Relationship of Air Traffic 

Control Specialist Age 

to En Route Operational 

Errors

Dana Broach

David J. Schroeder

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

December 2005

Final Report

DOT/FAA/AM-05/22

Office of Aerospace Medicine

Washington, DC 20591
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.
i

Technical Report Documentation Page 

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. 

DOT/FAA/AM-05/22 

4. Title and Subtitle 5. Report Date 

Relationship of Air Traffic Control Specialist Age to En Route Operational December 2005 

Errors 6. Performing Organization Code 

 

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

Broach D, Schroeder DJ 

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

FAA Civil Aerospace Medical Institute 

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

Oklahoma City, OK 73125 

12. Sponsoring Agency name and Address 13. Type of Report and Period Covered 

Office of Aerospace Medicine 

Federal Aviation Administration 

800 Independence Ave., S.W. 

Washington, DC 20591 14. Sponsoring Agency Code 

15. Supplemental Notes 

Work was accomplished under approved research task BHRR523 

16. Abstract 

Public Law 92-297, passed in 1971, requires that air traffic control specialists (ATCSs) hired after May 16, 1972 

retire at age 56. The underlying rationale was that as controllers aged, the cumulative effects of stress, fatigue 

(from shift work), and age-related cognitive changes created a safety risk (U.S. House of Representatives, 1971). 

This hypothesis has been considered in two recent studies of en route operational errors (OEs). The Center for 

Naval Analyses (CNA, 1995) found no relationship between controller age and OEs. Broach (1999) reported 

that the probability of involvement in an OE increased with age. The purpose of this study was to re-examine the 

hypothesis that controller age, controlling for experience, was related to OEs. En route OE records (3,054) were 

matched with non-supervisory ATCS staffing records for the period FY1997-2003. Poisson regression was used 

to model OE count as a function of the explanatory variables age and experience using the SPSS® version 11.5 

General Loglinear (GENLOG) procedure. Overall, the Poisson regression model fit the data poorly (Likelihood 

Ratio 2 = 283.81, p < .001). The Generalized Log Odds Ratio was used to estimate the odds ratio for age. The 

odds of OE involvement for older controllers (GE age 56) were 1.02 times greater than the odds for younger (LE 

age 55) controllers, with a 95% confidence interval of 0.42 to 1.64. This range of odds indicated that neither age 

group was less or more likely than the other to be involved in an OE, controlling for experience. The analysis 

does not support the hypothesis that older controllers are at greater risk of involvement in an OE. These results 

suggest that the original rationale for the mandatory retirement of controllers may need to be re-examined. 

Additional research on age and ATCS performance is recommended.

 

17. Key Words 18. Distribution Statement 

 

 

Air Traffic Control Specialist (ATCS) Retirement; FAA; 

Age; Performance; Operational Errors

Document is available to the public through the 

Defense Technical Information Center, Ft. Belvior, VA 

22060; and the National Technical Information 

Service, Springfield, VA 22161 

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price 

Unclassified Unclassified 22 

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

1

Relationship of Air Traffic Control Specialist Age

and En Route Operational Errors

Introduction

Background

In 1971, the United States Congress amended Section 

8335 of Title 5 of the United States Code to require the 

mandatory separation of an air traffic controller at age 56 

(Public Law 92-297). The law was justified in testimony 

on the basis of two arguments: (a) that the cumulative 

stress of the job and shift work would result in “burnout” 

for the controller, thereby increasing risks to the safety 

of flight; and (b) that controllers lost proficiency with 

age, thereby increasing risks to the safety of flight. The 

testimony of Donald Francke, Executive Director of the 

Air Traffic Control Association (ATCA) in 1971 drew a 

straight line between controller age and safety:

There must be an orderly phasing out of the older controllers when they can no longer make the rapid and accurate 

decisions essential to the preservation of human life. This 

calls for recognition of the highly important fact that 

early retirement is not solely a matter of fairness to the 

controller, it is primarily a safety measure. (U.S. House 

of Representatives, 1971, p. 98).

The primary evidence offered for these arguments were 

anecdotal reports of stress from controllers, self-reported 

“stress-related” symptoms, physiological correlates of stress, 

and medical disability retirements of controllers. Despite 

the strong assertion made by Mr. Francke, no testimony 

or data were presented in 1971 to demonstrate that older 

controllers were more likely to make errors that might 

compromise the safety of flight. This study, therefore, 

was designed to test the hypothesis implicit in Francke’s 

testimony that older controllers were more likely than 

younger controllers to commit errors that reduced the 

safety of flight.

Previous Research

Air traffic controllers ensure the safe, orderly, and 

expeditious flow of air traffic through the U.S. National 

Airspace System (NAS). To ensure the safety of flight, 

controllers maintain adequate separation between aircraft 

and obstacles to flight. They issue speed, altitude, and 

heading commands to pilots to provide and maintain 

required separation. An operational error (OE) results 

when an Air Traffic Control Specialist (ATCS, referred 

to in this paper as“air traffic controller,” or “controller”) 

fails to maintain appropriate separation between aircraft, 

terrain, and other obstacles to safe flight. OEs are rare 

compared with the number of operations handled in the 

U.S. air traffic system. For example, there were 1,145 

OEs in fiscal year (FY) 2000 compared to 166,669,557 

operations, or 6.8 OEs per million operations (Pounds 

& Ferrante, 2003; Department of Transportation Office 

of the Inspector General [DOT OIG, 2003a]). Despite 

their rarity, OEs may pose safety risks, depending on the 

degree to which separation is lost and are critical safety 

indicators for the operation of the air traffic control system 

(DOT OIG, 2003a,b).

As part of its strategy to enhance the safety of the 

nation’s air traffic system, the Federal Aviation Administration (FAA) has set a performance objective of reducing 

the number of the most serious air traffic control OEs 

by 15%, to no more than 563, by FY2008. However, 

the OE rate has increased in recent years (DOT OIG, 

2000, 2002, 2003b). For example, the number of OEs 

increased from 754 in FY1997 to 1,194 in FY2001. Errors 

declined in FY2002 to 1,061 with the downturn in air 

traffic following the events of 9-11-2001, but recovered 

in FY2003. The DOT Inspector General reported that 

there were 1,186 OEs in FY2003, as shown in Figure 1.

Previous research on OEs investigated factors such as 

controller workload, situation awareness, shiftwork, fatigue, 

aircraft flight characteristics, and sector complexity (Della 

Rocco, Cruz, & Clemens, 1999; Endsley & Rodgers, 1997; 

Rodgers & Nye, 1993; Rodgers, Mogford, & Mogford, 

1998; Schroeder & Nye, 1993). Personnel and organizational factors such as staffing, experience, training, and work 

attitudes have also been investigated (Broach & Dollar, 

2002; Center for Naval Analyses Corporation, CNAC, 

1995; Schneider, 2001; Schroeder & Nye, 1993).

The relationship of age to OEs has been considered 

explicitly in two studies. In 1976, the FAA Air Traffic 

Service requested that the MITRE Corporation conduct 

a “study of the performance of the human element in air 

traffic control ... to support identification of system error 

causes and recommend corrective actions” (Kinney, 1977, 

p. iii). As part of that study, Spahn (1977) drew on data 

from the System Effectiveness Information System (SEIS) 

to investigate the relationship of age to System Errors 

(SEs; now called Operational Errors1). Spahn analyzed 

data for 630 center and 564 terminal errors that occurred 

in the years 1974 through 1976. First, Spahn plotted the 
2

754

885

941

1,145 1,194

1,061

1,185

0

200

400

600

800

1,000

1,200

1,400

1997 1998 1999 2000 2001 2002 2003

Figure 1: Number of Operational (OEs) by Fiscal Year (FY) 

frequency distributions of the age of involved controllers 

and of the entire controller population by option and 

year. The frequency distributions, as shown in Figure 2 

for center controllers and SEs, were very similar. Based 

on this inspection and without formal statistical tests, 

Spahn observed that the similarity showed that “no age 

group has had neither more nor less than its proportional 

share of system errors” (pp. 3-35). He concluded that SEs 

behaved like random events and were not predictable on 

the basis of controller age (pp. 3-35).

The Center for Naval Analyses Corporation (CNAC) 

was tasked by the FAA to conduct an analysis of operational errors in the early 1990s. CNAC extracted en 

route operational error data for the period January 1991 

to July 1995 from the FAA Operational Error/Deviation 

System (OEDS). These data were then matched with 

agency personnel records and controller experience at 

the time of the error was estimated. The controllers (N

= 3,724) were then grouped by experience, and the total 

number of controllers with errors was divided by the total 

number with the same experience to derive an estimate 

of the “likelihood” of an OE for each experience group. 

CNAC found that experience and the likelihood of an 

OE were significantly related, with a quadratic equation 

fitting the data (R2 = .72). CNAC reported that the likelihood of an OE declined dramatically in the first few 

years of experience at an air route traffic control center 

(ARTCC) and then appeared to approach a constant 

value. Experience was significantly correlated with age 

(r = .48, p < .001), but CNAC did not examine controller 

age or control for age effects.

Broach (1999) re-analyzed the CNAC data from 

the perspective of controller age. The CNAC data set 

included the number of OEs (none, one, two, or more) 

committed between January 1991 and July 1995. OE 

dates were not available, so age at the time of the error 

could not be calculated. Therefore, the analysis by Broach 

were based on age and experience at the beginning of 

the 5-year observation period.2 Following the CNAC 

methodology, the controllers were grouped by age in 

1-year increments from 18 to 48. The average age at 

the beginning of the observation period was calculated 

(AGE). The total number of controllers with errors 

was divided by the total number of controllers in that 

age group to estimate the likelihood of an OE for each 

group (p(OE)). The average experience (EXP), defined 

as years of experience at the facility, was also computed 

for each age group. Two analyses were performed. First, 

OE likelihood was regressed on AGE, using the SPSS® 

CURVEFIT procedure. This provided an estimate of age 

effects. However, as age and experience were confounded 

in the CNAC data, a second analysis was conducted in 
3

Figure 2: Distribution of System Errors relative to the age distribution of 

en route controllers, 1974, 1975, and 1976 (Spahn, 1977) 
4

which OE likelihood was regressed on both age and 

experience to provide an initial estimate of the relative 

contributions of both. Age and experience accounted 

for about 45.0% of the variance in OE likelihood across 

age groups (R = .671, F(2,27) = 11.03, p < .001). The 

standardized regression coefficient (β) for experience 

was negative (β = -.452), compared with a positive coefficient for age (β = 1.06). The results of that regression 

analysis indicated that the likelihood of an en route OE 

might increase with controller age. This suggested that, 

as the post-strike controller population ages (Schroeder, 

Broach, & Farmer, 1998), the likelihood of OEs may 

increase. However, the regression analysis also found 

that experience might mitigate the risk of an OE with 

increasing age. Additional research on the relationship 

between chronological age, experience, and the incidence 

of operational errors was recommended in view of the 

aging of the controller workforce. The present study 

builds on that recommendation.

Methodological Considerations

As noted by Li (1994) and Broach (2004), analysis of 

adverse, rare outcomes such as OEs and aircraft accidents 

poses analytic and methodological challenges. Common 

approaches include (a) analysis of the characteristics of the 

adverse outcome of interest and (b) analysis of the rates 

at which an adverse outcomes occur. The description of 

the characteristics of “severe” en route OEs by Rodgers 

and Nye (1993) is an example of the first descriptive 

approach. The analysis of OE rates by CNAC (1995), 

Broach (1999), and Broach and Dollar (2003) are examples of the second approach. Both CNAC and Broach 

calculated the dependent variable of interest as the ratio 

of controllers with errors in an experience or age range 

to the total number of controllers in that experience or 

age range. CNAC labeled this ratio as the “likelihood” of 

involvement in an error. In fact, both CNAC and Broach 

calculated the proportion of controllers in a given category 

that were involved in an error during some observation 

period. The result is a person-based estimate of risk. 

However, a person-based estimate of risk does not take 

into account factors such as aptitude, experience, age, and 

exposure. Exposure is a critical variable in assessing the 

risk for the occurrence of an adverse event. For example, 

a controller working a busy, low-altitude transitional sector with multiple merging airways that feed a major hub 

during an afternoon rush will have a greater opportunity 

to commit an OE than another controller working a 

high-altitude sector with sparse cross-continental traffic 

in steady, predictable east/west flows. Time on position 

may vary as well. A controller working longer on a given 

position will have greater opportunity to commit an OE 

than another controller working less time on a position. 

As noted by Della Rocco, Cruz, and Clemens (1999), a 

measure of exposure is required to appropriately analyze 

the risk of being involved in an OE. However, detailed 

measures of controller exposure were not available for 

this study.

Without measures of exposure, the analytic focus shifts 

from rates to the occurrences themselves. OEs are rare 

events, relative to the number of controllers working and 

operations conducted in any given day or year. On one 

hand, an event such as an OE may occur in any of a large 

number of operations. On the other hand, the probability 

of occurrence in any given trial is small. Events with these 

characteristics often follow the Poisson distribution rather 

than the more familiar bell-shaped normal distribution 

(Cameron & Trivedi, 1998).

A Poisson distribution has a single parameter, λ, 

unlike the more familiar normal distribution that is 

characterized by two parameters, the mean and standard 

deviation. The normal distribution is typically portrayed 

as a symmetrical bell curve (Figure 3). In contrast, the 

Poisson distribution may be asymmetrical and skewed, 

depending on the value of λ. For example, for small 

values of λ, the resulting distribution is peaked nearer 

to 0 and has a long “tail” to the right (Figure 4). Larger 

values of λ result in a distribution that looks more like 

a normal distribution.

Examples of events that follow a Poisson distribution 

are doctor visits, absenteeism in the workplace, mortgage 

pre-payments and loan defaults, bank failures, insurance 

claims, and airplane accidents (Cameron & Trivedi, p. 

11). This statistical “law of rare events” might apply to 

air traffic control operations as well: There are a large 

number of aircraft under the control of a relatively large 

number of controllers at any given moment, but the 

likelihood of an OE for any given aircraft by any single 

controller is very small.

Familiar statistical techniques based on the normal 

distribution such as correlation, analysis of variance, and 

multiple linear regression are not appropriate for modeling rare events characterized by a Poisson distribution. 

Rather, techniques based on the Generalized Linear Model 

(GzLM) are more appropriate for modeling dependent 

variables following a Poisson, Binomial or other exponential distribution (Myers, Montgomery, & Vining, 

2002). In the GzLM, a discrete variable such as the count 

of errors during some observation period is modeled as a 

function of explanatory variables (which may also follow 

an exponential distribution). An important assumption 

in the GzLM is that the events are independently and 

identically distributed (iid).3 In other words, the occurrence of the event does not depend on other occurrences, 

but rather depends on the influence of the explanatory 

variables. For example, the iid assumption stipulates that 
5

Figure 3: Example of the normal distribution 

(From www.itl.nist.gov/div898/handbook/eda/section3/ 

eda3661.htm, January 6, 2004) 

Figure 4: Examples of Poisson distribution for 4 different 

values of Ȝ (From www.itl.nist.gov/div898/handbook/ 

eda/section3/eda366j.htm, January 6, 2004) 
6

involvement of a controller in an OE in sector X in the 

afternoon does not depend on the occurrence of an OE in 

sector Y in the morning, but rather depends on the characteristics of the involved controller, the traffic, or other 

explanatory variables. Given this assumption, each unit 

of observation (a fiscal year) is treated as an independent 

observation for each controller and the number of errors 

incurred each year modeled as a function of controller 

characteristics (e.g., age and experience).

Poisson regression is a specific technique from the broad 

class of GzLM approaches often used to model counts 

of an event as a function of explanatory or predictor 

variables in field such as economics and epidemiology. 

Given that OEs are rare events and their distribution approximates the Poisson distribution, Poisson regression 

was selected as the appropriate analytic methodology. 

The goal of the analysis was to model the number of en 

route OEs incurred by a controller as a function of age 

and experience (e.g., tenure in the FAA) over a specific 

period of observation.

Method

Source Data

Operational error data. The FAA Operational Error/Deviation System (OEDS) is the official source for 

operational error data. For this analysis, the Field Support 

Division (ATX-200) extracted records for en route errors 

occurring between October 1, 1993, and September 30, 

2003. An Excel® data file was received from ATX-200, 

with 6,337 error records for the period October 1, 1993, 

through September 30, 2003. The data elements from 

the FAA Form 7210-3 (08/02) included in the raw data 

file are listed in Table 1. Due to database changes, only 

records from October 1, 1996 through September 30, 

2003, were usable for the analysis. The following criteria 

were used to select error records for analysis:

•	 Event Date >= 10/01/1996

•	 Primary ATCS only (P)

•	 Valid ATCS (partial) ID (Last 4 or 6 digits of SSN)

•	 Valid ATCS Date-of-Birth (DOB)

•	 Valid ARTCC 3-letter facility ID

Review of resulting 3,524 records indicated that most 

records used the last 4 rather than the last 6 digits of the 

ATCS SSN as the record identifier. To standardize the 

identifier, all records were reviewed, and only the last 

4 digits of any ID string were used (e.g., last 4 digits of 

the SSN). Approximately 100 records were corrected.

Actual On-Board (AOB) data. The official system 

of records for personnel data is the FAA Consolidated 

Personnel Management Information System (CPMIS). 

Since 1996, the Civil Aerospace Medical Institute 

has received an extract of CPMIS data at the end of 

each fiscal year for research purposes. Each extract 

was appended to create a cumulative AOB file which 

was then reduced by selecting records for en route 

controllers using these criteria: (a) job series = 2152, 

(b) organization type = ARTCC, and (c) supervisory 

status = Non-supervisor.

The data file was aggregated by SSN, retaining date of 

birth (DOB) for matching with the OE data. A new variable was created with the last 4 digits of each controller’s 

SSN. These CPMIS records were then matched with the 

OE records using the SPSS table lookup procedure (see 

Figure 5). Two OE records with the same last 4 digits 

of the SSN and dates-of-birth were dropped. Overall, 

3,368 OE records were matched with CPMIS data. 

Selecting on supervisory status for non-supervisors only 

resulted in a total 3,054 OE records.

Table 1: OEDS data elements included in OE data extract 

Form Block Description 

Report Number 

Block 1 Date and time (local) of incident 

Block 9 Number of aircraft controller had responsibility for at the time of the error 

Block 10 Was training in progress? 

Block 11 Primary (P) or Contributory (C) 

Block 13 Employee’s 3-letter facility identification 

Block 15 Employee’s date of birth 

Block 16 Last (4 or 6) digits of employee’s social security number 

Block 17 Indicate the performance level of the employee 

Block 20 Is a medical certification issue related to the incident? 

Block 25 Time on position 

Block 28 Position function 
7

CPMIS OEDS 

DOB LAST4SSN YEAR YEAR LAST4SSN DOB N_OE 

01/01/53 1234 1997 1997 1234 01/01/53 1 

02/02/54 2345 1997 1997 2345 02/02/54 1 

... ... ... ... ... ... ... 

01/01/53 1234 2003 2003 1234 01/01/53 1 

... 

(SPSS Syntax) 

MATCH FILES 

 /FILE=[CPMIS] 

 /FILE=[OEDS] 

 /BY=YEAR SSN DOB

Figure 5: Logic for matching CPMIS and OEDS records by controller and year 

The 3,054 OE records were then matched with CPMIS 

records for non-supervisory controllers without errors in 

any given year to create an overall database comprised of 

controllers with and without errors for each fiscal year. 

The number of error and non-error records is presented 

by fiscal year in Table 2. For example, of the 7,178 nonsupervisory ATCS on-board at ARTCCs in FY1997, 6,864 

(95.6%) had no operational errors, while 303 controllers 

(4.2%) had one OE, 11 had 2 errors (0.2%), and none 

had 3 errors in that fiscal year. However, as shown in 

Table 2, there was one ATCS in FY2001 and another in 

FY2003 with three OEs for the year.

Data Transformations

Both age and tenure were continuous ratio variables. 

To simplify the analysis, they were recoded into specific 

ranges. The first category for tenure was based on the 

average of about three years required to complete on-thejob training for center controllers (Manning,1998). The 

next interval was 6-years wide (4 through 9), followed by 

5-year increments. Age was recoded into 2 groups: age 55 

and younger; and age 56 and older. This split was used to 

specifically assess the risk that might be associated with 

controllers older than the mandatory separation age.

Table 2: Number of non-supervisory ATCS on-board with 0, 1, 2, or 3 operational errors by fiscal year 

Number of ATCS with Operational Errors (OEs) 

Fiscal Year 0 1 2 3 AOB Total 

1997 6,864 303 11 0 7,178

1998 6,932 389 16 0 7,337

1999 6,869 422 21 0 7,312

2000 6,833 487 31 0 7,351

2001 6,827 549 45 1 7,422

2002 7,110 416 32 0 7,558

2003 7,410 313 17 1 7,741
8

Analysis 

The data were aggregated by fiscal year, age group, and 

tenure group to create a cross-classification table suitable 

for Poisson regression, as shown in Table 3. The columns 

labeled “Number of OEs (nij)” contain the counts of OEs 

reported for each age and tenure group combination. For 

example, there were 44 OEs in the period FY1997 to 

FY2003 for controllers age 55 or less and with 3 years or 

less tenure, and 4 OEs for controllers age 56 or older and 

with 3 years or less tenure. The column labeled “ATCS 

Population (Nij)” contain data representing the number of 

controllers “exposed” to the risk of incurring an OE during 

the observation period for each age-tenure combination. 

For example, there were 3,587 en route controllers age 

55 or less with 3 years or less tenure who were “at risk” 

of incurring an OE during the observation period. The 

goal of the Poisson regression analysis was to assess the 

relative effects of age and tenure on the ratios of errors 

to “at risk” population. The SPSS® version 11.5 General 

Loglinear (GENLOG) method was used to conduct the 

Poisson regression analysis.

Results

Overall, the Poisson regression model fit the data poorly 

(Likelihood Ratio χ2 = 283.81, p < .001; see Appendix 

A for statistical details). The parameter estimate for the 

main effect of age (3.50) was significantly different from 

0 (95% confidence interval = 3.29 to 3.70), as were the 

parameter estimates for tenure. To consider the effect of 

age across tenure, the two age groups were contrasted. 

The Generalized Log Odds Ratio was used to estimate the 

odds ratio for age, that is, the odds of OE invovlement 

for older (GE age 56) controllers (see SPSS, 1999, p. 202 

- 203). The odds of OE involvement for older controllers 

(GE age 56) were 1.02 times greater than the odds for 

younger (LE age 55) controllers, with a 95% confidence 

interval of 0.42 to 1.64. A confidence interval for the 

odds ratio that included 1.0 indicated that the odds of 

involvement for the two groups were equal: neither age 

group was less or more likely to be involved in an OE.

Discussion

The Poisson regression analysis of the number of OEs as 

a function of ATCS age and experience (years of employment) did not support the hypothesis that the likelihood 

of involvement in an en route OE increased with age. This 

finding casts doubt on the explicit characterization of the 

mandatory early retirement of controllers as “primarily 

a safety measure” (Testimony of Donald Francke, U.S. 

House of Representatives, 1971). 

As noted by Li et al. (2003), age in and of itself may 

have little bearing on safety-related outcomes if factors 

such as individual job experience, workload, traffic complexity, and time-on-position are taken into consideration 

(p. 878). Moreover, all other things being equal, age may 

influence performance through two conflicting pathways. 

On the one hand, the inevitable changes in cognitive 

function, particularly speed of processing, may result 

in slower and less efficient performance. On the other 

hand, experience is gained with age, and compensatory 

strategies and meta-strategies may result in safer and 

more efficient performance by controllers. Additional 

research is recommended to extend and confirm the initial 

analyses reported above and to examine the contribution 

of cognitive and other variables. For example, the en 

route OE data analyzed in this study might be linked 

to medical data for each of the controllers such that error involvement as a function of medical status might 

be investigated. Error data collected using the JANUS 

approach (Pounds & Isaac, 2002) might be analyzed to 

determine if certain types of errors, for example, in the 

cognitive domain, are more or less common for older 

and younger controllers involved in OEs.

Work on control strategies (D’Arcy & Della Rocco, 

2001) might be extended to examine differences in 

strategies by age and experience groups. In addition, a 

longitudinal study of a sample of incumbent controllers might be initiated in which, as part of the annual 

medical examination, additional measures of cognitive 

function are taken and any changes over time monitored. 

The sample of controllers might also undergo periodic 

Table 3: Tenure by age cross-classification table for Poisson regression analysis 

Number of OEs (nij) ATCS Population (Nij)

Tenure Group LE Age 55 GE Age 56 LE Age 55 GE Age 56 

LE 3 Years 44 4 3,587 110 

4 – 9 Years 488 10 7,574 191 

10 – 14 Years 1,112 20 15,758 280 

15 – 19 Years 1,007 2 14,816 128 

20 – 24 Years 343 2 5,615 67 

GE 25 Years 142 57 2,587 1,186 
9

simulator-based assessments of core technical performance 

for research purposes only. Other work might focus on 

investigations of the impact of shift work and fatigue 

on performance for older controllers and assessment of 

the efficacy of fatigue counter-measures by age. Annual 

medical examination data might be used to conduct 

large-scale epidemiologic studies to track factors such as 

blood pressure, gastro-intestinal complaints, and other 

medical outcomes commonly thought to reflect jobrelated stress.

Finally, in addition to these “controller factors,” future 

research on OEs needs to consider other potential explanatory variables such as traffic and sector architecture. 

Recent work in the United States (Pfleiderfer, 2003; Mills, 

Pfleiderfer & Manning, 2002) and Europe (Delahaye & 

Puechmorel, 2000) has focused on developing metrics for 

the representation of traffic complexity. While showing 

promise, there is no scientific agreement as yet on how 

to measure traffic complexity, much less its interaction in 

real-time with the controller. However, with continued 

research on both controller and traffic characteristics, and 

their interaction, it might be possible to reach a more 

definitive answer about the relationship of controller age 

and operational errors.

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accident and pilot inclusion criteria and analytic 

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Federal Aviation Administration Office of Aerospace Medicine.4

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employee attitudes and supervisor-controller ratio 

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(Available from www.oig.dot.gov).

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(2003a). Operational errors and runway incursions: 

Progress made, but the number of incidents is still 

high and presents serious safety risks. (DOT OIG 

AV-2003-040). Washington, DC: Department of 

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(Available from www.oig.dot.gov).

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(2003b). DOT’s Top management challenges. 

(DOT OIG PT-2004-006). Washington, DC: Department of Transportation Office of the Inspector 

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Endsley, M. R., & Rodgers, M. D. (1997). Distribution 

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directions for methodology of social and behavioral 

science 6: Issues in aggregation (pp. 65-75). San 

Francisco, CA: Jossey-Bass.

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control: Observations and analyses of the performance of controllers and supervisors in providing ATC separation services. (MITRE Technical 

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review of epidemiologic studies. Aviation, Space, 

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for reducing operational error causal factors. 

(DOT/FAA/AM-03/19). Washington, DC: Federal Aviation Administration Office of Aerospace 

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human error in ATM. (DOT/FAA/AM-02/12). 

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Office of Aerospace Medicine.4

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relationship of sector characteristics to operational 

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Federal Aviation Administration Office of Aviation 

Medicine.4

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traffic control centers. In M. D. Rodgers (Ed.), 

An examination of the operational error database 

for air route traffic control centers (pp. 11-25). 

(DOT/FAA/FAA/AM-98/22). Washington, DC: 

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Medicine.4

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control specialist workforce, 1981-1992 (pp. 43-

52). (DOT/FAA/FAA/AM-98/23). Washington, 

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the workload conditions associated with operational 

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Office of Aviation Medicine.4
11

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End Notes

1

The original terminology of System Error is used in describing the MITRE studies to be consistent with the reproduced figures. The more recent terminology of Operational 

Error (OE) is used in describing contemporary studies. Both 

terms refer to an event with less than standard separation 

between aircraft or aircraft and an obstacle as the result of 

controller action (or inaction).

2

As is commonly done in survival analyses, age at the beginning of the observation period was used as a predictor of 

subsequent outcomes (see Li et al., 2003 for an example).

3

It must be noted that traditional multiple linear regression 

makes the same assumption that the dependent variable is 

independently and identically distributed.

4

This publication and all Office of Aerospace Medicine 

technical reports are available in full-text from the Civil 

Aerospace Medical Institute’s publications Web site: www.

faa.gov/library/reports/medical/oamtechreports/index.cfm

A1

APPENDIX A:

Poisson Regression (GENLOG) SPSS Output 

General Loglinear: Without contrasts 

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

 GENERAL LOGLINEAR ANALYSIS 

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

Data Information 

 12 cases are accepted. 

 0 cases are rejected because of missing data. 

 3231 weighted cases will be used in the analysis. 

 12 cells are defined. 

 0 structural zeros are imposed by design. 

 0 sampling zeros are encountered. 

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

Variable Information 

Factor Levels Value 

TEN_GRP 6 Service from FAA EOD group 

 1 LE 3 years 

 2 4-9 years 

 3 10-14 years 

 4 15-19 years 

 5 20-24 years 

 6 GE 25 years 

AGE_56 2 Under or Over age 56 

 1 LE Age 55 

 2 GE Age 56 

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

Model and Design Information 

 Model: Poisson 

Design: Constant + AGE_56 + TEN_GRP 

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

Correspondence Between Parameters and Terms of the Design 

Parameter Aliased Term 

 1 Constant 

 2 [AGE_56 = 1] 

 3 x [AGE_56 = 2] 

 4 [TEN_GRP = 1] 

 5 [TEN_GRP = 2] 

 6 [TEN_GRP = 3] 

 7 [TEN_GRP = 4] 

 8 [TEN_GRP = 5] 

 9 x [TEN_GRP = 6] 

Note: 'x' indicates an aliased (or a redundant) parameter. 
A2

 These parameters are set to zero. 

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

 GENERAL LOGLINEAR ANALYSIS 

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

Convergence Information 

Maximum number of iterations: 20 

Relative difference tolerance: .001 

Final relative difference: 3.45689E-06 

Maximum likelihood estimation converged at iteration 6. 

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

Table Information 

 Observed Expected 

Factor Value Count % Count % 

TEN_GRP LE 3 years 

 AGE_56 LE Age 55 44.00 ( 1.36) 46.59 ( 1.44) 

 AGE_56 GE Age 56 4.00 ( .12) 1.41 ( .04) 

TEN_GRP 4-9 years 

 AGE_56 LE Age 55 488.00 ( 15.10) 483.36 ( 14.96) 

 AGE_56 GE Age 56 10.00 ( .31) 14.64 ( .45) 

TEN_GRP 10-14 years 

 AGE_56 LE Age 55 1112.00 ( 34.42) 1098.72 ( 34.01) 

 AGE_56 GE Age 56 20.00 ( .62) 33.28 ( 1.03) 

TEN_GRP 15-19 years 

 AGE_56 LE Age 55 1007.00 ( 31.17) 979.33 ( 30.31) 

 AGE_56 GE Age 56 2.00 ( .06) 29.67 ( .92) 

TEN_GRP 20-24 years 

 AGE_56 LE Age 55 343.00 ( 10.62) 334.86 ( 10.36) 

 AGE_56 GE Age 56 2.00 ( .06) 10.14 ( .31) 

TEN_GRP GE 25 years 

 AGE_56 LE Age 55 142.00 ( 4.39) 193.15 ( 5.98) 

 AGE_56 GE Age 56 57.00 ( 1.76) 5.85 ( .18) 

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 

Goodness-of-fit Statistics 

 Chi-Square DF Sig. 

Likelihood Ratio 238.8141 5 1.E-49 

 Pearson 505.8644 5 4.-107 
A3

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

 GENERAL LOGLINEAR ANALYSIS 

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Parameter Estimates 

 Asymptotic 95% CI 

Parameter Estimate SE Z-value Lower Upper 

 1 1.7666 .1235 14.31 1.52 2.01 

 2 3.4968 .1041 33.58 3.29 3.70 

 3 .0000 . . . . 

 4 -1.4221 .1608 -8.84 -1.74 -1.11 

 5 .9173 .0839 10.94 .75 1.08 

 6 1.7384 .0769 22.62 1.59 1.89 

 7 1.6234 .0776 20.93 1.47 1.78 

 8 .5502 .0890 6.18 .38 .72 

 9 .0000 . . . . 

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Covariance Matrix of Parameter Estimates 

Parameter

 1 2 4 5 6 7 8 

 1 .0152 

 2 -.0105 .0108 

 4 -.0050 .0000 .0259 

 5 -.0050 .0000 .0050 .0070 

 6 -.0050 .0000 .0050 .0050 .0059 

 7 -.0050 .0000 .0050 .0050 .0050 .0060 

 8 -.0050 .0000 .0050 .0050 .0050 .0050 .0079 

Aliased parameters are not shown. 

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Correlation Matrix of Parameter Estimates 

Parameter

 1 2 4 5 6 7 8 

 1 1.0000 

 2 -.8187 1.0000 

 4 -.2531 .0000 1.0000 

 5 -.4853 .0000 .3726 1.0000 

 6 -.5295 .0000 .4065 .7795 1.0000 

 7 -.5248 .0000 .4029 .7725 .8428 1.0000 

 8 -.4573 .0000 .3511 .6731 .7344 .7278 1.0000 

Aliased parameters are not shown. 

SAVE OUTFILE='D:\Count-oriented Regression\NonSup Data Agg by FY, Age Group & 

Tenure Group.sav' 

 /COMPRESSED. 

GENLOG

 ten_grp age_56 /GLOR = age_cont 

 /MODEL = POISSON 

 /PRINT = FREQ ESTIM CORR COV 

 /PLOT = NONE 

 /CRITERIA = CIN(95) ITERATE(20) CONVERGE(.001) DELTA(.5) 

 /DESIGN age_56 ten_grp . 
A4

General Loglinear: Age Contrast 

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

 GENERAL LOGLINEAR ANALYSIS 

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Data Information 

 12 cases are accepted. 

 0 cases are rejected because of missing data. 

 3231 weighted cases will be used in the analysis. 

 12 cells are defined. 

 0 structural zeros are imposed by design. 

 0 sampling zeros are encountered. 

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Variable Information 

Factor Levels Value 

TEN_GRP 6 Service from FAA EOD group 

 1 LE 3 years 

 2 4-9 years 

 3 10-14 years 

 4 15-19 years 

 5 20-24 years 

 6 GE 25 years 

AGE_56 2 Under or Over age 56 

 1 LE Age 55 

 2 GE Age 56 

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Model and Design Information 

 Model: Poisson 

Design: Constant + AGE_56 + TEN_GRP 

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Correspondence Between Parameters and Terms of the Design 

Parameter Aliased Term 

 1 Constant 

 2 [AGE_56 = 1] 

 3 x [AGE_56 = 2] 

 4 [TEN_GRP = 1] 

 5 [TEN_GRP = 2] 

 6 [TEN_GRP = 3] 

 7 [TEN_GRP = 4] 

 8 [TEN_GRP = 5] 

 9 x [TEN_GRP = 6] 

Note: 'x' indicates an aliased (or a redundant) parameter. 

 These parameters are set to zero. 
A5

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

 GENERAL LOGLINEAR ANALYSIS 

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Convergence Information 

Maximum number of iterations: 20 

Relative difference tolerance: .001 

Final relative difference: 3.45689E-06 

Maximum likelihood estimation converged at iteration 6. 

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Table Information 

 Observed Expected 

Factor Value Count % Count % 

TEN_GRP LE 3 years 

 AGE_56 LE Age 55 44.00 ( 1.36) 46.59 ( 1.44) 

 AGE_56 GE Age 56 4.00 ( .12) 1.41 ( .04) 

TEN_GRP 4-9 years 

 AGE_56 LE Age 55 488.00 ( 15.10) 483.36 ( 14.96) 

 AGE_56 GE Age 56 10.00 ( .31) 14.64 ( .45) 

TEN_GRP 10-14 years 

 AGE_56 LE Age 55 1112.00 ( 34.42) 1098.72 ( 34.01) 

 AGE_56 GE Age 56 20.00 ( .62) 33.28 ( 1.03) 

TEN_GRP 15-19 years 

 AGE_56 LE Age 55 1007.00 ( 31.17) 979.33 ( 30.31) 

 AGE_56 GE Age 56 2.00 ( .06) 29.67 ( .92) 

TEN_GRP 20-24 years 

 AGE_56 LE Age 55 343.00 ( 10.62) 334.86 ( 10.36) 

 AGE_56 GE Age 56 2.00 ( .06) 10.14 ( .31) 

TEN_GRP GE 25 years 

 AGE_56 LE Age 55 142.00 ( 4.39) 193.15 ( 5.98) 

 AGE_56 GE Age 56 57.00 ( 1.76) 5.85 ( .18) 

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Goodness-of-fit Statistics 

 Chi-Square DF Sig. 

Likelihood Ratio 238.8141 5 1.E-49 

 Pearson 505.8644 5 4.-107 
A6

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

 GENERAL LOGLINEAR ANALYSIS 

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Parameter Estimates 

 Asymptotic 95% CI 

Parameter Estimate SE Z-value Lower Upper 

 1 1.7666 .1235 14.31 1.52 2.01 

 2 3.4968 .1041 33.58 3.29 3.70 

 3 .0000 . . . . 

 4 -1.4221 .1608 -8.84 -1.74 -1.11 

 5 .9173 .0839 10.94 .75 1.08 

 6 1.7384 .0769 22.62 1.59 1.89 

 7 1.6234 .0776 20.93 1.47 1.78 

 8 .5502 .0890 6.18 .38 .72 

 9 .0000 . . . . 

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Covariance Matrix of Parameter Estimates 

Parameter

 1 2 4 5 6 7 8 

 1 .0152 

 2 -.0105 .0108 

 4 -.0050 .0000 .0259 

 5 -.0050 .0000 .0050 .0070 

 6 -.0050 .0000 .0050 .0050 .0059 

 7 -.0050 .0000 .0050 .0050 .0050 .0060 

 8 -.0050 .0000 .0050 .0050 .0050 .0050 0079 

Aliased parameters are not shown. 

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Correlation Matrix of Parameter Estimates 

Parameter

 1 2 4 5 6 7 8 

 1 1.0000 

 2 -.8187 1.0000 

 4 -.2531 .0000 1.0000 

 5 -.4853 .0000 .3726 1.0000 

 6 -.5295 .0000 .4065 .7795 1.0000 

 7 -.5248 .0000 .4029 .7725 .8428 1.0000 

 8 -.4573 .0000 .3511 .6731 .7344 .7278 .0000 

Aliased parameters are not shown. 
A7

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

 GENERAL LOGLINEAR ANALYSIS 

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Generalized Log-odds Ratio Coefficients 

Factor Value AGE_CONT 

TEN_GRP LE 3 years 

 AGE_56 LE Age 55 -1.000 

 AGE_56 GE Age 56 1.000 

TEN_GRP 4-9 years 

 AGE_56 LE Age 55 -1.000 

 AGE_56 GE Age 56 1.000 

TEN_GRP 10-14 years 

 AGE_56 LE Age 55 -1.000 

 AGE_56 GE Age 56 1.000 

TEN_GRP 15-19 years 

 AGE_56 LE Age 55 -1.000 

 AGE_56 GE Age 56 1.000 

TEN_GRP 20-24 years 

 AGE_56 LE Age 55 -1.000 

 AGE_56 GE Age 56 1.000 

TEN_GRP GE 25 years 

 AGE_56 LE Age 55 -1.000 

 AGE_56 GE Age 56 1.000 

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

 Generalized Log-Odds Ratio 

 95% Confidence Interval 

 Variable Value SE Wald Sig. Lower Upper 

 AGE_CONT -20.9810 .6248 1127.4890 3.5-247 -22.2056 -19.7563 

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

 Generalized Odds Ratio 

 95% Confidence Interval 

 Variable Value Lower Upper 

 AGE_CONT 7.728E-10 2.2710E-10 2.6300E-09