decreased for those age 44-49 before dropping sig-
nificancly for those 50 or older. The mean CBPM
score for controllers 50 and older was significantly
lower than that of controllers from a of the other age
groups. Peer and supervisor ratings are highest for the
ATCSs berween the ages of 44 and 49, before drop-
ping significantly for ATCSs age 50 and greater.

The results of the comparisons of current age and
measures of ATCS performance provide evidence of
a general decline in performance for controllers once
they reach their late 40s. The regression lines de-
scribed above indicate that the drop in performance
level begins to occur around age 45. The comparisons
of group means clearly demonstrate that the level of
performance, as measured by the CBPM and experi-
mental rating score, is significantly lower for those
controllers age 50 and older. Since the upper age
range is restricted due to mandatory retirement, the
extent to which this downward trend would continue
for older controllers is unknown. One caveat -with
these findings is that the number of people included
in this study who were 50 or over is much smaller
than the number of people from other age groups,
suggesting that the mean criterion scores for the older
ATCSs may not be as robust.

The finding that, as 2 group, older controilers have
lower ratings of job performance is consistent with
findings of previous researchers who have explored
this issue. However, previous studies have found this
to be a negative linear relationship, whereas the
current study provides evidence of a curvilinear rela-
tionship. Scores on the current measures of job per-
formance actually increased with age until ATCSs
entered their late 30s, indicating that job perfor-
mance increases with air traffic experience. The strong
positive correlation between current age and work
experience indicates that younger controllers had less
experience than older controllers. Given that more
experience on the job should result in increased skill
and learning, it is not surprising that people with
more experience would have higher performance
scores and ratings. As reported earlier, however, there
is a point at which scores on these job performance
measures no longer increase, and actually decrease,
with age and despite experience. In the current study,
performance scores decreased to the point that people
over the age of 50 performed at a level that was lower
than that of younger controllers who had much less
job experience.

10

Limitations of the Study

There are limitations to this study that must be
considered when reviewing the resuits and conclu-
sions that are presented. All of the ATCSs who
participated in this seudy were volunteers, It is there-
fore likely that controllers uncomfortable with their
level of performance chose not to participate. This
may be particularly likely for older controllers, since
their average level of performance was found to be
Jower than that of younger controllers. Consequently,
the older ATCSs included in this study may represent
the better performers from that particular age group,
resulting in an under-estimation of the age-related
decline in job performance.

Although the CBPM used in this study provided
valuable information about ATCS’s ability to per-
form simulated tasks, it does not provide evidence of
actual job performance since the ATCSs were not
performing tasks using the airspace they work with
on a daily basis. The experimental peer.and snpervi-
sor ratings, however, did relate to actual performance
on the job. The cross-sectional approach of this study
also creates limitations. Conclusions regarding
changes in ability are based on the assumption that
ATCSs possess skill and ability levels that are rela-
tively equal following training. Consequently, lower
performance levels among older ATCSs would be
attributed to decline in ability rather than to a low=>
level of abiliry throughout their career. One mitigat-
ing factor that must be considered is that the best
performers are often promoted into supervisory posi-
tions. This means that the average performance level
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 individuals are tracked throughout the course of
their career, is the only way to determine the extent to
which performance levels change over time, as well as
the manner in which this change occurs.

CONCLUSION

Over the years, researchers have speculated about
the possible causes of the decline in training and job
performance scores with age (Trites, 1961; Trites 8
Cobb, 1962; Cobb, 1968). The most likely cause of
the decline in job performance measures is an age-
related decline in cognitive ability. Such a decline has


AN INVESTIGATION OF THE RELATIONSHIP BETWEEN CHRONOLOGICAL AGE
AND INDICATORS OF JOB PERFORMANCE FOR INCUMBENT
Air TRAFFIC CONTROL SPECIALISTS

The relationship berween age and job performance
for Air Traffic Control Specialists (ATCSs) is an issue
that has been revisited many times over the past few
decades (Trites, 1961; Trites & Cobb, 1962; Cobb,
1968; VanDeventer & Baxter, 1984). Researchers
have consistently found a negative relationship be-
tween controller age and performance across studies
that have used different ATCS options (enroute,
terminal), career stages (age at entry into training,
current age on the job), and criterion measures (on-
the-job ratings, academy performance) (Trites, 1961;
Trites & Cobb, 1962; Cobb, 1968; VanDeventer &
Baxter, 1984). That research provided support for
congressional action that mandated both hiring age

” limits and retirement age requirements for the ATCS
job. As more ATCSs reach etirement age and the
FAA prepares for renewed hiring efforts, there is a
need once again to explore this issue. According to
Schroeder, Broach, and Farmer (1997), the potential
effects of aging on cognitive functioning, and the
consequences of these changes on job performance
and future training requirements, are important con-
siderations associated with the aging of the ATCS
workforce. The purpose of this paper is to use a
sample of incumbent ATCSs to explore the relation-
ship between entry-on-duty age, current age, and
indicators cf job performance.

In 1972, in an effort to address safety issues related
to the age-performance relationship, Congress began
requiring that applicants for the ATCS job not reach
their thirty-first birthday prior to initial appoint-
ment (VanDenventer & Baxter, 1984). Congress also
set 2 mandatory retirement age of 56, at which time
ATCSs are removed from positions requiring the
direct separation and control of air traffic (Aul,
1991). These age restrictions are not without contro-
versy, as there has been much criticism of these
policies by those people who are excluded from
employment (Aul, 1991). Despite the criticism, it is
hard to ignore the consistent results found by re-
searchers who have explored the relationship between

age and performance.

In his study of air traffic control trainees, Trites
(1961) found a negative relationship between age and
supervisor rating, which led to his conclusion that
older trainees were less likely to be viewed as the best
controllers. Using criterion measures that assessed
both FAA Academy training end job performance,
Trites and Cobb (1962) found a negative relation-
ship between age at entry into ATC training and
subsequent Academy and job performance. These
results led to the recommendation that there be a
maximum entry age for air traffic controllers. The
underlying causes of this negative relationship were
not explored by those researchers.

Cobb (1968) used a sample of journeymen radar
enroute ATCSs to provide further evidence of the

. negative relationship between age and performance,

using job performance rating as the criterion mea-
sure. Potential reasons for the lower ratings obtained
by older controllers were physiological aging, lower-
level aptitudes and abilities, and/or lower motivation
levels (Cobb, 1968). Cobb also suggested that the
lower ratings for older controllers could have been
due to biased attitudes toward older people.

Cobb and Mathews (1974) explored the relation-
ship between age, air traffic experience, and experi-
mental ratings of job performance for terminal ATCSs
at high-density airports. As found in previous stud-
ies, the results revealed a negative relationship be-
tween age and the experimental job performance
evaluations. The results also demonstrated that con-
trollers over age 40 were rated as less proficient in
their jobs than younger controllers. Cobb and
Mathews were reluctant to attribute these differences
to physiological aging alone. They speculated that
the older controllers may not have been as highly
motivated as younger controllers, that the older con-
iollers may not have been among the top controllers _
‘at any poine in their career, and that the low ratings
may also have been due to age bias.

VanDeventer and Baxter (1984), administered a
biographical questionnaire to trainees at the FAA Acad-
emy. The results revealed a negative linear relationship



180

160

140

CBPM Score

120

7 —

30 40 50 60

Current Age

Figure 1. Regression of Current Age on CBPM

© Linear

& Quadratic

Table 6. Regression of Current Age on Peer and Supervisor Rating Score

Variable B B ‘AR?
‘Current Age -160 1.26 00
Current Age” -.002 -1.19 02**
R2=.02
Adj. R2=.02
Re.15**

#9<01


a staff or supervisory position. The duration of these
assignments was quite short, as the average amount of
time spent in these positions was less than 6 months.
As shown in Table 1, only 10.9% of the participants
wereabove the age of 43. Although this number is not
proportional to the other age groups, it is consistent
with skewed age distributions reported by previous
researchers (Cobb, 1968; Cobb & Mathews, 1974).
The age of the workforce is most likely skewed due to
the firing of 11,000 controllers during the 1981

Professional Air Traffic Controllers Association
(PATCO) strike.

Procedure .

Data collection teams were assembled and trained
to conduct testing at each of the 12 ARTCCs in-
cluded 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
two-day period in a room provided by their facility.
One day of computer-based testing was devoted to
the predictor test, and one day was devoted to admin-
istration of the CBPM. Due to scheduling con-
straints, some volunteers took all parts of the test over
two consecutive days while others spread testing out
over the course of the on-site data collection period.

Each volunteer also identified two peers and two
supervisors to complete experimental job performance
assessment ratings. The supervisor and peer raters
participated in an orientation and training program
to ensure valid and accurate scaling. These raters were
then asked to complete the forms and submit them to
the researchers.

Measures

The criterion measures used in the AT-SAT con-
current validation study also served as the controller
performance measures in the current study. Both the
CBPM and peer and supervisor rating scales served as
measures of job performance that were compared
with both entry-on-duty age and current age.

Computer Based Performance Measure. The
CBPM served as a measure of the technical skills
necessary to effectively and efficiently separate traffic
on the job (Hanson, Borman, Mogilka, & Manning,
1998). It is a 38-item, 2-hour test where controllers

are presented a series of realistic air traffic scenarios
and 2 to 5 multiple choice questions pertaining to
each scenario. Each question has 3 to 5 response
options representing different ways in which the air
traffic problems might be addressed. During admin-
istration of the CBPM, controllers were presented
with scenarios and given up to 60 seconds to review
each one before it was presented for scoring purposes.
Each scenario lasted no more than 5 minutes. Re-
spondents were then given 25 seconds to answer each
question. Once a response was chosen, controllers
were unable to return to previous items or scenarios
to review information or change answers (Hanson et
al., 1998). Hanson etal. (1998) reported a coefficient
alpha of .59 for the CBPM, which was validated
against hi-fidelity air traffic simulations.

Peer and Supervisor Rating Scales. The peer and
supervisor ratings are behavior-based scales with 10
dimensions and one overall effectiveness scale (see
Appendix A). Rating standards, which describe con-
troller proficiency at different effectiveness levels in
an effort to make ratings more objective, are provided
below each of the 10 dimensions. These rating stan-
dards were developed as part of the AT-SAT concur-
rent validation study (Borman, Hedge, Hanson,
Bruskiewicz, Mogilka, Manning, Bunch, & Hogen,
1999). Raters were asked to read each category defi-
nition and rating standard, then compare the current
effectiveness of the controller being rated with that
standard. Ratings were made on a seven-point scale
and were later combined to produce an overall crite-
tion rating score. As shown in Table 2, the rating score
was moderately but significantly correlated with the
CBPM score. It should be noted that these ratings were
completed independently of CBPM administration.

The linear relationship between age and job per-
formance was analyzed using Pearson's product-mo-
mentcorrelation. Analysis of variance (ANOVA) and
post-hoc multiple comparison procedures were used
to identify significantly different group means.

Although previous research has found a linear
negative relationship between age and performance,
an examination of bivariate scatterplots raised. the
possibility of a curvilinear relationship. Therefore,
hierarchical polynomial regression analysis was used
to assess the form of the relationship between age and
performance. For each measure of job performance,
age was entered into the equation first, followed by


between age at entry and pass/fail status after initial
ATCS screening. These results supported the findings
of research from nearly two decades earlier. VanDeventer
and Baxter speculated that this negative relationship
might have a cognitive basis.

The present study revisited the issue of ATCS age
and performance using incumbent controllers and
newly developed measures of job performance. It is
important to note that ATCSs were not tested on
their own sectors, so their actual level of performance
on the job remains unknown. A recent Air Traffic-
Selection and Training (AT-SAT).concurrent valida-
tion study afforded an opportunity to investigate the
relationship between age and performance using cri-
terion measures that did not exist for previous stud-
ies. One of these measures, a computer based
performance measure (CBPM), served as a measure
of the technical skills necessary to effectively and
efficiently separate traffic on the job. Assessment
ratings of job performance by peers and supervisors
also served as a criterion measure for the current
study. Based on previous research, both current and
entry-on-duty age were hypothesized to be negatively
correlated with ATCS job performance.

METHOD

Participants

A total of 1083 Full-Performance Level (FPL)
enroute ATCSs, supervisors, and staff participated in
the AT-SAT concurrent validation study. A mini-
mum of 75 ATCSs from each of 12 enroute air traffic
control centers (ARTCCs) volunteered to partici-
pate. Personnel employed in supervisory or staff
positions were excluded from the present study, leav-
ing 828 FPL enroute ATCSs. All participants identi-
fied themselves as being FPL ATCSs at the time of the
concurrent validation; 141 had previously held a staff
position and 30 had previously been supervisors.
Demographic information is presented 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. The average num-
ber 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. Current age was
positively correlated with many work-related vari-
ables, such as number of years 2s an FPL ATCS and
number of years in current position. A positive cor-
relation was found between current age and years in

Table 1. Demographics

Variable Number Percent
Current Age
31 or Younger 144 175
32-37 391 476
38-43 197 AD
44-49 59 72
50 or greater 30 37
Entry-on-Duty Age
23 or Younger 252 33.6
24-25 208 279
26-27 127 16.9
28-29 107 14.3
30-31 52 69
Gender
Male 690 83.3
Female 138 16.6
Race
American Indian 7 21
. | Asian/ Pacific Islander 5 0.6
African American 36 43
Hispanic 33 40
White 731 88.1
Other 6 0.7
‘Education
HLS. or GED 8 9.1
Attended Trade School 3 04
Completed Trade Schoo! 15 20
Attended College less than 2 165 224
Attended College 2 or more yrs. 179 239
Completed College, 2 yr. Degree 55 TA
Completed College, 4 yr. Degree 225 20.1
Attended Graduate School 38 51



Table 2. Correlation of Age and Criterion Measures

Variabie ‘Mean | S.D. T 2 3
1. Current Age 364 [ 5.7 | 10
2. Entry-on-Duty Age 250 | 3.2 | 47 10
3.CBPM 1912] 143 | -04 02 10
4, Peer and Supervisor Rating 51 | 97 | -08* -07* — .25*
Toot
Table 3. Correlates of age
Mean | SD. 1 2 3 4 5 6 7
1. Carrent Age 364 | 5.7 [ 10
2. Entry-on-Duty Age 25.0 } 3.2 | 47** 10
3. Years FPL ATCS 72 | $4 | .78* -01 10
4, Years in Current Position 83 ] 59 | 72 00 81%" «1.0
5. Years in Previous Job 32 1} 27 | 16** 05 .18** 06 10
6. Years Staff Position 45} 12 | 40** 04 36%" 15 061.0
7. Years Supervisor 13 [| 69 | 278" 04 .21** = 0S 07 s35** 100
* pcs
peers
Table 4. Group Means and Results of One-way ANOVA of Criterion Measures by Entry-
on-Duty Age Group
Entry-on-Duty Age
Job Performance 23 and 24-25 26-27 28-29 30-31 F
Measure younger _
CBPM 191.72 191.12 190.47 192.53 193.90 72
Peer and Supervisor $.19 5.14 5.01 5.04 5.13 1.83
| Rating Score
*p<.05
Table 5. Regression of Current Age on CBPM Score
Variable B B ‘AR’
Current Age 4.68 1.82 00
Current Age” -.06 -1.88 “04e*
R04
Adj. R?=.04
R=21**
p01



DOT/FAA/AM-99/18 An Investigation of the Relationship
Between Chronological Age and

Office of Aviation Medicine Indicators of Job Performance for

Washington, D.C. 20591 Incumbent Air Traffic Control Specialists

Michael C. Heil

Civil Aeromedical Institute
Federal Aviation Administration
Oklahoma City, Oklahoma 73125

~ June 1999

“19990629 081

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

U.S. Department
of Transportation

Federal Aviation
Administration

pric QUALITY INSPECTED 4


190

8

Mean of final CBPM score

170
31 or Younger 32-37 38-43 44-49 50 or Greater

Current Age
Figure 3. Mean CBPM Score for Current Age Group

56

Sd
rs

a
i

g
o

»
o

Mean of Peer and Supervisor Rating

>

6
31 and Younger 32-37 38-43 44-49 50 or Gi
Current Age

Figure 4. Mean Peer and Supervisor Ratings for Current Age Group



’ 7
6
= eneee
3
e 44
&
a
z 34 _—
5 2 bnear
g@ 2 ® Quadratic
20 30 40 50 ry
Current Age

Figure 2. Regression of Current Age on Peer and Supervisor Ratings

Table 7. Group Means and Results of One-way ANOVA of Criterion Measures by Current

Age Group.
Current Age
Job Performance 31 and 32-37 38-43 44-49 50 or Older FE
Measure _younger
CBPM 189.10 192.51 193.36 190.54 178.54 7.33**
Peet and Supervisor 4.98 5.10 5.22 5.44 4.75 6.89**
Rating Score
** <.01

This relationship, plotted in Figure 2, suggests that
peer and supervisor rating scores begin to decrease at
approximately age 45.

Group Comparisons. A one-way ANOVA was
performed for each criterion measure to determine
differences in mean scores based on current age. The
results of these analyses, which are summarized in
Table 7, show significant age differences for each

measure of controller performance. Tukey post-hoc
comparisons were performed to determine which of
the age groups differed significantly in terms of scores
and ratings. .

Results revealed that the mean CBPM score of
178.54 for controllers who were 50 or older was
significantly lower than CBPM scores for controllers
of the other age groups (p<.01). Figure 3 shows that


the age-squared (quadratic) term. If the relationship
between age and job performance is primarily linear,
then age alone should explain a significant propor-
tion of the criterion variance, and the quadratic term
that is subsequently added should not account for a
significant change in R’. If the quadratic term ex-
plains a significant proportion of the variance beyond
the age term, then there is evidence of a curvilinear
relationship berween age and job performance.

RESULTS

Results are divided into two main sections based
on age classification. The first section contains re-
sults of analyses using entry-on-duty age, whereas
current age was used in analyses described in the
second section,

Entry-on-Duty Age

Correlations. As shown in Table 2, there was little
support for the hypothesized relationship between
entry-on-duty age and performance. Entry-on-duty
age was negatively correlated with peer and supervi-
sor rating score (r= -.07, p<.01); however, this corre-
lation is very small. The statistical significance of the
relationship is likely due to the power of the test as
influenced by the large sample size. Entry-on-duty
age was not significantly correlated with CBPM score.
Table 2 also shows a correlation between the CBPM
and peer and supervisor rating score. Table 3 contains
the results of Pearson product-moment correlations
between age and variables related to work history.
Entry-on-duty age was not significantly correlated
with any of the work-related variables included in
this table.

Prediction of Performance. The relationship be-
tween entry-on-duty age and criterion measure scores
was explored further using hierarchical polynomial
regression to predict performance. Two separate re-
gression analyses were performed with entry-on-duty
age as the predictor and CBPM and rating scores
serving as dependent variables. Results revealed that
entry-on-duty age did not significantly predict either
CBPM or the experimental rating scores. Entering
the quadratic term into the equation did not result in
a significant change in R?, providing no evidence of
a curvilinear relationship between entry-on-duty age
and the criterion measures.

Group Comparisons. Mean criterion scores for
different age groups were compared using one-way
ANOVA. Separate ANOVAs were performed for the
CBPM and rating score, the results of which are
presented in Table 4. No significant differences were
found between entry-on-duty age groups for either
the CBPM score or rating score.

Current Age

The results of analyses using current age as the
independent variable provide partial support for the
hypothesized relationship between age and perfor-
mance. The results demonstrate that the relationship
is curvilinear rather than linear.

Correlations. The results of Pearson product-
moment correlations between current age and crite-
sion measures are presented in Table 2. These
correlations do not support the hypothesized nega-
tive linear relationship between current age and job
performance. Although the very low correlation be-

" qween current age and rating score was statistically

significant (r=.08, p<.01), this is likely due to the
power of the analyses as influenced by sample size. As
shown in Table 3, current age was significantly cor-
related with entry-on-duty age (r=.47, p<.01), years
as an FPL ATCS (r=.78, p<.01), years in current
position (r=.72, p<.01), years in previous job (r=.16,
p<.OL), years in staff position (r=.40, p<.01), and
years as supervisor (r=.27, p<.01).

Prediction of Performance. The relationship be-
tween current age and performance on the criterion
measures was explored further using hierarchical
polynomial regression. Two regression analyses were
performed with current age as the predictor, and
CBPM and rating score serving as dependent variables.

As shown in Table 5, age did not significantly
contribute to the prediction of the CBPM score when
entered into the regression equation. However, the
quadratic term, or age-squared, did contribute sig-
nificantly when added (R?=.04), providing evidence
of a curvilinear relationship. This relationship is
depicted in Figure 1, which reveals that the predicted
CBPM score increases gradually until approximately
age 35, before declining after age 42.

Hierarchical polynomial regression also revealed a
quadratic relationship between current age and peer
and supervisor rating score. As shown in Table 6,
only the quadratic mode! produced a significant R?
change in predicting the overall rating score (R?=.02).


Reacting to and resolving potential conflictions effectively and efficiently; using proper air traffic separation
techniques effectively to ensure safety; sequencing aircraft effectively for arrival or departure; sequencing aircraft
to ensure efficient/timely traffic flow; controlling traffic in a manner that ensures efficient traffic flow;
controlling traffic in a manner that minimizes traffic problems (e.g., conflictions, traffic flow problems) for
other controllers and pilots.

H. Reacting co Stress
Remaining calm and cool under stressful situations; handling stressful air traffic conditions in a professional
manner.

I Teamwork

‘Working smoothly with supervisors and other controllers in the facility; pitching in and helping orher
controllers as necessary; accepting and reacting constructively to appropriate criticism from supervisors or peers;
avoiding arguments and interpersonal conflicts with other controllers, supervisors, or pilots.

J. Adaptability/Flexibility

Reacting effectively to difficult equipment problems, changes in weather, traffic situations, etc., or to
unexpected actions on the part of other controllers or pilots; using contingency or “fall-back” strategies
effectively when unforeseen/unanticipated air traffic problems emerge or if first plan doesn’t work; asking for
help when it’s needed; developing/executing innovative solutions to air traffic problems; dealing effectively with
situations for which there may not be clearly prescribed procedures, situations which require novel thinking;
adapting to equipment updates, new kinds of procedures, etc.


been found in other studies, including those that
have focused on cognitive decline in older pilots
(Hardy & Parasuraman, 1997). Cobb (1968) specu-
lated thar lower performance ratings could have been
the resulcof, among other interpretations, age bias on
the part of the rater. The current srudy does not
support that option: Controller’s scoreson the CBPM,
which is an objective criterion measure, were posi-
tively correlated with peer and supervisor critesien
assessment ratings. The pattern of relationship be-
tween current age and performance was relatively
consistent across all criterion measures. Such a.consis-
tent pattern would not be expected if either the CBPM
or the ratings were inaccurate or biased.

As the ATCS workforce ages and reaches retire-
ment age, the actual job performance of older con-
trollers becomes 2 more important issue. The present
study provides experimental evidence that control-
lers over the age of 50 perform, on average, at a level
that is lower than that of young controllers with litle
experience. They also perform, on average, at a level
that is significantly lower than ATCSs who are just a
few years younger. Although only a small percentage
of the controllers in this study were age 44 or older,
it is only a matter of time before the bulk of control-
lers reach this age. Almost one-half of the ATCSs
included in this study were between the ages of 32-
37. In 10 years time, the majority of these controllers
will begin entering the age range at which predicted
job performance level begins to decline.

Since work experience and current age are highly
correlated (as shown in Table 3), it is not possible to
disconnect the effects of these two variables. The
curvilinear nature of the relationship berween ATCS
age and performance scores suggests that the young-
est controllers are not necessarily the best. The regres-
sion lines shown in Figures | and 2 suggest that there
is a ten-year span of time, between the ages of ap-
proximately 35 and 45, in which ATCSs are at their
peak level of performance. It seems that ATCSs need
to have a certain amount of work experience, or
“seasoning,” before they reach this peak performance
level. The extent to which older controller's perfor-
mance would have declined had they not had at least
10 years work experience, is unknown. It is likely,
however, that they would nor have peaked at a level
as hig’: as other controllers before experiencing age-
relared decline.

The mandatory ATCS retirement age means that
the FAA loses its most experienced controllers when
they may no longer be the best performers. However,
this does not necessarily mean thar they are no longer
capable controllers. This raises questions about the
level of ATCS performance considered “good enough”
to continue separating traffic. Thus far, there is no
apparent answer to this question. ATCSs work in an
environment where there are sector differences and
workload variability. Since changes in sector or
workload may affect controller performance level,
additional information is needed to better under-
stand how they operate in sectors with different
workload demands and conditions. It is important to
understand whether or not expertise helps older con-
trollers cope with novel or demanding situations as
they control traffic. Another question that arises
when considering the mandatory retirement age is
whether or not modifications to the ATC work envi-
ronment would compensate for possible age-related
declines in performance. It should also be noted that
not all older ATCSs exhibit-2 lower performance
level; iz :fact many over the age of 45 achieved
performance scores higher than those attained by
younger controllers. When viewed as a group, older
ATCSsare found to have lower levels of performance.

Since ATCS work in a safety-related occupation,
the importance of understanding the relationship
beeween current age and job performance cannot be
over-emphasized. As has been the case with earlier
studies, the current study cannot provide definitive
reasons for the decline in performance scores that
occurs for older controllers. To have a better under-
standing of this dynamic, it is important that this
issue be studied in a more comprehensive manner,
where age and performance are the specific research
issues under investigation. An opportunity to conduct
a longitudinal study of age and performance exists now
that the FAA plans to increase its hiring efforts over the
next several years. It is recommended that new ATCSs
be tracked throughour their careers, with job perfor-
mance being measured periodically to study the manner
in which performance changes with age.

11


. Technical Report Documentation Page
T. Report No, 7%. Govemment Accession No. Recipient's Catalog No.
DOT/FAA/AM- 99/18
7 Tike and Subiite © Report Date
An Investigation of the Relationship Between Chronological Age and | June 1999
Indicators of Job Performance for Incumbent Air Traffic Control
Specialists
©. Performing Organization Code
7, hastens) '&. Performing Organization Report No.
Heil, M.C.
3. Performing Organization Name and Address 70. Work Unit No. (TRAIS)
FAA Civil Aeromedical Institute
P.O. Box 25082 11. Contract or Grant No.
Oldahoma City, OK 73125
12. Sponsoring Agency name and Address 13. Type of Report and Period Covered
Office of Aviation Medicine
Federal Aviation Administration 4. Sponsoring Agency Code
800 Ave., S.W.
| Washington, DC_20591
15. Supplemental Notes
16. Abetract

Over the last few decades, researchers have consistently found 2 negative relationship between the age of Air Traffic
Control Specialists (ATCSs) and both training success and ratings of job performance (Trites, 1961; Trites 8¢ Cobb,
1962; Cobb, 1967; VanDeventer 8 Baxter, 1984). As more ATCSs reach retirement age and the FAA prepares for
renewed hiring efforts, there is 2 need to once again explore this issue. According to Schroeder, Broach, and Farmer
(1997), the potential effects of aging on cognitive functioning, and the consequences of these changes on job
performance and future training requirements, are important considerations associated with the aging of the ATCS
workforce. The present study revisited the issue of ATCS age and performance using incumbent controllers and newly

| developed measures of job performance. A recent Air Traffic-Selection and Training (AT-SAT) concurrent validation

| study afforded an opportunity to investigate the relationship between age and performance using criterion measures that
did not exist for previous studies. One of these measures, a computer based performance measure (CBPM), served asa
measure of the technical skills necessary to effectively and efficiently separate traffic on the job. Assessment ratings of job
performance by peers and supervisors also served. as a criterion measure for the current study. Results of ANOVA and
regression analysis revealed that, on average, older ATCSs received lower scores on measures of job performance.

17. Key Worde 18. Dastibutor Satement
Air Traffic Control Specialist, Job Performance, Age, Document is available to the public through the
Personnel Selection National Technical Information Service,
; Springfield, Virginia 22161
19. Securly Classi. (of thie repon) 20, Security Classi, (of ths page) Bi. No. of Pages 2. Pree
Unclassified Unclassified 17
Form DOT F 1700.7 (8-72) Reproduction of completed page authorized



APPENDIX A

PERFORMANCE CATEGORIES FOR PEER AND SUPERVISOR RATING SCALES

A. Coordinating

Coordinating with other controllers to minimize traffic problems; coordinating clearances, changes in
aircraft destinations, alticudes, etc. as appropriate; initiating and receiving handoffs and pointouts in an effective
manner; presenting the rationale for instructions to pilots or other controilers as necessary.

B. Communicating and Informing

Using clear, concise, accurate language to get message across unambiguously; talking only when necessary and
appropriate; employing proper phraseology to ensure accurate communications; notifying pilots/controllers/
other personnel of information that might affect them as appropriate; issuing advisories and alerts to appropriate-
parties; providing complete and accurate position relief briefings; providing accurate and legible flight strip
information; listening carefully to requests and instructions (e.g., from pilots, other controllers) and ensuring
that they are understood; attending to readbacks and ensuring that they are accurate.

C. Maintaining Attention and Vigilance

‘Scanning properly for air traffic events, situations, potential problems, etc.; keeping track of equipment/
weather status; identifying unusual events, improper positioning of aircraft, equipment malfunctions, etc.;
recognizing when aircraft have potential for loss of separation; verifying visually that control instructions are
followed; recognizing potential problems in adjacent sectors; remaining vigilant during slow periods.

D. Managing Multiple Tasks

Keeping track of a large number of aircraft/events at a time; conducting wo or more tasks simultaneously
(e.g., issuing instructions while scanning the screen; monitoring pilot communications while writing on strips);
remembering and keeping track of aircraft and their positions; remembering what you were doing after an
interruption; returning to what you were doing after an interruption and following through; providing pilots
with additional services as time allows.

E. Prioritizing

Taking early or prompt action on air traffic problems rather than waiting or getting behind; knowing what
to do first and which are the most important situations to work on; recognizing that some problems or situations

_ are less important and can wait; preplanning before busy periods; organizing the board and using flight strips

effectively to keep priorities straight for handling air traffic situations; quickly and decisively determining
appropriate priorities.

EF. Technical

Knowing the equipment and its capabilities and using it effectively; knowing aircraft capabilities/limications
(speed, wake requirements, size, minimums) and using that knowledge; keeping up-to-date on letters of
agreement, changes in procedures, regulations, etc.; keeping up-to-date on seldom used procedures or skills. ©

G. Maintaining Safe and Efficient Air Traffic Flow

Al



IWOTICE

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 or use thereof.


mean CBPM scores gradually increased with age
until peaking for ATCSs in the 38-43 age range.
Mean CBPM was lower for controllers age 44-49, then
dropped significantly for ATCSs age 50 or older.

As shown in Table 7, significant age differences
also existed for peer and supervisor ratings (F=6.72
(4, 744), p<.01). Tukey Post-hoc comparisons re-
vealed that the mean rating score of 5.44 (SD=.65)
for controllers age 44-49 was significantly higher
than the ratings of controllers ages 31 and younger
(M=4.98, SD=.60), 32-37 (M=5.10, SD=.72), and
50 and older (M=4.75, SD=.87). Furthermore, mean
peer and supervisor ratings for controllers age 38-43
(M=5.22, SD=.68) was significantly higher than the
ratings given to controllersage 31 and younger (M=4.98,
SD=,60) and 50 and older (M=4.75, SD=.87).

The mean peer and supervisor criterion ratings for
each age group are plotted in Figure 4. Mean peer and
supervisor rating increased for each age group before
peaking for ATCSs between the ages of 44-49. As
discussed above, the decrease in mean rating for
__ATCSs ages 50 and older is statistically significant.

DISCUSSION

The results of statistical analyses, particularly hier-
archical polynomial regression, provide evidence ofa
curvilinear relationship between age and ATCS per-
formance: An examination of the linear relationship
between age and job performance generally yielded
non-significant results. However, the results of linear
analyses do not provide'a complete understanding of the
relationship that exists between age and performance.

Entry-on-Duty Age

Inclusion of entry-on-duty age in this study pro-
vided an opportunity to investigate the existence of
differences in performance that are based on the age
at which current FPLATCSs entered duty. Results of
the analyses described above demonstrate no rela-
tionship between entry-on-duty age and performance
on the CBPM. A small, but statistically significant,
negative correlation was found between entry-on-
duty age and peerand supervisor ratings. The ANOVA
yielded non-significant results; there were no signifi-
cant differences in CBPM score and rating score for
people who entered duty at different ages. The results
of regression analyses failed to demonstrate a rela-
tionship between entry-on-duty age and CBPM and
rating scores. Since the sample of ATCSs used in this

study was restricted by the hiring-age limit, no as-
sumptions can be made regarding the potential per-
formance levels of people hired over the age of 31.
However, it should be noted that earlier research
found differences in ratings when the age range went
to 39 years (Cobb, 1968). The current sample is also
restricted in terms of ATCS performance level, since
it is comprised of only those people who succeeded
through training; people who failed training are not
currently employed as controllers.

Current Age

There is strong evidence to suggest that the rela-
tionship between current age and job performance is
not linear. Although previous research revealed a
negative linear relationship (Trites, 1961; Tries &
Cobb, 1962; Cobb, 1968), such results were not
found in the current study. Analyses reveal that the
relationship berween current age and performance is,
in fact, curvilinear; performance scores increased
with age until ATCSs reached their mid 30s: Perfor-
mance then leveled off until ATCSs reached their
mid 40s, when scores dropped.

Evidence of 2 curvilinear relationship between
current age and performance was provided by the
results of hierarchical polynomial regression, as well
as analysis of variance. The patterns of relationship
that were found between current age and the two
criterion measures are similar, which is important
given that the CBPM represents an objective measure
of performance on air traffic-related tasks and was
administered independently of the more subjective
peer and supervisor ratings. The reported R-squares
of .04 and .02 indicate that current age does not
explain a large percentage of the variance in control-
ler performance. These results, however, do reveal the
trend of the relationship between these variables.
Predicted CBPM score increased with age before
peaking for ATCSs in their mid-30s through mid
40s. At approximately 45 years of age, predicted
CBPM score begins to decrease and drop to a level
below that predicted for the youngest aud newest
controllers. A similar pattern was found when using
curzent age to predict peer and supervisor rating.

Plotting the mean CBPM and rating scores for
controllers of different age groups provides another
illustration of the relationship between current age
and performance. Mean CBPM scores increased with
age until reaching their highest point for controllers
in the 38-43 year age range. The mean score then



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