trained in the GL2000 simulator would outperform MFS-trained and untrained pilots 

in upset-recovery maneuvering. Table 6 reflects the fact 

that our experiment is a 3x4 repeated measures factorial.

4. Results

4.1 Data Collected

As in previous flight testing sessions, various factors including airsickness and equipment malfunction 

precluded our obtaining complete data sets for every 

GL2000-trained pilot.25 Table 7 reports the total number 

of participants for whom we obtained usable data for each 

of the four upsets. Table 8 presents mean and standard 

deviation values for data collected during flight testing. 

Data for MFS-trained and control group participants 

were collected in March 2008, data for GL2000-trained 

participants in November 2008.

4.2 Data Analysis

Execution of the experiment resulted in one deviation 

from the experimental design. Motion sickness and other 

logistic difficulties resulted in participant pilots receiving 

only 2.5 hours (on average) of five scheduled GL2000 

training hours. To compensate, participants were allowed 

to observe the training sessions of other participants. An 

unavoidable execution drawback was that the ERAU 

class schedules required GL2000 training to take place 

during intensive two-day weekends. Five participants 

were trained each weekend for five successive weekends. 

This training schedule resulted in some GL2000 participants completing training as much as four weeks prior 

to flight testing. By contrast, MFS-trained participants 

received one hour of simulator training per week over a 

span of ten weeks and were tested in a two-week period 

immediately following the conclusion of their training.

4.2.1 A Priori Analyses on Altitude Loss

MANOVAs are the conventional choice for analyzing 

our data. Nevertheless, we first conducted a priori t-tests to 

determine altitude loss differences between pairs of groups 

for each upset. A number of factors justified this decision:

1. Altitude loss is the most important of the six 

dependent variables. As explained in Section 2.1, 

performance on the other five dependant measures 

directly influences how much altitude is lost.

2. Other variables affect altitude loss but could not be 

included in our experimental measurements:

•	Roll and pitch rates

•	Magnitude of elevator, aileron, and rudder inputs, 

especially rudder input to roll at low airspeeds

•	Use of available G in dive pullout

•	Promptness and uniformity of appropriate G input

Data for these measures would improve our ability 

to discriminate between trained and untrained pilot 

Table 7. Group Sizes for the Three Participant Groups 

Upset Nose-Low 

Upright 

Nose-High 

Upright 

Nose-Low 

Inverted 

Nose-High 

Inverted 

GL2000-Trained Group Size n=22 n=23 n=21 n=22 

MFS-Trained Group Size n=25 n=25 n=19 n=24 

Control Group Size n=26 n=26 n=17 n=26 

Combined Group Size n=73 n=74 n=57 n=72 

Table 6. The 3 x 4 Repeated Measures Factorial Design of the Expanded Research 

Experiment 

Upset Attitude (Repeated Measure) 3 x 4 Factorial Nose-High 

Upright 

Nose-Low 

Upright 

Nose-High 

Inverted 

Nose-Low 

Inverted 

10 Hours Classroom / 5 Hours 

MFS +5 Hours GyroLab-2000 

GL2000/MFSTrained PilotsGL2000/MFSTrained Pilots GL2000/MFSTrained Pilots GL2000/MFSTrained Pilots 

10 Hours Classroom / 10 Hours 

MFS Training 

MFS-Trained 

Pilots 

MFS-Trained 

Pilots 

MFS-Trained 

Pilots 

MFS-Trained 

Pilots 

Training 

None 

Control Group Pilots Untrained Pilots Untrained Pilots Untrained Pilots Untrained Pilots
7

Table 8. Mean and Standard Deviation Values for Each Upset and Dependent Variable 

Nose-Low Upright Nose-High Upright Upset GL2000 MFS Control GL2000 MFS Control 

Altitude Loss in Feet 600.00 

181.29

565.20 

75.28

728.46 

169.51

213.04 

157.87

331.20 

225.56

340.38 

184.75

Min Unload G in Rolls Not Applicable 0.10 

0.06

0.00 

0.12

-0.04 

0.15

Max G in Dive Pullout 3.78 

0.57

3.70 

0.64

2.90 

0.49

1.87 

0.42

2.41 

0.90

1.82 

0.30

Seconds to First Throttle 2.45 

1.68

3.0 

1.66

5.19 

2.43

1.83 

2.01

2.12 

1.62

3.27 

2.97

Seconds to First Roll 1.32 

0.57

1.28 

.46

1.85 

.68

1.91 

0.90

2.28 

.89

3.15 

1.38

Seconds to Recover 5.27 

1.24

5.40 

1.38

7.04 

1.64

10.26 

1.57

11.16 

1.43

12.88 

2.98

Nose-Low Inverted Nose-High Inverted Upset GL2000 MFS Control GL2000 MFS Control 

Altitude Loss in Feet 884.76 

179.41

948.95 

167.03

1069.41 

139.08

368.18 

169.19

382.08 

200.65

464.62 

169.59

Min Unload G in Rolls 0.57 

0.90

0.99 

0.86

1.41 

.63

-0.39 

0.18

-0.47 

.28

-0.43 

.26

Max G in Dive Pullout 4.42 

0.57

4.74 

0.62

3.98 

0.50

2.65 

0.49

2.90 

0.84

2.34 

0.45

Seconds to First Throttle 2.90 

2.10

2.79 

1.78

4.41 

2.81

2.14 

4.02

1.48 

.68

3.31 

3.21

Seconds to First Roll 2.48 

1.78

1.68 

.67

4.88 

3.30

4.41 

2.48

3.04 

1.30

6.15 

2.98

Seconds to Recover 7.05 

0.97

7.11 

1.29

7.88 

.99

12.00 

2.91

13.33 

1.74

15.23 

2.27

performance. However, we lacked funding to purchase 

and install a sophisticated flight data recorder in our 

test aircraft. Moreover, Embry-Riddle policy precludes 

the use of invasive instrumentation in a university 

airplane used for flight training.

3. High G force applied during dive pullout from a 

nose-high recovery is not invariably a reliable indication of superior performance. In an optimal nose-high 

upset-recovery in the Decathlon, the pilot immediately 

sets full throttle and unloads the airplane, then applies 

full rudder and aileron to roll the airplane toward an 

upright wings-level attitude. As the airplane continues 

to climb, airspeed decreases toward zero rapidly. The 

airplane remains unloaded as the nose falls slowly 

through the horizon and airspeed begins to increase. At 

15o

-20o nose-low, the wings are level and airspeed has 

increased to 65-70 mph. At this point the pilot applies 

available G in the range of 1.25-1.5 to raise the nose 

gently to the horizon. Altitude loss is typically 100-200 

feet; in some cases, an altitude gain actually results.

High G force during the low-speed shallow dive 

recovery is neither required nor available. However, 

novice pilots often apply inappropriate control inputs, 

resulting in a steep nose-down attitude and high airspeeds. At this point, a large altitude loss is inevitable, 

and high G is available and required to avoid losing 

additional altitude. GL2000-trained pilots were more 

successful on average than MFS-trained pilots or control group pilots in avoiding a steep nose-down, high 

speed dive recovery in nose-high upsets. This success 

is reflected in a statistically significant smaller altitude 

loss paired with a lower pullout G for the nose-high 

upright recovery.

4. There is considerable “noise” (high variability) in 

the data we collected. High standard deviations, of 

course, decrease the power of MANOVA and ANOVA

analyses to detect significant differences among the 

three groups.

Table10 presents the results of pairwise t-tests to determine significant altitude loss differences between every two 

groups—GL2000-Trained vs. Control, GL2000-Trained 

vs. MFS-Trained, and MFS-Trained vs. Control. GL2000-

trained participants lost significantly less altitude than 

MFS-trained participants in the nose-high upright upset. 

Otherwise the two trained groups performed equally in 
8

altitude loss, exceeding control group performance in both 

nose-low upsets. For ease of understanding the information in Table 10, Table 9 recapitulates altitude loss and 

standard deviation by group for each of the four upsets.

4.2.2 Multivariate Analyses

We also conducted MANOVAs for each of the four 

upsets. The results reflect a significant difference between 

the three groups at p = 0.000 for all four upsets. Table 11 

reports the Wilks Lambda, Alpha, and Partial Eta-squared 

values for each of the four analyses. The remarkably 

consistent Eta-squared values indicate that about onethird (30%, 33%, 41%, 33%) of the variance detected 

by the MANOVAS is accounted for by the model; i.e., 

approximately one-third of the performance difference in 

each upset stems from differences in average performances 

among the three groups, while the remaining two-thirds 

results from differences in individual performances within

each of the three groups.

Since the MANOVAs revealed a significant difference 

between the three groups for all four upsets, we conducted 

univariate analyses to assess the contribution of each of the 

dependent variables to statistical differences detected by the 

MANOVAs. The results of these ANOVAs are presented in 

Table 12, where bolding indicates a significant difference 

between the three groups for the upset and dependent 

variable indicated.

Whenever an ANOVA value in Table 12 indicated 

that a dependent variable contributed to a statistically 

significant difference between the groups, we conducted 

protected pairwise comparisons—GL2000-Trained vs. 

Control; MFS-Trained vs. Control; GL2000-Trained 

vs. MFS-Trained—using the Bonferroni adjustment to 

ascertain the nature of the difference. Table 13 presents 

the results of these pairwise tests. It reflects the fact that 

GL2000-trained participants and MFS-trained participants 

significantly outperformed control group participants 

in many areas. However, the only significant differences 

between GL2000- and MFS-trained participants occurred 

in the nose-high upright upset, where MFS-trained pilots 

unloaded more efficiently and applied more Gs in the 

dive pullout.

4.3 Implications of Univariate and 

Multivariate Analyses

We hypothesized that GL2000-trained pilots would 

outperform control group pilots. This hypothesis was 

confirmed. Univariate and multivariate statistical analysis 

of the data in Table 8 indicates that the overall performance of GL2000-trained pilots and MFS-trained pilots 

exceeded the performance of untrained control group 

pilots.26 Our results suggest that classroom and simulator 

training improves a pilot’s ability to maneuver an airplane 

out of serious upset.

We also hypothesized that GL2000-trained pilots would 

outperform MFS-trained pilots. This hypothesis was not 

confirmed. Although a priori univariate analyses reveal 

GL2000-trained pilots were superior to MFS-trained pilots 

in altitude loss on the nose-high upright upset, considering both statistical analyses one must conclude that the 

differences between the performances of GL2000-trained 

and MFS-trained pilots were minimal.

Table 9. Altitude Loss and Standard Deviation by Group for Each of Four Upsets 

Nose-Low Upright Nose-High Upright Upset GL2000 MFS Control GL2000 MFS Control 

Altitude Loss in Feet 600.00 

181.29 

565.20 

75.28

728.46 

169.51

213.04 

157.87 

331.20 

225.56

340.38 

184.75

Nose-Low Inverted Nose-High Inverted Upset GL2000 MFS Control GL2000 MFS Control 

Altitude Loss in Feet 884.76 

179.41

948.95 

167.03

1069.41 

139.08

368.18 

169.19

382.08 

200.65

464.62 

169.59

Table10. Statistically Significant Differences in Altitude Loss Between Paired Groups, as 

Determined by A Priori Univariate Analysis 

Dependent Upset 

Measure Nose-Low Upright Nose-High Upright Nose-Low Inverted Nose-High Inverted

Altitude 

Loss 

GL2000 < Control 

t(1,70) = 2.985 

p = 0.004 

MFS < Control 

t(1,70) = -3.924 

p = 0.000 

GL2000 < Control 

t(1,71) = 2.315 

p = 0.024 

GL2000 < MFS 

t(1,71) = 2.128 

(p = 0.037 

GL2000 < Control 

t(1,54) =3.447 

p = 0.001 

MFS < Control 

t(1,54) = -2.198 

p = 0.032 
9

Table 11. Group Sizes, Wilks Lambda, Alpha, and Partial Eta-Squared Values for Each of the 

Four Upsets 

Upset Nose-Low 

Upright 

Nose-High 

Upright 

Nose-Low 

Inverted 

Nose-High 

Inverted 

Wilks Lambda Value 

F(10,132) = 5.67

p = 0.000 

η2 = 0.300 

F(12,132) = 5.308

p = 0.000 

η2 = 0.325 

F (12,98) = 5.718 

p = 0.000 

η2 = 0.412 

F (12,128) =5.211

p = 0.000 

η2 = 0.328 

Table 12. Univariate Test F and Alpha Values for Each of the Four Upsets (Bold = 

Significant Difference) 

Dependent Upset 

Measure Nose-Low UprightNose-High Upright Nose-Low Inverted Nose-High Inverted

Altitude 

Loss 

F(2,70)=8.515 

p = 0.000 

F(2,71) = 3.252 

p = 0.045 

F(2,54) = 6.022 

p = 0.004 

F(2,69) = 2.063 

p = 0.135 

Minimum Unload 

G in Rolls Not Applicable F(2,71) = 10.053 p = 0.000 

F(2,54) = 4.931 

p = 0.011 

F(2,69) = 0.607 

p = 0.548 

Maximum G Load 

in Dive Pullout 

F(2,70) = 18.147 

p = 0.000 

F(2,71) = 7.404 

p = 0.001 

F(2,54) = 7.959 

p = 0.001 

F(2,69) = 5.216 

p = 0.008 

Seconds to 

First Throttle 

F(2,70) = 13.280 

p = 0.000 

F(2,71) = 2.769 

p = 0.069 

F(2,54) = 2.911 

p = 0.063 

F(2,69) = 2.447 

p = 0.094 

Seconds to 

First Roll 

F(2,70) = 7.618 

p = 0.001 

F(2,71) = 8.509 

p = 0.000 

F(2, 54) = 10.863 

p = 0.000 

F(2,69) = 10.787 

p = 0.000 

Seconds to 

Recover 

F(2,70) = 11.670 

p = 0.000 

F(2,71) = 9.574 

p = 0.000 

F(2, 54) = 3.269 

p = 0.046 

F(2,69) = 11.669 

p = 0.000 

Table 13. Statistically Significant Differences Between Paired Groups for All Dependent Variables

Upset Dependent 

Measure Nose-Low 

Upright 

Nose-High 

Upright 

Nose-Low Upright Nose-High Inverted

Altitude 

Loss 

GL2000 < Control 

MFS < Control GL2000 < Control 

Minimum Unload 

G in Rolls Not Applicable MFS < GL2000 

Control < GL2000 GL2000 < Control 

Maximum G 

Load 

in Dive Pullout 

GL2000 > Control 

MFS > Control 

MFS > GL2000 †

MFS > Control MFS > Control MFS > Control 

Seconds to 

First Throttle 

GL2000 < Control 

MFS < Control 

Seconds to 

First Roll 

GL2000 < Control 

MFS < Control 

GL2000 < Control

MFS < Control 

GL2000 < Control 

MFS < Control 

GL2000 < Control 

MFS < Control 

Seconds to 

Recover 

GL2000 < Control 

MFS < Control 

GL2000 < Control

MFS < Control 

GL2000 < Control 

MFS < Control 

† As explained earlier, the finding that MFS-trained pilots pulled significantly more Gs than GL2000-trained pilots 

while recovering from the nose-high upright upset does not necessarily indicate superior performance. 
10

5. Concluding Remarks

5.1 Proposed Improvements to Training Syllabus

Rogers et al. (2007) report training shortcomings in 

the use of MFS as an upset-recovery trainer, stressing in 

particular the importance of teaching aircraft-specific 

maneuvering techniques to minimize altitude loss, and 

conjecturing that improved use of the simulator would 

result in better flight testing results. The conjecture was 

verified in Rogers et al. (2009), where a second group of 

MFS-trained pilots performed much better than the first 

MFS-trained group after improved training procedures 

were implemented.

Similarly, we believe that if we conducted our experiment 

a second time with simulator training modified by what 

we learned during the research, flight testing results might 

reflect a much stronger performance by GL2000-trained 

pilots.27 We had a limited amount of time in which to 

conduct the GL2000 training—two days per group—and 

during the first day, GL2000 training time was limited 

when participants experienced varying degrees of motion 

sickness and reached a point where further training was 

ineffective. Nevertheless, participants adapted quickly to 

GYROLAB motion, and we were able to conduct motion training more aggressively on the second day. In the 

course of the research, we modified our GL2000 training 

approach slightly to provide both no-motion and motion 

time during the first day. The no-motion time was used to 

teach certain “rote” skills that are not motion critical. The 

limited motion time was used to teach the motion critical 

skills while simultaneously allowing participants to adapt 

gradually to simulator motion.

It appears that rote responses in upset-recovery maneuvering can be taught as effectively with MFS as with 

the motion based GL2000 simulator. The first of these 

responses involves using visual cues to determine pitch 

and bank angles during an upset. As shown in Figure 3, 

MFS, as implemented at ERAU, provides three simultaneous views outside the cockpit: 90o

 left, forward, and 

90o

 right. The forward view is used to categorize an upset 

as nose-high or nose-low, while the two side views allow 

a pilot to determine bank angle, including whether the 

aircraft is upright or inverted. The second rote response is 

proper use of throttle—application of full thrust in nosehigh upsets and reduction of throttle to idle in nose-low 

upsets. It is possible that participant pilots might have been 

better prepared for GL2000 training had they previously 

practiced these rote responses on MFS or received more 

GL2000 no-motion practice. Repetition is a powerful 

learning reinforcement for such responses.

Figure 3. Screen Captures of MFS Windows Configuration for Upset-recovery Training 
11

Table 14. Average and Observed Minimum Altitude Losses for Each of the Four Upsets 

Altitude Loss in Feet 

Data Source Nose-Low 

Upright 

Nose-High 

Upright 

Nose-Low 

Inverted 

Nose-High 

Inverted 

GL2000-Trained Pilot Average 600 213 885 368 

MFS-Trained Pilot Average 565 331 949 382 

Control Group Pilots Average 728 340 1069 465 

Observed Minimum during Safety Pilot Training 220 -50 350 -30 

The second change we would implement to our training 

syllabus involves utilization of the GL2000 simulator. Given 

the time required to adapt to GL2000 motion, our goal of 

giving participants five hours of GL2000 training in two 

days proved to be optimistic. Training had to be suspended 

for participants who experienced motion sickness until they 

could recover, since learning ends once a pilot becomes 

distressed. This slowed the training process significantly. 

As explained previously, we experimented with flying the 

GL2000 without planetary motion during the first training 

session for each participant, then introduced the use of G forces 

incrementally during subsequent sessions. Interestingly, we 

found a significant degree of adaptation to planetary motion 

between the first and second days of weekend training and, 

not infrequently, between successive sessions on the first day.

Were we to repeat our experiment, we would plan onehalf hour GL2000 training sessions and alternate planetary 

motion sessions with non-motion sessions depending on 

how well an individual participant tolerates the resulting G

forces. Additionally, we would extend the training period 

to three days. Under these circumstances, we believe five 

hours of GL2000 training per participant would be possible 

while training five pilots per group. The remaining five of a 

total of ten simulator hours would still be accomplished on 

the Microsoft Flight Simulator. We would also introduce 

upset-recovery maneuvering on MFS, as opposed to limiting 

the use of this desktop simulator to aerobatic training only.

5.2 Limitations of Ground-Based Simulators for 

Upset-Recovery Training28

Statistical analysis confirms our hypothesis that GL2000-

trained participants would outperform control group 

participants in upset-recovery maneuvering. Although 

GL2000-trained pilots lost less altitude in three of the four 

upsets and recovered faster in all four, they did not statistically outperform MFS-trained participants to the degree 

anticipated. More important, perhaps, neither trained group 

performed as well in altitude loss as we would have expected.

Table 14 summarizes altitude losses for GL2000-trained, 

MFS-trained, and control group pilots for all four upsets. 

The bottom row of the table reports minimum observed altitude loss for each upset during safety pilot training. Despite 

the fact that our research suggests that simulator training 

significantly improves a pilot’s ability to recover an airplane 

from a serious upset, there is a large disparity between trained 

participant altitude losses and the far smaller altitude losses 

achievable by pilots experienced in all-attitude maneuvering 

in an actual airplane.

The altitude disparities reflected in Table 14 seem to call in 

question the implicit assumption that airline simulator-based 

upset-recovery training programs impart flying skills sufficient 

to make it probable that a typical line pilot can recover an 

airliner from a serious upset with minimum altitude loss. U.S. 

airline pilots no longer come primarily from military flight 

backgrounds where training afforded them extensive opportunity to perform aerobatic flight maneuvers. For military 

trained pilots there are no unusual attitudes, only unexpected 

attitudes. By contrast, most air transport pilots flying today 

have never experienced the extreme pitch and bank angles 

and high G forces associated with severe airplane upsets. 

Indeed, most have never been upside-down in an airplane 

even once. Informal conversations with current airline pilots 

suggest that while virtually all regard the company-provided 

upset training they receive as useful, a significant number 

also perceive it as a pro forma approach to a serious safety 

problem—better than nothing but far from what would be 

desirable if training costs were not a paramount consideration. 

Although aerobatic training has not so far been authoritatively related to upset-recovery success in a transport type 

airplane, aerobatic flight in a light airplane would provide an 

opportunity for pilots to practice maneuvering in extreme 

attitudes across wide airspeed and energy level ranges. This 

might in turn lead to greater confidence and maneuvering 

proficiency in an actual upset situation.29

Upsets are known to be a primary cause of fatal commercial air transport accidents. Passenger and air crew safety 

considerations mandate that air transport pilots be able to 

recover from the infrequent but potentially catastrophic 

upsets that inevitably will occur from time to time in air 

transport operations. Although our research implies that 

simulator-based upset-recovery training is a value-added 

activity and that introducing higher levels of fidelity may 

to some extent enhance skills transfer, additional work is 

needed to optimize ground-based flight training devices 

and their utilization to ensure they provide highly effective 

upset-recovery training.
12

1. “Statistical Summary of Commercial Jet Airplane 

Accidents, World Wide Operations, 1959 – 2006,” 

downloaded 12 November 2008 from www.boeing.

com/news/techissues/pdf/statsum.pdf.

2. Rodney O. Rogers, Albert Boquet, Cass Howell, 

and Charles DeJohn (2007), “Preliminary Results 

of an Experiment to Evaluate Transfer of Low-Cost, 

Simulator-Based Airplane Upset-Recovery Training,” FAA Technical Report DOT/FAA/AM07/28, 

Office of Aerospace Medicine, Washington, DC. 

20591, p. 1. Available at www.faa.gov/library/

reports/medical/oamtechreports/2000s/2007/.

3. Rodney O. Rogers, Albert Boquet, Cass Howell, 

and Charles DeJohn (2009), “An Experiment 

to Evaluate Transfer of Low-Cost SimulatorBased Airplane Upset-Recovery Training,” FAA 

Technical Report DOT/FAA/AM-09/5, Office of 

Aerospace Medicine, Washington, DC. 20591. 

Available at www.faa.gov/library/reports/medical/

oamtechreports/2000s/2009/.

4. Most of this section is reproduced verbatim from 

Rogers et al. (2009). A more detailed summary may 

be found in Rogers et al. (2007), pp. 2-4.

5. www.calspan.com/upset.htm.

6. Valerie Gawron, (2004), Aircraft Upset Training 

Evaluation Report (revision 20), National Aeronautics and Space Administration (NASA): NAS2-

99070.

7. J.A Kochan (2005), The Role of Domain Expertise 

and Judgment in Dealing With Unexpected Events, 

Ph.D. Dissertation, Department of Psychology, 

University of Central Florida, Orlando, FL, 2005.

8. J.A. Kochan and J.E. Priest (2005), “Program Update and Prospects for In-Flight Upset Recovery 

Training,” Proceedings of the 13th International 

Symposium on Aviation Psychology, Oklahoma City, 

OK: Wright State University, 2005.

9. J.A Kochan, E. Breiter, M. Hilscher, and J.E. 

Priest, (2005), “Pilots’ Perception and Retention 

of In-Flight Upset Recovery Training: Evidence 

for Review and Practice,” Proceedings of the Human 

Factors and Ergonomics Society 49th Annual Meeting, 

Orlando, FL: September 26-30, 2005.

10. Environmental Tectonics Corporation, “ATFS-400 

Authentic Tactical Flight Simulator Model 400 

Executive Summary.”

11. Dick Leland, Curt Spenney, Tammy Chelette, 

and Ernie Lewis (2003), “Opportunities in Flight 

Simulation: Tactical Flight Simulation.”

12. Zulkeffeli Mat Jusoh and Dick Leland (2006), 

“Tactical Flight Simulation in the Royal Malaysian 

Air Force: the ATFS-400 Authentic Tactical Flight 

Simulator.”

13. Ian W. Strachan (2001), “Motion Cueing in the Real 

World and in Simulators: Principles and Practice,” 

Janes Simulation and Training Systems 2000-2001, 

pp. 149-175.

14. ETC officials advise that “experience shows disorientation can occur but does not always occur.”

15. Cezary Szczepanski and Dick Leland (2000), 

“Move or Not to Move? A Continuous Question,” 

Southampton, PA: Environmental Tectonics, 200l. 

©2000 ETC, published by the American Institute of 

Aeronautics and Astronautics with permission (AIAA

Paper 0161). Copy provided by the first author.

16. J.A. Kochan, E.G. Breiter, and F. Jentsch (2004), 

“Surprise and Unexpectedness in Flying: Database 

Reviews and Analysis,” Proceedings of the Human 

Factors and Ergonomics Society 48th Annual Meeting, 

New Orleans, LA: Human Factors and Ergonomic 

Society, 2004.

17. J.A. Kochan, E.G. Breiter, and F. Jentsch (2005), 

“Surprise and Unexpectedness in Flying: Database Factors and Features,” Proceedings of the 13th 

International Symposium on Aviation Psychology, 

Oklahoma City, OK: Wright State University, 2005.

18. Based on Kochan (2005).

19. J.A. Kochan, J.E. Priest, and M. Moskal (2005), 

“Human Factors Aspect of Upset Recovery Training,” 

17th Annual European Aviation Safety Seminar, Warsaw, Poland: Flight Safety Foundation and European 

Regions Airline Association, 14-16 March 2005.

20. J.A. Kochan, J.E. Priest, and M. Moskal (2005a), 

“The Application of Human Factors Principles to 

Upset Recovery Training,” 50th Annual Corporate 

Aviation Safety Seminar, Orlando, FL: Flight Safety 

Foundation and National Business Association, 

26-28 April 2005.

21. J.A. Kochan (2006), “Human Factors Aspects of 

Unexpected Events as Precursors to Unwanted 

Outcomes,” 18th Annual European Aviation Safety 

Seminar, Flight Safety Foundation and European 

Regions Airline Association: Athens, Greece, 13-15 

March 2006.

6. Endnotes
13

22. Jan J. M. Roessingh (2005), “Transfer of Manual 

Flying Skills from PC-Based Simulation to Actual 

Flight—Comparison of In-Flight Data and Instructor Ratings,” The International Journal of Aviation 

Psychology, 15 (1), 67-90.

23. The decision to forego a more traditional 2 x 4 

mixed-model analysis derives from the nature of 

the upset data themselves, which argues against 

the direct comparisons that characterize a repeated 

measures MANOVA. For example, a nose-high 

recovery may lead to an altitude gain whereas 

nose-low recoveries invariably result in significant 

altitude losses.

24. See Rogers et al. (2009) p. 7 for univariate F values 

associated with significant effects in Table 5.

25. There were, however, no unsuccessful recoveries; 

without exception, GL2000-trained participants 

returned the aircraft to straight and level flight 

without assistance from the safety pilot.

26. As reported in Section 3.1, prior two-group analysis 

had previously established the result that MFStrained pilots outperformed control group pilots.

27. The fact that univariate analysis showed GL2000-

trained participants superior to MFS-trained participants in altitude loss on the nose-high upright 

upset supports this possibility. So perhaps does the 

fact that multivariate analysis showed GL2000-

trained participant superior to control group participants in altitude loss on the nose-low upright 

upset when MFS-trained participants were not.

28. Some of the ideas expressed in this subsection are 

reproduced from Rogers et al. (2009), pp. 10-11.

29. Paul Ransbury, President, APS Emergency Maneuver Training (www.apstraining.com), has adduced 

evidence supporting this possibility. Ransbury and 

his colleagues formulated a set of objective criteria 

for evaluating in-flight upset-recovery maneuvering 

pilot skills. Using these criteria, they conducted pretraining and post-training flight testing of a group 

of 75 pilots currently flying air carrier turbojet or 

turboprop aircraft. The testing was conducted in 

an Extra-300 aerobatic general aviation aircraft. 

Eighty-eight percent of the 75 pilots had in excess 

of 1500 hours of flight experience; more than 

four-fifths had fewer than 10 hours of aerobatic 

experience. The flight testing rated participant 

pilot performance as ideal, safe, unsafe, or severe 

error on each of five upset scenarios, with ideal or 

safe considered successful performances. During 

pre-training testing, only about 40% of the pilots 

recovered successfully from any given scenario. 

Post-training success rates were 92.3% in one of 

the five scenarios and 97.5% in the other four.

Boeing Aircraft Company. “Statistical Summary of Commercial Jet Airplane Accidents, World Wide Operations, 1955 – 2006.” Available at www.boeing.

com/news/techissues/pdf/statsum.pdf.

Gawron, V. (2004). Aircraft Upset Training Evaluation 

Report (revision 20). National Aeronautics and Space 

Administration (NASA) NAS2-99070. (Airplane 

Upset Training Evaluation Report, February 25, 

2002, National Aeronautics and Space Administration NAS2-99070 is an earlier version of this 

research report.)

Kochan, J.A. (2005). The Role of Domain Expertise and 

Judgment in Dealing with Unexpected Events, Ph.D. 

Dissertation, Department of Psychology, University 

of Central Florida, Orlando, FL.

Kochan, J.A. (2006). “Human Factors Aspects of 

Unexpected Events as Precursors to Unwanted 

Outcomes,” 18th Annual European Aviation Safety 

Seminar. Flight Safety Foundation and European 

Regions Airline Association: Athens, Greece, 13-15 

March 2006.

Kochan, J.A., Breiter, E.G., and Jentsch, F. (2004). “Surprise 

and Unexpectedness in Flying: Database Reviews 

and Analysis,” Proceedings of the Human Factors and 

Ergonomics Society 48th Annual Meeting. New Orleans, 

LA: Human Factors and Ergonomic Society.

Kochan, J.A., Breiter, E.G., and Jentsch, F. (2005). 

“Surprise and Unexpectedness in Flying: Database 

Factors and Features,” Proceedings of the 13th International Symposium on Aviation Psychology. Oklahoma 

City, OK: Wright State University.

7. References
14

Kochan, J.A., Breiter, E., Hilscher, M., and Priest, 

J.E. (2005). “Pilots’ Perception and Retention of 

In-Flight Upset Recovery Training: Evidence for 

Review and Practice,” Proceedings of the Human 

Factors and Ergonomics Society 49th Annual Meeting. 

Orlando, FL: September 26-30, 2005.

Kochan, J.A and Priest, J.E. (2005). “Program Update and 

Prospects for In-Flight Upset Recovery Training.” 

Proceedings of the 13th International Symposium on 

Aviation Psychology. Oklahoma City, OK: Wright 

State University.

Kochan, J.A., Priest, J.E., and Moskal, M. (2005). “Human Factors Aspect of Upset Recovery Training,” 

17th Annual European Aviation Safety Seminar. Warsaw, Poland: Flight Safety Foundation and European 

Regions Airline Association: 14-16 March 2005.

Kochan, J.A., Priest, J.E., and Moskal, M. (2005a). 

“The Application of Human Factors Principles to 

Upset Recovery Training,” 50th Annual Corporate 

Aviation Safety Seminar. Orlando, FL: Flight Safety 

Foundation and National Business Association: 

26-28 April 2005.

Roessingh, J.J.M. (2005). “Transfer of Manual Flying Skills from PC-Based Simulation to Actual 

Flight—Comparison of In-Flight Data and Instructor Ratings.” The International Journal of Aviation 

Psychology, 15 (1), 67-90.

Rogers, R.O, Howell, C., Boquet, A, and DeJohn, C. 

(2007). “Preliminary Results of an Experiment 

to Evaluate Transfer of Low-Cost, SimulatorBased Airplane Upset-Recovery Training,” FAA 

Technical Report DOT/FAA/AM07/28. Office of 

Aerospace Medicine, Washington, DC 20591. 

Available at www.faa.gov/library/reports/medical/

oamtechreports/2000s/2007/.

Rogers, R.O, Boquet, A., Howell, C., and DeJohn, C. 

(2009). “An Experiment to Evaluate Transfer of 

Low-Cost Simulator-Based Airplane Upset-Recovery Training.” FAA Technical Report DOT/FAA/

AM-09/5, Office of Aerospace Medicine, Washington, DC 20591. Available at www.faa.gov/library/

reports/medical/oamtechreports/2000s/2009.

Strachan, I.W. (2001). “Motion Cueing in the Real 

World and in Simulators: Principles and Practice.” 

Jane’s Simulation and Training Systems 2000-2001, 

pp. 149-175.