. DOT/FANAM~97/23 · 

Office of Aviation Medicine 

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T h ec meal . D ocumentat1on p ag_e 

1. Report No. I 2. Government Accession No. 3. Recipienfs Ca1alog No. 

DOT/FAA/AM-97/23 

4. T~e and Subti11e 5. Report Oa1e 

The Use of Weather Information in Aeronautical Decision-Making: II November 1997 

6. Performing Organ~tion Code 

7. Aulho~s) 8. Performing Organization Report No. 

Driskill, W.E., Weissmuller, ].Q., Hand, O.K.;' and Hunter, D.R.2 

9. Performing Organization Name and Address 10. Wolk Un~ No. (TRAIS) 1 Metrica, Inc. I 'FAA Office of Aviation Medicine 11. Contract or Grant No. 

San Antonio, TX 78216 Washington, DC 20591 

12. Sponsoring Agency Name and Address 13. Type of Report and Perfod Covered 

Office of Aviation Medicine 

Federal Aviation Administration 

800 Independence Avenue, S. W. 

Washington, DC 20591 

15. Supplemen1ary Notes 

16. Abstract 

An investigation was conducted of the values, or worth functions, pilots attribute to weather and terrain variables in 

making decisions about flight in a single-engine aircraft under visual flight rules. This study replicated earlier 

exploratory research (Driskill, Weissmuller, Quebe, Hand, Dittmar, and Hunter, 1997) that used data from a 

single geographic area. The present study obtained data from pilots in six geographic regions of the United States. 

The results of this study confirm the three tentative hypotheses suggested by the data from the initial study: (1) 

Cognitive processes that pilots utilize in making aeronautical decisions can be modeled using regression methods; 

(2) The values pilots associate with varying levels of ceiling, visibility, and precipitation are a function of the terrain 

over which the flight is made; and (3) While values differ among pilots, specific policies can be found to describe 

how they assign weights in making decisions about beginning or continuing a flight. Generally, pilots use a 

compensatory decision strategy, combining the weather variables in making judgments about flight by 

compensating for poor conditions in one variable with better conditions in other variables. However, under some 

circumstances, pilots also tend to employ a worst-factor strategy; that is, pilots appear to have personal standards 

for either ceiling, visibility, or precipitation, below which they become reluctant to make a flight. 

17. Key Words 18. Distribution Statement 

Pilots, Aircraft Pilots, Decision-Making, Aviation Document is available to the public 

Safety, Linear Modeling, Policy Capturing through the National Technical 

Information Service, Springfield, VA 22161 

19. Securny ClassH. (of lhis report) 120. Security ClassH. (of lhis page) 121. No. of Pages 122. Price 

Unclassified Unclassified 55 

Form DOT F 1700.7 (8-72) Reprodu<mon of completed page aulhonzed 
TABLE OF CONTENTS 

Page 

I. INTRODUCTION ............................................................................................................................. 1 

II. BACKGROUND ................................................................................................................................. 1 

III. METHOD AND DATA COLLECTION ......................................................................................... 3 

Format Modifications .......................................................................................................................... 3 

Pilot Sample Selection and Description .............................................................................................. 3 

IV. ANALYSIS OF REGIONAL DATA .................................................................................................. 4 

Inter rater Agreement and Stability ...................................................................................................... 4 

Policy Group Identification and Description ..................................................................................... 5 

Policy Group Demographic Information ............................................................................................ 6 

Regional Comparisons ......................................................................................................................... 7 

Worst Flight Scenario .......................................................................................................................... 8 

V. COMPARISON WITH SOUTH TEXAS SAMPLE RESULTS ..................................................... 8 

Demographic Information ................................................................................................................... 8 

Comfort Level ...................................................................................................................................... 9 

Average R2 and Average Regression Weights ...................................................................................... 9 

VI. DECISION STRATEGY .................................................................................................................... 9 

VII. FINDINGS AND CONCLUSIONS ................................................................................................. 9 

VIII. REFERENCES ................................................................................................................................... 11 

LIST OF FIGURES 

Figure 1. Group comfort levels by route ...................................................................................................... 13 

Figure 2. Pilot comfort levels at 8 NM visibility ......................................................................................... 14 

iii 
TABLE OF CONTENTS (CONTINUED) 

LIST OF TABLES 

Table 1. 1997 sample demographics compared with demographics in 1995 Study .................................. 15 

Table 2. Sample distribution by terrain type and geographic region ......................................................... 15 

Table 3. Mean differences comparisons for total and geographic region samples ..................................... 16 

Table 4. Interrater agreement and stability for regional and total samples by terrain type ................. ~ ..... 17 

Table 5. Average regression weights for terrain policies ............................................................................. 18 

Table 6. Over-water subpolicy group mean regression weights ········•·····································-·········-·········· 18 

Table 7. Non-mountainous subpolicy group mean regression weights ...................................................... l9 

Table 8. Mountainous subpolicy group mean regression weights .............................................................. 19 

Table 9. Over-water scenario mean comfort ratings ................................................................................... 20 

Table 10. Non-mountainous scenario mean comfort ratings ....................................................................... 21 

Table 11. Mountainous scenario mean comfort ratings ............................................................................... 22 

Table 12. Over-water policy group comparison ............................................................................................ 23 

Table 13. Highlights of group characteristics (over-water route) ................................................................. 24 

Table 14. Non-mountainous policy group comparison ................................................................................ 25 

Table 15. Highlights of group characteristics (non-mountainous) .............................................................. 26 

Table 16. Mountainous policy group comparisons ...................................................................................... 28 

Table 17. Highlights of group characteristics (mountainous) ...................................................................... 29 

Table 18. Mean regional comfort ratings by terrain ..................................................................................... 29 

Table 19. Mean regression weights for geographical regions by terrain ....................................................... 30 

Table 20. Proportionality of group membership by region .......................................................................... 31 

Table 21. Comparison of total sample demographic data with South Texas sample .................................. 31 

Table 22. Regional and South Texas demographics (over-water) ................................................................ 32 

Table 23. Regional and South Texas demographics (non-mountainous) .................................................... 33 

Table 24. Regional and South Texas demographics (mountainous) ............................................................ 34 

Table 25. Comparison of compensatoty and non-compensatoty models .................................................... 35 

APPENDIX A- SOFTWARE AND ANALYSIS ......................................................................................... AI 

APPENDIX B- DATA COLLECTION PACKAGE .................................................................................. Bl 

APPENDIX C- REGRESSION MODELS ................................................................................................. Cl 

iv 
THE UsE oF WEATHER INFORMATION 

IN AERONAUTICAL DECISION MAKING: II 

I. INTRODUCTION 

This report describes research which assessed worth 

functions that a nationwide sample of pilots assign to 

weather information when making aeronautical deci~ 

sions about flight over varying terrain. It replicates 

exploratory research {Driskill, Weissmuller, Quebe, 

Hand, Dittmar, and Hunter, 1997), which was based 

on data from a single geographic area, that provided 

data tentatively confirming three hypotheses: First, 

cognitive processes pilots utilize in making aeronautical decisions can be modeled using regression methods; second, the values pilots associate with varying 

levels of ceiling, visibility, and precipitation are a 

function of the terrain over which the flight is made; 

and third, while values differ among pilots, specific 

policies can be found to describe how they assign 

weights in making decisions about a flight. 

Reported are results from data collected from pilots 

from six geographical regions. The report includes the 

following: identification of worth functions pilots 

attribute to weather variables, description of differences of policy decisions with respect to the worth 

functions, assessment of regional differences in use of 

weather information, analysis of the relationship of 

pilot demographic information and pilot decisionmaking with regard to weather and terrain information, comparison of results with the exploratory 

research findings, assessment of pilot decision strategies, and a summary of findings and conclusions. 

II. BACKGROUND 

The importance of understanding the cognitive 

processing of flight information in preparing for and 

during flight is evident from research reported by 

Jensen and the National Transportation Safety Board 

(NTSB). According to Jensen (1982), 80 to 85 percent of aircraft accidents can be assigned to pilot error 

and, further, over 50 percent of the fatal accidents 

result from faulty decision-making. The NTSB, in a 

study of 361 small aircraft accidents in which 583 

fatalities were reported for 276 of them {NTSB/SR89/01, July 14, 1989, p.1), reported that flight under 

visual meteorological rules {VFR) into instrument 

meteorological conditions {IMC), and pilot error are 

two of the most important factors in small aircraft 

fatal accidents. 

While considerable research is reported about the 

potential effects of Five Hazardous Thought Patterns 

on decision-making {see, for example, Jensen, Adrion, 

& Lawton, 1987) in aviation accidents, very little is 

reported on the cognitive aspects of aeronautical decision-making, i. e., on how pilots assess adverse 

conditions and assign and integrate appropriate weights 

to such flight data elements as weather and terrain in 

making their judgments. Two studies are reported by 

Curry {1976) and Flathers, Giffin, and Rockwell 

{1982), who noted differences in worth functions or 

values pilots assigned to various flight data elements 

in the landing and en route phases of flight. In 

addition, studies at the University ofillinois {Barnett, 

1989; Stokes, Kemper, & Marsh, 1992; Wickens, 

Stokes, Barnett, & Davis, 1987) have examined the 

relationship of pilot biographical and demographic 

information, along with knowledge and ability measures, to pilot performance and information processing during simulated flight performance. 

Whether or not worth functions can be modeled 

using regression methods was the focus of research 

reported in Driskill et a/., {1997). The approach 

involved collection of pilot judgments of scenarios 

describing flight over non-mountainous, over mountainous, and over-water terrain under various visibility, ceiling, and precipitation conditions. Each scenario 

consisted of a proposed cross-country VFR flight of 

approximately one-hour duration in a single-engine 

aircraft with an inoperative navigation radio and 

without a transponder over each of the three kinds of 

terrain under differing VFR into IMC weather conditions. The procedure and findings from that study are 

summarized below, however readers are directed to 
the original report {Driskill et al., 1997) for a complete description. 

A set of27 scenarios was developed for each terrain 

type. Specific routes were drawn on route maps for 

flight from Hereford airport to Dalhart airport in the 

Texas Panhandle (non-mountainous), from Las Vegas airport to SanJuan airport in New Mexico (mountainous), and from City County airport over Lake 

Michigan to Delta County airport {over water). Scenarios were described on 5 x 7 cards. Each card in a set 

described different combinations of visibility, ceiling, 

and precipitation conditions, developed with the assistance ofNational Oceanographic and Atmospheric 

Agency (NOAA) and U. S. Air Force weather experts 

to assure realistic weather combinations, i. e., that 

combinations could be observed in nature. Maps 

denoting the routes pilots were to follow and other 

information pertinent to flight over each terrain were 

provided for reference. 

Pilot judgments of their level of comfort in flying 

under each of the scenario conditions were the basis 

for assessing worth functions. The judgment task 

required two actions on the part of the subject. First, 

each pilot rank ordered the scenarios in a set in 

descending order based on how comfortable he or she 

would feel in flying over the specified terrain under 

the weather conditions described. Second, using the 

set of rank-ordered scenarios, each pilot then assigned 

a "comfort" rating to each scenario, using the accompanying comfort rating scale. This scale ranged from 

0, least comfortable, to 100, most comfortable. Pilots 

were not constrained {nor were they expected) to rate 

their most comfortable scenario 100. Instead, they 

could assign any lesser level of rating that described 

their comfort level. Many of the subjects, in fact, used 

a rating lower than 100 to indicate their "most comfortable" scenario. Each pilot completed three decks 

of scenario cards, one deck for each of the terrain 

conditions. 

Most of the data collected from 131 male and 19 

female pilots came from group administration during 

meetings of the FAA Safety Seminar Program held in 

the South Texas area (roughly from San Antonio, 

Austin, and Killeen, south to Victoria, and west to 

Rockport). After distributing the three decks of scenario cards {one deck for each kind of terrain), the 

2 

experimenter presented a motivational introduction, 

and then briefed and walked the pilots through the 

rating process, using sample scenarios. The experimenter remained through the data collection process 

to provide further instruction or clarification that 

may have been required. 

Analysis was based on computation of a multiple 

correlation for each subject for each terrain condition. 

The comfort ratings a pilot assigned to the scenarios 

within a terrain condition comprised the dependent 

variable. Independent variables were ratings from 22 

experienced pilots on 16 ceiling levels ranging from 

600 to 5,000 feet, 12 visibility levels ranging from 1 

1/2 NM to more than 8 NM, and eight precipitation 

levels ranging from freezing rain to no precipitation. 

Using a 0-100 point scale, the experts rated how safe 

it would be for an inexperienced pilot to fly under 

each of the levels {Driskill et a/., 1997). Multiple 

correlations for the each of the 150 pilots comprising 

the sample met at least the .05 level of statistical 

significance. 

Regression weights {from the individual multiple 

correlations) associated with the visibility, ceiling, 

and precipitation variables, which represent the value 

or worth pilots associated with each of the variables, 

were hierarchically clustered. The analysis package 

used was HIER-GRP {Appendix A), which is part of 

an Armstrong Laboratoty mathematical-statistical 

package. This analytical process formed clusters of 

pilots with similar within-cluster regression weights; 

between cluster weights {within terrain type), however, differed significantly with respect to the value or 

worth functions assigned to visibility, ceiling, and 

precipitation information. 

Hierarchical clustering produced four significantly 

different non-mountainous terrain policy dusters or 

groups; four groups for flight over the mountainous 

route; and two groups for the over-water route. Within 

terrain group policy differences seemed to vaty according to how conservative the subjects comprising each of 

the groups were in making their ratings. Some policy 

groups were formed by pilots who provided relatively 

low overall comfort levels even when flight was under the 

best weather conditions while some other policy groups 

exhibited considerably higher comfort levels for flight 

under all conditions, including the most adverse. 
Some significant differences of demographic data 

for clusters within a terrain condition were found, 

mostly based on kinds of flying experience, age, and 

aircraft ownership. Such differences, however, were 

not consistent across clusters within a terrain condition. Age of pilots comprising each of the four clusters 

for the non-mountainous clusters, for example, did 

not differ, but age in one or more of the clusters in the 

other two terrain types differed significantly from 

other clusters. Further analysis of the background 

information revealed that pilots changed group policy 

membership from one terrain to another, i. e., some 

pilots who were members of a policy group reporting 

overall low, conservative comfort levels changed group 

membership to policy groups under other terrain 

conditions having overall higher comfort levels. 

Analysis also addressed the decision strategies pilots employed in making their comfort level ratings. 

This analysis was undertaken because of decision 

theory research which suggests that judges may employ different decision-making strategies in judgment 

tasks such as those employed in the exploratory research (Payne, Bettman, & Johnson, 1992). Further, 

it is important to determine whether some pilots may 

employ inappropriate strategies when assessing flight 

information, so that training or interventions could 

be used to redirect their decision-making. In general, 

pilots tended to use a compensatory strategy (as opposed to multiplicative or non-compensatory) whereby 

good conditions in one weather variable compensate 

for poorer conditions in other variables. 

The fact that the sample of pilots in the exploratory 

research was drawn from pilots in the South Texas 

area limits the generalization of results to other geographical regions. Subsequent sections of this report 

describe use of the scenario-based judgment task to 

collect judgments from pilots from six other geographical regions of the United States. 

III. METHOD AND DATA 

COLLECTION 

The logistics of data collection from a pilot sample 

nationwide required a change from a proctored-group 

to a mail-out-and-return protocol. In turn, mail ad3 

ministration necessitated modification of the format 

of the data collection instruments. 

Format Modifications 

For efficient mail administration, the scenario descriptions were transcribed from cards to answer sheets. 

Only the format of the scenario information was 

changed. The same route maps were provided. Some 

minor modification of the Pilot Background Data 

form on which pilots provided demographic information was made. Gender, for example, was omitted and 

a few additional informational items were added. 

Instructions, demographic information forms, and 

answer sheets containing scenario information and 

places for respondent rankings and ratings (Appendix 

B) were packaged for mailing. 

Modifications of the scale used and instructions for 

making ratings were made based upon comments 

received from pilots during a pre-test of the instrument. In the original study, the scale ranged from 100 

for the most comfortable scenario to 0 for the least 

comfortable. This scale was reversed for mail administration with 100 to be used for the least comfortable 

and 0 for the most comfortable. For analysis of the data, 

pilot responses were transposed to match the original 

scale. 

Instructions for making comfort level ratings differed also. In the exploratory study, respondents were 

instructed that the maximum rating, e. g., 100, did 

not have to .be used for the terrain scenario for which 

they felt most comfortable. In this study, such instructions were not provided and, as shown in Section 

IV, respondents appeared to anchor their ratings on 

the extreme ratings, e. g., at 0 and 100. 

Pilot Sample Selection and Description 

The sample was randomly selected from FAA 

records. Only pilots who were actively flying as determined by the presence of a current flight physical record 

in their FAA records file were considered for selection. 

Pilots from the South Texas area were excluded. Data 

collection packages were mailed to 1 ,200 pilots in the 

Alaskan, Western Pacific, Southern, Northwest Mountain, Eastern, and New England regions. Packages 

contained a pre-addressed and post-paid envelope for 

.. 

... 
the raters to return their responses to the FAA in 

Washington, DC. 

Responses from 326 respondents were received and 

submitted for analysis. Answer sheets were transcribed 

into EXCEL and a floppy disk containing the data was 

prepared by the FAA for analysis. 

Demographic statistics describing the sample of 

326 respondents are shown in Table I. Since not all 

pilots completed all the scenarios, there were 319 

respondents for over-water, 315 for over non-mountainous, and 303 over mountainous terrain. The most 

noteworthy demographic characteristic is the 52-year 

average age. This average is almost I 0 years higher 

than the average in data compiled by the FAA (see 

Table 13, U.S. Civil Airmen Statistics, 1993). In this 

report, the average ages for private, commercial, and 

air transport pilots were 42.7, 41.9, and 44.1, respectively. Because of the 10-year difference, demographics of the present sample were compared with a 

mail-administered nationwide sample reported in 

Hunter (1995). As Table I shows, the demographics 

of the two mail-administered samples are quite similar, including the ages of the respondents. 

The distribution of pilots among six geographical 

regions is shown in Table 2 while Table 3 shows the 

background variables for which regions differ significantly (p < .05) 1 from the Total Sample. As shown, the 

sample of Alaskan pilots was considerably smaller 

than were the samples from other regions. 

Mean comfort level ratings were also computed, 

using GRPREL from the Comprehensive Occupational Data Analysis Programs (CODAP) (see Appendix A), for the total sample and for each terrain type. 

The grand mean (total sample) was 39.52, over-water 

39.80, over non-mountainous, 42.27, andover mountains, 36.38. The comfort rating for over-water terrain 

is slightly higher than the grand ·mean. Non-Mountainous average comfort level is about three points 

higher than the overall mean; and, the mountainous 

mean is over three points lower. 

IY. ANALYSIS OF REGIONAL DATA 

Data analysis replicated the method used in the 

exploratory research: interrater agreement, policy stability, computation of multiple correlations for each 

pilot respondent, identification of within-terrain policy 

groups based on hierarchical clustering of regression 

weights associated with the weather variables for each 

terrain type, testing of significance of differences of 

demographic variables among policy groups, and identification of decision strategy pilots employed when 

they made their judgments. Other analyses include 

the effect terrain exerts on comfort level, comparisons 

of regional comfort level, identification of the scenarios for each terrain for which pilots indicated the 

most and least comfort, and determination of relationships of demographic information to comfort level. 

Interrater Agreement and Stability 

lnterrater agreement and stability of ratings for the 

total group and for each regional group by terrain rype 

(Table 4) was computed, using GRPREL (See Appendix A for a description of this analysis package). 

lnterrater agreement is indicated by r 11 and rkk indicates the stability of the policies represented by the 

ratings. An r11 of .20 is considered an acceptable level 

of interrater agreement and any rkk above .90 represents high stability. (The authors recognize that these 

analysis procedures are little utilized outside military 

occupational analysis settings. For more detailed information on the procedures and their application, 

1 In Table 3, and Tables 14, 15, and 17 which will follow later, caution is needed in interpreting the results. Because a large number of 

comparisons are being made in each of these tables, it is likely that some of the observed differences reached statistical significance purely by 

chance. We have chosen, in order not to inflate the Type II error rate, not to make an adjustment to the individual comparisons so as to restrict 

the experimentwise error rate to the nominal O.OSlevel. The impact of this decision is to place slightly more burden on the reader, who must 

keep in mind, for example, that of the 174 comparisons made in Table 3, approximately 9 (.05 x 174) would reach statistical significance 

even if there were absolutely no differences among the regions on any of the demographic items. Clearly, since there were 37 comparisons 

that were found to be statistically significant in that table, there are some underlying differences among the regions. However, which of the 

37 comparisons truly reflect real differences and which are spurious statistical artifacts, we cannot say. Since our interest is observing the general 

pattern of differences (examine, for example, the flying experience of pilots in the Northwest Region), we do not find this to be a serious 

restriction. Rather, adopting more stringent significance levels for each individual comparison, so as to maintain the experimentwise error 

rate, would mask much of the underlying richness of the data by inflating the Type II error rate. On balance, the current approach which 

reports individual comparisons somewhat liberally seems the most prudent course, keeping in mind that any particular individual comparison 

should be regarded skeptically. 

4 
the reader may consult Albert, et al., 1994; Christal & 

Weissmuller, 1976, 1988; Phalen & Mitchell, 1993; 

Staley& Weissmuller, 1981; and Weissmuller, Phalen, 

& Tartell, 1997 .) lnterrater agreement across the total 

group of pilots and within each geographical region 

(r11 ) ranged from .31 to .58. Stability was high, rkk 

ranging from .82 to .99. In the table, k shows the 

average number of raters per scenario. Fractional 

entries for the number of raters are given because the 

software computes an overall or grand mean number 

of raters per scenario. For the Western region overwater 

route, for example, inspection of the number of raters 

for each scenario shows that 46 of the pilots rated 14 

scenarios and 47 pilots rated 13 scenarios; thus the 

average number of raters per scenario was 46.48, 

rounded to 46.5. The actual number of pilots rating 

a scenario was used to compute the scenario mean 

comfort level. The means of the comfort level for the 

27 scenarios were the basis for computing the regional 

grand mean comfort rating over all scenarios. 

Policy Group Identification and Description 

The high interrater agreement found for pilot comfort level judgments for each terrain type indicates 

that the judgments were based on a single, homogeneous, and common terrain-specific policy with respect to a terrain type. The pilots, in effect, defined 

three policies, one for each terrain type by their 

agreement about the ordering of the comfort level 

they would experience in making flights under the 

terrain-weather variable scenarios. Subpolicies within 

an overall terrain policy, if any, would be characterized by subtle differences among weather-variable 

worth functions from which comfort level ratings 

derived. Further analyses were required to determine 

if there were any such differences of weights for the 

weather variables sufficient to define one or more 

subpolicies within a terrain policy. To this objective, 

a multiple regression equation for each pilot was 

computed and the worth functions (regression weights) 

for each variable were hierarchically clustered, using 

HIER-GRP. This clustering methodology has the 

power to detect subtle differences of the kind anticipated when there is such high interrater agreement 

and to cluster the pilots attributing similar weights to 

the weather variables. 

5 

Worth Function Computation. To identify the 

worth functions (i. e., the relative emphasis) pilots 

attributed to the ceiling, visibility, and precipitation 

variables, coefficients for each variable were computed for each pilot by terrain type using the following regression equation: 

CF = A + B + C + AB + AC + BC + ABC 

where CF was a vector of a pilot's 27 weather 

scenario "comfort" ratings for a given type of terrain; 

and A, B, and C and their interactions were vectors of 

benchmark values for ceiling, visibility, and precipitation, respectively. The regression equations represent 

a pilot's worth function with respect to the given 

terrain type. A total of937 regression equations were 

computed (one for each pilot for each terrain type), 319 

equations for over water, 315 for non-mountainous, and 

303 for mountainous (not all 326 respondents completed each terrain set). 

Individual R values were high and all statistically 

significant (p < 0.05; null hypothesis R =zero). Overwater values were lowest; only 47 percent exceeded 

. 70. Seventy-three percent, however, exceeded .60. 

For non-mountainous terrain, 70 percent were .70 or 

higher, and for mountainous terrain, 84 percent exceeded .70. Average R2

s were .66, .73, and .77, respectively. A large part of the variance associated with the 

pilots' ratings is explained by their worth functions 

and scenario benchmark values. Average R's and regression weights for the weather variables are shown 

for each terrain in Table 5. 

Subpolicy Group Specification. Hierarchical clustering revealed that each terrain policy was composed 

of four subpolicies differing with respect to weathervariable worth function emphasis (i.e., differences of 

regression weights). Tables 6, 7, and 8 display descriptive data for each of the subpolicies, showing number 

of pilots in each group, average comfort level, average 

R', and highest regression weights (only weights > 

+0.20 or< -0.20 are shown in the Tables). 

Comparison of the numbers of pilots in the policy 

groups reveals that terrain type differentially affects 

group membership size. For the over-water route, the 

largest N is 139 for the policy group (W-2) having the 

second lowest comfort level and a relatively small 

number (24 for Group W-1) constitutes the group 

with the lowest mean comfort level. For the non-

 
mountainous route, the Ns decrease in order from the 

group (NM-4) with the highest comfort level to the 

group (NM-1) with the lowest. This pattern changes 

for the over-mountains route where the largest number is found in the group (M-1) with the lowest 

average comfort level. In terms of terrain effect, the 

pilots tended to be more conservative in their ratings 

of comfort level for over mountains flight. They 

became less conservative for over-water and over nonmountainous routes, respectively. 

Between subpolicy group comparisons of mean 

comfort level differences were all significant (p < .05 

or less). For the over-water route, mean ratings range 

from 13.70 to 55.29, non-mountainous from 20.85 

to 54.53, and mountainous from 21.61 to 53.72. The 

mean levels by policy groups within each terrain type 

are illustrated in Figure I in terms of deviation from 

the grand mean (39.52). 

Average R' indicates the degree to which a policy 

group seems to make use of the experts' benchmarked 

attribute values and combine them in a consistent and 

rational manner. Even though weights differ significantly from policy group to policy group with respect 

to ceiling, visibility, and precipitation variables, these 

factors were very important determinants for pilots 

when they rated their comfort levels, accounting for 

most of the variance (60 to 74 percent) associated with 

comfort level ratings. 

Regression weights in Tables 5, 6, 7, and 8 are 

displayed to demonstrate how groups differed with 

respect to the weighting of the weather variables. As 

the tables show, some type of interaction (i.e., A *B*C, 

A*B, A*C, B*C) accounts for the larger regression 

weights. For six of the nine sub policy groups having 

the lowest comfort level means (the more cautious), 

for example, comfort level is primarily accounted for 

by the three-way interaction of ceiling, visibility, and 

precipitation (A*B*C). The three subpolicy groups 

having the highest comfort-level means (W-4, NM-4, 

M-4) have a slightly different pattern of weights. The 

largest over-water subpolicy weights are for the twoway interaction of ceiling and precipitation (A *C) and 

visibility and precipitation (B*C); for non-mountainous, two-way interaction of ceiling and precipitation 

(A*C) and visibility; and for over mountains, precipitation (C) and ceiling and visibility (B*C) interaction. 

The ceiling-visibility-precipitation interactions can 

he discerned, though not easily, in a careful inspection 

of the scenarios and associated mean comfort levels 

displayed in Tables 9, 10, and II. Some insight about 

the weather-condition interactions may he gained 

from Figure 2, showing ceiling-precipitation interaction for each terrain when visibility is held constant at 

8 nautical miles (NM). Two features should he noted. 

First, the dramatic decrease in over-water comfort 

level from 5000 feet ceiling and no rain to freezing 

rain at the same ceiling. Second, the shapes representing the ceiling-precipitation interactions for overwater and non-mountainous routes are essentially 

alike. It also seems likely that the shape for the 

mountainous route would he more similar if the 

scenarios for that route had contained precipitation 

conditions for ceiling levels between I 000-5000 feet. 

Policy Group Demographic Information 

Demographic information (from the Pilot Background Data form) for policy groups is summarized in 

Tables 12, 14, and 16. Demographic information 

about the total terrain group is included with each of 

the three tables under the headings TOTAL W (for 

over-water), TOT ALNM (for non-mountainous), and 

TOTALM (for mountainous) for reference purposes. 

Means for terrain, precipitation, visibility, and ceiling 

are based on the 5-point scale shown on the Pilot 

Background Data form (Appendix B). The scale was 

1, "Never;" 2, "Once or Twice;' 3, "Several Times;" 4, 

"Many Times;" 5, "Numerous Times." 

Significant differences among groups, computed 

from t-tests (.05 level)' of policy-group to policygroup comparisons, are detailed in Tables 13, 15, and 

17. Across the various comparisons, the most consistent differences seem to be associated with experience 

in flying over the terrain types and personal ceiling. In 

general, as group comfort level increases, terrain 

2 Readers are reminded of the earlier caution regarding interpretation of these comparisons. Some of the individual comparisons which attained 

statistical significance may be spurious simply as a matter of chance. Patterns of rdationships, rather than individual comparisons, should 

therefore be the principal focus. 

6 
experience tends to increase and personal ceiling tends 

to decrease. 

Multiple regression methodology was used for two 

purposes. First, we examined the degree to which pilot 

comfort level was predicted from demographic variables. The R2 obtained was 0.156. Secondly, we assessed the degree to which pilots used weather 

information consistent with the ratings by the expert 

pilots. Two regression equations were computed; one 

which used demographic variables and pilot comfort 

ratings to predict individual R2

s, and one which used 

only demographic variables as predictors. The R2s 

were 0.176 and 0.149, respectively. Results of the 

regression analyses are displayed in Appendix C, which 

shows that zero-order correlations (ZOC) and unique 

contribution of each of the variables to the separate 

criteria are small. These results indicate that age, 

certification and flying experience have little influence on pilots' ratings of comfort level. Further, in 

view of the high individual multiple Rs reported in an 

earlier section, the worth functions pilots attributed 

to the weather variables (I) accounted for most of the 

variance in the pilots' comfort ratings and (2) were 

consistent with expert pilot ordering of the importance of the weather variables. 

Regional Comparisons 

Analyses addressed differences of mean comfort 

level among regions by terrain type, regional worth 

function comparisons, demographic characteristics, 

and the proportionality of regional membership among 

sub policy groups. In reviewing these analyses, readers 

should bear in mind the limited sample of Alaskan 

pilots (N = 13), which makes interpretation of results 

for that region problematic. 

Mean Comfort Levels. The mean comfort levels 

for each region by terrain type are given in Table 18. 

Significant (p < .05) differences in the mean comfort 

levels among the regions for each terrain type are 

summarized below: 

Over-Water Terrain 

• Northwest Mountain region is significantly lower 

than Total Group, Southern region, Western Pacific region means 

7 

• Southern region is significantly higher than Total 

Group mean 

Non-Mountainous Terrain 

• Alaska region mean is significantly higher than the 

Total Group and all other regional means 

• Northwest Mountain region is significantly lower 

than Total Group mean 

Mountainous Terrain 

• Alaska region mean is significantly higher than 

Total Group and all other regional means 

Worth Functions. Average R2 and regression 

weights for the geographical regions by terrain type 

have been previously shown in Table 5. Overall, the 

amount of comfort-level variance accounted for by 

the expert ratings of the weather variables was not only 

higher for the Alaskan pilots than the total sample (all 

terrains) but was higher than the other regions for both 

the non~mountainous and mountainous terrains. 

For over-water terrain, the R 2s were similar among 

regions, accounting for 62 to 70 percent of the variance 

in comfort levels. Except for Alaska, worth functions 

are similar with the highest weight attributed to the 

interaction of ceiling, visibility, and precipitation 

(A *B*C). The Alaskan weights are somewhat different; the largest weight is attributed to an interaction 

of ceiling and precipitation (A *C) followed by the 

A*B*C interaction. 

Non-mountainous terrain R2s are larger than those 

for over water, ranging from .71 to .82. R2 are the 

product of ceiling-visibility (A *B) and ceiling-precipitation (A *C) interactions for all regional groups. 

Average R2

s for the mountainous terrain are the 

highest of all. Expert ratings contribute from 7 4 to 79 

percent of the variance in comfort levels. Generally, 

pilots attribute most weight to the ceiling-visibilityprecipitation interaction. 

Regional Group Membership Proportionality. The 

issue of proportionality of policy group membership 

was addressed by determining if membership in any 

given subpolicy group was comprised of a larger or 

smaller number of pilots than should be expected 

from the proportion of pilots for the region comprising the total sample. As shown in Table 20,6 ofthe72 
comparisons were found to be statistically different 

from the mean proportions (p < .05). Since we would 

have expected 4 comparisons to attain statistical sig~ 

nificance simply because of chance, the results do not 

present strong evidence of differences in the proportionality of policy group membership. However, since 

4 of the 6 significant differences were found for 

Alaskan pilots, the data are suggestive (though certainly not definitive) of a real difference in proportionality for that group. More Alaskan pilots than 

should be expected were members of the terrain groups 

(W-4, NM-4, and M-4) having the highest comfort 

levels (55.29 for over water, 54.53 for non-mountainous, and 53.72 for mountainous) for each terrain type. 

Given the results of the analysis of regional membership in subpolicy groups, comparisons of the regional groups against the Total Group were made (see 

Table 3). Differences were noted for 19 of the 30 

experience items in the Pilot Background Data form. 

Many of the differences are in the direction to be 

anticipated. That is, Alaskan pilots reported more 

experience with heavy snow; the Western Pacific and 

Northwest Mountains pilots have more experience in 

flight over mountains. While Table 3 provides some 

insight for the differences of Northwest Mountains 

membership, there is nothing to support the Alaskan 

proportionality findings. Northwest Mountain pilots, although reporting more experience over water 

and in the mountains, have significantly less experience in all the ceiling and visibility conditions. With 

respect to the Alaskan region, it may very well be that 

pilots there represent a different sample with respect 

to comfort level than found elsewhere in the United 

States. Such an assumption, however, can only be 

tentative, because of the small Alaskan sample size. 

Worst Flight Scenario 

Pilots were asked to choose from among the scenarios (i. e., the 27 combinations of weather variables} 

the one scenario that represented the worst flight 

scenario under which they would consider continuing 

the flight described for each terrain type. Data for this 

item, however, were not usable, since some pilots 

followed the instructions, while others simply listed 

the scenario they had ranked worst for a particular 

terrain type. 

V. COMPARISON WITH SOUTH 

TEXAS SAMPLE RESULTS 

8 

There is a striking similarity between the hierarchical clustering results from the regional and South 

Texas samples for flight over non-mountainous and 

mountainous routes. Sub policies for each terrain condition were alike with respect to how pilots viewed 

flight under the ceiling, visibility, and precipitation 

conditions. By route type, both samples were comprised of groups of pilots who made uniquely conservative ratings of comfort level, even for the best 

combinations of conditions, as well as other groups 

making progressively less conservative ratings. Overwater route results differed with respect to number of 

subpolicy groups identified. Where there were four 

subpolicies for the regional sample, there were only 

two for the South Texas sample. The policies from 

both samples, however, are characterized by progressively less conservative comfort ratings. 

Less similarity is noted for other factors. Comparisons of the South Texas and regional samples for 

demographic information, average comfort level, and 

for average R' and weather variable average regression 

weights are described in the following paragraphs. 

Demographic Information 

Comparison of the data from the 326 pilots in six 

geographical regions with the ISO pilots from South 

Texas revealed, first, the South Texas sample was 10 

years younger and more nearly matches the 1993 

national average age of 42.7 years (Table 21). Second, 

the South Texas pilots had fewer total flying hours 

(1694 to 3845 hours logged} and were less experienced in flight over the three terrains. In addition, for 

a large number of the remaining variables, the South 

Texas pilots had less experience in flying under the 

different ceiling, visibility, and precipitation levels. 

The variables for which South Texas pilots reported 

higher means are for flying hours logged in the past 90 

days, use of the F AA/FSS weather source, and for 

larger number holding private pilot licenses. A variable-by-variable comparison of the two samples is 

shown in Tables 22, 23, and 24. Subpolicyalignment 

follows for the groups having the lowest comfort level 

means to a comparison of groups with the highest 
means. The South Texas sample data are shown under 

the columns labeled GP. 

Comfort Level 

For all subpolicy groups, the South Texas pilots 

reported much lower mean comfort level ratings. The 

lower ratings, however, may, in part, be attributable to 

the differences in instructions for providing the ratings 

(see Methodology and Data Collection, Section III). 

Average R2 and Average Regression Weights 

Regional average R2 and regression weights for 

sub policy groups are displayed in Tables 6, 7, and 8. 

Comparable information for the South Texas subpolicy 

groups are shown in Driskill et al., 1997. While 

average R2s are fairly similar (that is, the amount of 

total variance in comfort ratings ranges from approximately 60 to 80 percent), inspection of the two sets of 

tables reveals different patterns in the average weights 

the pilots assign to ceiling, visibility, and precipitation and their interactions. 

VI. DECISION STRATEGY 

To determine the extent to which pilots used compensatory or noncompensatory strategies when assigning weather scenario comfort ratings, the 

correlations between comfort ratings and sets of compensatory and noncompensatory models were examined and compared. Specifically, the standardized 

scenario benchmark ratings (which were standardized 

to a mean of5.0 and a standard deviation of 1.0 for all 

scenarios) were transformed (using sets of transformations designed to simulate compensatory and noncompensatory policies) and then correlated with the 

pilots' scenario mean comfort ratings for each terrain 

rype. The models consisted of additive, multiplicative, worst-factor cut-off, single-factor, continuous, 

and cut-off models, one each for ceiling, visibiliry, 

and precipitation. 

Within the compensatory model set, the additive 

model summed the three benchmark factors and implied equal weights for the three factors. The multiplicative model, both two and three factor models, 

cross-multiplied benchmark values. This model implies a policy in which one or more factors may 

9 

introduce a dampening effect which can overpower 

the level of the other factors. 

The worst-factor cutoff models used the lowest of 

the three benchmark values. This model implies a 

policy in which the poorest of the three factors became 

the focus. 

Noncompensatory models were represented by 

continuous single factors (A, B, or C) as the decision 

variable and single-factor cut-offs. A pilot using a 

single continuous factor to establish a cut-off value 

would represent a policy in which the focus was on 

either ceiling, visibility, or precipitation (A, B, or C) 

or some established cut-off value (e. g., X> 4.5) as a 

single judgment factor. 

Table 251ists the compensatory, worst-factor, and 

noncompensatory models and their respective correlations with the pilot comfort ratings. This Table 

shows, just as in the case of the South Texas sample, 

the multiplicative and additive models consistently 

showed the strongest relationships with assigned comfort ratings. 

It should be noted, however, that the worst-factor 

model where X > 4.5 also had relatively strong relationships with comfort ratings. These data suggest 

that values less than one-half (.5) standard deviation 

below the mean of the expert (benchmark) rating for 

any one of the three variables were used as the determining judgment factor when the pilots made their 

comfort ratings. That is, individual pilots tend to have 

a self-defined level, a personal standard, for ceiling, 

visibiliry, or precipitation that establishes the point at 

which they become very uncomfortable and, most 

probably, would be reluctant to make a flight under 

that condition. 

VII. FINDINGS AND CONCLUSIONS 

Although the six geographical region and South 

Texas samples are markedly different samples of the 

continental United States pilot population, there is 

high substantive agreement of results. To the degree 

that these samples are representative of the general 

pilot population, therefore, this agreement strongly 

suggests that the findings and conclusions can be 

generalized to the national pilot population. 
In summary, this study found: 

1. Pilot-unique worth functions or regression weights 

attributed to ceiling, visibiliry, and precipitation 

conditions for differing terrain in aeronautical 

decision-making can be modeled using regression methods. 

2. In the context of the scenarios used in this study, 

weather variable regression weights accounted for 

a large proportion (66 to 77 percent) of the 

variance associated with pilot decisions to make a 

flight, as reflected in their comfort levels. 

3. These worth functions consistently parallel the 

emphasis experts assign to flight under the various levels of ceiling, visibility, and precipitation 

over the three terrain rypes. 

4. Although there appears to be a common ordering 

of the weather variables in terrain policy, each 

pilot attributes uniquely different weights to weather 

conditions for each terrain rype. Hierarchical 

clustering of these pilot-unique regression weights, 

with respect to the worth functions attributed to the 

weather variables, produced four statistically different policy groups of pilots for each terrain route. 

5. Mean policy group comfort levels, the product of 

the regression weights for the weather variables, 

differ markedly among terrain subpolicy groups 

and range from conservative to progressively 

higher levels. 

6. Demographic characteristics of pilots forming 

the subpolicy groups account for 15-17 percent 

of policy variance at most. From a training perspective, this result is disappointing, because it is 

not possible to identify pilots for whom targeting 

training interventions might be most useful. 

7. The interactive effect of variation in ceiling, visibiliry, and precipitation in decision-making is 

complex and, except for freezing precipitation 

where pilots are reluctant to make a flight, generalization about any one of the variables is inappropriate. In combining the weather variables, 

pilots tend to trade-off a poor value for one 

variable for good values in others. 

8. While pilots tend to use compensatory (multiplicative and additive) decision strategies in considering the weather variables, there is evidence that 

a worst-factor strategy operates for some weather 

scenarios. That is, pilots appear to have personal 

standards for either ceiling, visibiliry, or precipitation below which they become reluctant to 

make a flight. 

9. Since neither weather condition nor demographic 

variable weights individually nor cumulatively 

account for the total variance associated with 

pilot comfort levels, other factors that can affect 

pilot decision-making warrant investigation. 

Other personal characteristics and the willingness of pilots to take risks may be potential sources 

of some of the variance. In addition, the current 

study attempted to hold constant motivational 

factors- the urgency of making a given flightthrough the specification of the flight scenarios. 

Had motivational factors been allowed to vary, 

different weighting and integration of the weather 

information may have occurred. 

Given these findings, how then does one make use 

of the results. First, let us note that, on average, the 

pilots agree with the risk assessments given by experts 

of the various weather combinations. This replicates 

the previous finding (Driskill, et a!., 1997) and the 

findings of a study using a considerably different 

assessment instrument (Driskill, eta!., in press). Given 

that accidents are relatively rare events, it should not 

be too surprising that experts and the general population of pilots share a common view of what constitute 

safe and hazardous weather conditions. Still, there is 

some variabiliry in risk assessments, as indicated by 

differences in comfort levels among the hierarchical 

groups for each terrain. If, as this and other studies 

have found, demographic variables do not predict 

differential comfort level (which may equate to risk 

tolerance), then the present instrument may be one 

way to assess those differences. 

10 

The next question, of course, is whether or not 

those differences in comfort level or group membership have any real utiliry. For example, are they 

associated with differential involvement in accidents, 

incidents, or critical flying events? If these measures 

are found to be valid predictors of those criteria, then 

using them to make pilots aware of their risk tolerance 

might be worthwhile. Similarly, pilot awareness of 

their preferred decision model (i.e., compensatory, 

cut-offlevels) might also prove worthwhile if it led to 
a change of behavior toward safer operations. One 

might also speculate that low-time, VFR pilots should 

use a non-compensatory model to reduce the risk of 

weather accidents. All of these issues are open to 

study, and may be addressed in future studies. 

Additionally, a reviewer (K. Joseph, personal communication, July 2, 1997) suggested that the findings 

relating to decision strategy types may be useful to 

designers of flight planning software. For example, 

knowledge about pilot decision strategies when considering weather information could be used to enhance weather-related decisions made by pilots during 

flight planning. Flight planning software could be 

designed to include weather-related decision aids that 

reduce and possibly eliminate poor decision strategies. 

While the present study has shown that weatherrelated decision making may be modeled successfully 

using linear modeling techniques, much remains to be 

done to determine whether this approach can make a 

significant contribution to improving aviation safety. 

Future studies will address the relationships of measures derived from this modeling technique with criteriaofinterest and how best to apply the results to achieve 

a safery benefit. 

VIII. REFERENCES 

Albert, W.G., Phalen, W.J., Selander, O.M., Dittmar, 

M.J., Tucker, O.L., Hand, O.K., Weissmuller, 

J.J., & Rouse, !.F. (1994). Large-scale laboratory 

test of occupational survey software and scaling 

procedures. Proceedings of the 36th Annual Conference of the International Military Testing Association (pp 241-46). Rotterdam, The Netherlands. 

Barnett, B. J. (1989). Modeling information processing 

components and strnctural knowledge representatiom 

in pilot judgment. Unpublished doctoral dissertaion, 

University of Illinois, Urbana-Champaign. 

Christal, R. E & Weissmuller, J. J. (1988) The job-task 

inventory. InS. Gael (Ed.), The job analysis handbook for business, industry, and government (Vol2). 

New York: John Wiley & Sons 

II 

Christal,R. E. &Weissmuller,J.J. (1976).NewCODAP 

programs for analyzing task foetor information. 

(AFHRL-TR-76-3,AD-A025 121). LacklandAFB, 

TX: Occupational and Manpower Research Division, Air Force Human Resources Laboratory. 

Curry, R. (1976). Worth assessment of approach to 

landing. Proceedings of the annual conference on 

manual control. NASA report TX-73, University 

of Illinois, Urbana, IL: 585-91. 

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

K., Dittmar, M. J., & Hunter, D. R. (1997). The 

use of weather information in aeronautical decisionmaking. (DOT/FAA/AM-97/3). Washington, DC: 

Office of Aviation Medicine, Federal Aviation 

Administration, Department ofTransportation. 

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

K., & Hunter, D. R. (in press). Evaluating the 

decision-making skills of general aviation. (DOT I 

FAA/AM-98/XX). Washington, DC: Office of 

Aviation Medicine, Federal Aviation Administration, Department ofT ransportation. 

Flathers, G. W., Giffin, W. C., & Rockwell, T. (1982). 

Behavioral decision-making behavior of aircraft 

pilots deviating from a planned flight. Aviation, 

space, and environmental medicine, 958-63. 

Goody, K. (1976). Comprehemiveoccupationaldataanalysis programs (CODAP): Use ofREXALL to identifY 

divergent raters. (AFHRL-TR-76-82, AD-A025 

847). Lackland AFB, TX: Occupation and Manpower Research Division, Air Force Human Resources Laboratory 

Hunter, D.R. (1995). Airman &search Questionnaire: 

Methodology and overall results. (DOT/FAA/AM95/27). Washington, DC: Office of Aviation 

Medicine, Federal Aviation Administration, Department of Transportation. NTIS ordering # 

ADA300583. 

Jensen, R. S. (1982). Pilot judgment: Training and 

evaluation. Humanfoctors, 24,61-73. 

National Transportation Safery Board Safery Report 

(1989). General aviation accidents involving visual 

flight rules into imtrnment meteorological conditiom. NTSB/SR-89/01. National Transportation 

Safery Board, Bureau of Safery Programs: Washington, DC. 
Payne, J. W., Bettman, J. R., & Johnson, E. J. {1992). 

Behavioral decision research: A constructive processing perspective. In Rosenzweig, M. R. & Porter, L. W. (Eds), Annual review of psychology. Palo 

Alto, CA: 43, 55-86. 

Phalen, W.J., & Mitchell, J.L. {1993). Innovations in 

occupational measurement technology for the U.S. 

military. Proceedings of the Eighth International 

Occupational Analysts Workshop {pp 12-16). San 

Antonio, TX: USAF Occupational Measurement 

Squadron. 

Staley, M. R. & Weissmuller, J. J. {1981). Interrater 

Reliability: The development of an automated analysis tool. (AFHRL-TP-81-42, AD-A108 400). 

Brooks AFB, TX: Technical Services Division, Air 

Force Human Resources Laboratory. 

Stokes, A. F., Kemper, K.L., & Marsh, R. (1992). Timtstressed flight decision making: A study of expert and 

novict aviators. (Technical Report ARL-93-1). Savoy, IL: University of Illinois, Aviation Research 

Laboratory. 

US Civil airmen statistics (1993). Federal Aviation Administration: Washington, DC. 

Weissmuller, J. J., Phalen, W. J., & Tartell J. Uune 

1997) Defining and characterizing subgroups using interrater reliability and cluster analysis techniques. Proceedings of the Ttnth International Occupational Analysts' Workshop. San Antonio, TX: 

USAF Occupational Measurement Squadron. 

Wickens, C.D., Stokes, A., Barnett, B., & Davis. T. 

( 1987 ). Componential analysis ofpilot decision making. (Technical Repott ARL-87/4). Savoy, IL: U niversity of Illinois, Aviation Research Laboratory. 

12 
FIGURES 

Figure!. 

Group Comfort Levels By Route 

20.00.------------------------------, 

15.00 

10.00 

5.00 

0.00 

-5.00 

·10.00 

·15.00 

·20.00 

-25.00 

Overwater 

Route 

Non-Mountainous 

Route 

Mountainous 

Route 

-3o.oo L----------------------------__J 

13 
---·-

Figure2. 

Pilot Comfort Levels at BNM Visibility 

100 

90 

80 

70 

a; 60 > " .J 

t: 50 .e 

E 

8 40 

30 

20 

10 

0 

0 500 1 000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 

Ceiling 

14 
Terrain 

Water 

NonMountain 

Mountain 

TABLES 

Table 1. 1997 Sample Demographics 

Compared with Demographics in 1995 Study 

Item 1997 1995 

Mean Age 52 yrs. 50 yrs. 

CFI or CFll certificate 34% 34% 

Private pilot license 33% 38% 

Commercial license 44% 43% 

Airline transport license 20% 18% 

Multi-engine rated 49% 49% 

Instrument rated 77% 71% 

Employed as pilot 19% 34% 

Flying hours 90 days 36 29 

Total flying hours logged 3845 3465 

Mean comfort level - total group 39.52 

Mean over-water comfort level 39.80 

Mean non-mountain comfort level 42.27 

Mean mountain comfort level 36.38 

1 From Hunter (1995) 

Table 2. Sample Distribution by Terrain Type and Geographic Region 

N Alaska Northwest Western Southern Eastern 

319 12 56 49 50 57 

315 13 57 48 50 55 

303 13 54 47 48 52 

15 

New 

England 

82 

81 

79 
Table 3. Mean Differences Comparisons for Total and Geographic Region Samples' 

Demographic Item Total AK NW WP so EA NE 

Age 52 yrs. H' CF1 or CF11 certificate 34% 

Private pilot license 33% 

Commercial license 44% 

Airline Transport license 20% 

Multi-engine rated 49% 

Instrument rated 77% 

Employed as pilot 

Own aircraft 

19% H H L 52% 

Hours logged last 90 days 36 

Total hours H L logged 3845 

Number L FAA Seminars 1.69 L attended 

% Time weather from 58 

FAA!FSS 

% Time weather from 41 

radiotrV L H 

% Time weather from DUAT 26 

% Tune weather other sources 16 

Lowest ceiling day VFR L 2184 ft. L H L Flying experience' 

Terrain 

over water 2.84 H H L H H L over non-mountain 4.42 

over mountains 3.48 

Precipitation conditions 

light rain 3.94 

moderate H H L rain 3.36 H L heavy snow 2.14 H 

Visibility conditions 

I mile 3.15 L H L 4 miles 4.18 L 

8 miles 4.72 L H Ceiling conditions 

800feet 3.14 L H 1800 feet 4.05 L 

4000feet L 4.78 L 

'Significance of differences computed by t-tests at 0.05level for individual comparisons 2

H indicates significantly higher mean than Total Group, L significantly lower than Total Group, - no difference 

'Rating scale used: 1, Never: 2, Once or twice; 3, Several times; 4, Many times; 5, Numerous times 

16 
Table 4. lnterrater Agreement and Stability for Regional 

and Total Samples by Terrain Type 

Over Water 

Region R11 Rkk k 

Alaska 0.3450 0.8528 11.0 

Western 0.4772 0.'1770 46.5 

Southern 0.5177 0.9810 48.2 

Northwest 0.4944 0.9818 55.3 

Eastern 0.5778 0.9873 56.8 

New England 0.5219 0.9888 80.9 

Total 0.5130 0.9968 300.0 

Non-Mountainous 

Region R11 Rkk k 

Alaska 0.4948 0.9112 10.5 

Western 0.5264 0.9811 46.9 

Southern 0.5314 0.9815 46.7 

Northwest 0.5265 0.9840 55.3 

Eastern 0.5506 0.9851 53.9 

New England 0.4938 0.9870 77.8 

Total 0.5214 0.9968 286.9 

Mountainous 

Region R11 Rkk k 

Alaska 0.3082 0.8200 10.2 

Western 0.5028 0.9794 46.9 

Southern 0.5369 0.9811 44.7 

Northwest 0.5866 0.9867 52.3 

Eastern 0.5521 0.9837 49.0 

New England 0.5529 0.9895 76.5 

Total 0.5430 0.9970 275.9 

17 
Table 5. Average Regression Weights for Terrain Policies 

Over Water Non-Mountain Mountains 

Number of Members 319 315 303 

Average Comfort Level* 39.80 42.27 36.38 

Average R2 0.66 0.73 0.77 

Regression Weights 

Ceiling(A) -0.41 -0.38 

Visibility (B) 

Precipitation (C ) -0.31 

(MULT A*B*C) 1.48 0.99 

(MULTA*B) -0.54 0.75 0.23 

(MULTA*C) 0.40 

(MULTB*C) -0.33 

*Comfort level means are significantly different (p<.05) 

Table 6. Over-Water Subpolicy Group Mean Regression Weights 

Total Groul! W-1 

TOTALW 

Grou!!W-2 Grouu W-3 Grouu W-4 

Number of Members 319 24 139 72 84 

Mean Comfort Level* 39.80 13.79 31.76 46.73 55.29 

MeanR2 0.66 0.67 0.70 0.61 0.65 

Regression Weights 

Ceiling(A) -0.24 

Visibility (B) 0.20 

Precipitation (C ) -0.31 -0.38 -0.33 -0.38 

(MULT A*B*C) 1.48 1.41 2.05 1.84 0.26 

(MULTA*B) -0.54 -0.82 -0.58 -0.76 -0.21 

(MULTA*C) 0.60 

(MULTB*C) -0.38 -0.41 0.47 

*Comfort level means are significantly different (t-test, p<0.05) 

18 
Table 7. Non-Mountainous Subpolicy Group Mean Regression Weights 

Total GrOUJ:!NM-1 GrouJ:!NM-2 GrouJ:!NM-3 Groul! NMTOTALNM ~ 

Number of Members 315 49 72 93 101 

Average Comfort 42.27 20.85 38.45 43.82 54.53 

Level* 

Average R2 0.73 0.71 0.73 0.74 0.74 

Regression Weights 

Ceiling (A) -0.41 -0.63 -0.78 

Visibility (B) -0.20 -0.28 0.39 

Precipitation (C ) 0.20 

(MULT A*B*C) -0.29 1.62 -0.84 

(MULTA*B) 0.75 1.18 -0.41 1.93 0.27 

(MULTA*C) 0.40 0.56 0.57 0.47 

(MULTB*C) -0.23 

*Comfort level means are significantly different (t-test, p<0.05) 

Table 8. Mountainous Subpolicy Group Mean Regression Weights 

Total GrOUJ:!M-1 GrOUJ:!M-2 GrouJ:!M-3 GrOUJ:! M-4 

TOTALM 

Number of Members 303 94 78 63 68 

Average Comfort 36.38 21.61 35.57 40.71 53.72 

Level* 

Average R2 0.77 0.69 0.62 0.67 0.63 

Regression Weights 

Ceiling (A) -0.38 -0.48 -0.86 

Visibility (B) 

Precipitation (C ) 0.48 

(MULT A*B*C) 0.99 1.53 1.96 

(MULTA*B) 0.23 0.20 0.34 0.75 0.46 

(MULTA*C) -0.35 1.38 

(MULTB*C) -0.33 -0.47 -0.44 -0.30 

*Comfort level means are significantly different (t-test, p<.05) 

19 
Table 9. Over-Water Scenario Mean Comfort Ratings 

No. Ceiling (in feet) Visibility (in NM) Precipitation N Mean 

104 5000 >8 none 318 90.94 103 4000 4 none 318 83.51 101 4000 7 moderate rain 318 72.93 112 1600 8 none 105 2000 317 72.22 3 none 316 70.28 114 4000 5 light snow 316 63.90 110 1800 7 moderate rain 107 1800 317 57.02 7 light snow 317 54.80 125 1800 5 moderate rain 102 5000 314 51.34 1\12 light rain 315 43.45 122 900 5 106 1800 none 314 40.06 3 heavy rain 124 1600 313 37.80 4 moderate snow 313 37.41 119 600 7 none 123 800 313 32.99 4 moderate rain 118 600 312 27.66 8 moderate rain 313 25.98 Ill 800 1 y, none 108 3500 313 25.80 1 heavy rain 113 1600 314 25.63 3 heavy snow 127 900 315 25.02 4 heavy rain 120 1600 309 21.08 

116 1600 

1\12 moderate snow 313 20.83 1\12 heavy rain 109 900 312 20.29 1\12 moderate rain 126 1800 314 20.09 1\12 117 5000 none 314 18.45 1\12 moderate rain 115 700 312 17.50 IV. heavy snow 121 5000 312 5.69 8 freezing rain 313 4.95 

20 
Table 10. Non-Mountainous Scenario Mean Comfort Ratings 

No. Ceiling (in feet) Visibility (in NM) Precipitation N Mean 

206 4000 8 light rain 313 85.18 

201 5000 8 light snow 312 80.27 

226 2000 >8 light rain 310 74.83 

227 4000 5 light snow 308 70.89 

210 1800 4 none 311 70.27 

204 4000 3 light rain 311 66.76 

205 2000 7 light snow 310 65.92 

217 1800 7 moderate rain 309 59.86 

213 1800 5 moderate rain 311 57.68 

212 800 8 none 308 46.31 

223 1600 4 moderate snow 309 41.21 

220 2000 1 \12 none 305 40.34 

211 8000 >8 light snow 308 38.19 

202 4000 1 light rain 306 37.5 

224 800 3 none 308 36.61 

221 1800 3 heavy rain 308 35.141 

216 5000 1\12 moderate rain 304 34.84 

218 900 5 light snow 306 32 

203 1600 3 heavy snow 309 29.29 

214 600 8 moderate rain 303 28.17 

215 900 4 heavy rain 302 23.16 

225 900 1\12 light rain 305 20.87 

207 3500 \12 heavy rain 306 14.75 

208 1800 \12 moderate snow 306 11.83 

209 600 \12 light snow 307 9.38 

219 900 ll2 light rain 306 8.08 

222 2000 \12 freezing rain 304 3.92 

21 
-- -- ---- ---- --- - -

Table 11. Mountainous Scenario Mean Comfort Ratings 

No. Ceiling (in feet) Visibility (in NMl Precipitation N Mean 

307 4000 8 none 298 86.31 301 5000 5 none 298 84.83 316 2000 >8 light rain 295 67.19 303 3500 >8 modernte rain 295 66.00 325 2000 4 none 295 64.55 313 4000 4 light snow 293 60.63 321 2000 5 light snow 293 52.39 309 1800 7 moderate rain 294 49.95 327 1800 5 moderate rain 292 46.54 326 5000 I Yz light rain 292 39.99 317 2000 IV. none 291 38.03 323 900 8 light rain 292 34.13 315 1600 3 heavy rain 290 32.20 322 900 5 light rain 292 31.60 314 3500 I light snow 291 31.28 324 1699 3 heavy snow 288 25.52 319 600 8 moderate rain 290 24.17 310 800 I none 290 23.17 306 900 4 heavy rain 288 22.84 308 600 3 moderate snow 291 16.74 305 2000 Yz moderate rain 291 15.87 311 4000 Y2 heavy snow 289 12.49 312 600 I modernte snow 289 11.94 304 900 I heavy snow 290 11.24 320 3500 3 freezing rain 291 7.39 318 1600 I freezing rain 291 4.39 302 600 5 freezing rain 291 4.29 

22 
Table 12. Overwater Policy Group Comparison 

GROUP Total W-1 W-2 W-3 W-4 

Number of Cases 319 24 139 72 84 

Mean Comfort Level 39.98 13.79 31.76 46.73 55.29 

BACKGROUND VARIABLE 

Flying History 

Total Hours Mean 3845 hrs 2711 hrs 3174hrs 3950 hrs 3771 hrs 

Last 90 Days Mean 36 hrs 32 hrs 38 hrs 32 hrs 38 hrs 

Mean Age 52 56 52 51 52 

Percent Owning Aircraft 52 67 50 50 52 

Mean Terrain Experience 

Route I - Overwater 2.84 2.29 2.80 3.17 2.77 

Route 2 - Flatland 4.42 4.29 4.38 4.46 4.47 

Route 3 - Mountain 3.48 3.70 3.57 3.30 3.44 

Mean Precipitation Experience 

Light Rain 3.94 3.65 3.93 4.04 3.95 

Moderate Rain 3.36 3.00 3.31 3.44 3.46 

Heavy Snow 2.14 1.83 2 .. 08 2.43 2.07 

Mean Visibility Experience 

INM 3.15 2.88 3.03 3.46 3.18 

4NM 4.18 3.91 4.14 4.33 4.20 

8NM 4.72 4.52 4.72 4.81 4.70 

Mean Ceiling Experience 

800ft. 3.14 2.92 3.04 3.39 3.16 

1800 ft. 4.05 3.75 3.98 4.23 4.09 

4000ft. 4.78 4.71 4.78 4.81 4.76 

Percent Using Weather Source 

FAA/FSS 58% 57% 54% 57% 66% 

DUAT 26% 32% 26% 27% 25% 

TV/Radio 41% 36% 43% 38% 40% 

Other 16% 12% 20% 13% 14% 

Percent Certificates Held 

Private 33% 29% 32% 33% 35% 

Commercial 44% 54% 45% 38% 45% 

Airline Transport 20% 13% 21% 26% 15% 

Other 3% 4% 2% 3% 5% 

Multi-Engine Rating 49% 33% 50% 44% 55% 

CFI/CFII 34% 33% 37% 28% 32% 

Instrument Rating 77% 71% 78% 78% 77% 

Percent Flying Job 

Employed as a Pilot 19% 17% 17% 29% 15% 

Mean Number of FAA Safety Seminars 1.69 1.75 1.40 2.08 1.81 

Mean Lowest Ceiling for Day VFR Trip 2148 ft 2775 ft 2312 ft 2093 ft 1882 ft 

23 
Table 13. Highlights of Group Characteristics (Overwater Route) 

24 
Table 14. Non-Mountainous Policy Group Comparison 

GROUP Total NM-1 NM-2 NM-3 NM-4 

Number of Cases 315 49 72 93 101 

Mean Comfort Level 42.45 20.85 38.43 43.82 54.53 

BACKGROUND VARIABLE 

Flying History 

Total Hours Mean 3835 hrs 1849 brs 3950 brs 3372 brs 3955 brs 

Last 90 Days Mean 35 brs 18 brs 37 brs 41 brs 37 brs 

Mean Age 52 yrs 53 yrs 52 yrs 50yrs 53 yrs 

Percent Owning Aircraft 52% 59% 51% 47% 53% 

Mean Terrain Experience 

Route I - Overwater 2.82 2.29 2.93 2.80 3.01 

Route 2 - Flatland 4.41 4.26 4.26 4.49 4.53 

Route 3 - Mountain 3.47 3.43 3.61 3.27 3.59 

Mean Precipitation Experience 

Light Rain 3.95 3.65 3.99 4.01 4.00 

Moderate Rain 3.36 3.00 3.32 3.44 3.49 

Heavy Snow 2.13 1.83 1.96 2.21 2.32 

Mean Visibility Experience 

I NM 3.15 2.83 3.17 3.18 3.26 

4NM 4.18 3.67 4.14 4.34 4.31 

SNM 4.72 4.38 4.69 4.76 4.86 

Mean Ceiling Experience 

800ft. 3.13 2.73 3.18 3.17 3.25 

1800 ft. 4.05 3.63 4.10 4.10 4.16 

4000ft. 4.77 4.52 4.74 4.85 4.86 

Percent Using Weather Source 

FAA/FSS 58% 56% 53% 61% 60% 

DUAT 26% 32% 28% 26% 23% 

TV/Radio 41% 50% 34% 45% 37% 

Other 16% 17% 14% 18% 13% 

Percent Certificates Held 

Private 33% 43% 33% 30% 30% 

Commercial 45% 39% 38% 47% 50% 

Airline Transport 19% 10% 28% 19% 18% 

Other 3% 8% 1% 3% 2% 

Multi-Engine Rating 49% 33% 51% 49% 53% 

CFI/CFII 34% 27% 33% 34% 37% 

Instrument Rating 77% 67% 81% 83% 73% 

Percent Flying Job 

Employed as a Pilot 19% 6% 21% 24% 19% 

Mean Number of FAA Safety Seminars 1.74 1.49 2.11 1.66 1.66 

Mean Lowest Ceiling for Day VFR Trip 2174 ft 2718 ft 2490 ft 1979 ft 1863 ft 

25 
-- ----~--

-~-~- -,__- ---.,_ ... 

Table 15. Highlights of Group Characteristics (Non-Mountainous) · 

Hiahllahts of Group Characteristics (Non-Mountainous Route\ I I 

I . I 

Probability NM-1 NM-2 NM·3 NM-4 Compared to NM-4 NM-2 has: 

0.00 20.85 38.43 43.82 54.53 a lower comfort level I 

for non-mountainous route 

0.00 271811 249011 197911 183611 a hiaher oersonal ceiHna for a dav VFR trio 

0.02 4.26 4.26 4.49 4.53 less e>werience over non·mountainous terrain 

r-- 0.04 1.83 1.96 2.21 2.32 less ex(:lerience fl~ing in heai/Y snow -- 0.02 4.38 4.69 4.76 4.86 less exoerience flvina with visibilih'_at 8NM 

0.05 39% 38% 47% 50% a lower percentaQe of commercial pilots 

I I 

Probabilitv NM-1 NM-2 NM-3 NM-4 Comoared to NM-3 NM-2 has: 

r-- 0.00 20.85 38.43 43.82 54.53 a lower comfort level I 

I-· - for non-mountainous route 

0.00 271811 249011 197911 186311 a higher (:lersonal ceiling for a da),' VFR trill 

0.03 50% 34% 45% 37% a lower oercentaae obtain weather 

from TV/Rjdio sourcej 

Probability NM-1 NM-2 NM-3 NM-4 Compared to NM-1 NM-2 has: 

0.00 20.85 38.43 43.82 54.53 a hiaher comfort level I 

for non-mountainous route 

0.01 _18% 10% 19% 28% a lower oercentaae of ATP rated Pilots 

0.05 67% 81% 83% 73% a hiaher oercentaae of instrument rated (:lilots 

0.02 33% 51% 49% 53% a hioher oercentaae of multi-enoine rated pilots - -~ ··-

O.D1 6% 21% 24% 19% a higher percentage employed as a pilot 

0.01 18hrs 37hrs 41hrs 37hrs more flvino hours in the last 90 davs 

0.01 1849hrs 3950hrs 3372hrs 3955hrs a hiaher number of total flvina hours -- 0.01 50% 34% 45% 37% a lower percentaoe obtain weather rfrom TV /Radio sources 

- 0.01 2.29 2.93 2.80 3.01 more exgerience flying overwater --

r-----0.04 3.65 :)_~_ 4.01 4.00 more exoerience flvin!l in liaht rain ·-· 

0.01 3.67 4.14 4.34 4.31 more experience flyirm._ with 4NM visibility_ 

0.02 4.38 4.69 4.76 4.86 more experience flvina with 8NM visibilitv 

0.04 2.73 3.18 3.17 3.25 more experience flyirm._in 80011 ceilings 

0.01 3.62 4.10 4.10 4.16 more exoerience flvina in 180011 ceilinas 

I I - Probability NM-1 NM-2 NM-3 NM-4 Compared to NM-3 NM-4 has: 

0.00 20.85 38.43 43.82 54.53 a hiaher comfort level I 

for non-mountainous route 

0.05 3.43 3.61 3.27 3.59 more expelience over 

1

mountainous terrain 

26 
I 

I 

Probabllitv 

0.00 

c-.......... QJJ3 

0.01 

0.02 

0.00 

0.01 

0.00 

0.02 

r------- . 0.00 

0.03 

0.02 

O.D1 

0.02 

0.03 

0.00 

0.00 

O.Q1 

0.00 

0.00 

Probabilitv 

0.00 

... 

0.02 

0.03 

0.00 .. 

0.00 

0.01 

0.02 

0.02 

0.03 

0.03 

0.04 

0.00 

0.00 

0.04 

0.01 

0.00 

Table 15. Highlights of Group Characteristics (Non-Mountainous) 

(Continued) 

NM-1 NM-2 NM-3 NM-4 Conmared to NM-1 NM-4 has: 

20.85 38.43 43.82 54.53 a hioher comfort level I 

for non-mountainous route 

39% 38% 47% 50% a hioher oercentaoe of commercial oilots 

33% 51% 49% 53% a higher percentage of multi-engine rated pilots 

6% 21% 24% 19% a hiaher oercentaae emoloved as a oHot 

271811 249011 197911 186311 a lower personal ceiling for a day VFR trip 

18hrs 37hrs 41hrs 37hrs · more flvino hours in the last 90 davs 

1849hrs 3950hrs 3372hrs 3955hrs a hioher number of total flyino hours 

50% 34% 45% 37% a lower percentage obtain weather 

from TV/Radio sources 

2.29 2.93 2.80 3.01 more experience living overwater 

4.26 4.26 4.49 4.53 more experience over non-mountainous terrain 

3.65 3.99 4.01 4.00 more exoerience flvino in lioht rain 

3.00 3.32 3.44 3.49 more experience flyina in moderate rain 

1.83 1.96 2.21 2.32 more exoerience flvino in heaw snow 

2.83 3.17 3.18 3.26 more experience flyina with 1 NM visibility 

3.67 4.14 4.34 4.31 more exoerience flvina w~h 4NM visibility 

4.38 4.69 4.76 4.86 more experience flying with BNM visibility 

2.73 3.18 3.17 3.25 more exoerience flvino in 800ft ceilinos 

3.62 4.10 4.10 4.16 more experience !lving in 180011 ceilings 

4.52 4.74 4.85 4.86 more experience flyina in 400011 ceilinas 

I I 

NM-1 NM-2 NM-3 NM-4 Compared to NM-1 NM-3 has: 

20.85 38.43 43.82 54.53 a hioher comfort level I 

for non-mountainous route 

67% 81% 83% 73% a hiaher percentaae of instrument rated pilots 

33% 51% 49% 53% a hioher oercentaoe of multi-enoine rated oilots 

6% 21% 24% 19% a hiaher percentaae employed as a pilot 

271811 249011 197911 186311 a lower oersonal ceilino for a dav VFR trio 

18hrs 37hrs 41hrs 37hrs more flyina hours in the last 90 days 

1849hrs 3950hrs 3372hrs 3955hrs a higher number of total flying hours 

2.29 2.93 2.80 3.01 more experience flvina overwater 

3.65 3.99 4.01 4.00 more exoerience flvino in lioht rain 

3.00 3.32 3.44 3.49 more experience flyina in moderate rain 

1.83 1.96 2.21 2.32 more exoerience flvino in heaw snow 

3.67 4.14 4.34 4.31 more experience flying w~h 4NM visibility 

4.38 4.69 4.76 4.86 more experience flvino whh BNM visibility 

2.73 3.18 3.17 3.25 more experience flying in 80011 ceilings 

3.62 4.10 4.10 4.16 more experience flvina in 180011 ceilinas 

4.52 4.74 4.85 4.86 more exoerience flvino in 400011 ceilinos 

27 
'I I I 

il I 

'~I 

[JI 

! 

I. 

I ,, 

Table 16. Mountainous Policy Group Comparisons 

GROUP Total M-1 M-2 M-3 

Number of Cases 303 94 78 63 

Mean Comfort Level 36.38 31.61 35.57 40.71 

BACKGROUND VARIABLE 

Flying History 

Total Hours Mean 3775 hrs 3013 hrs 3605 hrs 3246 hrs 

Last 90 Days Mean 36 hrs 30hrs 47 hrs 33 hrs 

Mean Age 52 yrs 53 yrs 51 yrs 51 yrs 

Percent Owning Aircraft 52% 56% 60% 46% 

Mean Terrain Experience 

Route I - Overwater 2.81 2.68 2.94 2.97 

Route 2 - Flatland 4.41 4.40 4.45 4.45 

Route 3 - Mountain 3.46 3.46 3.46 3.37 

Mean Precipitation Experience 

Light Rain 3.94 3.83 4.04 3.97 

Moderate Rain 3.35 3.24 3.39 3.35 

Heavy Snow 2.12 2.09 2.01 2.06 

Mean Visibility Experience 

lNM 3.13 2.94 3.20 3.34 

4NM 4.19 3.99 4.22 4.32 

8NM 4.72 4.58 4.68 4.86 

Mean Ceiling Experience 

800ft. 3.11 2.99 3.20 3.11 

1800 ft. 4.04 3.96 1.01 4.03 

4000 ft. 4.78 4.70 4.74 4.87 

Percent Using Weather Source 

FAAIFSS 58% 54% 61% 53% 

DUAT 26% 31% 29% 24% 

TV/Radio 40% 45% 39% 42% 

Other 15% 16% 13% 18% 

Percent Certificates Held 

Private 33% 37% 27% 37% 

Commercial 45% 43% 50% 44% 

Airline Transport 19% 19% 18% 19% 

Other 3% 1% 5% 0% 

Multi-Engine Rating 49% 41% 47% 49% 

CFI/CFII 34% 29% 38% 33% 

Instrument Rating 77% 76 83% 79% 

Percent Flying Job 

Employed as a Pilot 19% 14% 22% 21% 

Mean Number of FAA Safety Seminars 1.69 1.62 1.9 1.86 

Mean Lowest Ceiling for Day VFR Trip 2167 ft. 2323 ft. 2164 ft 2051 ft. 

28 

M-4 

68 

53.72 

3801 hrs 

32 hrs 

53 yrs 

43% 

2.68 

4.36 

3.55 

3.95 

3.47 

2.32 

3.14 

4.29 

4.83 

3.18 

4.20 

4.83 

62% 

19% 

32% 

13% 

31% 

41% 

22% 

6% 

59% 

38% 

69% 

21% 

1.41 

2063 ft. 
Table 17. Highlights of Group Characteristics (Mountainous) 

~_ights of Group Chara_£t~rlstics (Mountainous Route)_ _ . I· _ -~t-·-:_-__ -+-------

Probability M-1 M-2 M-3 M-4 Compared to M-2, M-1 has: 

0.00 21.61 35.57 40.71 53.72 a lower comfort level for mountainous route 

_: __ 0.03 _ 30hrs 47hrs 3~hrs 32hrS- less flying ~ours in~asi 90 ~~-=-==== 

Probability 1 M-1_ --~-2 __ M-3 M-4 Compared to M-4, M-1 has: 

__ 0.00 --l-____g1J>l- 35.57 _ 40.71 53.72 a_lower comfo_rt level for n1_ountainous route . -··. 

O.Q1 · 41% 47% - 49%--s§<>;;-- a lower percentage of multi-engine rated pilots 

0.02 - _11 _ _?6_ .. %_ .!. 8.3% r __ .79%_t -.- 69_o/o __ a higherperc_e[ltage of in_strument rated pilots . ····-

_ _().0~ 56% ~_6()o/o_ _' . 46% 1 _ 43% a higher percentage own their own airp_lane ___ _ 

· · 0.02 31%j 29% 24% · 19% 'ahigherpercentageobtainweatherfrom DUAT 

-~ __ ().02 -~---45% ______ _39-% _ 42% t 32%-- a high-er percenTage obtain_:__::_ T_ __: __ -_:_ 

weather from TV/Radio sources _c:::_:__: 

0.03 t 3.99 4.22 ~ 4.32 4.29 - less experience flying with 4Nfv1 visibility ---- -

··· _ _(),_01- -4.5_8_-_i _ __i,~S:= I 4.86_-_ f- 4.83 lessexperi~nceflying_f!h_8Nll!_1ibility --·-

Probability I M-1 M-2 , M-3 i M-4 Compared to M-3, M-1 has: L_ 

___ o,oo r 2f61 + 3S~57T-40 71 -53.72 a lowercol11tmfli3vet f9rmountafnous-,Ou~e =-_ 0.04 2.94 3.20 3.34 3.14 less experience flying with 1 NM visibility 

~ , 3.99 4.22 4.32 I 4.29 ,tess experience flymg with 4NM visibility 

- _O.D1__ j_ 4.58 1_: 4.68 -~- _4:86 T 4.83 less experijnce flyTngTith BNM visibility__-_ -~ 

Probability I _M-1 _ 1

_ M:2 j M-3-i~- M-=4-lcompared to M-4, M-2_ has: __ _[_ _ __ 

- (lj>()_ __ 1 21.61 I 35.57 

1

_,10.71 , 53.72 .alower_comfortlevelformountainousroute __ 

0.02 76% I 83% 79% 69% i a higher percentage of instrument rated pilots 

-0.02 __ ' _ _56_o/,_'l--6o% 1 __ 46% l_ 43'% __ I a higher percentage _owntheir o-vn_airplan" ---_:-----: 

__ 0.05 j 4.5~ -~-6~-~ -4~6 i . 4.83 -11ess experiince flying j'th 8NM vi~ibility 

Probability . M-;_1. M-2 f- M-3 M-4 Compared to M-3, M-2 has: . . i . 

_o.o·o· __ t 21.6~---. 35.§7_ _4{),f1--=-ss-:-72 -- a lowercomfort level for mountaino~uc;cs~rco:="t~e __ -----i 

__ ()_,05 f §{)%! _6_0% 46% 43% a higher percentage ()IIJnlheir_own airplane 

0.03 1 4.58 4.68 4.86 --4.83 less experience flying with 8NM visibility . - - ..... 

____ 0.05.--l. 4.7_Q_ ! _:{74_ ·~ -4.87- - - 4.83 less experience flyingln_4000fl_c_.,!~n_(l'l __ n ----

_Prollat>llity ----M-1 ; M-2 M-3_ __ f---M-4- CompareiJiO-M~,-M-4 has: I __ 0.00 r· 21.61 I 35.57 40.71 53.72 I a higher comfort level for mountainous route 

Table 18. Mean Regional Comfort Ratingll by Terrain 

Terrain Type 

Region Water Non-Mountainous Mountainous 

Alaska 42.69 56.43 48.23 

Northwest 36.23 38.14 34.29 

Western 41.95 45.09 37.09 

Southern 42.76 45.49 37.50 

New England 39.48 40.08 35.25 

Eastern 38.99 40.07 34.44 

Total Sample 39.98 42.45 36.38 

29 
Table 19. Mean Regression Weights for Geographical Regions by Terrain 

Over Water 

Variable AK NW WP so NE EA 

Average R2 0.69 0.68 0.63 0.62 0.67 0.70 

Ceiling(A) -0.44 

Visibility (B) 

Precipitation (C) -0.63 -0.33 -0.25 -0.38 -0.33 

A*B*C 0.65 1.60 1.30 1.17 1.75 1.60 

A*B -0.26 -0.43 -0.34 -0.23 -0.89 -0.67 

A*C 0.79 

B*C 0.27 -0.22 -0.25 

Non-Mountainous 

Variable AK NW WP so NE EA 

AverageR2 0.82 0.73 0.75 0.72 0.71 0.76 

Ceiling (A) -0.29 -0.47 -0.43 -0.41 -0.33 -0.52 

Visibility (B) 0.23 

Precipitation (C ) 

A*B*C 0.25 

A*B 0.57 0.70 0.85 0.72 0.72 0.82 

A*C 0.31 0.22 0.32 0.32 0.21 

B*C -0.25 -0.23 -0.43 -0.33 -0.40 

Mountainous 

Variable AK NW WP so NE East 

Average R2 0.79 0.76 0.79 0.76 0.74 0.78 

'I'' -0.39 -0.49 -0.39 -0.32 -0.42 ,, Ceiling (A) -0.32 

''I' Visibility (B) II' Precipitation (C ) ,~I' A*B*C 0.47 1.00 0.74 0.91 1.03 1.34 I' li i A*B 0.38 0.22 0.32 0.32 0.21 I A*C 0.31 0.40 II 

,, B*C -0.25 -0.23 -0.43 -0.33 -0.40 I 

" 

,, 

';I 

I 

30 
Table 20. Proportionality of Group Membersbip by Region 

OVERWATER ROUTE 

TOTAL CASES 319 24 139 72 84 

TotaiW W-1 W-2 W-3 W-4 

Alaskan Reaion 12 2 2 1 .,.. 

Southern I'Leaion 50 2 23 9 16 

New Enaland Reaion 82 7 36 22 17 

Western Pacific Reaion 49 5 19 11 14 

Northwest Mountains Region 56 5 33 9 9 

Eastern Reaion 57 2 22 16 17 

NON-MOUNTAINOUS ROUTE 

TOTAL CASES 315 49 72 93 101 

TotaiNM NM-1 NM-2 NM-3 NM-4 

Alaskan Reaion 13 1 o• 2 10** 

Southern Reaion 50 5 10 14 21 

New England Reaion 81 19** 18 24 20 

Western Pacific Reaion 48 5 10 14 19 

Northwest Mountains Reaion 57 12 19 15 11** 

Eastern Reaion 55 6 13 21 15 

MOUNTAINOUS ROUTE 

TOTAL CASES 315 49 72 93 

TotaiM M-1 M-2 M-3 

Alaskan Reaion 13 4 1 1 

Southern Reaion 48 15 14 10 

New Enjlland Region 79 27 22 15 

Western Pacific Reaion 47 14 10 11 

Northwest Mountains Reaion 54 15 17 9 

Eastern Region 52 18 11 13 

Legend 

• = S1a1isticallv sianificant !-test, (.05) below the mean 

** = Statisticallv sionificant !-test (.051 above the mean 

Table 21. Comparison of Total Sample Demographic Data 

with South Texas Sample 

101 

M-4 

7** 

9 

15 

12 

13 

10 

Variable CUrrent Sample South Texas 

N 

Average Age 

Mean Total Flying Hours 

Mean Comfort Level 

Mean Over-Water Comfort Level 

Mean Non-mountain Comfort Level 

Mean Mountain Comfort Level 

31 

326 

52 years 

3845 

39.80 

42.27 

36.38 

ISO 

42 

1694 

30.29 

30.15 

30.29 
Table 22. Regional and South Texas Demographics 

--~~---F~~r: 13% n/a ,_ ·········----------------- . ------ ..................................................................•. -------------------

32 

31.76 40 

__ !!9.___ _38::.::__ ____ _ 

4.78 3.53 

54% 65% 

n/a 

24% 

7% --~~--~-

32% +;;;;;:;"··· • I l 63% 33% ! 35% l 

45% 

21% 

2% 

21% ·------ n/a 

17% 19% ----------------- ------------- 1.40 n/a 

---·--------------- --------------~- 2312ft n/a 

0.67 

-----------···············-~---··························) 

38% : 45% I ------4--- 26% i 15% i . --------------~-+-----~---------4 

3o/o \ 5% l ............................................................... l 

44% 55% ' 

I I 

29% I 15% I 

-~~aa·-r:ca1---~ 

I I 

·--··-············1·······························1 

···· ~~---1·····~~~~---1 

··········---·-········----1........ _I 
Table 23. Regional and South TUIB DemiJII'IIIIbks 

ROUTE 2 NON-MOUNTAINOUS 

MEAN COMFORT LEVEL 20.85 17 38.43 28 43.82 32 54.53 50 

NUMBER OF CASES (3151150) 49 37 72 52 93 34 101 27 

BACKGROUND VARIABLE NM-1 GP4 NM-2 GP2 NM-3 GP1 NM-4 GP3 

Flying History 

Total Hours 1849hrs 1679htS3950h~ 1516htS 3372hrs 1942htS 3955hrs 1869hrs 

Last 90 Days 18hrs 25htS 37hrs 37hffl 41hrs 51 hiS 37hrs 47htS 

Average Age 53yrs 46ytS 52yrs 41ytS 50yrs 44YtS 53yrs 38yrs 

Percent Owing Aircraft 59% 41% 51% 44% 47% 32% 53% 19% 

Percent Female n/a 14% n/a 17% n/a 12% n/a 4% 

Mean Terrain Experience 

Route 1 - Overwater 2.29 n/a 2.93 n/a 2.80 nla 3.01 nla 

Route 2 - Flatland 4.26 3.49 I 4.26 I 3.65 I 4.49 I 3.50 I 4.53 I 3.41 

Route 3 - Mountainous 3.43 n/a 3.61 n/a 3.27 nla 3.59 n/a 

Mean Precipitation Experience 

Ught Rain 3.65 2.11 3.99 2.50 4.01 2.62 4.00 2.41 

Moderate Rain 3.00 1.27 3.32 1.71 3A4 1.79 3.49 1.63 

Heavy Snow 1.83 0.38 1.96 0.33 2.21 0.38 2.32 0.67 

Mean Visibility Experience 

1 NM 2.83 1.38 3.17 1.79 3.18 1.62 3.26 1.85 

4NM 3.67 2.24 4.14 2.92 4.34 2.74 4.31 3.00 

SNM 4.38 3.30 4.69 3.77 4.76 3.38 4.86 3.70 

Mean Ceiling Experience 

800ft 2.73 1.62 3.18 1.75 3.17 1.65 3.25 1.78 

1800 It 3.63 2.46 4.10 3.13 4.10 2.88 4.16 2.93 

400011 4.52 3.38 4.74 3.81 4.85 3.56 4.86 3.67 

Percent Using Weather Source 

FAAIFSS 56% 68% 53% 66% 61% 74% 60% 56% 

DUAT 32% nla 28% n/a 26% nla 23% n/a 

TV/Radio 50% 26% 34% 32% 45% 33% 37% 35% 

Other 17% 13% 14% 12% 18% 10% 13% 7% 

Percent Certificates Held 

PP 43% 70% 33% 58% 30% 53% 30"Ao 56% 

COM 39% 24% 38% 37% 47% 32% 50% 22% 

ATP 10% n/a 28% n/a 19% n/a 18% n/a 

Other 8% n/a 1% n/a 3% n/a 2% n/a 

Multi-Engine Rating 33% n/a 51% n/a 49% n/a 53% n/a 

CFI/CFII 27% 16% 33% 21% 34% 21% 37% 26% 

Instrument Rating 67% nla 81% 38% 83% n/a 73% nla 

Percent Flying 

Job-employed as a pilot 6% 3% 21% 17";6 24% 21% 19% 23% 

Average Number 1.49 n/a 2.11 n/a 1.66 n/a 1.66 n/a 

FAA Safety Seminars Attended 

Lowest Ceiling for Day VFR Trip 2718ft n!a 2490tt I n1a I 1979ft I n!a I 1863ft I nla 

Average R2 o.SB I o.77 o.79 o.5o 

33 
1!!-

,,, ' 

',,I 

Table 24. Regional and South Texas Demographics 

1iisirum~eni Rating~---- · :·l'eninff'iYIIis ___ ~~ ----------~---- -

: -Jot.:emiiioie<fasa!lifol --- 14% 5% iAver&9e--Numil&r ~~---- --- ~- -, ·· 1:si- n;a_ -~ 

:·FJ;As&f8tis&ii1iniirsAueiiii&CiT __________ _ 

[L:owelitceiiin910r-·oa-y·vfiri-'friii:··-2323fl 

~~VIIJ'119.11 ... fl'.......... ·······-······-···-···-···-···-···-············-···-·········-··-·······r···-· 

IVa 

0.60 

~~~~f IVa 

0.83 ............. < ........ . 

34 

' 

2063ft ..... ; 

IVa ' .. ; 

0.75 
Table 25. Comparison or Compeusatoey and Non-Compensatory Models 

Model CorreJatioo 

Over-water Non-Mountainous Mountainous S. Tex. Regions S. Texas Regions S. Tex. Regions 

Compensatory Models 

A*B*C 

A+B+C (Additive) 0.83 0.80 0.94 0.92 0.93 0.92 

A*B 

(Multiplicative) 0.89 0.86 0.96 0.94 0.96 0.95 

A *C {Multiplicative) 

(Multiplicative) 0.65 0.62 0.86 0.89 0.84 0.85 

B*C 

0.79 0.74 0.76 0.73 0.82 0.83 (Multiplicative) 0.73 0.72 0.79 0.76 0.86 0.83 

Worst Factor Models 

Worst 

Worst 

Factor 

Factor (X>5.0) 0.54 0.59 0.69 0.74 0.71 0.73 

Worst Factor 

(X>4.5) 0.78 0.85 0.80 0.85 0.82 0.86 (X>4.0) 0.74 0.83 0.76 0.82 0.79 0.83 

Non-Compensatory Models 

A 

A(X>5.0) 

0.46 0.43 0.48 0.50 0.53 0.56 

A(X>4.5) 

0.46 0.44 0.44 0.47 0.55 0.60 

A(X>4.0) 

0.35 0.41 0.38 0.45 0.42 0.48 0.35 0.41 0.38 0.45 0.42 0.48 

B 

B 

0.48 0.50 0.67 0.69 0.62 0.59 (X>5.0) 

B 

0.41 0.43 0.56 0.59 0.53 0.50 (X>4.5) 

B 

0.46 0.53 0.62 0.66 0.46 0.45 (X>4.0) 0.46 0.53 0.62 0.66 0.46 0.45 

c 

C(X>5.0) 

0.62 0.58 0.60 0.52 0.73 0.72 

c 

0.57 0.53 0.60 0.57 0.67 0.68 (X>4.5) 

C(X>4.0) 

0.50 0.49 0.53 0.52 0.56 0.58 0.41 0.42 0.35 0.30 0.56 0.57 

35 
APPENDIX A 

Software and Analysis 

Data analysis for this project was accomplished on a Unisys II 00 series mainframe computer at the Human 

Resources Directorate of the USAF Armstrong Laboratory (ALIHR) and on the ffiM RS-6000 RISC model 530 

machine at the Metrica facility. All software used was developed by and is the property of the USAF Armstrong 

Laboratory, Brooks AFB, San Antonio, Texas. More specifically, in addition to utility and custom software required 

to reformat input files, data analysis was carried out using software packages described in the Mathematical and 

Statistical Library of the Armstrong Laboratory. This library is reviewed in Albert, W. G. & Whitehead, L. K., 

MATHEMATICAL AND STATISTICAL SOFTWARE INDEX: SECOND EDIDON (AFHRL-TP-85-47, 

August 1986). This index provides a brief description of each package and external references when available. 

Detailed program documentation is only available on the Unisys under their "@DA *DA.ADOC" retrieval system. 

(ALIHR internet address: "http://www.brooks.af.mil/HSC/ALJHR/hr-horne.htr..l") 

The Unisys II 00 is scheduled for shut-down and replacement in October 1997 by an ffiM RISC machine 

at ALIHR which is already in operation, but not yet accessible to outside users. It is unknown to this contractor as 

to which Unisys-resident programs are being converted for operation on the RISC platform and which will be "lost". 

Metrica has already produced the RISC-based version of ASCIT CODAP. 

This project used the following packages in this order: TRICOR (Correlation and Regression Package, 

Albert & Whitehead, 1986, p 23), GEN-IllER-GRP (Generate IllER-GRP input data, Albert & Whitehead, 1986, 

p I 0), IDER-GRP (Regression Equation Grouping, Albert & Whitehead, 1986, p II), GRPREL (Group Reliability, 

Staley & Weissmuller, 1981), and the ASCIT CODAP system (the Comprehensive Occupational Data Analysis 

Programs, Albert & Whitehead, 1986, p 53). A description of each package and its applicability to this project is 

provided below. 

TRICOR 

The first package used was TRICOR which stands for Triangular Correlation & Regression system. This 

system was developed by the United States Air Force Human Resources Laboratory (AFHRL) which is now known 

as the Human Resource Directorate ofthe USAF Armstrong Laboratory. This system can handle up to 400 variables 

in correlation matrices and regression analysis problems. Its matrix-inversion (exact solution) method accounts for 

its speed in processing hundreds of queued regression problems while screening-out linear dependencies while 

could lead to spuriously high results. In addition to hard-copy reports, TRICOR produces a Regression Master File 

which tracks every problem submitted to the system. This Regression Master File is input to the following program 

which extracts all the required statistics without error-prone human transcription. 

In this research stream, "rater policy" is defmed in terms of"level of comfort" associated with flying into 

a given weather scenario and the "worth functions" the pilot gives to the weather attributes being varied in this study 

which are ceiling, visibility, and precipitation. In statistical terms, the "level of comfort" is the criterion being 

predicted in the regression equation and the ''worth functions" are the regression weights being computed for each 

of the predictors of ceiling, visibility, and precipitation. 

AI 
Injudgingtheir own personal comfort level, some people may evaluate the weather factors in the proposed 

scenarios in different ways. Some people may look at each oftheweathervariables in turn, one at a time, like ceiling, 

then visibility, then precipitation. Other pilots may consider those factors in pairs, or all three simultaneously. For 

example, a given pilot may view ceiling AND visibility as a single factor which means if either is bad, he or she will 

not fly, regardless of the other value. These interaction forms introduce a non-compensatory notion into normal 

linear forms which permit multiple "high" variables to overwhelm a lone" low" variable. The regression model used 

in this project had to be expanded to include all "likely interactions." This means that the regression model used 

was robust enough to capture not only simple (compensatory), one-factor policies, but also, two-, three- (noncompensatory), or hybrid policies. The individual pilot's data is evaluated in terms of all possible models and the 

"best fit" becomes the policy ascribed to that pilot. 

Because the goal was to determine pilot policy under each of three terrains, the TRICOR correlations and 

regressions were run for each of the three terrain scenarios, each with about three hundred cases. Each of the 

TRICOR Master Regression Files was submitted separately to the following GEN-HIER-GRP, HIER-GRP, and 

CODAP analyses. 

GRPREL 

GRPREL is the standard interrater reliability in the CODAP system. For a given list of items rated by a set 

of Subject Matter Experts (SMEs), this program reports two measures of interrater agreement (R and R ). The 

R value indicates the reliability of the observed set of ratings- while 0.1 0 is considered a minlfu.um fo'J!' usable 

ra~h agreement (lower values or negative values indicate the presence of deviant raters-typically those who are 

using a reversed rating scale), a value of0.20 or greater is desired. The R value is driven by the number of raters 

actually used. Although an R of 0.90 is usually desired, it may not be p~ctical in a particular study because of a 

small number of raters (SMEs}lliat may be subdivided even further into smaller groups based on policy differences. 

The GRPREL program also computes means and standard deviations for each item in the list. Item-level reports 

are printed in three orders: original sequence, ordered descending y-mean value, and ordered descending on 

standard deviation. GRPREL computes each rater's correlation with the full-group mean vector and uses a 

probability evaluation to recommend the removal of deviant (non-cooperative or reversed scale) raters. The 

program can automatically iterate and remove flagged raters until either a sufficienct level of agreement(R = 0.90) 

is reached or no raters can be found with a probability (of deviant rating) above 0.95. •• 

GEN-IDER-GRP 

Because of the complexity and magnitude of the data required by the HIER-GRP program below, another 

program, GEN-HIER-GRP was created to mechanicaly reformat data from regression packages into the form 

required by HIER-GRP. While the Albert eta! reference indicates that GEN-HIER-GRP only accepts a TRICOR 

Regression Master File as input, personal communication with Janice Buchhorn of ALIHR indicates the program 

MAY have been modified to accept matrix information as output by SPSS. Note that the documentation indicates 

the software is limited to 200 cases. Metrica provided Ms. Buchhorn the source code updates to increase this limit 

to 400 cases to accommodate this project. 

A2 
IDER-GRP 

Given the regression analysis ()()llducted above ( detenniDalioo. of regteSSion weigbls IIOd "constant"), 

IllER -DRP performs a hierarchical clustering which begins wilh individuals (i.e., theirpolicy/regresllioo equation) 

and iteratively forms larger and larger policy groups until a single group remains. At each stage of the process, the 

two most similar people (or groups) are combined into a single, group policy equation. ~ people are oonsidered 

"most similar" if !he malhematics shows that (at !hat stage of the process) combining !hose two people loses the least 

amount of predictive efficiency, i.e., losses are minimized compared to all oilier alternative pairings. By studying 

diagrams of the clustering stages, it can clearly be seen when fairly large groups are being "forced" together by the 

precipitous drop in the amount of variance accounted for (i.e., the R2 value.) Note !hat the documentation indicates 

the software is limited to 200 cases. Metrica provided the source code updates to increase !his limit to 400 cases 

to accommodate this project. 

HIER-GRP was run once for each terrain scenario. While IDER-GRP provided a text~utput report 

identifYing "significantly different" branches within each terrain, !he cluster solution data for each terrain was 

reformatted for display in CODAP below. 

CODAP 

CODAP stands for the Comprehensive Occupational Data Analysis Programs and represents over 100 

programs designed to input, process, organize and report job-related data. CODAP includes procedures for 

clusteringjobs into structured job families, performing interrater reliability assessments on expert raters, comparing 

and reporting job descriptions for selected groups, reporting within-group frequency distributions for selected 

variables as well as statistical comparisons between groups. Note that the reference, Albert& Whitehead is outdated 

and program names have changed. For current program names and information, access the internet URL "http:// 

metricanet.com/groups/codap" .. 

For the remainder of this project, the primary data processing was moved to the RISC machine for 

processing with the RISC version of CODAP. Only the experimental versions of V ARSUM and CONHGP 

(explainedbelow)wererunontheUnisysafterthispoint. DatawereloadedintoaCODAPdatabaseusingAUDITR, 

INPSTD, and AUDITD (one for each terrain). The interrater reliability program (GRPREL) was run on each of the 

terrain samples. The reliability levels were very high indicating a strong consensus in prioritizing the 27 weather 

scenarios from "good" to "bad". Note that while pilots agreed on how to prioritize the weather scenarios, they still 

could have arrived at those decisions along different lines and have different personal comfort level about entering 

those conditions. 

A3 
To address the components of their personal policies, the cluster solution from HIER-GRP was converted 

to a CODAP cluster solution file using CONHGP (not documented on the internet). For each cluster solution the 

CODAP DIAGRM program was run to display a summary of the clustering. Analyzing the DIAGRM lead to the 

identification of four policy groups for each of the three terrains. Summary variables such as "average comfort level" 

and "group membership" were computed and added to the data base using V ARGEN. The people in "stages of 

interest" on the DIAGRM were identified using the STGJOB program while demographic groups (based on FAA 

region) were identified using the MEMSEL program. Once identified by either method, the interrater reliability 

program (GRPREL) was run to assess agreement as well as to produce a mean vector of ratings from the group. Each 

mean vector was shown in graphic form using PL TTSK (a derivative ofPL TV AL ). These mean vectors were input 

to FACMA T to produce an intercorrelation matrix showing how close the results of the policies are in prioritizing 

the weather scenarios. In addition, for each group identified, demographics were reported and cross-compared using 

the V ARSUM (experimental version with t-tests) program. 

Availability Notes 

TruCOR: 

While the TruCOR system may be replaced by any reasonable correlation and regression package (I 

criterion, 7 predictors), reformatting outputs for HIER-GRP may be tedious. 

GEN-HIER-GRP & HIER-GRP: 

No known substitute is available for the (GEN-HIER-GRP & HIER-GRP) programs. Much of the 

underlying math and all known references are contained in Ward, J. H. Jr, Treat, B. R., & Albert, W. G. GENERAL 

APPLICATIONS OF HIERARCHICAL GROUPING USING THE HIER-GRP COMPUTER PROGRAM. 

(AFHRL-TP-84-42). 

CODAP: 

While Metrica maintains ASCII CODAP for the Air Force, all release authorizations must go through the 

Air Force. Coordinating the release of ASCII CODAP from the Air Force should start by contacting the USAF 

,till AETC Occupational Measurement Squadron atRandolphAFB (OMSq). OMSq personnel are the operational users 

•:It, I~ I ofCODAP in the Air Force and underwrote the conversion from the Unisys to the ruse platform. Their home page 

!'Ill:.·.',,. 1 is "http:!/www.omsq.af.miVomy_pl.htrn". I . I'. 

:~~',' 1 Other versions of the CODAP system have been programmed over the years and include versions 

[jll,r · known as IBM 7040, IBM 360/370, Univac/Sperry!Unisys 1100, CDC 6600, CODAP 80, IBM MVS, IBM 

·II',, , VM, IBM ruse and atCODAP. The only systems known to be available at the present time include the 

1

11'

11 · , government-owned ASCII CODAP family (Unisys 1100, IBM MVS, and ruse CODAP) and the commercial, 

. ,,, pc-based product atCODAP from Sensible Systems, Inc., also in San Antonio, Texas. 

11 1

: 

11 

! I 

, j1'

11 A4 

,, 
APPENDIX B 

PILOT INFORMATION FORM 

The aeronautical charts used in this study were identical to those used in the previous study (Driskill 

et al., 1997); therefore, they have not been reproduced here. 

Bl 
us. Department 

ot 1iansporlc!ion 

F•CI•ral Avlatlan 

Administration 

Dear Airman: 

800 lnd_,denoe Ave .. s.w. 

WasNngtan, D.C. 20591 

I need your help. I am conducting a study of how pilots like you use weather 

information in making decisions. As you probably know, weather is a significant 

factor in a very large number of aviation accidents every year. The results of this 

study will help us develop new and more effective training programs to improve 

safety. 

This study examines how pilots use information on ceiling, visibility, and 

precipitation in their flight planning. It also examines how terrain influences the 

pilot's use of those weather data. Briefly, the study asks you to rank order 

various combinations of these weather factors from most to least comfortable and 

to then assign specific numerical ratings to each weather scenario. This process 

is repeated for three routes over widely different terrain. When we ran this study 

with some local pilots it took them about an hour to complete the exercise. 

This is not an easy exercise. You will really have to think about just what 

conditions you would rather fly under, and what factors are most important to you 

in making weather decisions. However, the more we learn about this decision 

making process, the safer we can make flying for everyone, so I hope you will 

take the time to complete the materials and send them back to me. If you have 

any questions you c;:~n reach me at (202) 366-6935. 

Sincerely, 

(}ar!) £ ;/rDavid R. Hunter, Ph.D. 

Program Scientist 

YOUR PARTICIPATION IS VOLUNTARY. If you do not wish to participate, 

simply discard these materials. If you decide to participate, your responses will 

remain anonymous. There are no codes or other identifying marks on the 

materials you are asked to complete and return to us that would identify you 

personally. 

This data collection is covered under OMB Approval Number 2120-0587 

B3 
p·l 

,II I 

INSTRUCTIONS 

First. let me make it clear that we are not asking whether or not you would actually 

make the flights described in these scenarios. Some pilots, for example, would never 

fly a single-engine aircraft across Lake Michigan, regardless of the weather conditions. 

For this study, it does not matter whether or not you would make a particular flight. 

What is important is how you think the various scenarios compare against each other. 

In other words, if you were flying along the routes shown, which weather combination 

would you most like to fly in and which would be your last choice. (By the way, those 

are usually the easy combinations to pick out. The going gets much tougher when 

deciding on all the in-between combinations.) 

Since you may never have flown over water long distances or through high mountain 

areas or through some of the weather conditions, you may have to use your 

imagination and think about what flying those routes under those conditions might be 

like. For a few of the weather combinations (such as heavy snow and five miles 

visibility), you may also have to use your imagination, since such a combination may be 

somewhat unlikely. 

Remember, there are no right or wrong answers. We are not trying to trick you into 

admitting you would fly under illegal conditions. We just want to know how you think 

these combinations of weather rate, and how comfortable you would be flying through 

them. 

1. Look at the Map for Route 1. It shows a flight from the City County Airport across 

Lake Michigan to the Delta County Airport in Escanaba, Michigan. Familiarize yourself 

with this route of flight and read the information about your aircraft and the departure 

and destination weather printed in the insert box on the map. 

2. Look at the response sheet for Route 1 which shows the Enroute Weather. You will 

see that a weak cold front is across your route of flight and that several combinations of 

reported ceilings, visibility and precipitation are given. For example, in the row for 

Scenario 101, the ceiling is reported to be 4,000 feet, the visibility is 7 miles, and 

moderate rain has been reported. 

3.1 Your first task is to decide under which set of conditions you would be most 

comfortable flying this particular route. Look carefully over all the combinations of 

ceiling, visibility, and precipitation and find the combination under which you would be 

most comfortable flying. When you have found that combination, write the number 1 

in the box headed Rank Order for that combination. 

B4 
INSTRUCTIONS - Page 2 

3.2 As you fill in the boxes on the forms, please print the numbers clearly. If there are 

two boxes for a particular answer (like there are for the Rank Order responses) and you 

want to enter a single number (for instance, you want to enter the number 1 for your 

most comfortable set of conditions) be sure to put that number in the box on the right. 

Example: Your entry for Rank Order 1 would be: 

[]] 

Your entry for Rank Order 15 would be: 

[]] 

Note that you do not need to fill in the leading boxes with zeros. 

3.3 Now look over the remaining combinations of ceiling, visibility and precipitation and 

find the combination under which you would be most comfortable flying from those 

remaining. Once you have found it, write the number 2 in the box headed Rank Order 

for that combination. 

3.4 Then just keep on narrowing the list of combinations down by finding the 

combination from among those remaining under which you are most comfortable and 

marking that combination with the next number. Finally, you will end up with only one 

combination left, and that will be the combination under which you would be least 

comfortable flying. If you have done the numbering properly, that combination will get 

numbered 27. 

3.5 At this point you should have gone through all the combinations and rank ordered 

them in terms of your comfort so that the combination you are most comfortable with 

is numbered 1 and the combination you are least comfortable with is numbered 27. 

All the other combinations will have some number between those two values, and no 

value will appear more than once. That is, you can't use the same rank number on 

more than one combination. 

3.6 Now, look back at the various weather combinations and decide which combination 

is the WORST COMBINATION under which you would continue the flight. That is, if 

any of the three factors (ceiling, visibility, or precipitation) got any worse, then you 

would not continue the flight and would divert. Once you have decided which is the 

worst combination in which you would still fly, write the SCENARIO NUMBER of that 

combination in the box marked WORST FLYABLE SCENARIO. If you would not make 

this flight under any of the combinations we have given, then enter "0 0 0" in the box 

marked WORST FLYABLE SCENARIO. 

85 
INSTRUCTIONS -- Page 3 

4.1 Your second task is to assign a level of comfort to each individual scenario 

using a scale from 1 to 100, where: 

1 = Most Comfortable about completing the flight VFR under these enroute 

weather and terrain conditions. 

100 = Least Comfortable about completing the flight VFR under the enroute 

weather and terrain conditions. 

4.2 Use any numbers in the range from 1 to 100 to indicate your level of comfort, but 

you May Not use the same number more than once. If, for instance, you feel that two 

choices are both "least comfortable" then rate one as "99" and the other as "1 00". 

5.1 After you have rank ordered all the weather scenarios for Route 1 and have 

assigned a comfort rating to each scenario for that route, do the same thing for Route 

2 and Route 3. 

5.2 Beginning with Route 2, review the map and read the information on the map. 

Then rank order each of the combinations of weather conditions. After you have 

completed the rank ordering, decide on the worst flyable scenario, then assign a 

comfort rating to each combination, again using the 1 to 100 scale. 

5.3 After you have completed Route 2, go on to Route 3 and repeat the rank ordering 

and comfort rating process for that route. 

6.1 To help in the analysis of the weather ratings, we need some information about you 

(for example, your age), your aviation ratings and experiences, and what area of the 

country you live in (We get that from the Zip code), so please complete the sheet 

labeled Pilot Background Data. Do not put your name or other identifying information 

on the sheet. 

6.2 If you have any comments or suggestions, please feel free to write them on a 

separate sheet of paper and send it back to us. 

7.1 Put the three Weather Information Study sheets on which you have written your 

rank order and comfort ratings and the Pilot Background Data Sheet in the selfaddressed business reply envelope and drop it in the mail. No postage is required. 

That's all. 

Thank you for taking the time to help me on this project. If you would like 

information on the results of this study, just send me a note with your name and 

address (don't put them on your answer sheets) and I will send you a copy of the 

report. 

B6 
~ 

• Weather Information Study 

Route 1 - City County to Delta County • 

Scenario Ceiling Visibility Precipitation Rank Comfort [Scenario Ceiling Visibility Precipitation Rank Comfort 

Number (feet) (miles) Order Level Number (feet) (miles) Order Level 

101 

102 

103 

104 

105 

106 

107 

108 

109 

110 

111 

112 

113 

~~ 

• 

4,000 7 moderate rain [ [ [j [ [ [1114 

5,000 1.5 lightrain I Lilli l1115 

4,000 4 none [_ I J I JJ l1116 

5,000 8+ none [ J_j I_Jj J 1117 

2,000 3 none I_[__[ Ll_[j 1118 

1,800 3 heavyrain [-T-J[TTII119 

1,800 7 light snow [[] [[_TJ 1120 

3,500 1 heavy rain []] [[IJ 1121 

900 1.5 moderate rain 1-LJ I.J~T] 1122 

1.8oo. 7 moderate rain LIJ ~-~---[11123 

800 1.5 none I_]][]]] 1124 

1,600 8 none [-[] [IIJ 1125 

1,600 3 heavy snow [0 [ I I l1126 

ENROUTE WEATHER: A weak cold 

front has stalled along a line from 

Sturgeon Bay to Namistique. The 

reported ceilings, visibility, and 

precipitation are given above. 

-, '127 

WORST 

4,000 5 light snow 

900 0.5 heavy snow 

1,600 1.5 heavy rain 

5,000 0.5 moderate rain 

600 8 moderate rain 

600 7 none 

1 ,600 1.5 moderate snow 

5,000 8+ freezing rain 

900 5 none 

800 4 moderate rain 

--------- . -- ----·· 

I .I --I l J·-IJ ----- ----- ----- -- OJ l-lll 

wr· r n 

ITH I I I 

COli I I 

[TI[ I I I 

CTIIIII 

CTIIIII 

[Dill I 

[Dill I 

1 .6oo 4 moderate snow rn I I I I 

1,800 5 moderate rain []] [ I I I 

1,8oo o.5 none rn [I I I 

900 4 heavy rain [[] [ I I I 

FLYABLE SCENARIO I I I I -----·--··-·---- ------·-· 

For office use only 

Scan Code: [Q~ITI OJ • 
·~~~ '"· -~ -~ 

~~--::::. ~---' --==-~ -=- --=-~ ~- ~~~--:~~ 

t>O 

00 

:-=- -- = 

- -

• · Weather Information Study 

Route 2 - Hereford Airport to Dalhart Airport 

Scenario Ceiling Visibility Precipitation • 

Number (feet) (miles) Rank Comfort /Scenario Ceiling Visibility Precipitation Rank Comfort 

Order Level 

201 

202 

203 

204 

205 

206 

207 

208 

209 

210 

211 

212 

213 

~~ 

• 

Order Level Number (feet) (miles) 

5,000 

4,000 

1,600 

4,000 

2,000 

4,000 

3,500 

1,800 

600 

1,800 

800 

800 

1,800 

8 

1 

3 

3 

7 

8+ 

0.5 

0.5 

0.5 

4 

8+ 

8 

5 

light snow 

light rain 

heavy snow 

light rain 

light snow 

light rain 

heavy rain 

moderate snow 

light snow 

none 

light snow 

none 

moderate rain 

CD I I I /1214 

CD I I I /1215 

CD/ I I /1216 

CD I I I l1211 

CD I I I /1218 

CD I I I /1219 

CD I I I /1220 

CD I I I /1221 

CD/ I I /1222 

CD/ I I /1223 

CD/ I I /1224 

CD/ I I /1225 

CD/ I I /1226 

600 8 moderate rain 

900 4 heavy rain 

5,000 1.5 moderate rain 

1 ,800 7 moderate rain 

900 5 light snow 

900 0.5 heavy rain 

2,000 1.5 none 

1,800 3 heavy rain 

2,000 0.5 freezing rain 

1,600 4 moderate snow 

800 3 none 

COlli I 

COl I I -J 

COII/I 

CD/III 

COII/I 

CD/III 

COII/I 

CD/III 

CD/III 

CD/III 

COlli: 

9oo 1.5 light rain CD I I I ' 

2,000 8+ light rain rn rn 

ENROUTE WEATHER: A weak cold 

front has stalled along a line running 

west from Amarillo into New Mexico. 

The reported ceilings, visibility, and 

precipitation are given above. 

227 4,000 5 light snow rn rn 

IWORST FLYABLE SCENARIO 1 1 1 

• 

For office use only 

Scan Code: I / I / / I QD 

OMB Approval Number 2120-0587 
• Weather Information Study • Route 3 • Las Vegas Airport to San Juan Pueblo Airport 

Scenario Ceiling Visibility Precipitation Rank Comfort Scenario Ceiling Visibility Precipitation Rank Comfort 

Number (feet) (miles) Order Level Number (feet) (miles) Order Level 

301 5,000 5 none [IJI I I I 314 3,500 1 light snow DJII l I 

302 600 5 freezing rain [IJI I I I 315 1,600 3 heavy rain OJI I l l 

303 3,500 8 moderat rain [IJI I I I 316 2,000 8+ light rain OJI l ! l 

304 900 1 heavy snow COl I I 1317 2,000 1.5 none OJ! I j I 

305 2,000 0.5 moderate rain [01 I I I 318 1,600 1 freezing rain [01 I I l 

306 900 4 heavy rain [01 I I 1319 600 8 moderate rain [I] I I I 

307 4,000 8+ none [IJI I I 1320 3,500 ;) freezing rain [I] I I l 

"' moderate snow CD I I I I 321 [I] I I l '0 308 600 3 2,000 5 light snow 

309 1,800 7 moderate rain [01 I I 1322 900 5 light rain [01 l l 

310 800 1 none [01 I I 1323 900 8 light rain [01 I ! I 

311 4,000 0.5 heavy snow [01 I I 1324 1,600 3 heavy snow [01 I l i 

312 600 1 moderate snow rn I I I 1325 2,000 4 none [01 I I l 

313 4,000 4 light snow [01 I I 1326 5,000 1.5 light rain [IJl I I l 

ENROUTE WEATHER: A weak cold 327 1 ,800 5 moderate rain []Jj I l 1 

~~ front has stalled along a line running //woRsT FLYABLE SCENARIO I I I I 

from Santa Fe northest into Colorado. For offioe use only The reported ceilings, visibility, and 

• precipitation are given above. Scan eooe: I I I I I I@J • OMB ~I .Number 2121l-05B7 
-~~- -

• 

-0 "' 

il~ 

• 

~ ~~~~~ ----=- = 

--=- = 

Weather Information Study 

Pilot Background Data 

What is your highest pilot certificate 

o Private o Commercial o Airline Transport 

Do you have an instrument rating? ONo 

Do you have a multi-engine rating? ONo 

Are you primarily employed as a pilot? ONo 

Do you hold a CFI or CFII certificate? ONo 

Do you own your own aircraft, either by 

yourself or as a member of a partnership? ONo 

How many FAA-sponsored Safety 

Seminars have·you attended in the last rn 

24 months? 

OYes 

OYes 

OYes 

OYes 

OYes 

What is the lowest ceiling under which 

you would start a cr~ss-country day VFR I I I I I feet 

trip from your local arrport? 

What is your age? rn 

What is your home zip code? I I I I I I 

How many hours have you logged in 

the last 90 days? I I I I 

How many Total Hours have you logged? I I I I I I 

What percent of the time do you obtain weather 

information for flight planning from each of these sources? 

FAA/FSS 1 1 1 r· 

TV/Radio I I I jlo 

DUAT I I I r 

Other I I I jlo 

Please indicate how often during your flying 

experiences you have flown under these or 

highly similar circumstances? 

NEVER 

ONCE or TWICE 

SEVERAL TIMES 

[ 

J MANY TIMES 

NUMEROUS TIMES 

0 [[ ~--... •-••••-•m' 

0 0 0 0 0 Over non-mountainous terrain similar to that In ROUTE 2 

0 0 0 0 0 Over mountainous terrain similar to that In ROUTE 3 

0 0 0 0 0 Light Rain 

0 0 0 0 0 Moderate Rain 

0 0 0 0 0 Heavy Snow 

0 0 0 0 0 Visibility 1 mile 

0 0 0 0 0 Visibility 4 miles 

0 0 0 0 0 Visibility 8 miles 

0 0 0 0 0 Ceiling BOO feet 

0 0 0 0 0 Ceiling 1,800 feet 

0 0 0 0 0 Ceiling 4,000 feet 

Thank you for your help. 

Please return these four answer sheets in the 

self-addressed business reply envelope. 

For office use only 

Scan Code: I I I I I I GO 

OMB Approval Number 2120-0587 • 

-, 

• 
r 

I 

I APPENDIXC 

Regression Models 

Predicting Policy-Related Metrics from Demographics 

Regression Model I: 

Predicting (COOl!) Average Comfort Level 

r = 0.39471 

RSq = 0.15580 

Note: "ZOC" stands for Zero-Order Correlation with ... 

Variable ID (Predictor) 

1 zoe with Comfort Level 

I I zoe with Person's RSq- Degree Expert Info was used) 

I I I Unique Variance Accounted for by this Variable 

I I I I Variable Description (Predictor) 

I I I I I 

VAR w/COOII w/RSQ! Unique I 

I I I I I 

V0025 .1905 

V0009 -.1848 

C0023 -.1409 

V0026 .1250 

C0015 .1235 

V0004 .1144 

V0014 .1141 

V0028 .0949 

V0017 -.0884 

V0005 .0701 

COOlS .0612 

C00!6 .0609 

C0020 -.0557 

V0027 .0524 

C0013 .0487 

V0007 -.0399 

C0021 .0210 

V0003 .0202 

V0008 .0174 

V0020 -.0163 

.020353 EXPERIENCE- VISIBILITY 4 MILES 

.017303 LOWEST CEILING FOR DAYVFR TRIP 

.023269 ROUTE 3 (MOUNTAIN) 

.001216 EXPERIENCE- VISIBILITY 8 MILES 

.016317 ALASKA REGION 

.017114 MULTI-ENGINE RATING (I= YES, O=NO) 

.003797 PCT TIME OBTAIN WEATHER INFO- FANFSS 

.004001 EXPERIENCE- CEILING 1800 FEET 

.004344 PCT TIME OBTAIN WEATHER INFO- OTHER 

.002925 EMPLOYED AS A PILOT (!=YES, O=NO) 

.007541 WESTERN PACIFIC REGION 

.001354 SOUTHERN REGION 

.001034 EASTERN REGION 

.002153 EXPERIENCE- CEILING 800 FEET 

.000873 HIGHEST CERTIFICATE- COMMERCIAL 

.001431 OWN AIRCRAFT (!=YES, O=NO) 

.002777 ROUTE I (WATER) 

.001264 INSTRUMENT RATING (!=YES, O=NO) 

.000893 NUMBER OF FAA SAFETY SEMINARS 

.003723 EXPERIENCE- ROUTE 3 

CI 
r 

C0014 -.0070 .005119 HIGHEST CERTIFICATE- AIRUNE TRANSPORT 

Regression Model 2: 

Predicting (RSQ1) Person's RSq- Degree Expert Info was used. 

(Model without using Comfort Level as a predictor.) 

r = 0.38716 

RSq = 0.14989 

Note: "ZOC" stands for Zero-Order Correlation with ... 

Variable ID (Predictor) 

1 zoe with Comfort Level 

I I zoe with Person's RSq- Degree Expert Info was used) 

I I I Unique Variance Accounted for by this Variable 

I I I I Variable Description (Predictor) 

I I I I I 

VAR w/COOII w/RSQ! Unique I 

I I I I I 

C0021 -.2612 .069710 ROUTE I (WATER) 

VOO!O -.0994 .001646AGE 

V0014 -.0988 .006845 PCT TIME OBTAIN WEATHER INFO- FAA/FSS 

V0019 

V0026 

C0020 

C0016 

C0022 

C0015 

C0014 

V0022 

V0024 

C0013 

V0013 

COOl? 

V0006 

V0023 

V0012 

V0028 

V00!6 

V0021 

.0930 .007995 EXPERIENCE - ROUTE 2 

.0790 .005269 EXPERIENCE- VISIBILITY 8 MILES 

.0759 .000958 EASTERN REGION 

-.0686 .002257 SOUTHERN REGION 

.0659 .005700 ROUTE 2 (FLAT) 

.0624 .003744 ALASKA REGION 

.0618 .009200 HIGHEST CERTIFICATE- AIRLINE TRANSPORT 

-.0612 .006049 EXPERIENCE- MODERATE RAIN 

-.0580 .001329 EXPERIENCE- VISIBILITY I MILE 

-.0579 .001243 HIGHEST CERTIFICATE- COMMERCIAL 

-.0525 .003875 TOTAL HOURS LOGGED 

-.0445 .001455 NEW ENGLAND REGION 

-.0429 .004380 CFI OR CFII CERTIFICATE (!=YES, 0=N0) 

-.0416 .000503 EXPERIENCE- HEAVY SNOW 

.0390 .001357 HOURS LOGGED IN LAST 90 DAYS 

.0319 .000603 EXPERIENCE- CEILING 1800 FEET 

.0158 .002992 PCT TIME OBTAIN WEATHER INFO -lV/RADIO 

-.0083 .001260 EXPERIENCE- LIGHT RAIN 

C2 
Regression Model 3: 

Predicting (RSQI) Person's RSq- Degree Expert InfO was wed. 

(Model using Comfort l.evd as a predictor.) 

r = 0.41940 

RSq = 0.17590 

Note: "ZOC" stands for Zero-Order Corrdation with ... 

Variable ID (Predictor) 

1 zoe with Comfort t.evd 

I I zoe with Person's RSq- Degree Expert Info was used) 

I I I Unique Variance Accounted for by this Variable 

I I I I Variable Description (Predictor) 

I I I I I 

VAR w/COOII w/RSQ! Unique I 

I I I I I 

C0021 .0205-.2612 .060171 ROUTE I (WATER) 

C0011 1.0000-.1640 .026108 AVERAGE COMFORT RATING 

VOOI0-.0103-.0994 .001723AGE 

V0014 .1038 -.0988 .005004 PCT TIME OBTAIN WEATHER INFO- FAA/FSS 

V0019 .0480 .0930 .008659 EXPERIENCE- ROUTE 2 

V0026 .1742 .0790 .010557 EXPERIENCE- VISIBILI1Y 8 MILES 

C0020 -.0468 .0759 .004221 EASTERN REGION 

C0022 .1189 .0659 .002370 ROUTE 2 (FLAD 

C0015 .1124 .0624 .010397 ALASKA REGION 

COO 14 -.0175 .0618 .005369 HIGHEST CERTIFICATE -AIRLINE TRANSPORT 

V0022 .0899 -.0612 .005670 EXPERIENCE- MODERATE RAIN 

C0013 .0635 -.0579 .000598 HIGHEST CERTIFICATE- COMMERCIAL 

V0013 .0378 -.0525 .003143 TOTAL HOURS LOGGED 

V0006 .0481 -.0429 .005361 CFI OR CFII CERTIFICATE (I= YES, O=NO) 

V0023 .0610 -.0416 .000997 EXPERIENCE- HEAVY SNOW 

V0012 .0070 .0390 .001398 HOURS LOGGED IN LAST 90 DAYS 

V0027 .0474 -.0341 .001608 EXPERIENCE- CEILING 800 FEET 

V0009 -.1898 -.0273 .002502 LOWEST CEILING FOR DAY VFR TRIP 

V0016 -.0527 .0158 .002957 PCTTIME OBTAIN WEATHER INFO- TV/RADIO 

V0021 .0766-.0083 .001507 EXPERIENCE- LIGHT RAIN 

V0004 .0974 .0058 .001805 MULTI-ENGINE RATING (!=YES, O=NO) 

COOlS .0722 .0045 .003173 WESTERN PACIFIC REGION 

C0019 -.0828 -.0009 .001111 NORTHWEST MOUNTAINS REGION 

V0020 -.0149 -.0003 .000836 EXPERIENCE- ROUTE 3 

C3 -ku.s. GOVERNMENT PRINTING OWICE; 19t7. "'"-'60047 
Regression Model 3: 

Predicting (RSQ I) Person's RSq - Degree Expert InfO was used. 

(Modd using Comfort Level as a predictor.) 

r = 0.41940 

RSq = 0.17590 

Note: "ZOC" srands for Zero-Order Correlation with ... 

Variable ID (Predictor) 

I zoe with Comfort Level 

I I zoe with Person's RSq - Degree Expert InfO was used) 

I I I Unique Variance Accounted for by this Variable 

I I I I Variable Description (Predictor) 

I I I I I 

VAR w/COOll w/RSQI Unique I 

I I I I I 

C0021 .0205-.2612 .060171 ROUTE I (WATER) 

COOll 1.0000-.1640 .026108 AVERAGE COMFORT RATING 

VOOIO -.0103 -.0994 .001723 AGE 

V0014 .1038-.0988 .005004 PCTTIMEOBTMNWEATHERINFO- FAA/FSS 

V0019 .0480 .0930 .008659 EXPERIENCE- ROUTE 2 

V0026 .1742 .0790 .010557 EXPERIENCE- VISIBIU1Y 8 MILES 

C0020 -.0468 .0759 .004221 EASTERN REGION 

C0022 .1189 .0659 .002370 ROUTE 2 (FLAT) 

C0015 .1124 .0624 .010397 ALASKA REGION 

C0014 -.0175 .0618 .005369 HIGHEST CERTIFICATE- MRLINE TRANSPORT 

V0022 .0899 -.0612 .005670 EXPERIENCE- MODERATE RMN 

C0013 .0635 -.0579 .000598 HIGHEST CERTIFICATE- COMMERCIAL 

V0013 .0378 -.0525 .003143 TOTAL HOURS LOGGED 

V0006 .0481 -.0429 .005361 CFI OR CFII CERTIFICATE (!=YES, O=NO) 

V0023 .0610-.0416 .000997 EXPERIENCE- HEAVY SNOW 

V0012 .0070 .0390 .001398 HOURS LOGGED IN LAST 90 DAYS 

V0027 .0474-.0341 .001608 EXPERIENCE- CEIUNG 800 FEET 

V0009 -.1898 -.0273 .002502 LOWEST CEIUNG FOR DAYVFR TRIP 

VOOI6 -.0527 .0158 .002957 PCT TIME OBTMN WEATHER INFO- TV/RADIO 

V0021 .0766 -.0083 .001507 EXPERIENCE- UGHT RMN 

V0004 .0974 .0058 .001805 MULTI-ENGINE RATING (!=YES, O=NO) 

COOlS .0722 .0045 .003173 WESTERN PACIFIC REGION 

COOI9 -.0828 -.0009 .001111 NORTHWEST MOUNTMNS REGION 

V0020 -.0149 -.0003 .000836 EXPERIENCE- ROUTE 3 

C3 fru.s. GOVERNMENT PRINTING omCE: 199'7. ~