Elaine M. Pfl eiderer

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

May 2003

Final Report

This document is available to the public

through the National Technical Information

Service, Springfi eld, Virginia 22161.

Offi ce of Aerospace Medicine

Washington, DC 20591

Development of an 

Empirically-Based Index 

of Aircraft Mix

DOT/FAA/AM-03/8
NOTICE

This document is disseminated under the sponsorship of

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

information exchange. The United States Government

assumes no liability for the contents thereof.
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1

DEVELOPMENT OF AN EMPIRICALLY-BASED INDEX OF AIRCRAFT MIX

Aircraft mix has been proposed as one of the traffi c characteristics that contributes to sector complexity 

in en route air traffi c control (Robertson, Grossberg, 

& Richards, 1979; Federal Aviation Administration 

[FAA], 1984; Grossberg, 1989; Mogford, Murphy, 

Roske-Hofstrand, Yastrop, & Guttman, 1994). “Sector 

complexity” describes static and dynamic characteristics 

of the air traffi c control environment that combine with 

controller taskload (i.e., the air traffi c events to which the 

controller is exposed) to produce controller workload (i.e., 

the controllers’ reaction to and perceived effort involved 

in managing these events) (Grossberg, 1989; Manning, 

Mills, Fox, Pfl eiderer, & Mogilka, 2001). As changes 

are introduced into the air traffi c control environment 

such as the recent implementation of the Display System 

Replacement (DSR), or the proposed introduction of 

“free fl ight” (Radio Technical Commission for Aeronautics 

[RTCA], 1995) it becomes increasingly important that 

measures are developed to evaluate the impact of these 

changes on controller performance. In spite of a growing 

body of work dedicated to the measurement of workload, 

taskload, sector complexity, and controller performance 

(for a list of 162 of these measures, see Hadley, Guttman, 

& Stringer, 1999) little attention has been focused on 

quantifying aircraft mix. This is possibly because, until 

recently, aircraft mix had not been clearly defi ned.

In the initial studies identifying aircraft mix as a 

factor contributing to sector complexity, aircraft mix 

was assumed to refer to problems associated with the 

disparate performance capabilities of propeller and jet 

aircraft (e.g., Robertson, Grossberg, & Richards, 1979; 

Grossberg, 1989). In the preliminary stages of a study 

conducted by Mogford and co-workers (1994), aircraft 

mix was defi ned as the proportion of commercial, private, 

and military aircraft. The number of aircraft fl ying Visual 

Flight Rules (VFR) versus Instrument Flight Rules (IFR) 

was considered to be a distinct factor. A subject-matter 

expert who provided detailed factor defi nitions introduced 

engine type as an element of aircraft mix. In the fi nal list 

of 19 sector complexity factors, aircraft mix was defi ned as 

“VFR, IFR, props, turboprops, jets, etc” (p.37). Though 

this defi nition seems extensive, these verbal representations of aircraft mix might be only marginally related 

to the parameters necessary for quantifi cation of the 

construct.

Pfl eiderer (2000) conducted an investigation of the 

salient features of aircraft mix as it relates to aircraft performance characteristics. For this analysis, 30 Certifi ed 

Professional Controllers (CPCs) from various Air Route 

Traffi c Control Centers (ARTCCs) across the United 

States provided average speed, climb, and descent rate 

estimates for a sample of 30 distinct aircraft types. A 

matrix of squared Euclidean distances derived from 

summary estimates (i.e., means of speed, climb, and 

descent) was used to construct a classical multidimensional scaling (CMDS) model of the aircraft. Multiple 

regression interpretation of the two-dimensional solution 

revealed that Dimension 1 was related to engine type, 

whereas Dimension 2 was associated with weight class. 

The results of the analysis were interpreted as evidence 

of performance-based prototypes. However, it was also 

evident from the position of the elements (i.e., aircraft 

types) in the derived stimulus space that it might be 

possible to develop a measure of aircraft mix using these 

two easily-obtained variables.

The present study is a continuation of that investigation (i.e., Pfl eiderer, 2000). Phase I was designed to 

determine whether controllers’ perceptions of aircraft 

performance and the actual recorded performance 

of aircraft were comparable (i.e., would demonstrate 

similar dimensionality in repeated CMDS analysis). For 

this analysis, a matrix of squared Euclidean distances of 

controller estimates of mean speed, climb, and descent 

rates for 24 distinct aircraft types was compared with a 

matrix of mean speed, climb, and descent rates of the 

same aircraft types calculated from routinely-recorded 

System Analysis Recording (SAR) data. It was expected 

that the two dimensions noted in the previous CMDS 

analysis of controllers’ perceptions would be the same as 

those in the SAR sample, but it was possible that the two 

matrices might differ with regard to the relative salience 

and importance of each dimension. Characteristics of the 

CMDS model of SAR data could be used to confi rm, 

amend, or replace previously-gathered information regarding the salient features of aircraft mix.

Phase II focused on the development of an index of 

aircraft mix based on the results of the Phase I multidimensional scaling analyses. Because multidimensional 

scaling translates patterns of responding into patterns of 

elements in a dimensional space, it should be possible to 

assign base values to aircraft and then calculate distances 

representing differences in performance capabilities to 

compute an aircraft mix index.

Finally, the aircraft mix index was computed for all 

aircraft present in a particular traffi c sample. If the index 

has suffi cient variability and precision, it should be able 
2

to discriminate between low-altitude sectors (i.e., sectors 

with a high probability of aircraft with disparate performance capabilities) and high-altitude sectors (i.e., sectors 

with a low probability of aircraft mix due to the relatively 

lower service ceilings of many piston-driven aircraft). If 

the aircraft mix index passes the “discriminability” test, 

future research will be conducted to determine whether 

or not it adds unique information to an existing suite of 

Performance and Objective Workload Evaluation Research (POWER) measures (Mills, Pfl eiderer, & Manning, 

2002). It is possible that the complexity associated with 

aircraft mix is redundant with other variables. It is also 

possible that aircraft mix is characteristic of so few sectors 

so as to be of little use within the larger suite of measures. 

One thing is certain: Aircraft mix’s relative contribution 

to sector complexity and controller workload cannot be 

assessed until it has been quantifi ed.

Phase I: Comparison of SAR Data and 

Controller Estimates

Method

Design and Procedure

Multidimensional scaling refers to a group of descriptive 

procedures that transform data into mapped elements in 

one or more spatial dimensions (Kruskal & Wish, 1978). 

The appropriate data for CMDS analysis are proximities, 

numbers that indicate the similarity or dissimilarity of 

a set of objects. Proximities may be obtained directly or 

derived mathematically from a set of variables. In this 

application, two matrices of dissimilarity measures were 

computed: One matrix was based on summary controller 

estimates of aircraft performance, the other was based 

on summary measures of aircraft performance derived 

from SAR data.

Controller Estimate Matrix. This matrix represents 

a subset of the data used in a previous study (Pfl eiderer, 

2000) in which 30 Certifi ed Professional Controllers 

(CPCs) provided estimates of average cruising speed, climb 

rate, and descent rate for each of 30 distinct aircraft types. 

In the present study, mean speed, climb, and descent rate 

estimates were calculated from data provided by 24 of the 

original 30 controllers for 24 of the original aircraft types. 

The subset of 24 controllers was selected from the larger 

sample because these CPCs met currency requirements 

at the same ARTCCs represented in the SAR sample: 

Kansas City (N = 17) and Boston ( = 17) and Boston (N = 7). The aircraft = 7). The aircraft 

list was reduced because 6 of the 30 aircraft types did not 

appear in the Kansas City or Boston airspace during the 

time sampled. The squared Euclidean distance between 

vectors of controller summary estimates was computed 

to create a matrix of distances representing controllers’ 

perceptions of each aircraft’s capabilities relative to other 

aircraft in the sample. For example, if summary estimates 

for the fi rst and second aircraft were:

The squared Euclidean distance for these aircraft 

would be: (1-4)2 + (2-5) 2 + (3-6) 2 = 27. A table listing 

summary controller estimates used to compute the matrix 

of distances is provided in Appendix A. For information 

about participants’ professional experience, detailed descriptions of the materials used to collect estimates, and 

other points of methodology, see Pfl eiderer (2000).

SAR Data Matrix. The information used to construct 

this matrix was recorded at the Kansas City and Boston 

centers. The Kansas City sample consisted of 168 hours 

of continuous SAR data recorded from January 19, 1999 

through January 25, 1999. The Boston sample comprised 

a total of 27 hours of SAR data, recorded on March 16, 

1998 from 14:00 to 20:59 ZULU (7 hours); March 17, 

1998, from 14:00 to 20:59 ZULU (7 hours); March 19, 

1998 from 15:00 to 19:59 ZULU (5 hours); and March 

20, 1998, from 15:00 to 22:59 ZULU (8 hours). Raw 

data were extracted through the use of “log” and “track” 

reports produced by the Data Analysis Reduction Tool 

(DART). Within the sample time frame, 7095 fl ights 

corresponded to the selected aircraft types. The modal 

fl ight duration of these fl ights was 27 minutes. The modal 

number of updates (observations) per fl ight was 283.

Aircraft type was derived from designators (alphanumeric labels that indicate the make and model of an 

aircraft) that are printed on the fl ight progress strip (FPS) 

and appear within the fl ight plan readout display. The 

contents of both fl ight progress strips and fl ight plan 

readouts are recorded by the Host system and output in 

the DART log report.

Mean climb and descent rate estimates were calculated 

from altitude information recorded in the DART track 

reports. Climb and descent rate estimates represent the 

amount of change divided by duration of change for all 

detected altitude changes converted into feet per minute 

(fpm) and then averaged across changes for each aircraft 

(set to missing if no altitude changes were detected). 

For example, from 8:12:08 to 8:17:02 fl ight XMPL01 

climbed from 26,400 feet to 33,000 feet – a total of 6,600 

feet in 4 minutes and 54 seconds (1,454 fpm). From 8:

30:00 to 8:35:00, XMPL01 climbed from 33,000 feet 

to 35,000 feet – a total of 2,000 feet in 5 minutes (400 

fpm). The climb rate estimate for XMPL01 would then 

be 927 fpm (the mean of the two changes.) Mean climb 

and descent rate estimates were calculated in this 

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3

 manner for each flight and then averaged across fl ights 

for each designator. Of course, not all fl ights made altitude 

changes during the time sampled, and so the number of 

observations used to calculate mean climb and descent 

rates varied between aircraft designators. Appendix A lists 

the number of climb and descent rate observations upon 

which mean climb and descent rates were based.

Mean speed estimates were calculated by fi rst computing the mean of all ground speeds recorded in the DART 

track report for each fl ight (distinguished by a unique 

Aircraft Identifi er [AID] Computer Identifi er [CID] 

combination) and then averaging across fl ights for each 

designator. The number of updates per fl ight varied as a 

function of control time. However, the number of speed 

estimates used to compute the average speed for each designator is equal to the number of aircraft corresponding 

to that designator (column N in the table in Appendix 

A). Please note that, unlike the computation of mean 

climb and descent rates, mean speed calculations did not 

involve speed changes. The measure simply represents the 

average speed for each aircraft type based on the average 

ground speed for all individual aircraft of that type.

Squared Euclidean distances were calculated from mean 

speed, climb, and descent rates in the same manner as 

controller observations. Distances in the resulting matrix 

represented each aircraft’s performance relative to other 

aircraft in the sample.

Variables for Interpretation of CMDS Models. A separate 

set of variables was collected for the purpose of interpreting the dimensions of the CMDS models. The engine 

number, engine type, and weight class of each aircraft 

was obtained from information provided in Appendix 

A of 7110.65N, the most recent version of Air Traffi c 

Control (FAA, 2002). (FAA, 2002).

Custom Software. As mentioned previously, the SAR 

data matrix was computed from DART log and track 

information for the 7,095 fl ights with aircraft designators corresponding to the selected aircraft types. These 

data were extracted from a considerably larger sample of 

fl ights. Consequently, it was impractical to manipulate the 

raw log and track text reports manually. Therefore, two 

Visual Basic programs were used to extract and organize 

information and to compute the summary measures. The 

National Airspace System (NAS) Data Management System (NDMS) program transforms the information in 

log and track reports into organized database fi les that 

provide effi cient storage and access (for a more detailed 

description of this program and its output, see Mills, 

Pfl eiderer, & Manning, 2002). A second program, Aircraft 

Mix – Phase I, was specially designed to derive pertinent 

information (i.e., aircraft type, speed, altitude) for each 

fl ight from NDMS output tables, compute summary 

measures, and print this information to SPSS database 

fi les for analysis.

Results and Discussion

Controller estimates of speed, climb, and descent rates 

were compared with mean speed, climb, and descent rates 

computed from SAR data using Pearson’s product moment 

correlation coeffi cient. All associations were statistically 

signifi cant at the p < .01 level. However, the magnitude 

of the correlation between SAR and ATC speed estimates 

was slightly higher than the others with an r = .94. The = .94. The 

comparison of mean ATC climb rate estimates and mean 

climb rates calculated from SAR data produced an r = 

.89, whereas the association between ATC descent rate 

estimates and SAR descent rate measures resulted in an 

r = .79.

Classical Multidimensional Scaling Analyses (CMDS)

Dimensionality. SAR data and ATC estimate matrices 

were submitted to separate, non-metric CMDS analyses. 

The squared correlation is a measure of fi t that describes 

the relationship between the original distances and the 

derived stimulus coordinates. Squared correlations of the 

two-dimensional (r2 = .9990) and three-dimensional (r2 = 

.9989) models of SAR data, and of the two-dimensional 

(r2 = .9995) and three-dimensional (r2 = .9996) models of 

ATC estimates suggest that the proximities were described 

well in either two or three dimensions.

Kruskal’s stress formula 1 is a measures of how well the 

confi guration represents the original data. This measure 

ranges from zero (best fi t) to one (worst fi t). Stress values 

for the two-dimensional (.0164) and three-dimensional 

(.0171) models of SAR data, and of the two-dimensional 

(.0123) and three-dimensional (.0116) models of ATC 

estimates indicated that the confi guration fi t the data well 

in either two or three dimensions. However, because stress 

is not a reliable measure in a degenerate solution, scatterplots describing the relationship between proximities and 

derived distances (i.e., scatterplot of non-linear fi t) and 

disparities (i.e., transformation scatterplot) were examined 

for both analyses. None of the scatterplots demonstrated 

patterns characteristic of a degenerate solution. (For an 

explanation of the causes and caveats of degenerate solutions, see Kruskal & Wish, 1978, p. 29-30.)

In both analyses, the two- and three-dimensional solutions demonstrated excellent fi t. Though the three-dimensional solution demonstrated the best fi t for the model 

of ATC estimates, examination of stimulus coordinates 

and regression analysis failed to uncover any distinctive, 
4

interpretable features of the third dimension. Additional 

dimensions are of little use if they fail to contribute to the 

interpretation of the solution (Kruskal & Wish, 1978). 

Moreover, the two-dimensional model demonstrated the 

best fi t for the SAR data set. Therefore, the two-dimensional solution was selected for interpretation.

Regression Method of Dimensional Interpretation. The 

most objective technique available for dimensional interpretation is the regression method in which variables 

believed to correspond with the stimulus confi guration 

are regressed over coordinates. For this application, engine 

type was coded according to performance capabilities 

associated with each engine type, from lowest (pistondriven) to highest (jet propelled). Weight class was also 

coded into three ordered levels: Small aircraft are those of 

41,000 lbs. or less maximum certifi cated takeoff weight; 

Large aircraft are those of more than 41,000 lbs. up to 

255,000 lbs. maximum certifi cated takeoff weight; Heavy 

aircraft are those capable of takeoff weights of more than 

255,000 lbs. whether or not they are operating at this 

weight during a particular phase of fl ight (FAA, 2002).

According to Kruskal and Wish (1978), two conditions are necessary for satisfactory multiple regression 

interpretation of a dimension. First, the multiple correlations must be extremely high (correlations in the .90s are 

recommended, although those in the .70s will suffi ce). 

As shown in Tables 1 and 2, only engine type and weight 

class achieved the recommended degree of association 

with the dimensions. In general, the two data sets were 

remarkably similar: Correlations were in the .90’s for 

engine type and in the .80’s for weight class. However, 

the two models differed with respect to the relationship 

between the dimensions and these criterion variables. 

Notice that in the model of SAR data, the standardized 

regression weights of both engine type and weight class 

are more closely associated with Dimension 1 than with 

Dimension 2. However, in the confi guration derived from 

ATC estimates, the standardized regression weights of 

engine type were more closely associated with Dimension 1, whereas weight class was more closely associated 

with Dimension 2.

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5

Neighborhood Interpretation of the CMDS Confi gurations. Neighborhood interpretation focuses on identifying discrete clusters of elements in the CMDS stimulus 

confi guration. Because neighborhood interpretation 

capitalizes on small distances, this method can sometimes reveal patterns in the data that are not discernable 

using multiple regression, which attends mostly to large 

distances. In this particular application, neighborhood 

interpretation was considered to be an important supplement to dimensional interpretation because it was highly 

possible that characteristics of the confi guration might 

correspond with categorical variables unsuitable for 

multiple regression.

In general, the confi gurations (Figures 1 and 2) were 

similar. In both models, Group A consisted primarily 

of piston-driven aircraft. The exception to this was the 

C208 (Cessna Caravan), a turboprop that did not perform like other turboprops. (As a point of interest, most 

of the controllers in the sample misclassifi ed the C208 as 

a piston-driven aircraft.) Group B consisted entirely of 

turboprops. In both confi gurations, all aircraft positioned 

to the right of the dashed gray line are jets. However, the 

number of groups differed between the confi gurations. 

In the model of ATC estimates, high-performance jets 

are clearly distinguished from other jet aircraft (Group 

D in Figure 2). In the SAR data model, jets formed a 

single, loosely-knit group.

Perhaps the most striking difference between the 

confi gurations had to do with weight class. Most of the 

aircraft types in the top portion of the stimulus confi guration of ATC estimates (Figure 2) are of the Small and 

Large weight classes: Heavy aircraft are positioned in the 

bottom portion of the confi guration. This refl ects the association of weight class with Dimension 2 in the model 

of ATC estimates. In the stimulus confi guration of SAR 

data (Figure1), Heavy aircraft are scattered throughout 

the cluster of jets (Group C).

Conclusions

The clusters of aircraft identifi ed in the neighborhood 

interpretation of the stimulus confi gurations present the 

simplest means by which to code aircraft types for the 

aircraft mix variable. For the most part, these groups 

were defi ned by engine type. Though high-performance 

jets were not clearly distinguished from other jets in the 

confi guration of SAR data, it seems reasonable to classify 

these aircraft separately in the computation of the aircraft 

mix index. On the average, the controllers who contributed 

estimates for the ATC sample had approximately 10 years 

of experience at their current ARTCCs. The SAR sample 

represented 195 hours of traffi c. Given the concordance 

of the two matrices in other respects, it is possible that 

high-performance jets might have emerged as a separate 

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6

group in the SAR confi guration had the sample been large 

enough to better approximate the years of experience 

represented by the controllers in the ATC sample.

It is unlikely that the incorporation of weight class 

is crucial to the precision of the aircraft mix index. To 

begin with, weight class is a correlate of engine type (i.e., 

most piston-driven aircraft are Small, most turboprops 

are Large, all Heavy aircraft are jets). Because of the 

nature of this relationship, incorporation of the weight 

class dimension would only involve jet aircraft (i.e., 

separating jets into Heavy/other subgroups). However, 

the tight clustering of the jet aircraft in Group C of the 

stimulus confi guration of ATC estimates (Figure 2) suggests that this differentiation is probably not necessary. 

Heavy aircraft may perform somewhat differently than 

other jet aircraft, but this difference appears to be only 

slightly perceptible to air traffi c controllers (other than 

procedural considerations addressing the wake turbulence 

associated with Heavy aircraft and B757s).

Phase II: Development and Testing of the 

Aircraft Mix Index

Method

Sample

The sample selected for testing the aircraft mix index 

consisted of SAR data from 15 high-altitude sectors and 

13 low-altitude sectors within the Kansas City airspace. 

The Kansas City ARTCC was selected because of the 

availability of sector information for that particular center 

(e.g., sector strata, number of underlying airports, sector 

combinations). The data were recorded on Friday, December 22, 1999 from 15:15 to 16:15 (local time) when 

most sectors within the Kansas City en route center were 

open (i.e., sector combinations were minimal).

Procedure

Given the fact that a total of 562 aircraft crossed the 

Kansas City airspace during the hour sampled, it would 

be highly impractical to calculate the aircraft mix index 

manually. Therefore, a Visual Basic program (Aircraft 

Mix – Phase II) was written to accomplish this task. This 

program was designed to process SAR information stored 

in NDMS output tables on a sector-by-sector basis. Based 

on Phase I results, aircraft were assigned aircraft type codes 

with values ranging from one to four. Piston-driven aircraft 

were assigned a value of 1, turboprops a value of 2. With 

some exceptions, jet aircraft were assigned a value of 3. 

High-performance jets (i.e., aircraft types that perform 

within similar parameters as the aircraft in Group D of 

Figure 2) are coded as such in the system fi les of all en 

route Host computers. These fi le codes were used to assign 

a value of 4 to all high-performance jets in the sample. 

Program algorithms were written to produce a half matrix 

of aircraft type differences between pairs of aircraft within 

a given sector. Table 3 lists aircraft type differences for the 

sample of aircraft in Figure 3. For instance, DAL589 is a 

commercial jet and has been assigned an aircraft mix code 

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7

of 3. N149RJ is a turboprop with an aircraft mix code 

of 2. The aircraft mix difference between N149RJ and 

DAL589 is 1. The fi nal step in the computation of the 

index involved summing all items in the half matrix. For 

example, the aircraft mix index for the group of aircraft in 

Figure 3 is 17.00 (see Table 3). For each minute of data, 

the aircraft mix index was calculated for all aircraft pairs 

at approximately 12-second intervals and stored in an 

array. At the end of each minute, the mean and standard 

deviation of the aircraft mix measure were calculated and 

sent to an array for the purpose of calculating the mean 

and standard deviation of the aircraft mix measure for 

each 15-minute interval processed.

Results and Discussion

Computing aircraft mix at 15-minute intervals for 

high- and low-altitude sectors clearly did not produce 

a normal distribution (evidenced by the discrepancies 

between the means and medians of the distributions 

described in Table 4). For that reason, the Mann-Whitney U statistic (Mann & Whitney, 1947) was employed statistic (Mann & Whitney, 1947) was employed 

to examine whether the aircraft mix index was reliably 

different in high- versus low-altitude sectors. The MannWhitney U is a distribution-free statistic that tests the is a distribution-free statistic that tests the 

null hypothesis that two sets of observations were sampled 

from identical populations. The minimal assumption of 

the Mann-Whitney U is the independence of observations istheindependenceofobservations 

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8

(Marascuilo & McSweeney, 1977). Given the fact that 

aircraft cannot be controlled by more than one sector at 

any given time, it logically follows that aircraft in one 

sector were independent of aircraft in another within 

each of the 15-minute intervals.

As shown in Table 5, the null hypothesis was rejected 

for all comparisons, indicating that the distributions of the 

aircraft mix index were reliably different in high- versus 

low-altitude sectors. The sum of ranks assigned to each 

of the original values for high- and low-altitude sector 

groups is consistent with the expectation that the mix 

of aircraft with different performance characteristics 

(ergo the aircraft mix index) would be higher in lowaltitude airspace.

Conclusions

Because the aircraft mix index was able to reliably 

detect distribution differences in high-and low-altitude 

sectors, the measure warrants further investigation. Plans 

for future tests include conducting a CMDS analysis of 

a much larger sample of aircraft types to include helicopters and other rotorcraft to determine whether they 

fi t into one of the existing aircraft categories or require 

the introduction of a separate code. Then, the aircraft 

mix index will be introduced into the current set of Performance and Objective Workload Evaluation Research 

(POWER) variables (Mills, Pfl eiderer, & Manning, 2002) 

to determine whether or not the aircraft mix index adds 

unique information to that set. Each step in this process 

brings us closer to determining the relative contribution of aircraft mix to sector complexity. Constructing 

the elements that create sector complexity may help us 

understand the nature of controller workload, and thus 

provide insight into the relationship between controller 

workload and performance.

References

Federal Aviation Administration (1984). Establishment and validation of en route sectors. (FAA Order No. 7210.46). Washington, DC: Author.

Federal Aviation Administration (2002). Air traffi c 

control. (FAA Order No. 7110.65N) Washington, DC: Federal Aviation Administration Air 

Traffi c Operations Program. Available online: 

http://www.faa.gov/atpubs/ATC.

Grossberg, M. (1989). Relation of sector complexity 

to operational errors. Quarterly Report of the 

Federal Aviation Administration’s Offi ce of Air 

Traffi c Evaluations and Analysis. Washington, 

DC: Federal Aviation Administration.

Hadley, G.A., Guttman, J.A., & Stringer, P.G. (1999, 

June). Air traffi c control specialist performance 

measurement database. (Report No. DOT/FAA/

CT-TN95/45).

Kruskal, J.B., & Wish, M. (1978). Multidimensional 

scaling. Beverly Hills, CA: Sage.

Mann, H.B., & Whitney, D.R. (1947). On a test 

of whether one of two random variables is 

stochastically larger than the other. Annals of 

Mathematical Statistics, 18, p. 50-60.

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9

Manning, C.A., Mills, S.H., Fox, C., Pfl eiderer, 

E., & Mogilka, H. (2001). Investigating the 

validity of performance and objective workload 

evaluation research (POWER). (Report No. 

DOT/FAA/AM-01/10). Washington, DC: 

Offi ce of Aerospace Medicine.1

Marascuilo, L.A., & McSweeney, M. (1977). Nonparametric and distribution-free methods for the 

social sciences. Monterey, CA: Brooks/Cole.

Mills, S.H., Pfl eiderer, E.M., & Manning, C.A. 

(2002). POWER: Objective activity and taskload 

assessment in en route air traffi c control. (Report 

No. DOT/FAA/AM-02/02). Washington, DC: 

Offi ce of Aerospace Medicine.1

Mogford, R.H., Murphy, E.D., Roske-Hofstrand, 

R.J., Yastrop, G. & Guttman, J.A. (1994). Application of research techniques for documenting 

cognitive processes in air traffi c control: Sector 

complexity and decision making. (Report No. 

DOT/FAA/CD-TN94/3). Atlantic City, NJ: 

Federal Aviation Administration.

Pfl eiderer, E.M. (2000). Multidimensional scaling 

analysis of controllers’ perceptions of aircraft performance characteristics. (Report No. DOT/

FAA/AM-00/24). Washington, DC: Offi ce of 

Aviation Medicine.1

Robertson, A., Grossberg, M., & Richards, J. (1979). 

Validation of air traffi c controller workload 

models. (Report No. DOT/FAA/RD-79/83). 

Cambridge, MA: U.S. Department of Transportation Research and Special Programs Administration – Volpe National Transportation 

System Center.

RTCA (1995). Final Report of RTCA Task Force 3: 

Free Flight Implementation, Washington, DC: 

RTCA Incorporated.

1

This publication and all Offi ce of Aerospace Medicine technical 

reports are available in full-text from the Civil Aerospace Medical 

Institute’s publications Web site: 

http://www.cami.jccbi.gov/aam-400A/index.html

Copies may be purchased from the National Technical Information 

Service, Springfi eld, Virginia 22161. 

A1

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