Table 11. Regression of Current Age on LF Planning and Thinking Ahead

Variable B B AR?
Current Age .00 AS -10**
Current Age” .00 ~76 00

R’=.10
Adj. R°=.10
R=.32
**p<.01
Table 12. Regression of Current Age on Scan
Variable B B AR?
Current Age AQ 04 O1**
Current Age” .00 -.15 .00
R*=.01
Adj. R°=.01
R=.10**
*"p<.01
b
3


60

50

30

20

© Linear

LF Situational Awareness Score

10 5 Quadratic
20 30 40 50 60

Current Age
Figure 7. Regression of Current Age on LF Situational Awareness Score

© Linear

=
o

LF Planning and Th#kirig Ahead Score
we

| Quadratic

8

30 40 50 60
Current Age

Figure 8. Regression of Current Age on LF Planning and Thinking Ahead

ll


under intense attentional demands when precise con-
trol was required. The results of Tsang and Shaner’s
study suggest that both expertise and Practice can
reduce age-related declines in performance. How-
ever, the modulation of age-related declines in per-
formance by expertise may be limited, with the
exception of performance that is highly domain-
specific, (Tsang and Shaner, 1998). In their study of
pilots, Morrow, Menard, and Stine-Morrow (1999)
found that expertise did not reduce age differences in
teadback accuracy. The issue of such age-related
cognitive declines is relevant to the ATCS job be-
cause the Separation and Control Hiring Assessment
(SACHA) job analysis identified prioritization, situ-
ational awareness, planning, execution, thinking
ahead, reasoning, and timesharing as some of the
cognitive functions most relevant to the ATCS job
(Nickels, Bobko, Blair, Sanda, & Tartak, 1995).

Based on the findings of previous researchers
(Becker & Milke, 1998; Hardy & Parasuraman,
1997; Salthouse, 1994; Tsang & Shaner, 1998),
older ATCSs would be expected to demonstrate lower
levels of performance on measures of cognitive abil-
ity. Although Salthouse (1994) stated that poor per-
formance on cognitive ability measures does not
necessarily mean that there is a corresponding de-
crease in job performance, Heil (1999) found that
Scores on experimental ATCS performance measures
declined with age for ATCS in their mid 40s; how-
ever it is important to note that the lower levels of
performance found among older ATCSs does not
mean that they are unable to safely manage the flow
of traffic within their areas of certification. The
cognitive measures used to select people into the
ATCS occupation are linked to the knowledge, skills,
abilities, and other characteristics (KSAOs) critical
to successful job gerformance. Consequently, an
understanding of ghe relationship between age and
performance on thése tests may help provide insight
into possible age-related declines in performance
among incumbent ATCSs.

The purpose of the current study was to investigate
the relationship berween age and performance on
tests of cognitive ability for incumbent ATCSs. The
data used in the current study was collected during
the Air Traffic-Selection and Training (AT-SAT)
concurrent validation study. It was hypothesized that
the age of the ATCS would be negatively correlated
with performance on those cognitive measures and
on tasks that require the greatest amount of fluid

intelligence, processing speed, and time-sharing. In
addition, it was hypothesized that no significant
relationship will be found between age and tests that
require less time-sharing and are based primarily on
existing knowledge.

METHOD

Participants

A total of 1083 Full-Performance Level (FPL)
enroute ATCSs, supervisors, and staff voluntarily
participated in the AT-SAT concurrent validation
study, including a minimum of 75 ATCSs from each
of 12 enroute air traffic control centers. Current staff
and supervisors were excluded from the present study,
leaving 828 FPL enroute ATCSs. All Participants
identified themselves as being FPL ATCS at the time
of the concurrent validation, although 141 had pre-
viously held a staff position and 30 had previously
been supervisors. Demographic information is pre-
sented in Table 1. Almost half of the controllers were
between the ages of 32 and 37 (M=36.4, SD=5.7),
and the majority of participants were Caucasian males,
reflecting the current demographic makeup of the
FAA’s ATCS workforce. The average number of years
in current position was 8.3, the average entry-on-
duty age was 25.0, and the average number of years as
an FPL ATCS was 7.2. As shown in Table 1, only
10.9% of the participants were above the age of 43,
Although this number is not proportional to the
other age groups, it is consistent with skewed age
distributions of ACTSs reported by previous re-
searchers (Cobb, 1967; Cobb et al., 1973;
VanDeventer & Baxter, 1984).

Procedure

Data collection teams were assembled and trained
to conduct testing at each of the enroute air traffic
control centers (ARTCCs) included in the study.
Each team was comprised of a test site manager and
two to four team members who were responsible for
administration of the predictor battery and criterion
measures. All participants were volunteers in the AT-
SAT concurrent validation study who were recruited
through on-site briefings and an informational memo,
Volunteers were tested over a 2-day period in a room
provided by their facility. One day of computer-
based testing was devoted to the predictor tests and
one day was devoted to administration of the crite-
tion measure.


Table 5. Regression of Current Age on Angles Score

Variable B B AR?
Current Age 1.46 99 004
Current Age” -.02 ~.94 O1L**
R*=.02
Adj. R=.01
R=. 12**
**p<.01
Table 6. Regression of Current Age on AT Efficiency
Variable B B - AR?
Current Age 13 06 .08**
Current Age” -01 -.34 .00
R=.08
Adj. R?=.07
R=.27**
<0
Table 7. Regression of Current Age on AT Safety
, LNariable B B AR?
>» Current Age -.30 -11 OS**
4 Current Age” .00 -12 00
R=.05
Adj. R’=.05
R=.23**
**p<.01



Schroeder, D.J., Broach, D., & Farmer, W.L. (1997).

Current FAA controller workforce demograph-
ics, future requirements, and research questions.
Proceedings of the Ninth Annual International Avia-
tion Psychology Symposium, 1, 135-41.

Trites, D.K. (1961). Problems in air traffic management:

L Longitudinal prediction of effectiveness of air
traffic controllers, (DOT/FAA/AM-61/ 1). Okla-
homa City, OK: FAA Civil Aeromedical Re-
search Institute. Available from: National Tech-
nical Information Service, Springfield, VA 22161;
order # AD268954.

Trites, D.K. & Cobb, B.B. (1962). Problems in air

traffic management: III. Implications of age for
training and job performance of air traffic control-
fers. (DOT/FAA/AM-62/3). Oklahoma: City, OK:
FAA Civil Aeromedical Research Institute. Avail-
able from: National Technical Information Ser-
vice, Springfield, VA 22161; order # N62-10353.

wer

Tsang, P.S., & Shaner, T.L. (1998). Age, attention,

expertise, and time-sharing performance. Psychol-
ogy and Aging, 13, 323-47.

VanDeventer, A.D., & Baxter, N.E. (1984). Age and

performance in air traffic control specialist train-
ing. In VanDeventer, A.D., Collins, W.E., Man-
ning, C.A., Taylor, D.K., & Baxter, N.E, Studies
of poststrike air traffic control specialist trainees:
I. Age, biographical factors, and selection test
performance related to academy training success,
(DOT/FAA/AM-84/6), Washington, D.C. Fed-
eral Aviation Administration Office of Aviation
Medicine. Available from: National Technical
Information Service, Springfield, VA 22161; or-
der # ADA147892,

Verhaeghen, P. & Salthouse, T.A. (1997). Meta-analyses

of age-cognitions relations in adulthood: Esti-
mates of linear and nonlinear age effects and struc-
tural models. Psychological Bulletin, 122, 231-249,


Dial Reading Test. The Dial test is designed to
test the subject’s ability to quickly identify and accu-
tately read certain dials on an instrument panel.
Subjects are asked to choose from one of five response
alternatives for each question about a given display.
The test consists of 20 questions. Individual items are
self-paced against the display of time left in the test as
a whole. Subjects are advised to skip difficult items
and come back to them at the end of,the test. Each
panel consists of seven dials in two rows, a layout that
remains constant throughout the test. Each of the
seven dials contains unique flight information. (To-
tal Time: 12 minutes.)

Analogies. The Analogies test measures the subject’s
ability to apply the correct rules to solve a given
problem as well as their efficiency in using the avail-
able information to solve that problem. Analogies are
based on words, pictures, or figures and appear in
three “windows” on the same screen for a given item.
Subjects use a mouse to move freely between the three
windows, view the different parts of the analogy, and
select their answer. However, they can view only one
window ata time. Window A presents the first part of
the analogy that requires subjects to infer the under-
lying rule. Window B contains that second part of the
analogy that requires subjects to apply the inferred
rule. Finally, Window C provides subjects the oppor-
tunity to confirm their choice by selecting their
answer from the available response options. The test
has 57 items: 30 word analogies and 27 visual (i.e.,
either pictorial or figural) analogies. (Total Time: 45
minutes.)

RESULTS

Pearson’s product-moment correlations and hier-
archical polynomia§ regression were used to test the
hypotheses. Curregt ATCS age was positively corre-
lated with many work-related variables, such as num-
ber of years as an FPL ATCS (t=.78; p<.01) and
number of years in current position (t=.72; p<.01).
As shown in Table 2, age was negatively correlated
with scores on ATST Efficiency, ATST Safety, Analo-
gies: Reasoning, LF Situational Awareness, LF Plan-
ning and Thinking ahead, and Scan.

Principal components analysis with varimax rota-
tion was performed on all scores generated by the
cognitive tests. Three factors were extracted, ac-
counting for 60.1% of the variance. Loading of
variables on factors, communalities, and percent of

variance are shown in Table 3. Loadings of .40 and
above were used for interpretation of the factors. The
tests that require the greatest amount of multi-task-
ing, perceptual speed, and fluid intelligence load
together on the first factor. These tests are also
presented in the form of dynamic computer situa-
tions or scenarios. The tests that predominately mea-
sure reasoning as well as use of existing knowledge
load on the second factor. These tests do not require
multi-tasking and may be classified as “page turner”
computer tests. The ATST: Procedural Accuracy
score loads alone on the third factor.

Hierarchical polynomial regression was used to
assess the form of the relationship between age and
cognitive test performance. For each cognitive test,
age was entered into the regression equation first,
followed by the age-squared (quadratic) term. If the
relationship between age and cognitive test perfor-
mance is primarily linear, then age alone should
explain a significant proportion of the test score
variance, and the quadratic term that is subsequently
added should not account for a significant change in
RY. If the quadratic term explains a significant pro-
portion of the variance beyond the age term, then
there is evidence of a curvilinear relationshi ip between
age cognitive test performance.

Regression analyses were performed for each of the
cognitive tests included in the study. Age did not
significantly contribute to the prediction of the ap-
plied math score when entered into the regression
equation. However, as shown in Table 4, the qua-
dratic term, or age-squared, did contribute signifi-
cantly when added (AR?=.016, p<.01; Adjusted
R?=.02), providing evidence of a curvilinear relation-
ship. This relationship is depicted in Figure 1. The
regression of age on the angles score is depicted in
Figure 2. This relationship is also curvilinear, as only
the quadratic model produced a significant R? change
in predicting angles score (AR?=.011, p<.01; Ad-
justed R*=.01). The results of this analysis are sum-
marized in Table 5. As demonstrated in Figures 3 and
4, the relationship between age and both the ATST
efficiency and the ATST safety scores is linear. Only
the linear model produced a significant change in R?
for ATST efficiency (AR2=.08, p<.01; Adjusted
R?=.07), shown in Table6, and ATST safety (AR?=.05,
p<.01; Adjusted R2=.05), shown in Table 7. Both the
linear and quadratic models failed to predict the
ATST procedural accuracy score.


Measures

The measures used in the current study are tests of
cognitive ability that were included in the predictor
test battery used during the AT-SAT concurrent
validation study. There is a time limit for all tests;
however, this limit is set so that applicants should be
able to complete all items. The applied math and
angles tests were used to assess knowledge-related
abilities, or crystallized intelligence. The letter fac-
tory, air traffic scenarios, and scan tests were used to
assess abilities requiring processing speed, time-shar-
ing, and fluid intelligence. The dial reading and
analogies tests both contain elements of fluid and
crystallized intelligence: both require use of existing
knowledge as well as reasoning and processing. How-
ever, the processing speed and timesharing require-
ments of these two tests are limited. A description of
each test is provided below.

Applied Math. This test contains 30 multiple-
choice questions. The test presents five practice ques-
tions before the test begins. Questions such as the
following are contained on the test: A plane has flown
for 3 hours with a ground speed of 210 knots. How far
did the plane travel? These questions require the
subject to be able to factor in such things as time and
distance in order to identify the correct answer from
among the four answer choices. (Total Time: 30
minutes.)

Angles. The Angles test measures the subject’s
ability to recognize angles. This test contains 30
multiple-choice questions. There are two types of
questions on the test. The first presents picture ofan
angle and the subject chooses the correct size of the
angle (in degrees) from among four response options.
The second presents a measure in degrees and the
subject chooses the angle (among four response op-
tions) that $epresents that measure. (Total Time: 10
minutes.) J

Letter Factory Test (LF). This test simulates a
factory assembly line that manufactures only four
letters of the alphabet (A, B, C, and D) in one of three
colors. The test has 18 sections and requires that
subjects use a mouse to perform multiple and often
concurrent tasks. Each test section begins with letters
appearing at the tops of the conveyor belts moving
down toward the loading area. Based on those letters,
subjects immediately begin selecting and moving
boxes to the loading area to provide just the right
number and color of boxes to correctly place all
letters. Other tasks performed during the simulated

factory settings include: (1) picking up letters of
various colors, (2) ordering new boxes when supplies
become low, and (3) calling Quality Control when
defective letters appear. Each section lasts between 30
seconds and 2 1/2 minutes. The LF test produces two
scores: LF situational awareness and LF planning and
thinking ahead. (Total Time: 91 minutes.)

Air Traffic Scenarios Test (ATST). This is a low-
fidelity simulation of an air traffic control (ATC)
radar screen that is updated every seven seconds. The
goal is to maintain separation and control ofa varying
number of simulated aircraft (represented as data
blocks) within the designated airspace as efficiently
as possible. Aircraft in flight can pass through the
airspace or land at one of two airports within the
airspace. Each aircraft's data block indicates its present
heading, speed, and altitude. There are eight differ-
entheadings representing 45 degree increments, three
different speeds (slow, moderate, fast), and four dif-
ferent altitude levels (1=lowest and 4=highest). Sepa-
ration and control are achieved by communicating
and coordinating with each aircraft by using the
computer mouse to click on the data block represent-
ing each aircraft and providing instructions such as
changes to current heading, speed, or altitude. The
ATST produces three scores: ATST Efficiency, ATST
Safety, and ATST Procedural Accuracy. (Total Time:
95 minutes.)

Scan. In the Scan test, subjects monitor a field that
contains discrete objects (called data blocks) which
are moving in different directions. Data blocks ap-
pear in the field at random, travel ina straight line for
a short amount of time, then disappear. During the
test, the subject sees a blue field that fills the screen,
with the exception of a l-inch white bar at the
bottom. In this field, up to 12 green data blocks may
be present. Each data block contains two lines of
letters and numbers separated by a horizontal line.
The upper line is the identifier and begins with a
letter followed by a 2-digit number. The lower line
contains a 3-digit number. Subjects are scored on the
speed with which they notice and respond to the data
blocks that have a number on the lower line outside
a specified range. Throughout the test, this range is
displayed at the bottom of the screen (e.g., 360-710).
To “respond” to a data block, the subject types the 2-
digit number from che upper line of the block (ignor-
ing the letter that precedes it), and then presses
“enter.” (Total Time: 18 minutes.)


Table 8. Regression of Current Age on Analogies Reasoning

Variable B B AR?
Current Age 1.33 1.14 O1F*
Current Age” -.02 -1.26 .02**
R?=.03
Adj. R?=.03
R=.18**
**p<.01

Table 9. Regression of Current Age on Dial Reading Test

Variable B B AR?
Current Age 1.46 90 .00
Current Age” -.02 -.95 O1e*
R’=.01
Adj. R?=.01
R=11
**p<.01
4
é
Tabi 10. Regression of Current Age on LF Situational Awareness
Variable B B AR?
Current Age 90 69 O7*
Current Age” -.01 -.95 01"
R=.08
Adj. R’=.07
R=.28**
**p<.0l

16


performance would choose not to participate. This is
particularly likely for older controllers, since their
average level of performance was generally found to
be lower than that of other controllers (Heil, 1999).
Consequently, the older ATCSs included in this
study may represent the better performers from that
particular age group, resulting in an under-estima-
tion of the age-related decline in cognitive ability. It
should also be noted that the R-squares generated
during the regression analyses indicate that age does
not explain a large percentage of the variance in
cognitive test performance. Although the effect sizes
are not large, the results of the regression analyses
reveal a consistent trend in the relationship between
age and cognitive test performance.

The cross-sectional approach of this study also
creates limitations. Conclusions regarding changes
in cognitive ability are based on the assumption that
ATCSs possess ability levels that are relatively equal
following training. Consequently, lower cognitive
test scores among older ATCSs would be attributed
to a decline in ability rather than to a lower level of
ability throughout their career. One mitigating fac-
tor that must be considered is that the best performers
are often promoted into supervisory positions. This
means that the average cognitive test scores of older
controllers may be lower because the “best” ATCSs of
this age group are no longer actively controlling
traffic. A longitudinal study of age, so that individu-
als are tracked throughout the course of their career,
is the only way to determine the extent to which
cognitive ability changes over time for an individual,
the manner in which this change occurs, and its
potential influence on job performance.

Conclusion

Over the years, igsearchers have speculated about
the possible causesigf the decline in job performance
with age (Cobb, 1968; Trites, 1961; Trites 8& Cobb,
1962). As the ATCS workforce ages and reaches
retirement age, the actual job performance of older
controllers becomes a more important issue. The
results of the current study support the suggestion
that declines in performance on simulations of air
traffic control situations and experimental ratings
may be associated with the age-related declines in
cognitive ability and processing speed. One impor-
tant question is whether or not there ate ways to
mitigate this age-related decline. Some researchers
have speculated that age-related decline in actual job

performance might be delayed with experience (Becker
& Milke, 1998; Heil, 1999; Salthouse, 1994;
Schroeder et al., 1997; Tsang & Shaner, 1998). The
extent to which this may be the case with the ATCS
job should be further investigated.

REFERENCES

Becker, J.T., & Milke, R.M. (1998). Cognition and
aging in a complex work environment: Relation-
ships with performance among air traffic control
specialists. Aviation, Space, and Environmental
Medicine, 69, 944-51.

Cobb, B.B. (1968). Relationships among chronologi-
cal age, length of experience, and job performance
ratings of air route traffic control specialists. Aero-
space Medicine, 39, 119-24.

Hardy, D.J., & Parasuraman, R. (1997). Cognition
and flight performance in older pilots. Journal of
Experimental Psychology: Applied, 3, 313-48.

Heil, M.C. (1999). An investigation of the relationship
between chronological age and job performance
for incumbent air traffic control specialists. (DOT/
FAA/AM-99/18). Oklahoma City, OK: FAA Civil
Aeromedical Research Institute. Available from:

National Technical Information Service, Spring-
field, VA 22161.

Morrow, D. G., Menard, W.E., & Stine-Morrow,
E.A.L. (1999). The influence of aging and expertise
on pilot communication. Paper presented at the
Tenth International Symposium on Aviation Psy-
chology, Columbus Ohio.

Nickels, B.J., Bobko, P., Blair, M.D., Sands, W.A., &
Tartak, E.L. (1995), Separation and Control Hir-
ing Assessment (SACHA): Final job analysis re-
port. Washington, D.C. Federal Aviation Ad-
ministration Office of Personnel.

Salthouse, T.A. (1991). Theoretical Perspectives on cog-
nitive aging. Hillsdale, NJ: Erlbaum.

Salthouse, T.A. (1994). Age-related differences in basic
8
cognitive processes: Implications for work. Ex-
perimental Aging Research, 20, 249-255.


BSETE

DOT/FAA/AM-99/23

Office of Aviation Medicine
Washington, D.C. 20591

Air Traffic Control
Specialist Age and
Cognitive Test Performance

Michael C. Heil

Civil Aeromedical Institute
Federal Aviation Administration
Oklahoma City, Oklahoma 73125

August 1999

Final Report

This document is available to the public
through the National Technical Information
Service, Springfield, Virginia 22161.

@

U.S. Department
of Transportation

Federal Aviation
Administration


100

a
o
1

b
o

a
°o

AT Efficiency Score

© Linear

5 Quadratic

Lv)
o

30

40
Current Age

50

Figure 3. Regression of Current age on AT Efficiency Score

120

100

&

ater

8

AT Safety Score
>
Oo

n
i}

Dy
3

Figure 4. Regression of Current Age on AT Safety Score

30

40
Current Age

50

60

60

© Linear

| Quadratic


FIGURES
120

100

8

L
3S

© Linear

8

Applied Math Score

°

5 Quadratic

30 40 50 60
Current Age
Figure 1. Regression of Current Age on Applied Math Score

yp
3

110

100 4

90 OT

g

© Linear

~
So

Angles Score

8

| Quadratic

30 40 50 60
Current Age
Figure 2. Regression of Current Age on Angles Score

nN
3S


Both the linear and quadratic models contributed
to the prediction of the analogies reasoning score. As
shown in Table 8, when entered into the regression
equation, age contributed significantly to the predic-
tion of the analogies reasoning score (AR2=.01, p<.01;
Adjusted R?=.01). The age-squared, or quadratic
term, also contributed significantly when added
(AR?=.02, p<.01; Adjusted R2=.03), suggesting that
the relationship is best described as curvilinear. This
relationship is depicted in Figure 5. Figure 6 shows
that the relationship between age and the dial reading
test score is best described as curvilinear, or qua-
dratic. Only the quadratic term, age-squared, con-
tributed significantly to the prediction of the dial
reading score when entered into the regression equa-
tion (AR?=.01, p<.01; Adjusted R2~.01), as shown in
Table 9.

Table 10 reveals that age contributed significantly
to the prediction of LF situational awareness when
entered into the regression equation (AR?=.07, p<.01;
Adjusted R?=.07), although the quadratic term also
produced a significant change in R? when added to
the equation (AR2=.01, p<.01; Adjusted R2=.07).
The relationship between age and LF planning and
thinking ahead is best explained by a linear model, as
shown in Figure 8. As shown in Table 11, only the
linear model produced a significant change in R?
when predicting the LF planning and thinking ahead
score (AR*=.10, p<.01; Adjusted R*=.10). Table 12
shows chat age contributed significantly to the pre-
diction of the scan test score (AR?=.01, p<.01; Ad-
justed R2=.01), whereas the quadratic term did not.
This indicates linear relationship between these two
variables, as shown in Figure 9.

DISCUSSION
5

The rdgults of the current study provide evidence
of a negdtive relationship between ATCS age and
performance on several measures of cognitive ability.
Examination of the linear relationships between age
and cognitive abilities suggests that older ATCSs
have lower levels of performance on those tasks that
require fluid intelligence and high levels of | cognitive
Processing and multi-tasking. The ATST and letter
factory test require high levels of awareness, time-
sharing, prioritization, execution, scanning, plan-
ning, thinking ahead, tolerance for high intensity
activities, perceptual speed and accuracy, decisive-
ness, and dynamic visual-spatial ability. These cognitive

abilities have been identified as the most televanc to
the ATCS job (Nickels et al., 1995). The results
Suggest an age-related decline in those cognitive
abilities that are most important to successful job
performance.

Ic is interesting to note that no relationship was
found between ATST: procedural accuracy and age.
The ATST: procedural accuracy score also loads
alone on its own factor. It may be that procedural
accuracy is based less on fluid intelligence, since the
procedures that must be followed remain the same
regardless of the scenario. Both the safety and effi-
ciency scores, however, depend upon quick assess-
ment and response to new and increasingly complex
situations and scenarios. The procedural accuracy
score seems to reflect ability, or willingness, to follow
procedures and does not necessarily tap into a specific
knowledge or skill level. The negative correlations
between scan and analogies reasoning, and age were
low, despite being statistically significant. Although
the Scan Test requires some of the same cognitive
abilities as the ATST and Letter Factory Test, it is not
as complex and does not tequire the same degree of
multitasking.

Hierarchical polynomial regression revealed acur-
vilinear relationship between age and several of the
cognitive tests, such as applied math, angles, analo-
gies, letter factory situational awareness, and dial
reading. Performance on these tests increased with
age until ATCSs entered their mid- to late- 40s. This
pattern is consistent with that found when investigat-
ing the relationship between ATCS age and indica-
tors of job performance (Heil, 1999). Consequendy,
it seems that a decline in ATCS performance coin-
cides with a decline in cognitive abilities relevant to
the ATCS occupation. This decline occurs despite
continued experience with time-sharing activities on
the job, which is consistent with the findings of
Tsang and Shaner (1998), who studied pilots. This
does not mean that the older ATCSs are performing
at an unacceptable level but, rather, that they are
performing at a level that is lower than that of their
younger colleagues,

Limitations

There are limitations to this study that must be
considered when reviewing the results and conclu-
sions that are presented. All of the ATCSs who
Participated in this study were volunteers. It is likely
that controllers uncomfortable with their level of


|
|
i
|

Air TraFFic CONTROL SPECIALIST AGE AND CoGnrrive Test PERFORMANCE

INTRODUCTION

Researchers have visited the issue of Air Traffic
Control Specialist (ATCS) age and performance many
times over the past few decades (Cobb, 1968; Heil,
1999; Trites, 1961; Trites & Cobb, 1962; Schroeder,
Broach, & Farmer, 1997; VanDeventer & Baxter,
1984). These researchers have consistently found a
negative relationship between the age of Air Traffic
Control Specialists (ATCSs) and both training suc-
cess and ratings of job performance. A recent study
found a curvilinear relationship between ATCS age
and performance on acomputerized simulation of air
traffic situations, with performance decreasing for
people in their mid 40s (Heil, 1999). To date, there
has been no systematic study of factors associated
with this decline in performance. However, some
researchers (Heil, 1999; Schroeder, Broach, & Farmer,
1997) have speculated that it may be due to a decline
in cognitive ability with age.

In the general population, the negative relation-
ship between age and performance on tests of cogni-
tive ability has been well-documented (Salthouse,
1994; Vethaeghen & Salthouse, 1997). Salthouse
(1994) distinguished between two broad categories
of age and performance relationships: 1) age and
cognitive processes; and 2) age and work perfor-
mance. In the first category, a negative relationship
exists between age and measures of cognitive process-
ing efficiency. However, this relationship does not
generalize fp all cognitive abilities. According to
Salthouse ($991), there is little age-related decline in
the amounfof information or knowledge possessed
by a person. In fact, there is evidence that knowledge
increases well into the 60s. The lack of an age effect
for knowledge, in conjunction with an age effect for
cognitive processing, led to cognitive ability being
categorized in one of two ways: Fluid intelligence,
which refers to the efficiency of processing at the time
of assessment; and crystallized intelligence, which re-
fers to the cumulative products of Processing from
earlier periods in a person’s life (Hardy &
Parasuraman, 1997; Salthouse, 1994). Although fluid

intelligence tends to decline with age, there has been
little evidence of such a relationship between crystal-
lized intelligence and age (Salthouse, 1994),

The second relationship discussed by Salthouse
(1994) is that between cognitive ability and perfor-
mance in the work environment. Although the aver-
age magnitude of the relationship has varied across
studies, the relationship between cognitive ability
and job proficiency has been positive. According to
Salthouse, results of research demonstrate that higher
levels of cognitive ability, as measured by cognitive
test performance, are related to higher levels of per-
formance in work situations. He pointed out, how-
ever, that most researchers have found little convincing
evidence that older workers are either less productive
or less competent than younger workers. Salthouse
postulated that this paradox might be due to selective
attrition in the workplace or the use of job perfor-
mance measures that are not sensitive to dimensions
of the job requiring cognitive ability. Salthouse also
suggested that age trends may be observed on cogni-
tive measures but not in work performance due to
relevant experience. Specifically, increased age is as-
sociated with greater levels of experience, which may
be advantageous at work, but not necessarily on tests
of specific abilities. Salchouse investigated whether
or not experience moderates the relationship between
age and work performance through either preserva-
tion of a high level of cognitive ability or a larger
accumulation of relevant knowledge with age. The
results of this research failed to demonstrate that
experience moderates the relationship between age
and basic cognitive processes (Salthouse, 1994).

According to Tsang and Shaner (1998), inconsis-

- tent reports of the interactive effects of age and

experience may result from the domain-specificity of
expertise effects. In their study of age, attention,
expertise, and time sharing, Tsang and Shaner found
age-related deficits in time-sharing efficiency and
resource allocation for people over the age of 60.
These deficits seemed to occur when subjects were


300
200
a

100

2

8 a
25) © Linear
<

G
Ho | Quadratic

20 30 50 60

40
Current Age
Figure 9. Regression of Current Age on Scan Score

site


Table 3. Factor Loadings, Communalities, and Percent of Variance for Principle

Components Analysis of AT-SAT Cognitive Tests.

Factor
Test 1 2 3 7
ATST: Efficiency 83 04 24 75
ATST: Safety 81 07 19 -70
LF: Planning and Thinking Ahead 77 -20 -.10 64
LF: Situational Awareness 59 34 -.16 50
Scan 40 13 -11 19
Applied Math 19 .76 05 61
Angles 07 73 09 55
Dial Reading 05 66 07 A4
Analogies: Reasoning 3h 67 -.21 59
ATST: Procedural Accuracy 03 Al 91 84
Percent of Variance 32.1 17.8 10.2
Table 4. Regression of Current Age on Applied Math Score
Variable B B ARTM
Cyrrent Age 3.03 1.22 004
Opprent Age? -.04 “1.16 016**
R’=.02
Adj. R’=.02
R=.14**

*p<01



Technical Report Documentation Page

1. Report No.
DOT/FAA/AM-98/23

2. Goverment Accession No.

3. Recipient's Catalog No.

‘4. Title and Subtitie

Air Traffic Control Specialist Age and Cognitive Test Performance

5, Report Date
August 1998

6. Performing Organization Code

7. Author(s)

Heil, M.C.

&. Perlonming Organization Report No.

‘9. Partorming Organization Name and Address
FAA Civil Aeromedical Institute
P.O. Box 25082

Oklahoma City, OK 73125

10. Work Unit No. (TRAIS)

1. Contract or Grant No.

12, Sponsoring Agency name and Address
Office of Aviation Medicine
Federal Aviation Administration
800 Independence Ave., S.W.
Washington, DC 20591

13. Type of Report and Period Covered

14. Sponsoring Agency Code

15. Supplemental Notes

16. Abstract

Researchers have explored the issue of air traffic control specialist (ATCS) age and performance many times over the past

few decades (Cobb, 1968; Heil, 1999; Trices, 1961; Trites & Cobb, 1962; Schroeder,
VanDeventer & Baxter, 1984). These researchers have consistently found a negati
ATCSs and both training success and ratings of job performance. A recent study (|
relationship between ATCS age and performance on a computerized simulation o

Broach, & Farmer, 1997;

ve relationship between the age of Air
Heil, 1999) found a curvilinear
f air traffic situations, with

performance decreasing for people in their mid 40s. Some researchers ¢

Heil, 1999; Schroeder, Broach, & Farmer, 1997)

have speculated that these relationships may be due to a decline in cognitive ability with age. The purpose of the current
study is to investigate the relationship between age and performance on tests of cognitive ability for incumbent ATCSs.
As part of a concurrent validation study, 1083 incumbent ATCSs from 12 enroute centers took a newly developed air
traffic control selection test. The tests included in the 6 hour battery were developed to measure the knowledge, skills,
abilities, and other characteristics (KSAOs) relevant to the ATCS job. Some of the KSAOs measured by the battery
include: ability co prioritize, situational awareness, planning, execution, thinking ahead, short-term memory, reasoning,
decisiveness, concentration, perceptual speed and accuracy, mathematical reasoning, and ability to deal with dynamic
visual movement. The relationship between current age and performance on these cognitive tests was compared using
regression analysis and analysis of variance procedures. The results of these analyses suggest some age-related decline in
those cognitive abilities chat are most important to successful job performance.

4
%
4
17, Key Words 18. Distribution Statement
Air Traffic Control Specialist, Age, Personnel Selection, | Document is available to the public through the
Cognitive Abilities National Technical Information Service,
Springfield, Virginia 22161

19. Security Classif. (of this report)
Unclassified

20. Security Classi, (of this page)
Unclassified

21, No. of Pages
20

22. Price

Form DOT F 1700.7 (8-72)

Reproduction of completed page authorized


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.

vw



8

nN
o

=
°o

© Linear

Analogies Reasoning Score

°

@ Quadratic

iS)
3

30 40 50 60
Current Age

Figure 5. Regression of Current Age on Analogies Reasoning Score

110

100

oO
o

@
oO

fo _
S Qa

a
oO

© Linear

| Quadratic

p
oO

Dial Reading Test Score

o
oO

30

NO
oO

40 50 60
Current Age

Figure 6. Regression of Current Age on Dial Reading Test Score

10


TABLES

Table 1. Demographics

Variable Number Percent
Current Age
31 or Younger 144 17.5
32-37 391 47.6
38-43 197 24.0 ©
44-49 59 7.2
50 or greater 30 3.7
Gender
Male 690 83.3
Female 138 16.6
Race
American Indian 17 21.
Asian/ Pacific Islander 5 0.6
African American 36 43
Hispanic 33 4.0
White 731 88.1
Other 6 0.7
Education
HLS. or GED 68 9.1
Attended Trade School 3 0.4
Completed Trade School 15 2.0
Attended College less than 2 165 22.1 ,
Attended College 2 or more 179 23.9
Completed College, 2 yr. 55 TA
Completed College, 4 yr. 225 30.1
Attended Graduate School 38 5.1
%
Table 2. Qgrrelation of Age and Cognitive Test Scores
Variable Mean SD. [1 a ce ee |
T. Current Age 364 ——57—«| «LO
2. Applied Math 882 137 | 07 10
3. Angles 911 814 | 06 47* 10
4, ATST: Efficiency 600 121 24* Te 10
5. ATST: Procedural Accuracy 70.1 147 O77 10
6. ATST: Safety 660 146 | -  15* 08 72" 06 10
7, Analogies: Reasoning 280° 65 | 2° 46e 37" 19" 0098 1.0
8. Dial Reading 87.4 90 | -05 35" 30" 10" 05 04 29* 10
9. LF: Situational Awareness 365073 | 5 30" 19" 34 0529 gor 719
10. LF: Planning & Thinking Ahead 24 7 | 2S Ae SY 03 * 30” tase
HL. Scan W791 232 | 2) m2 56220 27 10
*p<.01
13