William Knecht1

Jerry Ball1

Michael Lenz2

1

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

2

FAA Headquarters

 Washington, DC 20591

March 2010

Final Report

Effects of Video Weather Training 

Products, Web-Based Preflight Weather 

Briefing, and Local Versus Non-Local 

Pilots on General Aviation Pilot Weather 

Knowledge and Flight Behavior, Phase 2

DOT/FAA/AM-10/6

Office of Aerospace Medicine

Washington, DC 20591

OK-10-0077-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-10/6 

4. Title and Subtitle 5. Report Date 

Effects of Video Weather Training Products, Web-Based Preflight Weather March 2010 

Briefing and Local Vs. Non-Local Pilots on General Aviation Pilot Weather 

Knowledge and Flight Behavior, Phase 2 

6. Performing Organization Code 

 

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

Knecht WR, Ball J, Lenz M 

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

FAA Civil Aerospace Medical Institute 

P.O. Box 25082 11. Contract or Grant No. 

Oklahoma City, OK 73125 

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-A-07-HRR-521 

16. Abstract 

This research had two main phases. Phase 1 investigated three major questions, one of which was whether or not 

video weather training products could significantly affect general aviation (GA) pilot weather knowledge and 

flight behavior in marginal meteorological conditions. Fifty GA pilots took a general weather knowledge pre-test, 

followed by exposure to either one of two weather training videos (the Experimental groups) or to a video having 

nothing to do with weather (the Control group). They next took a post-test to measure knowledge gain induced 

by the training product. Finally, they planned for and flew a simulated flight mission through marginal weather 

from Amarillo, TX, to Albuquerque, NM. Multivariate modeling implied that a combination of higher pilot age, 

receiving either weather training product, and takeoff hesitancy could significantly, correctly predict 86.7% of 

diversions from deteriorating weather and 77.8% of full flight completions. 

 The question then became whether or not this model would be robust over time. In the present study (Phase 

2), after a time lapse of 3-4 months, 44 of the 50 original Phase 1 pilots returned for further testing. Again, they 

were tested for weather knowledge and flew a simulated flight mission similar to Phase 1’s. 

 No significant change in weather knowledge was evident from Phase 1 to 2, nor were any significant 

differences seen between the three treatment groups. Additionally, the 3-factor model of Phase 1 failed to 

significantly predict flight diversions or flight completions in Phase 2. 

 The combined results of Phases 1 and 2 imply that the effects on weather knowledge and flight behavior of a 

single 90-minute training video seem minimal in comparison to the complexities of weather itself and flight into 

weather. This is consistent with intuition. Moreover, what small effects are produced seem to decay with time. 

 None of this is unexpected. It merely means that weather is complex, and effective weather training must be 

intensive to begin with and ongoing to remain effective. 

 

17. Key Words 18. Distribution Statement 

 

 

Weather, Training, Pre-Flight Briefing, Weather 

Knowledge, Flight Behavior 

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 24 

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

iii 

Acknowle

Stanley Roberts, Manager, AFS-630 was invaluable in providing item difficulties to FAA test questions. 

Tammy Harris, FAA, and Janine King, Sally Glasgow, and Suzanne Thomas of Xyant Technology, Inc. provided 

key scheduling and subject payment support. Again, we extend our heartfelt gratitude to the pilots who made 

this effort possible.

v 

Co

Introducti

Purpose of This Research. 

Method

Background. 

Research Design—Original Assignment to Group and Order of Treatment

Participants and Attrition. 

Apparatus  

Procedure  

Result

 Data Normality, Phase 

Specific Effects.

Modeling Flight Behavior  .

Discuss

Phase 1, Brief Summary. .

Phase 2, Brief Summary. .

Findings Common to Both Studies. 

References

Appendix A: Frequency Histograms

Appendix B: Phases 1 & 2 Correlational Structure

1 

Effects of Video Weather Training Produ

Web-Based Preflight Weather Briefing, 

Versus Non-Local Pilots on General Aviati

Weather Knowledge and Flight Behavio

Intr

Purpose of This Research

FAA Civil Aerospace Medical Institute (CAMI) researchers were tasked by the FAA Flight Standards division (AFS-810) to explore a number of issues in genera

aviation, including:

1. Do video weather training products significantly

affect pilot weather knowledge and flight behavior 

in the face of potential instrument meteorological 

conditions (IMC)?

a. If so, what are the immediate effects?

b. Do these effects persist over tim

2. How are modern Web-based weather products used 

during preflight briefing?

3. Do local1 Oklahoma pilots differ appreciably fro

non-local pilots in either weather knowledge or 

weather-related flight behavior?

Issue 1 is consequential because bad weather is a perennial concern to general aviation, and therefore remains a 

continual focus of FAA investigation. 

Issue 2 begins human factors study of what promises to 

be the future of preflight weather briefing—self-briefing 

by pilots using World-Wide Web-based tools.

Issue 3 addresses the question of whether or not Oklahoma pilots are representative of U.S. pilots in general

Presumably, they are similar but, so far, this has not been 

directly investigated. Since many of the FAA’s general 

aviation studies are conducted by the Oklahoma-based

Civil Aerospace Medical Institute, this is a statistical

validity issue calling for study.

Me

Background

This research was conducted in two phases. Phase 1 

examined data collected from January to July, 2008, and 

was the subject of a prior report by similar name. Phase 

2 data were collected from July to September, 2008, and 

1

A “local” pilot was defined as one living in Oklahoma at the time of

the study. For the most part, this meant long-term Oklahoma residents.

However, there were a few instances of pilots living in Oklahoma

whose state-of-legal residence was not Oklahoma because they were

attending local flight schools.

are the subject of the current report. Most of the current 

methodology is detailed in the Phase 1 report. Therefore, 

only key findings from Phase 1 will be reiterated here.

The reason for conducting a longitudinal study was 

to investigate the effect of time on any learning taking 

place due to the video training product during the Phase 

1 study. Phase 1 found no statistically significant single, 

direct effect of the training product on general weather 

knowledge or hazardous flight behavior (in simulo). Instead, what was found through modeling was a significant 

multiple effect of a combination of

• Training product(the two training products differed 

from the Control, but not from each other

• Pilot age

• Takeoff hesitancy(pilots’ initial yes/no decision whether 

they would even attempt the flight)

on subsequent completion of the entire flight (although 

not directly on objectively observable flight hazard 

variables).2

 Since risk was arguably cumulative, an argument could be made that completion of the entire flight 

was an indicator of increased flight hazard.3

Research Design—Original Assignment to Group and 

Order of Treatments

Because Phase 2 was a follow-up study, no further 

experimental treatments were introduced. Table 1 shows 

the original experimental structure, which was maintained 

in Phase 2. Pilots had been assigned to one of 3 primary 

Training product groups, each of which was sub-divided 

into 2 secondary Instrument rating groups, each of which 

was sub-divided into 2 Pilot residence groups containing 

“local” (Oklahoma residents) versus non-local residents.

This produced a 3x2x2 mixed design with 12 treatment 

cells, 4 Ss per cell. Pilots had been assigned to cells in 

order to equilibrate cell means for age and flight hours.

2

Severe non-normality of flight hours and the hazard variables prevented 

doing standard continuous-data linear regression modeling. Instead, 

a more-tolerant stepwise binary logistic regression was done on the 

yes/no outcome of whether or not pilots completed the entire flight.

3

The way the Phase 1 flight scenarios were physically constructed, 

there was a -.384 (p = .006) correlation between how close pilots 

finally got to the destination (Albuquerque, NM) and the number

of minutes spent < 500' AGL (above ground level)
2 

Participants and Attrition

Fifty GA pilot volunteers had originally been selected to 

participate with informed consent, but six were unable to 

participate in Phase 2, dropping the final  to 44. Table

2 describes key demographic factors and changes. None

of these changes was statistically significant.

Apparatus 

Weather training products/control materials. Two 

well-known video weather training products had been 

selected from a list of candidate products. The Phase 

1 report details the selection method. The authors of 

these products graciously provided them on condition of 

confidentiality; therefore their wishes for confidentiality 

shall be maintained in this report as well.

Weather Knowledge Tests. In Phase 1, three parallel 

forms of a 30-question general weather knowledge test 

were constructed and matched on item difficulty. Administration order of the parallel forms was counterbalanced 

across pilots, controlling for the event that the three forms 

might not be exactly equivalent in difficulty. Each test was

administered on a laptop computer using software written 

by the experimenters in Microsoft Visual Studio 2005TM.

In Phase 1, one of the 3 forms had been given as a 

pre-test, followed by the video training product, followed 

by a second form as the post-test. Now, in Phase 2, the

remaining form was given to assess any groupwise change 

in weather knowledge scores.

Advanced General Aviation Research Simulator 

(AGARS). Again, the CAMI AGARS constituted the

flight simulator platform for assessing flight behavior. 

Details of AGARS can be found in the Phase 1 report

Procedure 

The Phase 2 simulator mission was engineered to be 

highly similar to Phase 1. To recap briefly, pilots were asked 

Table 1. Experimental structure.

Independent variables (IV) Dependent variables (DV) Training product Instrument- rated pilot? Pilot’s state of residence Age Flight hr Flight duration Minimum dist- ance to ABQ Minutes spent in IMC Minutes scud running Minutes spent < 500’ AGL

Takeoff 

decision (Y/N)

1 No OK

12 treatment cells were equilibrated for 

age and flight hr.

1 No Non-OK

1 Yes OK

1 Yes Non-OK

2 No OK

2 No Non-OK

2 Yes OK

2 Yes Non-OK

Control No OK

Control No Non-OK

Control Yes OK

Control Yes Non-OK

Table 2. Phases 1-2 attrition characteristics for cell ns, median age, median flight hours, and % instrument-rated.

Training Product 1 Training Product 2 Control Combined group

n Age FH %IR n Age FH %IR n Age FH %IR N Age FH %IR

Phase 1 16 39 280 53 16 38 235.5 47 18 42 262.5 50 50 39 268 50

Phase 2 15 39 300 60 15 34 227 53 14 42 225 57 44 39 280 57
3 

to plan an east-to-west, visual-flight-rules (VFR) flight 

from Amarillo, TX (AMA) to Albuquerque, NM (ABQ).

This route takes approximately 90 minutes to fly in the 

Piper Malibu configuration at high-speed cruise. Pilots had 

2 cockpit VORs (VHF OmniRange Navigation Syste

and an ADF (Automatic Direction Finder). Again, the

had the ability to access a Web-based weather emulation 

on a stand-alone PC during preflight. Upon finishing thei

flight planning, they took the final version of the weather 

knowledge test as dictated by the counterbalance order. 

Next, a 15-minute convenience break was given to each

pilot. Following the break, an abbreviated refamiliarization session with AGARS was given. Again, pilots were 

allowed to ask for assistance with flight settings at any 

time during the course of the flight scenario.

As before, the route consisted of gradually rising terrain during the first two-thirds of the flight, followed 

by a dramatic elevation change during the last third. 

During the course of the flight, pilots were exposed to

deteriorating VFR weather conditions. Again, visibility 

was initially set at 8 nautical miles and gradually decreased 

to 5 miles by the time the pilots had flown approximately 

two-thirds of the route. Cloud ceilings were lowered from

4500 feet AGL to 3500 AGL across the same terrain. A

a result, the ceilings again gradually squeezed the pilots 

closer to the ground. 

Shortly into the flight, the barometric pressure dropped 

from the preflight planning level of 30.10 to 29.98. This 

afforded a potential error between actual and intended 

altitude for pilots not receiving a barometric update (either from AFSS or AWOS) after departure. Specifically,

pilots failing to update their Kollsman setting would 

fly an actual altitude approximately 120' below their 

intended altitude. The authors acknowledge that, given 

the aircraft’s blind transponder encoder plus the departure 

airspace, in real life an air traffic controller would have 

normally detected the altitude discrepancy and issued a 

correction to the pilot. However, since the study’s purpose 

was specifically to study both errors of commission and 

omission, this correction was purposely skipped to study 

the consequences.

Because the flight situation essentially did not change 

from Phase 1 to 2, the issue of learning effect had to 

be addressed.4

 Pilots might fly better in Phase 2 simply 

because they would now know better the airframe and 

the physical terrain. 

Two methods were therefore used to distract pilots 

from the similarities between Phases 1 and 2. First, the 

direction of approach for Phase 2 weather was made 

symmetrical to, and counterbalanced with, whatever 

each pilot had experienced during Phase 1. For instance, 

if a pilot had experienced Phase 1 weather approaching 

from 45° (their 4 o’clock) on this east-to-west flight, the 

Phase 2 weather approached symmetrically from 135° 

(their 8 o’clock). This symmetry was purely a distractor, 

not a variable of interest.

The second distractor was the introduction of a primary flight display (PFD). The PFD incorporated th

basic flight instruments that provided aircraft attitude, 

airspeed, altitude, vertical speed, heading, rate-of-turn, 

slip-and-skid, navigation, transponder, and systems annunciation data (See Figure 1). The inset map view (containing map, traffic, and terrain information) was removed 

4

Two reasons dictated not using 2 different, counterbalanced routes. 

First, the AGARS scenery database did not encompass the entire 

continental U.S. Second, even if it had, pilots would have easily spotted

the essential flight features (i.e., slowly rising terrain terminated by 

hills, with deteriorating visibility and ceiling along the way).

Figure 1. AGARS primary flight display. Figure 1. AGARS primary flight display.
4 

for consistency with the Phase 1 display information. 

Navigational information was coupled to the autopilot

and the NAV/COM radio heads. Temperature and ti

were also presented on the display in the lower left and 

right corners respectively. Pilots had the ability to adjust 

the barometric pressure, course direction indicator, and 

heading bug. Each pilot received a 20-30 minute training

session using the display. During this training, pilots were

asked to fly a short flight from an airport out to a VOR at

a requested altitude and then return back to the departure 

airport to land. They were asked to use the autopilot to 

fly a direct course to the station and then reverse their 

course and fly an outbound leg back to the airport. This 

gave the pilots time to utilize and familiarize themselves 

with all of the display’s functionality.

Resu

Data Normality, Phase 2

Normality of data frequency distributions is a prerequisite for the use of parametric statistics. Therefore,

a preliminary check of all data was made.

Normality of weather knowledge test scores.Acceptable 

normality of weather knowledge test scores was supported 

by 2-tailed Shapiro-Wilk tests (ppre-test =.297, ppost-test =.786, 

pfinal = .653, all non-significant (NS))

Normality of Web-based weather information data. In 

Phase 1, the Web-emulation page view duration data 

presented considerable departure from normality. Many 

pages received very little viewing time while some received 

a great deal. Moreover, some of the longest viewing times 

reflected mistakes (pilots forgetting to close out a particular page). Therefore, medians and quartiles were used in 

Phase 1 to more accurately reflect group usage patterns. 

That approach was continued in Phase 2.

Normality of flight simulator data. Appendix A shows 

Phase 1 AGARS flight data displayed in the left column, 

with Phase 2 data in the right column. Phase 1 flight duration was the only distribution to pass the Wilk-Shapiro 

test of normality.

Most of these data were simply too extreme to be 

corrected by any standard mathematical transform such 

as a log or square root. Therefore, most analysis was subsequently done using distribution-free (non-parametric) 

statistics.

Equivalency of weather knowledge test forms A, B, and 

C. With Phase 2 complete, we can now analyze the 3 

test forms for equivalency of difficulty. As usual, we 

first visually inspected the data frequency distributions. 

Figure 2 shows frequencies collapsed over all 3 forms for 

percent correct and elapsed time (time spent taking the 

test). Elapsed time appeared unexpectedly non-normal.

This will be elaborated upon shortly.

Table 3 shows the knowledge test means and standard 

errors of the mean for the N=42 surviving score triplets

(2 of the 44 returning pilots had originally failed to take 

the Phase 1 knowledge pre-test).

A Shapiro-Wilk test supported normality across forms A,B,C for percent

correct (N=42, pA=.313, pB =.715,

pC =.359). However, the distributions failed 

Mauchy’s Test of Sphericity (p=.043), so 

a corrected repeated-measures ANOV

(Greenhouse-Geisser) was used to examine 

mean differences. 

The results implied that Forms A, B, 

and  were not equivalent in difficulty

(p =.001). Examination of the .95 confidence intervals revealed that Form A

scores were significantly higher than both 

B and C, with B andC otherwise bein

about equal. 

We therefore concluded that Form A 

was a somewhat easier test. Fortunately, the 

treatment order counterbalance statistically 

controlled for this, so it presented no fatal 

flaw to the overall analysis.

0.400 0.500 0.600 0.700 0.800 0.900

K score

0

10

20

30

Count

1000 2000 3000

Elapsed time

10

20

30

40

50

Count

Figure 2. Score frequency distributions for a) the knowledge test, 

and b) time spent taking the test. Histograms represent combined 

data from all three administrations (Forms A, B, and C). 

Table 3. Knowledge-score means, collapsed across all 3 test forms.

Mean Correct Standard error of the mean

Form A 70.7% 2.0

Form B 62.9% 2.0

Form C 64.2% 2.7
5 

Similar to Table 3, Table 4 shows means for elapsed 

time (ET), collapsed across Phases 1+2. This is the time

each pilot spent taking each of their 3 tests, with results 

grouped by test form A, B, or C

Here, Shapiro-Wilk rejected normality on all 3 test 

forms for elapsed time (N=42, pA=.015, pB <.001, p

<.001). Therefore, a nonparametric Friedman test was 

used to test differences between group medians. 

Results implied that pilots spent more time 

taking some forms of the test than others 

(p =.017). Followup Wilcoxon pairwise difference tests pinpointed that pilots spent significantly more time taking form B than 

([pA-B =.130, pB-C =.006, pA-C =.103). Fortunately, the treatment order counterbalance again protected the analysis 

from fatal harm. 

At this time, other than sampling error, we have no 

explanation for why form A should be easiest, or for why 

form  should be fastest

Specific Effects

Effect of the weather training products on GA pilot weather 

knowledge. Stepping back to view the big picture, one of 

the most interesting questions is whether the weather training product had any effect on pilots’ weather knowledge. 

After Phase 1, we saw no significant change. 

Now, what about over the entire time course

of the experiment?

Reanalysis across all 3 groups (pre-treatment, post-treatment, and final) found acceptable Shapiro-Wilk normality for weather 

knowledge scores (all p >.297), and no trouble 

with sphericity (pMauchy = .079). But, repeated 

measures ANOVA showed neither significan

weather knowledge effects across time (pF

= .396) nor effect of training product (p = 

.908). Adding instrument rating and pilot’s 

locality of residence to the analysis failed to 

uncover any interaction effects (smallest p = 

.389). Table 5 shows the means (with 95% 

confidence interval in parentheses).

There might be more than meets the eye 

here, however. When the data were collapsed 

across training products, it was clear that 

pilots spent, on average, significantly less 

time taking their knowledge tests after the 

pre-test than before it. These elapsed time 

distributions failed Shapiro-Wilk (all ps < 

.005), so the Friedman test was used, which 

revealed a significant overall decrease in ET

over the course of the experiment (refer to 

column averages, pFriedman = .000005). Table 

6 (bottom row, light gray) shows this trend. 

Could the fact that pilots were simply spending less

time on the later tests be the reason we failed to see 

any significant knowledge boost for the weather training groups? In Phase 1, we discovered that the elapsed 

time spent taking the test (ET) did not predict weather

knowledge score. On average, pilots who spent more time

taking the test actually got lower average scores (rSpearman = 

-.187), although the effect was not statistically significant. 

Therefore, ET was useless as a covariate in ANOVA a

could not be used to enhance the power of our analysis.

Table 6 shows that the largest average time drop occurred between the first and second administrations. This 

“pre-post drop” in ET can probably be explained simply

by 1) on first testing, all pilots were unfamiliar with the 

software and 2) over half the pilots took their first test in 

the undisturbed privacy of their hotel rooms or homes, 

with little time pressure, whereas all took the post-tests 

and finals in the laboratory, where they were under supervision, often with a plane to catch that afternoon, and

none could fly the simulator until after their knowledge 

test was finished. This does not necessarily mean they 

were less attentive or serious in the later sessions. It may 

merely mean they were highly motivated to get the test 

over with and get into the simulator, and likely concentrated more intently on the last 2 tests.

Table 4. Elapsed time means, collapsed across all 3 test forms.

Mean ET (seconds) Standard error of the mean

Form A 1077 73.8

Form B 1193 85.1

Form C 958 82.6

Table 5. Weather knowledge means--% correct, N=42—and (.95 CI).

Pre-treatment Post-treatment Final

Trg Prod 1 66.7 (± 10.2) 68.1 (± 9.2) 63.3 (± 10.1)

Trg Prod 2 65.0 (± 7.8) 67.4 (± 6.3) 66.4 (± 6.3)

Control 66.2 (± 7.9) 66.9 (± 6.0) 63.6 (± 5.6)

Table 6. Elapsed time (ET) means (seconds, N=42).

Pre-treatment Post-treatment Final Row Ave.

Trg Prod 1 1486 1034 982 1167

Trg Prod 2 1414 1021 924 1120

Control 1117 831 873 940

Column Ave. 1339 962 927 1076
6 

Figure 3. Whole-group page view durations for the part-task 

emulation of www.aviationweather.gov. Top: Phase 1 (N=50). 

Bottom: Phase 2 (N=44). Partial box plots show 25th, 50th

(median), and 75th percentiles. Note that the y-axis is logarithmic. 

Conclusion. In Phase 1, we interpreted the 

overall results as implying that weather is simply 

too complex a subject for a single, brief weather 

training product to have much effect on general weather knowledge. Now, we can restate

that finding to say that individual test score 

variability—“noise,” if you will—seems just too 

large to detect whatever “signal” may have been 

generated by the training products. Methodologically, the way to get around that problem would 

be to develop a product-specific knowledge test 

designed to better assess the specific details of 

each training product. Of course, that would

require establishing internal and external validity and reliability of the individual test items, 

which is an entire project in its own right (and 

the very reason we used pre-validated FAA items 

to begin with). In other words, the test would 

have to be more specific and sensitive to be able 

to detect any effect of a 90-minute training video 

on weather knowledge.

Comparison of Web preflight briefings. Figure 3 

compares Phase 1 page view durations (top) with 

Phase 2 (bottom). Figure 3’s logarithmic y-axis 

makes it easier to see the various small values, 

yet still represent large values. By comparing 

medians (rather than means), we expect more 

stable viewing estimates for each individual page 

since the effect of extreme values will be reduced.

However, Figure 3 is sufficiently complex to 

make meaningful visual inspection difficult. 

Some statistical analysis and visual reinterpretation of the data may help make more sense of 

what they imply.

There were 15 main Web pages, plus an 

additional 3 sub-pages for page 4, making 18 

Phase 1-2 pairs we can correlate. This should say 

something about relative viewing time and consistency of 

preferred pages between Phase 1 and Phase 2. 

In Figure 4 (left), Web pages below the 45° dashed 

“identity line” showed an increase in groupwise median 

page view duration from Phase 1 to Phase 2; pages above 

the identity line showed a decrease.5

 In comparing all 18 

page-pairs, the pattern of median view durations showed 

no significant change between Phases 1 and 2 (pWilcoxon = 

5

The low Phase 1-2 correlation (embodied by the linear fit line) was 

partly due to large decreases in Phase 2 median view duration for 2 

outliers—pages 7 and 11—the only two text-based pages (all others 

were graphical). Page 7 was winds/temps as text; page 11 was the 

area forecast (FA) as text. From this, we might be tempted to argue 

that text-based products are less efficient than graphical products, 

but that would not take into account differences in the amount 

of information per page. Plus, when pages 7 and 11 are ignored, 

rs → .399—less of a change than might be expected.

.14, NS). Stated another way, the group behaved only 

a bit differently the first time compared to the second. 

Some pages were viewed longer, others less.

In contrast, Figure 4 (right) shows how the raw number of page views decreased significantly (> 20%) from 

Phase 1 to Phase 2 (pWilcoxon = .0002). In fact, the best-fit 

(solid) line of Figure 4 (right) shows that the pattern of 

this decrease was remarkably uniform across pages (rs

 = 

.972, p <.000001).7

 Moreover, the statistical significance 

was not just trivially due to the loss of 6 pilots in Phase 2 

because the horizontal (x) axis of Figure 4 (right) reflects 

6

An alternate way to look at the pattern was to note a correlation 

between each page’s Phase 1 and 2 median views, but not a significant 

one (rs

 = .374, NS). 

Almost all the data points fall above the 45° (dashed) identity line, 

indicating more time spent in Phase 1 than in Phase 2.
7 

correction for that attrition (all Phase 2 page views were 

multiplied by 50/44). 

Did the overall decrease in page views mean that, by

Phase 2, pilots started disregarding their preflight weather 

briefings? Not necessarily. The simplest explanation for

the drop may be just that pilots had already used the 

computerized system once. If some remembered which 

pages they preferred (or did not prefer), that would predictably decrease the overall Phase 2 page views. 

In a sense, some pages did seem “more popular” than 

others. In Figure 4 (left and right) the “least popular” pages 

are clustered near the lower left, and the “most popular” 

near the upper right. For instance, the page 4 variants 

(Convective Outlook) uniformly received very few views

In contrast, pages 9 (TAF Java Tool) and 14 (Looping

NEXRAD) received the highest number of vie

Examining this issue of “popularity” more deeply,

we can assume that people tend to dwell on things they 

find either

1. highly informative or

2. hard to understand

Unfortunately, explanation 1 implies something good,

explanation 2 not so good. Moreover, either explanation 

(or both) may be working in any given pilot for any given 

page. This confound would have to be untangled in future 

studies using more sophisticated methods. 

For now, let us just imagine a simple “group dwell 

index,” calculated by multiplying each page’s median 

number of page views times its median view duration. 

Dwell can then represent “groupwise amount of attention

paid to each page.

Dwell = N1 * T1 (1)

where  = the ith page’s number of views and T = view

duration (~ is the symbol for “median”). Figure 5 shows 

the result.

For example, Figure 5 (right) shows that the page 4 

variants (convective outlook) were low-dwell pages in 

both Phases 1 and 2. Again, pages below the 45° dashed 

identity line (e.g., 8, 12, 15) gained dwell in Phase 2.

Higher-dwell pages of interest (labeled in Figure 5) were

page content

Dwell increased 

during Phase 2?

07 Winds/temps (text) N

09 TAF Java tool N

11 FA (area forecast, text) N

13 METAR Java tool 

12 PIREPs Ye

08 Prog charts (surface) Yes

Interestingly, PIREPs gained considerable dwell in

Phase 2—even though there were no PIREPs. The reason

is unclear. METARs and TAFs may not truly have lost

value in Phase 2—just dwell. These pages were based 

on a mouse float-over system (floating the mouse cursor 

over a small black “station-reporting” rectangle caused 

METAR or TAF text to pop up). So, their decrease in

dwell perhaps only reflected the fact that it took less time 

for pilots to figure them out in Phase 2.

Flight behavior. Figure 6 shows the Phase 1 flight paths 

(left column) and Phase 2 flight paths (right column), 

grouped by training product (rows 1-3). Non-instrumentrated pilots’ paths are shown as black lines; instrumentrated pilots’ are white lines

Figure 4. Left: Scatterplot of each page’s median view duration for Phase 1 (y-axis) vs. Phase 2 (x-axis). Right: 

Scatterplot of numbers of times each page was viewed by all pilots considered as a single group (page views). 
8 

Figure 6 shows us mainly how similar the flight patterns were, across training groups, instrument rating, and 

Phases 1 versus 2. Most pilots who did complete the flight 

picked their way relatively directly, straight through the 

mountain passes east of ABQ, even though this required 

great care to simultaneously maintain adequate cloud and 

ground clearance. Interestingly, the one Phase 1 Control

group instrument-rated pilot (lower-left box, white path) 

seen to flank the weather by flying north and later south 

in Phase 2 was the same pilot. He reported that, after 

being successful the first time, he was simply trying the 

same strategy, just going the opposite direction.

We can check flight pattern consistency—namely, if 

a pilot did or did not make it all the way to ABQ during Phase 1, did he or she do the same thing in Phase 2? 

Table 7 shows the 2x2 consistency matrix for 42 pilots.8

Most pilots behaved more consistently than expected 

by chance (gray-highlighted cells, 22+11=33 of 42 = 

78.6%, pFisher’s Exact Test = .0007). Pilots tended to repeat 

whatever flight decision they made the first time (e.g., if 

they flew all the way to ABQ in Phase 1, they generally 

did so in Phase 2 as well).

Next, looking only at Table 7’s 9 inconsistent pilots,

we might ask whether the 6 Phase 2 “new risk-takers” 

significantly outnumber the 3 “new diverters.” However, 

odds-ratio analysis disconfirms this (pz

=.32, NS), meaning

there was no statistically greater tendency for risk-taking 

in Phase 2 than in Phase 1. 

8

44 completed both Phases 1 and 2, but 2 experienced controlled 

flight into terrain and so were excluded from this analysis

Finally, we logically tried to see if some of our independent variables might correlate with consistency. The 3 

“new diverters” were coded as -1 (signifying a decrease in 

risk-taking), consistent pilots as 0, and “new risk-takers” 

as 1 (signifying an increase in risk-taking). However, 

subsequent Spearman correlations between consistency 

and major independent variables (pilot age, flight hours, 

instrument rating, average weather knowledge test score 

and elapsed time [[pre+post+final]/3], and pilot’s locality 

of residence) were all non-significant.9

Thus, we can offer no explanation for this consistency, 

other than to note the universal human tendency for 

many of us behave in similar ways over time when faced 

with similar circumstances.

AGARS intercorrelations. Table 8 shows nonparametric 

Spearman correlations between key variables. Statistically 

significant correlations are highlighted in gray. Pointbiserial correlations (rpb) are used when one variable is 

dichotomous, the other continuous.

Trivial relations. Four of the largest correlations are 

statistically significant, but trivial. These cells are marked 

in a light shade of gray.

1-2. Instrument rating x pilot age (rpb = .382) and instrument rating x flight hours (rpb = .379) simply mean 

that instrument-rated pilots tend to be older and 

have more flight hours.

9Of course, the -1,0,1 coding scheme was logical, but purely speculative

and arbitrary. The Spearman correlation was also not the method 

of choice for some of these tests. However, given that none of the 

resulting 2-tailed p-values was smaller than .10, readers may excuse 

us for not pursuing the matter.

Figure 5. Scatterplot of our value metric (group dwell) for each Web page (left), plotted as a linear-linear, 

with linear best-fit line (right) plotted as a log-log, with power function best-fit line. Again, Phase 2 values are 

adjusted for plot attrition. The log-log plot (right) makes it easier to see values near (0,0), which are obscured 

in the linear plot (left).
9 

Figure 6. Top-down, flight-profile views for Phases 1 and 2, laid over the terrain map. Terrain slowly rose as 

pilots flew east to west, squeezing them between clouds and ground, especially near the north-south ridge 

just before ABQ. Digital elevation data were obtained from National Geophysical Data Center (2008) and 

drawn by Mathematica (2008).

Figure 6. Top-down, flight-profile views for Phases 1 and 2, laid over the terrain map. Terrain slowly rose as pilots flew east 

to west, squeezing them between clouds and ground, especially near the north-south ridge just before ABQ. Digital elevation 

data were obtained from National Geophysical Data Center (2008) and drawn by Mathematica (2008). 

Table 7. Consistency of flight decisions from Phase 1 to 2.

Phase 2

Phase 1 No Yes

Made it to ABQ

No 22 (16.7) 6 (11.3)

Yes 3 (8.3) 11 (5.7)

1st number is actual n (2nd is expected n). pFisher = .0007 (2-tailed)
10 

Table 8. Correlations between key Phase 2 variables.

Variable 2

Variable 1

Instrument Rating 

(1=instrument-rated)1

Locality of Residence 

(1=Local)1

Pilot Age2Pilot Flight Hours2Ave. Wx Knowledge2, 4Web Pre-flight duration2Flight Duration2Minimum Dist to ABQ2Minutes scud running2Minutes in IMC2

Instrument Rating 1.0

State of Residence 3 1.0

Pilot Age .382 

(.010)

-.275 1.0

Pilot Flight Hours .379 

(.011)

 .060 .736 

(<.001)

1.0

Ave. Wx Knowledge4 .247 -.255 -.066 .154 1.0

Web Preflight Duration -.058 -.421 

(.004)

 .475 

(.001)

 .371 

(.013)

 .225 1.0

Flight Duration .130 -.281 -.107 .043 .070 .109 1.0

Minimum Dist to ABQ -.083 .215 .231

 .030

-.064 -.023 -.908 

(<.001)

1.0

Minutes scud running .077 .206 -.049 -.037 -.036 .038 .090 -.194 1.0

Minutes in IMC .088 -.202 -.148 -.235 -.260 -.076 .138 -.248 .234 1.0

Minutes < 500’ AGL -.147 .034 -.139 -.053 -.175 .049 .373 

(.013)

-.422 

(.004)

-.033 .446 

(.002)

1

r

pb = Point-biserial. 2

r

s = Spearman rho. Low p-values are in parentheses (all others are non-significant (NS)); 3

No 

correlation run because sample had been partitioned for these factors. 4

(pre-test+post-test+final test)/3
11 

3. Pilot age x flight hours (rs = .736) merely means that 

older pilots tend to accumulate more flight time. 

4. Flight duration x minimum distance to ABQ 

(rs

 = -.908) only means that the longer pilots fly, the 

closer they tend to get to the destination (ABQ). 

Non-trivial relations. Discounting trivial relations, a few

interesting ones remain. In Table 8, these are boldfaced 

and marked in a darker shade of gray. They are:

1. Web preflight duration x Locality of residence(

pb=-.421)

2. Web preflight duration x Pilot age(rs=.475)

3. Web preflight duration x Pilot flight hours(rs

=.368)

4. Minutes < 500' AGL x Flight duration(r

=.373)

5. Minutes < 500' AGL x Minimum distance to

ABQ(rs

=-.422)

6. Minutes < 500' AGL x Minutes in IMC(

=.446)

Correlations 1-3 imply that non-Oklahoma pilots

older pilots, and higher flight-hour pilots tended to 

spend slightly more time using the Web preflight briefing tool. The effect sizes were no more than modest, 

accounting for r2

pb = 18, 23, and 14% of the variance, 

respectively.

Correlations 4-6 imply that pilots who flew longer, got

closer to ABQ, and those who spent more time in IM

tended to spend slightly more time < 500' AGL. Effec

sizes were also modest, accounting for r2

pb = 14, 18, and 

20% of the variance, respectively.

Durability of non-trivial relations. A “durable” relation 

can be defined as one remaining statistically significant 

across both Phases 1 and 2. Table 9 shows the only two 

durable relations.

1. Web preflight duration x Locality of residence

(rpb=-.348/-.421)

2. Web preflight duration x Pilot age (rs

=.417/.475)

In other words, both local pilots and younger pilots 

spent slightly less time on their Web weather preflight 

briefing. Arguably, local pilots were more familiar with 

local terrain and weather patterns. And, older pilots may 

have been either slightly more careful briefers, or might 

have simply been a bit less familiar with Web-based briefing, especially www.aviationweather.gov.

Effect of pilot weather knowledge on subsequent flight 

safety. As in Phase 1, having higher weather knowledge 

(as measured by these test questions) did not significantly 

predict flight safety. As Table 8 showed, average weather 

Table 9. Durable, non-trivial relations between Phases 1 and 2 variables.

Variable 2

Variable 1

Instrument Rating 

(1=inst rated)1

Locality of Residence 

(1=Local)1

Pilot Age2

Pilot Flight Hours2

Ave. Wx Knowledge2

Web Pre-flight duration2

Flight Duration2

Minimum Dist to ABQ2

Minutes scud running2

Minutes in IMC2

Instrument Rating 1.0

State of Residence 1.0

Pilot Age 1.0

Pilot Flight Hours 1.0

Ave. Wx Knowledge4 1.0

Web Preflight Duration -.348 (.013) Ph1

-.421 (.004) Ph2

 .417 (.003) Ph1

 .475 (.001) Ph2 1.0

Flight Duration 1.0

Minimum Dist to ABQ 1.0

Minutes scud running 1.0

Minutes in IMC 1.0

Minutes < 500’ AGL

1

r

pb = Point-biserial. 2

rs = Spearman rho. P-values are in parentheses.
12 

knowledge10 did not correlate significantly 

with any Phase 2 flight behavior variables.11

Also as in Phase 1, neither did it seem related 

to age, flight hours, locality of residence, 

or instrument rating (rs

 = -.066, .154, rpb = 

-.255, .247 respectively, all NS)

Effect of Web preflight briefing time on 

subsequent flight safety. Were pilots who 

spent more time on their Phase 2 Web-based 

weather briefing safer pilots? As in Phase 1, 

not significantly. Table 8 shows all non-significant Spearman correlations of Web preflight duration with flight 

duration, minimum distance to ABQ, minutes scud 

running, minutes in IMC, and minutes < 500' AGL

with correlation values ranging from -.076 < rs 

< .109.

We now have a bit more sophisticated sense of how 

these pilots used the Web-emulation to brief themselves 

on the weather. It might seem logical to explore relations 

between, say, individual page view durations and our flight 

safety variables. However, there is arguably far too much 

variability in the data to be able to do this confidently.12

Effect of the weather training products on takeoff hesitancy. 

In Phase 1, we saw evidence that the weather training 

product may have induced takeoff hesitancy. Did this

hesitancy persist over time? In Phase 1, 12 of 50 pilots 

initially stated that having to fly this mission VFR, they 

would choose not to even take off. In Phase 2, 7 of the 

44 returning pilots made the same decision. Overall, the

degree of change was not significant (pC2 = .330, NS).

Stated the opposite way, Phase 1 and Phase 2 decisions 

were significantly correlated13 rf = .323 (p = .032). 

Table 10 shows that 4 + 30 = 34 pilots (the 2 cells 

highlighted gray) repeated their Phase 1 takeoff decision in Phase 2, while 3 + 7 = 10 (23%) reversed their 

decision—7 of those 10 (70%) being pilots who formerly 

did not want to take off, who now did want to take off, 

even though the flight situation was essentially identical 

to Phase 1.

10(pre+posttest+final score)/3.

11Spearman correlations with flight duration, minimum distance to 

ABQ, minutes scud running, minutes in IMC, and minutes < 500'

AGL ranged from -.260 < rs < .07, all NS. 12First, the variation in numbers of page views-per-dependent variabl

(DV) was enormous (range 1-92, mean 44.8, SD 28.0), meaning tha

correlations and models would either be based on wildly different 

numbers of cases or would be saddled with huge numbers of zero 

values. Second, the frequency distributions for the 18 Web pages’ 

durations were, without exception, unacceptably non-normal for 

parametric techniques. Even excluding non-zero values, all ShapiroWilk ps were < .001 except Collaborative Convective Forecast Produ

(CCFP) = .011 and Convective Outlook-Wind = .183 (but, wh

was based only on n=3). Currently, there is no widely accepted

method of nonparametric multiple regression. So, in short, we would 

mistrust the results.

13The correlation used here was f (phi), which measures the relation 

between 2 dichotomous variables (in this case, Phase 1 hesitancy yes/

no vs. Phase 2 hesitancy yes/no).

Table 11 shows numbers of Phase 1 and Phase 2 pilots 

who initially hesitated, versus the values expected by 

chance (in parentheses). In Phase 1, we saw significant 

effect (lack of hesitancy) centered in the Control group.

In Phase 2, this effect was not significant. The Yatescorrected Phase 2 pC2

 is .554, implying that the training 

groups did not differ significantly.

This implies that training product-induced takeoff 

hesitancy did not persist over time. This is important 

because Phase 1 evidence for effect of training products 

was sparse and rested on the effect of a 3-variable model 

(consisting of training product + pilot age + takeoff hesitancy) to predict whether or not pilots would complete 

the entire flight to ABQ. Were hesitancy to cease to exert 

an effect, that would probably obviate the model.

In Phase 2, this hesitancy did appear to diminish and 

2 changes seemed to drive it. First, as Table 11 shows, the 

Yes/No ratio for training product 2 changed from 9/7 to

12/3. Second, the Control group’s Yes/No ratio went fro

17/1 to 13/1 (due to the serendipitous attrition of 4 Phase 

1 non-hesitating controls).

Serendipity or not, unlike Phase 1, no assertion can now 

be made that the training product induced takeoff hesitancy 

in Phase 2. This finding is critical, because it destroys a 

putative causal chain, namely that:

training product → takeoff hesitancy → degree of penetration 

into adverse weather → degree of risk

This causal chain was Phase 1’s only viable candidate 

model for asserting that a 90-minute weather training product 

might have an demonstrable effect on pilot behavior—the 

primary motivation for doing this study. Now, the Phase 2

analysis implies that, if a “Takeoff Hesitancy Effect” ever

existed, it was not durable.

Effect of takeoff hesitancy on subsequent flight safety. The 

above argument reduces to the following question: Did the

7 Phase 2 hesitators perhaps still end up somehow flying 

more safely than the remaining 37 pilots? 

Not appreciably. There were no significant differences

between hesitators and non-hesitators for minutes spent 

in IMC, minutes scud running, or minutes < 500' A

(2-tailed Mann-Whitney pU = .268, .089, .950 respectively, 

all NS). In Phase 1, hesitators seemed to continue their

conservatism into their flight, making significantly briefer 

Table 10. Takeoff decision across phase for 44 pilots who participated in both Phase 1 and 2 (“Yes” means “Would take off”).

Phase 2

No Yes

Phase 1

No 4 (1.8) 7 (9.3)

Yes 3 (5.3) 30 (27.8)

1st number is actual n (2nd is expected n)
13 

flights, with consequently less penetration into the marginal 

weather close to ABQ. This was not true in Phase 2 (pU = 

.550, .450, respectively, NS)

Effect of the weather training products on subsequent flight 

safety. When all was said and done, did viewing a weather 

training product significantly affect flight safety? In Phase 

1, there was indirect indication of this. Seeing a weather 

training video related to takeoff hesitancy, which related 

to flight duration, which related to minutes spent < 500' 

AGL—although seeing the weather video did not significantly directly relate to minutes spent < 500' AGL (nor t

scud running or time spent in IMC)

However, as we saw, the same indirect correlational chain 

did not seem at work in Phase 2. Nor could any direct relation be seen between training product and subsequent flight

safety, as measured by flight duration, minimum distance to 

ABQ, minutes in IMC, minutes scud running, or minutes

< 500' AGL (.154 < pKruskal-Wallis < .768, NS)

IMC penetration. Despite highly emphasized instructions

to fly VFR-only, a small number of Phase 1 and 2 pilots still 

penetrated IMC. Phase 2 penetration ranged in duration from

0.03-84.7 min. Most pilots avoided IMC altogether, and the

number of penetrations decreased from Phase 1, although 

not technically significant when duration was analyzed.14

We can argue that those who spent less than a minute 

in IMC probably did so inadvertently. Seven Phase 2 pilots

spent more than 1 minute in IMC (versus 16 in Phase 1).

Five spent more than 4 minutes (versus 10 in Phase 1). 

Figure 7 illustrates there were only 3 “repeat offenders” for 

long-duration penetration (>4 min) across both studies—2 

of these were VFR pilots, all resided outside Oklahoma15

providing no solid evidence why these 3 particular individuals 

behaved as they did. It may prove useful to explore this in 

a follow-up report. 

14Wilcoxon signed-rank Phase 1-2 difference test results for a) all 

IMC penetrations, b) > 0.5 min, c) > 4 min = p = .614, .140, 158,

respectively, (all NS). 15Given that about half the pilots were Oklahomans, the odds of 

randomly selected pilots all being non-Oklahomans is still roughly

1/23

, or .125 (NS), meaning it can be considered a chance occurrence

Modeling Flight Behavior

One of the questions we want to answer is, “What

differentiated pilots who chose to complete the flight through 

deteriorating weather from pilots who chose not to complete 

the flight?” To investigate this question meant constructing models—simplifications of the situation that still 

captured its essential features.

Correlations are, at heart, the simplest possible kinds

of model—a set of relations between single variables. 

Table 8 was a good place to start investigating these 

relations. However, it is rare that real-world events are 

ever completely explained by one, single factor. More 

often, multiple causative (or facilitative) factors are at 

work simultaneously. To try to get at such multi-factor 

relations, we turned to multivariate modeling.

Cluster analysis. In Phase 1, we saw how a subset of 

the candidate variables formed 2 significant similarity 

clusters (Table 12 was derived from Table 6 of the Phase 

1 report).

In Phase 2, however, a repeat cluster analysis failed to 

find any variables related sufficiently to sort the pilots 

into even 2 clusters. The logical significance of this will 

become apparent shortly.

Binary logistic regression analysis. In Phase 1, stepwise 

forward likelihood-ratio binary logistic regression analysis 

produced the 3-variable model referred to earlier. However, when this was again attempted, it was unproductive. 

The candidate variables from Table 12 not only failed to 

cluster but also failed to significantly predict the binary 

outcome variable ToABQ (meaning whether or not a 

given pilot made it all the way through the deteriorating 

weather to the destination ABQ).

We next retested the Phase 1 3-variable logistic model 

for durability. In Phase 1, a model based on training 

product, age, and takeoff decision16 was able to predict 

64% of the explainable (Nagelkerke) variance in ToAB

(p=.000004) and correctly predicted 83.3% of the cases 

16Note that takeoff decision reflects “takeoff hesitancy” as discussed

earlier.

Table 11. Takeoff hesitancy, Phase 1 vs. 2.

Phase 1 Phase 2

Trg Prod 1 Trg Prod 2 Control Trg Prod 1 Trg Prod 2 Control

Initial takeoff decision

Yes 12 (12.2) 9 (12.2) 17 (13.7) 12 (12.6) 12 (12.6) 13 (11.8)

No 4 (3.8) 7 (3.8) 1 (4.3) 3 (2.4) 3 (2.4) 1 (2.2)

Pairwise odds-ratios, 1-tailed p

← .152 →

← .004 → NS

← .037 →
14 

(diversion vs. continuation on to ABQ), compared to a 

baseline prediction rate of 62.5%.17

In retesting this model with Phase 2 data, however, the 

identical model predicted only 19.9% of the Nagelkerke variance and 72.1% of the cases, compared to its baseline rate

of 60.5%. This was not a significant improvement (p=.145, 

NS) over an educated guess (that is, the baseline, “constantonly” model)

This performance degradation of the Phase 1 model was 

certainly not simply due to the raw number of pilots who 

actually made it all the way to ABQ (18 of 4818 in Phase 1 vs. 

17 of 43 in Phase 2). Nor was it due to pilot age (because pilots

were only 3-4 months older than they were during Phase 1). 

By default, the Phase 1 model seemingly collapsed due 

to inconsistencies in takeoff decision from Phase 1 to Phase 

2. In other words, whatever coherent effect, or “signal,” the 

weather training products may have engendered in Phase 1 

dissolved amongst the “noise” of individual variation in Phase 

2. Table 10 showed the 2x2 consistency matrix. Ten pilots 

(3+7) reversed their Phase 1 takeoff decisions in Phase 2. 

While Fisher’s Exact Test gives this probability at p=.054

technically non-significant—it is arguably close enough to 

suspect that we have the culprit that disabled the Phase 1 

3-variable model.

17The baseline rate is the model’s ability to predict an outcome by 

chance alone, given only knowledge of average group behavior. For 

instance, if 50% of Americans voted Democratic, that would be the

baseline rate, assuming we had no other knowledge about individual 

voters. However, if we knew individual voters’ incomes, educational 

levels, ethnicities, genders, religious preferences, and job categories, we 

might expect to predict individual votes at greater than a 50% success 

rate. The primary purpose of multivariate modeling is to maximize 

that kind of additional predictability.

18The original Phase 1 N=50, with 1 missing data, 1 eliminated for CFI

® 48. The original Phase 2 N=44, with 1 eliminated for CFIT ® 4

Disc

The purpose of this research was to investigate the 

following questions:

1. Video weather training products 

a.What are the immediate effects on pilot weather 

knowledge and flight behavior in the face of potential 

instrument meteorological conditions?

b. Do these effects persist over time

2. How are modern Web-based weather products used 

during preflight briefing?

3. Do Oklahoma pilots differ appreciably from nonOklahoma pilots in either weather knowledge 

weather-related flight behavior?

Phase 1, Brief Summary

Main findings, Question 1. In Phase 1 of this project, 50 

GA pilots participated in a study designed to collect data on 

both pilot weather knowledge and flight behavior. Pilots took 

a weather knowledge pre-test, followed by exposure either to 1 

of 2 weather training videos (the Experimental groups), or to a

video having nothing to do with weather (the Control group).

They then took a knowledge post-test to measure knowledge 

gain induced by the training product. Next, they planned for,

and flew, a simulated flight mission through deteriorating 

weather from Amarillo, TX, to Albuquerque, NM (ABQ).

Numerical flight data were collected and flight behaviors noted

A limited number of significant effects were seen in Phase 

1. For one, there was a tendency for pilots who viewed either 

of the 2 weather videos to hesitate taking off into the marginal 

weather. These “hesitators” flew only after encouragement. In 

contrast, 17 of 18 control group pilots took off without any 

encouragement.

Table 12. Phase 1 variables contributing significantly to clustering.

Continuous Categorical

Age Training product

Flight hours Takeoff hesitancy

Final minimum distance to ABQ Wx recheck just before takeoff

Minutes flying < 500’ AGL Flew all the way to ABQ

Clustering

Cluster 1 tendencies Cluster 2 tendencies

Younger Older

Lower flight hr Higher flight hr

Closer final minimum dist to ABQ Farther final minimum dist to ABQ

More minutes at < 500’ AGL Fewer minutes at < 500’ AGL

Control group (no wx trg product) Received a wx training product

Initial takeoff response was to fly Initial takeoff response was to not fly

No wx check just before takeoff Wx check just before takeoff

Flew all the way to ABQ Diverted before ABQ
15 

Subsequently, the hesitators continued their conservatism, tending to make shorter flights than non-hesitators. 

Since the bulk of the scenario’s danger lay near the flight’s 

destination, we might conclude that watching a weather 

training video induced takeoff hesitancy, which then 

induced shorter flights, which then led to lower groupwise risk exposure. There was correlational evidence to 

support each individual link of this chain.

However, the overall chain of logic was not that simple. 

Ultimately, none of the beginning-state variables (e.g.,

pilot age, flight hours) ended up directly correlating with 

the end-state flight-risk variables (scud running, time 

spent in IMC, or time spent at < 500' AGL). Therefore

no simple model based on video training product alone 

could be ultimately shown to modulate flight risk.

Therefore, to try to explain weather-related risk-taking, 

we next turned to multivariate modeling. Non-normalities

in the data frequency distributions19 precluded the use of 

more-powerful parametric multiple regression statistical 

techniques. So, a binary logistic regression model was 

used, which took multiple candidate variables (continuous and discrete) to find the best combination capable of 

explaining the variation in a single, discrete dependent 

variable—namely, whether or not a pilot completed the 

entire flight to ABQ. This model was based on the assumption that the farther one flew into the deteriorating 

weather, the greater the overall risk.

In Phase 1, that analysis led to a reasonably wellperforming, 3-variable model of weather-related risk 

taking based on

• Video training product

• Pilot age

• Takeoff hesitancy

19Key data frequency distributions are shown in Appendix A.

We can interpret this to mean that—if a brief video 

weather training product did exert a measurable effect—it 

probably did so less by directly influencing pilots' weather 

knowledge or preflight briefing habits and more by appealing to a subset of somewhat older pilots with latent 

conservative behavioral (flying) tendencies to begin with. 

Those pilots may arguably have been sensitized, less by 

specific training product content than by the fact of having been exposed to “something safety-related,” which 

subsequently made them more cautious about the specific 

flight scenario to which they were exposed.

Secondary findings. As Figure 10 (Appendix B) shows, 

in Phase 1, there were slight-but-significant tendencies for 

older pilots to spend a bit more time on their Web-based 

preflight briefing, whereas local pilots spent less time on it. 

Instrument-rated pilots spent slightly less time too close 

to the ground (< 500' AGL). So did higher flight hour

pilots. However, instrument-rated pilots also tended to 

be older, with higher flight hours. So, it was difficult to 

pinpoint whether rating, age, or flight hours was most 

related to ground clearance. 

There was also a slight tendency for older pilots and 

higher flight hour pilots to fly shorter flights (meaning 

they penetrated the weather slightly less). Age and experience may engender some risk aversion. Alternatively, 

younger pilots might have been merely “gaming the 

system,” treating the flight more like a game than a real 

flight. It is difficult to say.

As an incidental side effect noticed during this 

research, these pilots’ long-term retention of weather 

knowledge proved somewhat lower (19%) than national norms on FAA certification exam scores obtained 

by freshly licensed pilots. This may or may not be an 

important issue having bearing on how the FAA constructs test questions and assesses weather competency. 

Figure 7. (Left) Number of Phase 1 pilots (y-axis) by time-spent-in-IMC (x-axis) for the time range 0.5 – 82.5 

minutes. (Right) Phase 2 pilots, showing drop-off in long-term IMC dwell time.
16 

However, it is sufficiently complex to merit review in a 

separate “Phase 3” report.

Finally, one instance of actual controlled flight into 

terrain (CFIT) was seen in Phase 1. This underscored

the humbling fact that genuine accidents rarely follow 

the exact pattern implied by group statistics. This incident happened because of nothing more elaborate than 

momentary in-flight attentional lapse while the pilot was 

studying the sectional. It was a Control group pilot, not

younger or lower flight hour, as our model would have 

led us to believe.

Phase 2, Brief Summary

Main findings, Question 1. As Figure 11 (Appendix 

B) shows, the pattern of non-trivial relations between 

variables differed substantially from Phase 1. Most relations did not replicate, other than trivial ones. 

As previously mentioned, particularly salient was the 

failure of the 3-variable Phase 1 model to again significantly predict risk-taking behavior in Phase 2. In Phase 

2, not only was there no significant evidence of a direct 

effect of the training products on anything, but even the 

partial (or, perhaps, indirect) effect represented by the 

Phase 1 3-variable model disappeared.

The most straightforward explanation for the 3-variable model’s collapse may simply be what behavioral 

psychologists call desensitization. In Phase 2, pilots were 

under less pressure: They had fewer tests to take; they were 

already familiar with equipment and procedure; they now 

fully understood that the FAA researchers were benign. 

They could relax and act naturally. And, when they did, 

it became hard to discern any great differences between 

the weather training groups and the Control group

In the Phase 1 report, we discussed a possible cognitive 

priming hypothesis. First-time exposure to the weather 

training product could have “tipped off the participants” 

that the study was to be about weather. Given the context 

of FAA officials conducting an experiment within an FAA 

facility, a devil’s advocate could argue that any Phase 1 

effect of training product might owe more to priming 

about weather and risk than to any permanent cognitive 

or behavioral change the products themselves might have 

induced. Most likely, any effect was affective (emotional), 

and merely decayed/desensitized over time.

Secondary findings. As in Phase 1, a single episode of 

controlled flight into terrain also occurred in Phase 2. 

Figure 8 shows the flight profile for this individual, an 

older, instrument-rated, high flight hour pilot. As in 

Phase 1, the explanation was not complex—the pilot 

appeared to suffer a lapse of attention and ran straight 

into the rising terrain (Figure 8, left). The flight notes 

recorded that the individual had unusually great initial 

trouble mastering the controls of the Malibu. Shortly 

after takeoff, there was direct ascent into brief IMC.

Recovery from this led to direct, steady descent, which 

nearly resulted in impact with the ground (Figure 8, right, 

minimum AGL = 9.78'). Many would agree that such

an near-impact could be disorienting and could lead to 

subsequent “tunnel vision,” with difficulty attending to 

normal multiple piloting tasks, including awareness of 

terrain clearance.

Findings Common to Both Studies

Durable relations. As Table 9 and Appendix B show, 

a few non-trivial durable relations persisted from Phase 

1 to 2. There was a slight tendency for older pilots, and 

for Oklahoma pilots, to spend a bit more time on their

Web-based preflight briefing. This is readily explained 

if we assume that older pilots were likely to be slightly 

less familiar with Web-based preflight briefing, and that 

local pilots tended to know the terrain better.

Consistency of flight behavior. Table 7 showed that in 

Phase 2, more than 78% of pilots made the same ultimate 

choice about either diverting or continuing on to the 

Figure 8. The one Phase 2 CFIT. (Left) Horizontal view of the pilot’s flight profile. (Right) 

Expanded view of the takeoff and subsequent events, including a near-collision with terrain. 
17 

destination that they first made in Phase 1. Only 6 pilots

grew more daring from Phase 1 to Phase 2, while 3 grew 

more conservative—not a significant groupwise change. 

This consistency was remarkable, and what inconsistency 

there was could not be explained by any of our major 

independent variables.

Rule violations. Despite this being stressed as a VFRonly flight, in Phase 1, 10 pilots ascended immediately

into IMC and spent at least 4 minutes there. In Phase

2, the number dropped to 5, although that decrease was 

not statistically significant. Three pilots were “repeat offenders” across both phases. This is particularly perplexing, given that these pilots were obviously well aware 

of being involved in a study sponsored by the aviation 

regulator, and yet still violated cloud clearances and 

minimum visibility requirements. At this time, we have 

no explanation for this behavior but intend to pursue it 

in a “Phase 3” report.

Question 2: Use of Web-based preflight weather briefing 

materials. Both Phase 1 and 2 studies show the study of 

Web-based preflight briefing to be more complex than 

anticipated. However, we note that the present studies are 

among the first human factors studies of NWS Web-based

preflight briefing to be done (if not the first). Therefore, 

methodological imperfection is unsurprising. 

First, there was an issue with page view duration outliers, as detailed in the Phase 1 report. Second, users take 

time to explore a new system, so “first impressions” of 

usage are bound to differ from those of persons trained 

to an asymptotic skill level. Third, the number and distribution of page views can be inflated by users merely 

quickly going to a page, finding out there is nothing of 

value there, and moving on. 

Finally, the amount of time spent per page (view 

duration) confounds two completely different concepts, 

namely information importance and information difficulty. 

In other words, long dwell times do not necessarily mean 

a page is chock full of information. It can be, but it also 

can mean that the information on that page is simply 

hard to access or to understand. Untangling this confound

will require more sophisticated methodology, possibly 

eye-tracking or self-report surveys, and possibly even 

physiological measurement of workload.

Resolution of these issues will be fundamental to 

continued human factors study of Web-based preflight 

briefing. However, such study is necessary, since the Web 

is so obviously the future of GA preflight weather briefing.

Question 3: Equivalency of local Oklahoma pilots to 

non-locals. We did not see significant differences between 

local pilots and non-local pilots. The only significant 

finding was that locals took slightly less time to brief 

for this relatively local flight. But, Oklahoma pilots are

arguably more familiar with their own local terrain and 

weather patterns, and need less briefing time for a flight 

such as this, so that issue is trivial. 

Importantly, this addresses the issue of whether CAMI

studies are generalizable to the national population of 

U.S. GA pilots. To recap the Phase 1 report, the fact

is that U.S. pilots study a fairly uniform curriculum

(largely driven by the licensing exams). This guarantees 

a measure of pilot uniformity. What is certainly far more 

important to research planning is the individual variation 

in knowledge and skill present between one pilot and 

another—not where a particular pilot happens to live. 

Yes, there are specific regions where certain flying skills 

are more called-upon than others. The high winds in 

the Midwestern U.S. are a good example. But—unless

the task to which a given group of pilots is put depends 

critically on some small, specific set of skills—geographical 

region-of-residence probably will not matter a great deal. 

What this means is that researchers simply need to 

adhere to good practice in selecting pilots and assigning 

them to treatment conditions. As long as designs are 

counterbalanced, and pilots are reasonably well-matched 

for age, flight hours, and instrument rating over treatment cells, there is probably only occasional need to 

recruit non-locally. Our final cost figures put the human

effort and dollar cost of testing a non-local pilot at approximately 5-10 times the expense of recruiting a local 

pilot. Therefore, what non-local pilots are probably best 

used for is precisely when an elite sample is required but 

not locally available. For instance, if high-hour, young, 

VFR pilots were needed for some reason, then we would 

probably want to consider recruiting non-locally.

Ref

National Geophysical Data Center (2008). NOAANGDC-MGG-GLOBE Custom Data Selection Pa

Digital elevation data downloaded Oct. 23, 200

from www.ngdc.noaa.gov/cgi-bin/mgg/ff/nphnewform.pl/mgg/topo/customdatacd.

Wolfram Research (2008). Mathematica V7.0. 

A-1 

APPENDIX A 

Figure 9. Frequency histograms for numbers of pilots (y-axis) by flight duration, minimum distance to Albuquerque, minutes 

in IMC, minutes below 500’ ground clearance, and minutes scud running (x-axes). Phase 1 data are at left, Phase 2 at right. 

B-1 

APPENDIX B 

Figure 10. Phase 1 univariate and multivariate correlational structure. 

Figure 11. Phase 2 correlational structure.