AM 65-19 

PROBLEMS IN AIR TRAFFIC MANAGEMENT: 

V. Identification and Potential of Aptitude Test Measures 

for Selection of Tower Air Traffic Controller Trainees 

Bart B. Cobb, M.S. 

Approved by 

STANLEY R. MoHLER, M.D. 

DIRECTOR, CARl 

July 1965 

FEDERAL AVIATION AGENCY 

Office of Aviation Medicine 

Civil Aeromedical Research Institute 

Oklahoma City, Oklahoma 

Released by 

M. s. WHITE, M.D. 

FEDERAL Am SuRGEON 

Problems in Air Traffic Management: 

V. Identification and Potential of Aptitude 

Test Measures for Selection of Tower Air 

Traffic Controller Trainees 

BART B. CoBB, M.S. 

ABSTRACT 

A study of over 200 Terminal Air Traffic Control Specialists indicated that their training performance could be well 

predicted by a composite of four aptitude tests measuring: 

numerical ability, non-verbal abstract reasoning, ability to 

solve simplified air traffic problems, and verbal abstract 

reasoning. Pre-employment experience directly related to air 

traffic control was also found to contribute to the prediction 

of training performance. Although the ai.)titude tests were 

related in the expected direction with a measure of job performance, the relationships were small. The aptitude of 

verbal abstract reasoning is both unique and important for 

the prediction of Terminal ATCS performance. Previous 

resear-ch has failed to identify such an aptitude as being of 

importance for the prediction of Euroute ATCS performance. With the exception of verbal abstract reasoning, the 

group of Civil Service Commission tests presently being 

used to select all ATCS trainees provides adequate measures of those aptitudes which this study identified as most 

significant for the prediction of Terminal performance and 

is still appropriate for selection of Terminal ATCS trainees. 

-'""rHE RESULTS OF previous research conducted at 

.1. the Federal Aviation Agency's (FAA) Civil Aeromedical Research Institute (CARl) have indicated the 

contribution which aptitude test measures can make in 

the selection of personnel for air traffic control training. 

From the Selection Section, Psychology Branch, Federal 

Aviation Agency, Civil Aeromedical Research Institute, Aeronautical Center, Oklahoma City, Oklahoma. 

Opinions or conclusions contained in this report are those of 

the a)lthor. They are not to be construed as necessarily reflecting 

the views or the endorsement of the Federal Aviation Agency. 

More specifically, it has been shown that aptitude test 

measures can be used to predict 2 s.chool performance in 

centralized basic training courses, and 4 supervisory ratings of on-the-job performance at operational facilities 

one to five years after completion of basic training. 

However, there are two different groups or types of 

air traffic controllers (ATC's) involved in the FAA's 

management of air traffic. The present study represents 

the first major effort toward identifying specific aptitude 

measures that may have potential for further improving 

the prediction of performance criteria for one of the 

two types of trainees, i.e., Terminal (or Tower) trainees. 

Much of the previous research has been .focused upon 

analysis of data for trainees preparing for control work 

at any one of the. 24 continental Air Route Traffic Control Centers. Such controllers are referred to as Enroute 

or Center controllers, while those working in Terminal 

or Airport Tower facilities are designated as Terminal 

controllers. 

Separate basic training courses were formerly provided by the FAA Academy at the Aeronautical Center 

in Oklahoma City for personnel recruited for training 

in the different types of control. However, the Enroute 

course was discontinued in October of 1962 with further 

emphasis being directed toward on-the-job training at 

operational facilities. The Terminal cotirse continued 

to be offered. 

The Academy's two courses are characterized by both 

differences and similarities in regard to instruction and 

objectives. Common to both courses are seven academic 

or study areas. One of the seven pertains to Air Route 

Traffic Control ( ARTC) which receives major emphasis 

in the Enroute Course. Another is that of Terminal Area 

Traffic Control ( T ATC) which receives greater emphasis 

Reprinted from AEROSPACE MEDICINE 

Vol. 35, No. 11, November, 1964. ©Aerospace Medical Association 
PROBLEMS IN AIR TRAFFIC MANAGEMENT-COBB 

in the Terminal course. Instruction and training in 

either course is oriented toward qualification on an Air 

Traffic Control Specialist Certification Examination. The 

examination and qualification requirements are identical 

for trainees of either type. A considerable portion of 

training also consists of laboratory work simulating that 

which each type of trainee will experience after assignment to an operational facility. · 

Until mid-1962, all trainees were selected on the basis 

of medical and job-experience requirements-with attainment of the latter usually resulting from military 

service. No formal assessment of aptitudes by testing 

procedures was involved in the screening of applicants 

for training. 

As part of a research program aimed at improvement 

of selection techniques, administration of an extensive 

experimental aptitude test battery to all Enroute and 

Terminal trainees arriving at the Academy was initiated 

in September of 1960. The objective was to determine 

the specific aptitude areas having the best potential for 

prediction of performance measures. Due to substantially larger class inputs, data in sufficient quantity to 

permit an analysis was first accumulated for the Enroute trainees. Prediction equations, which were developed through iterative multiple regression techniques, and validated on subsequent samples, revealed 

that the best composite for prediction of Enroute training-course performance criteria should include tests of 

numerical ability, space relations, abstract reasoning, air 

traffic problems and figure analogies. These Enroute 

prediction equations were also applied and found to be 

highly valid for Terminal trainees. Results for early 

phases of this research were reported at the 32nd annual meeting of the Aerospace Medical Association. 

In subsequent studies, prediction equations based on 

combinations of aptitude tests found most valid for 

criteria of Enroute samples, have been applied to data 

of Terminal samples with repeated success. However, 

the present study-in contrast to those previously reported-constituted the first major detailed analysis actually based on Terminal-trainee data and aimed at 

determining the specific composite of test measures and 

respective weighting factors most useful for the prediction of performance criteria for Terminal controller 

trainees. 

PROCEDURE 

During the period September 1960 through July 1961, 

an extensive battery of commercially developed· psychological tests, yielding 44 different scores, was administered to 212 incoming students of the Academy's 

Terminal ATC training course. Data regarding age, 

previous job-related experience, and educational background, were systematically obtained and coded for all 

212 cases. Five performance criteria were developed for 

which the number of cases with complete data records 

varied in accordance with the attrition rates for various 

stages of training. Four of the criteria reflected performance for the basic eight-week course. The fifth represented post-training-course job performance at an operational facility. A listing of all aptitude, background, and 

1020 Aerospace Medicine • November 1964 

criterion variables is presented in Table I with descriptions provided for those specific measures which the 

study later revealed as most useful for predictive purposes. 

TABLE I. LISTING OF VARIABLES INCLUDED 

IN THE ANALYSES 

(Descriptions are provided for only those variables which one or more 

of the regressions indicated as being significant in the prediction of 

training-course criteria.) 

Criteria 

A. Academic Grade Average for the Terminal ATC Training Course 

B. Laboratory Grade Average for the Terminal ATC Training Course 

C. Combined Academic-Laboratory Grade Average for. the Terminal 

ATC Course' 

D. Pass-Fail Status for the Terminal ATC Training Course 

E. Average of Supervisors' Ratings for Performance at an Operational 

Facility 

Aptitude Tests 

1. DAT Space Relations: A 45-item test of identifying solid figures 

figures that can be made from unfolding patterns. 

2. DAT Numerical Ability: A 40-item test of arithmetic computational skill. 

3. DAT Abstract Reasoning: A SO-item test wherein the task is to 

indicate which of a series of choices (figures) properly carries 

out a principle of logical development exhibited by a sequence of 

figures. 

4. DAT Language II. 

5. DAT Mechanical Reasoning: For each of 68 items, the student 

studies a drawing depicting the operation of a physical or 

mechanical principle to determine the correct statement describing 

a situational outcome resulting from application of the principle. 

6. Air Traffic Problems, Part I: A 30-item test presenting highly 

simplified version~ of air traffic control situations. Performance 

is not necessarily dependent upon past ATC experience. Given 

a basic 5-minute time separation rule, the student must determine 

whether aircraft may be permitted to change altitude without 

violating the rule. 

7. Air Traffic Problems, Part II. 

Variables 8-19 pertain to twelve subtests of the California Test of Mental 

Maturity (CTMM, Advanced Form A, 1957 edition). 

8. CTMM Immediate Recall. 

9. CTMM Delayed Recall. 

10. CTMM Sensing Right and Left. 

11. CTMM Manipulation of Areas. 

12. CTMM Opposites. 

13. CTMM Similarities. 

14. CTMM Analogies: Seven drawings of different objects are presented for each of 15 items. The first object has a definite relationship to the second which must be recognized in order to 

identify, by analogy, the drawing among the last four which is 

similarly related to the third drawing. 

15. CTMM Inference: For each of 15 items, the task is to comprehend 

printed statements presenting two premises; then select, from 

among four alternatives, the most logical conclusion. 

16. CTMM Number Series. 

17. CTMM Number Quantity, Coins: A 15·item test necessitating 

mental manipulation of interrelated amounts of money and 

number of coins. 

18. CTMM. Number Quantity, Arithmetic: Solving of fifteen word 

problems presenting simple arithmetical and mathematical situa 

tions. 

19. CTMM Verbal Concepts. 

Personality Scales 

Variables 20-37 represent 18 personality scales derived from the 480-iten, 

California Psychological Inventory 

20. CPI-Ac (Achievement via Conformance). 

21. CPI-Ai (Achievement via Independence) : This scale identifies 

those factors of interest and motivation which facilitate achievement in any setting where autonomy and independenc~ are 

positive factors. 

22. CPI-Cm (Communality). 

23. CPI·Cs (Capacity for Status). 

24. CPI-Do (Dominance), 

25. CPI-Fe (Femininity), 

26. CPI-Fx (Flexibility), 

27. CPI-Gi (Good Impression), 

28. CPI-Ie (Intellectual Efficiency), 

29. CPI-Py (Psychological Mindedness). 

30. CPI-Re (Responsibility). 

31. CPI-Sa (Self Assurance). 
PROBLEMS IN AIR TRAFFIC MANAGEMENT-COBB 

32. CPI-Sc (Self Control). 

33. CPI-So (Socialization) : An indication of the degree of social 

maturity, integrity, and rectitude which the individual has 

attained. 

34. CPI-Sp (Social Presence). 

35. CPI-Sy (Sociability). 

36. CPI-To (Tolerance). 

37. CPI-Wb (Sense of Well Being). 

Speeded Tests 

Variables 38-44 represent the highly speeded tests of the Moran Repetitive 

Measurements (RPM) battery. 

38. RPM Aiming. 

39. RPM Flexibility of Closure. 

40. RPM Number Facility. 

41. RPM Perceptual Speed: The task is to detect and cross out all 

numbers in a row that are like a circled number at the beginning 

of each row. 

42. RPM Speed of Closure. 

43. RPM Visualization. 

44. RPM Social Memory : Measures the ability to remember faces or 

photographs. 

Background Variables 

45. Age: Chronological age when tested. 

Variables 46A9 represent data coded on a 9-point scale and variables 50-51 

each represent a summation of similarly derived codes for several elements. 

46. Educational Background: Coded years of formal education. 

47. GCI (Ground Control Intercept) Experience. 

48. Pilot Experience. 

49. Air·to-Ground Radio Operator Experience. 

50. Sum of Air Traffic Experience: Summation of codes for several 

types of previous experience directly related to air traffic control. 

Types included were VFR Tower, Approach Control Tower, 

Radar Approach Control, Center, GCA, and RAPCON. 

51. Sum of Communications Experience: Sum of coded amounts of 

Station Experience, Ground-to·Air, and Point-to-Point communi· 

cations experience. 

Enroute Equation Score 

52. Composite Score Predicted by Previously Developed Enroute ATC 

Equation. 

Psychological Test Variables:-All of the 44 scores 

derived from the test battery were included in the analysis. The battery was heterogeneous, consisting mainly of 

commercially-developed tests providing measures of 

various aptitudes, attitudes, and perceptual abilities. 

Some were highly speeded; others were "power" tests. 

Numerous considerations were involved in the selection 

of tests comprising the battery. Some had been demonstrated by previous research to have predictive potential for ATC trainees; a few were selected to supplement, or substitute for, previously used tests on the 

assumption that they would provide more comprehensive and reliable measures of specific areas, and several 

were included because of their apparent relevance to 

investigation of potential for additional aptitude areas. 

Background Variables:-The background variables included: Age of Trainees; Coded Educational Background, and five variables representing coded amounts 

of varying types of previous job experience. Age was 

simply the chronological age (rounded to nearest birthday) of an individual at the time of test battery administration. Coded Educational background represented a 9-point scaling of educational attainment level. 

For example, a non-graduate of high school was coded 

as 1; a high school graduate as 2, and so forth. Six or 

more years of college was coded as 9. 

Amounts of experience in each of twelve different 

types of related work areas were also coded on a 9-point 

scale. For analysis purposes, however, the number of 

experience variables was reduced from twelve to five by 

development of two summary variables which included 

all but three of the individual types of experience. The 

first summary variable, designated as Sum of Air Traffic 

Experience, represented a total of coded amounts of experience directly related to control of air traffic. The 

second, named Sum of Communications, represented a 

summation of codes for communications experience not 

directly related to air traffic control. Previous research 

had indicated appropriateness of such grouping procedures. Codes for Ground Control Intercept ( GCI), 

Pilot Experience, and Air-to-Ground Radio Operator 

Experience constituted the three remaining and nonsummary experience variables included for analysis. 

Criterion Variables:-The four training-course performance variables were: Academic Grade Average; 

Laboratory Grade Average; the Combined AcademicLaboratory Grade Average, and Pass-Fail status. All 

were derived from data entered by training school personnel on each student's final "Evaluation of Performance" record. 

The Academic Grade Average was based on a summation of all achievement and examination grades attained at various training stages for seven different academic study areas; the Laboratory Grade Average represented the final performance grade assigned each individual for laboratory-simulated air traffic control work, 

and the Combined Academic-Laboratory Grade Average was simply an arithmetic mean of the two averages. All such criterion scores were rounded to overcome difficulties of dealing with fractional values. 

For the Pass-Fail criterion, all students successfully 

completing the training course were considered as pass 

cases; those unsuccessful, as fails. Satisfactory completion of the course and A TCS certification demanded 

ultimate attainment of a satisfactory grade at every 

training stage for every study area. At any stage of 

training, failing grades of less than 70 in three or more 

of the seven subject areas or in laboratory work, generally resulted in automatic elimination from training. 

Students having failing grades in no more than two of 

the subjects were normally permitted a retake of pertinent examinations in order to avoid elimination. It 

should be pointed out that a few of the fail cases included in the present study actually have higher grade 

averages than do some of the passes. This is due to the 

fact that they might have done quite well in some of the 

subjects other than those which they failed, whereas a 

few of the pass students were able to attain only minimal grades in all subjects. Previous studies have shown 

that the failure rate for the Terminal course is generally 

less than ten per cent. From a research standpoint, the 

low failure rate contributes toward additional significance of the grade averages as performance criteria. 

The Average Supervisory Rating represented a mean 

of all performance evaluations submitted by work supervisors of an individual at an operational facility. The 

use of a special rating form permitted each supervisor 

to rate a controller, on a five-point scale, for each of a 

series of work-related and proficiency items." For the 

present study, these element ratings were coded on a 

five-point scale, and a weighted average was computed 

to represent the overall rating by each supervisor. In 

most instances, an individual was rated by more than 

Aerolf{Jace Medicine • November 1964 1021 
PROBLEMS IN AIR TRAFFIC MANAGEMENT-COBB 

one supervisor and the assigned criterion value represents an average of all such ratings rendered. Unfortunately, the usefulness of this criterion in the present 

study may be questioned because of probable biasing 

factors relevant to data collection. More specifically, 

several individuals who had been administered the 

aptitude tests and whose records were included in other 

phases of the study;had left the FAA before the facilities received a request for performance evaluations. 

Ratings, made in retrospect, were submitted for only a 

portion of such cases. The data obtained for these individuals were excluded from the study on the presumption that mere acknowledgement of their separated 

status might have tended to evoke artificially lower and 

more critically-reflective ratings from supervisors. On 

the other hand, the non-availability or exclusion of data 

for the employment-terminated cases forces a consideration of probable attenuation effects and severely limits 

the definitiveness with which results of the present 

study may be interpreted. 

Samples:-A total of 212 incoming students for the 

Terminal course were administered the battery of aptitude tests during the period of September 1960 through 

July 1961. Two samples of comparable .size were established. The first, based on 99 pass and 7 fail cases of 

eight classes tested during the earlier portion of the time 

period, was designated as the Experimental Sample. 

These data were scheduled for analyses aimed at development of prediction equations for the various criteria. Data for the remaining 96 pass and 10 fail cases 

constituted the Validation Sample on which to test the 

predictive efficiency of the equations. 

Regression Analyses:-After deletion of 5 cases which 

had incomplete data records, two matrices of intercorrelations were computed for the Experimental Sample. 

The first matrix was based on combined data for 95 

pass and 6 fail cases and included intercorrelations of 

all psychological, background, experience, and criterion 

variables. An additional variable was included for pl,ll'-

poses of comparison and reference only. It represented 

the composite scores derived through application of an 

equation which previous research had shown to be most 

predictive of criteria for trainees of Enroute ATC 

classes. 

The second matrix was based on data of only the 95 

pass cases of the Experimental Sample and excluded the 

Pass-Fail criterion variable. The correlations within 

each matrix, except those involving the Supervisory Rating criterion, were based on a constant or common N 

of cases. 

Using these data and following an iterative multiple 

regression technique developed by Greenberger and 

Ward 3 and Botten berg, 1 the best combinations of predictors for the performance criteria were selected. Inclusion of variables in a predictor equation was terminated when no further significant increase in the magnitude of the multiple correlation could be obtained. 

For each criterion, efforts were first focused on development of a series of equations wherein selection of 

variables for the predictor composite was successively 

restricted to a consideration of different groupings of 

only the psychological measures. It should be pointed 

out that: 19 of the 44 test measures reflected performance on rather general and typical aptitude tests, 18 

TABLE II. TRAINING-COURSE-CRITERIA VALIDITY COEFFICIENTS FOR EACH 

VARIABLE INCLUDED AS A PREDICTOR IN ONE OR MORE REGRESSION EQUATION 

Experimental Sample t Validation Sample t 

90 74 

For Combined 95 Pass Passes For Combined 96 Pass Passes 

Variables Significant in One and 6 Fail Cases Only and 10 Fail Cases Only 

Or More Prediction Equation Acad. Lab. A-L. Supv. A cad. Lab. A-L. Supv. 

Aptitude: 

DAT Spatial Ability .19 .10 .16 .03 .33 .07 .17 .02 

DAT Number Ability .37 .12 .31 .06 .51 .17 .37 .19 

DAT Abstract Reasoning· .34 .30 .38 -.14 .53 .34 .45 .15 

DAT Mechanical Reasoning .18 .00 .13 -.19 .19 -.23 .01 .06 

Air Traffic Problems I .40 .29 .42 .00 .38 .28 .35 .13 

CTMM-Analogies .30 .03 .22 -.04 .18 .04 .10 .06 

CTMM-Inference .43 .32 .44 .11 .21 .30 .20 .06 

CTMM-Number Quantity, Coins .34 .39 .42 .06 .38 .17 .28 -.02 

CTMM-Number Quantity, Arith. .34 .20 .35 .19 .52 .26 .43 .25 

Personality: 

CPI-Ai, Achievement Ind. .26 .09 .21 .08 .18 -.07 .09 .12 

CPI,So., Socialization , .46 .25 .43 .31 -.02 .17 .06 -.07 

Speed Tests: 

RPM-PS, Perceptual Spd. .18 .26 .26 -.01 .10 .10 .05 .26 

RPM-SM, Social Memory -.03 .10 .02 -.20 .17 .07 .14 -.22 

Background : 

Sum of Air Traffic Exper. .01 .28 .14 -.12 .19 .38 .26 .16 

Intercorrelations of Criteria 

Acad. : Academic Grade A vg. -45 .87 .44 .49 .76 .23 

Lab.: Laboratory Grade Avg. .45 .82 .32 .49 .89 .12 

A-L; Acad.-Lab. Grade Avg. .87 .82 -46 .76 .89 .22 

Supv.; Supervisor's Rating Avg. .44 .32 .46 .23 .12 .22 

t For the experimental sample, all supervisory·rating correlations (r's) are based on a common N of 90 cases 

and those involving other criteria on a common N of 101. In contrast to the common·N procedure~ every r 

for the validation sample is based on the maximum N of cases with data available for each pair of variables. 

For r's involving supervisory ratings, the N was or approached 74; for other criteria, the N Usually was 106 

or approached 106. The minimum r's for the respective significance levels of .OS and .01 are: ~ .20 and ~ .27 

for N = 90; :=:.23 and~ .30 for N = 74; and ~ .19 and ~ .25 for N = 101 or N = 106. 

1022 Aerospace Medicine • November 1964 
PROBLEMS IN AIR TRAFFIC MANAGEMENT-COBB 

were actually personality scales, and 7 represented 

scores on very highly-speeded perceptual-type tests. 

Data for the respective background and experience 

variables were also quite distinctive. Therefore, additional series of regressions, representing successive considerations of age, education, and previous-job-experience measures, were accomplished to determine the 

supplemental predictive contribution of these variables 

when used in combination with various groupings of 

psychological test measures. 

A total of 38 different regressions were accomplished 

in only the initial phase of the study and 18 additional, 

but simplified, equations were developed in subsequent 

phases. Validity coefficients for the Experimental 

Sample are presented in the first portion of Table II for 

each variable that emerged as a significant predictor in 

one or more of the 56 equations. Intercorrelations for 

four of the five criteria are also shown. (The latter portion of the table presents similar data obtained for the 

Validation Sample during later phases of the study.) 

The ultimate objective of these extensive analyses 

was determination of the most practical and valid equation(s) for prediction of the various criteria of Terminal-Trainee performance. Within the limitations of this 

report, presentation and comparison of data for all 56 

equations is not feasible. However, several aspects concerning their general nature will be described in conjunction with the subsequent summary of developmental and predictor-composite selection procedures. 

As a first step toward selection of the most useful prediction equation, 7 of the initial 38 regressions were discarded at the outset. The 7 were for the pass-fail 

criterion and represented analyses based on intercorrelations of the first matrix computed for the Experimental 

Sample. Inasmuch as the data of only 6 fail cases were 

included in the total of 101 cases upon which all correlations of the matrix were based, the 7 pass-fail 

equations were considered as unreliable and therefore 

deleted. 

The remaining 31 regressions were carefully reviewed · 

to ascertain similarities and differences in data for those 

exhibiting the best predictive potential. After a comparison of the multiple correlations, the variables of 

each predictor composite, and the beta weights of the 

respective variables, a total of 14 regression equations 

were selected for retention and further study. The conventional b-weights for these initially retained regression equations were next computed in order that they 

might be applied to the data of the Validation Sample. 

The b-weight for each variable of a predictor composite 

represented a conversion of the beta in accordance with 

the proportional relationship of the standard deviation 

(S.D.) of the predictor variable to that of the criterion 

variable. 

Development of Simplified Prediction Equations:-

Before initiating validation procedures, two additional 

groups of equations were developed. The first such 

group consisted of 14 equations designed to test the 

assumption that no appreciable loss in predictive accuracy would result through use of more simplified 

weights substituted for the conventional and more complex system of b-weights. In order to establish integer 

weights for these 14 equations, two sets of independently-derived single-digit weights were first computed. 

Based on a consideration of data for variables of each 

predictor composite, one of the digital weights was 

grossly proportionate to the inverse of a predictor variable's standard deviation and the other was grossly proportionate to its conventionally established b-weight. 

Except for rounding procedures to avoid fractions, the 

final assigned integer weight for each variable included 

in any of the 14 equations actually represented an average of the two independently-derived digital weights. 

These equations, were numbered as 1A through 14A, 

with 1A corresponding to previous regression 1; 2A 

with 2, and so forth. 

A final group of 4 equations, representing even further 

simplification, was developed. These were designated 

as "Summarily-Derived" equations because of the method 

underlying their development. No variables were included in the predictor composites except those which 

preceding equations had indicated to be most potentially useful. The weights for the variables of each predictor composite were derived under an arbitrarily 

imposed restriction that no weight would exceed the 

magnitude of 4. The b-weights and integer weights for 

the most significant of the previous equations were first 

converted, on a grossly proportionate basis, to weights 

within the restricted range of 1 through 4. The procedure resulted in establishment of two new sets of 

weights for each composite. After comparing all sets, 

weights were adjusted to establish four new "Summarily-Derived" equations, numbered as 15, 16, 17 and 

18, which will be presented and discussed in subsequent portions of this report. 

Application and Validation:-For purposes of validation, the 14 initially retained regression equations, the 

14 corresponding and simplified equations, and the 4 

summarily-derived equations were all applied to data 

representing the second sample of Terminal cases. For 

purposes of permitting a more reliable and comparative 

assessment of the contrasting weighting techniques, the 

equations were also applied to data of the Experimental 

Sample. To facilitate further comparison, and to determine the efficacy of each equation in predicting 

criteria other than that for which it was specifically designed, all predicted values were correlated with the 

actual data of every criterion variable. 

RESULTS 

The results indicated that no equation was valid for 

the prediction of Average Supervisory Rating. As previously explained, this was the criterion variable for 

which the data was suspect of being attenuated. For 

the remaining criteria, all equations 'validated when 

applied to the data of the second sample. In addition to 

the criterion for which designed, each equation also 

exhibited relatively good potential for the prediction of 

multiple criteria. Excluding Supervisory Rating, all 

validities and correlations were statistically significant 

at the .01 level. 

Prediction results for the 14 simplified equations 

were highly comparable to those obtained for the corAerospace Medicine • November 1964 1023 
PROBLEMS IN AIR TRAFFIC MANAGEMENT-COBB 

responding and more complex b-weighted regression 

equations. In many instances, the corresponding correlations were identical and there were no differences exceeding .04, thus supporting the contention that no 

appreciable sacrifice in predictive accuracy would result through use of the more practical and simplified 

equations. 

Of the 14 simplified equations, the best potential for 

the prediction of multiple criteria was exhibited by three 

of the four equations originally formulated for the prediction of the Academic Grade Average. These equations, numbered as 2A, 3A and 4A, constituted the first 

of two groups retained for further comparative study in 

determination of the most practical valid equation. As 

further evidence regarding the efficacy of simplified 

weighting techniques, the results stemming from application of the four summarily-derived equations also 

warranted retention and consideration of this entire 

group for the selection of the best equation ( s) . 

Comparison of Seven Equations:-The data of Table 

III permit a comparison of equations 2A, 3A and 4A 

with the four summarily-derived equations in terms of 

predictor-variable weights, validities, and other correlations. Data for the Enroute equation are also presented. 

In reviewing the contents of the seven Terminal equations, the predominance of four predictor variables 

should first be noted. Six of the predictor composites 

include the CTMM-Inference test and all seven equations include the tests of Air Traffic Problems, CTMMNumerical Quantity-Coins and CTMM-NumericalQuantity-Arithmetic. These four tests apparently constitute a common "core," providing measures of those 

aptitudes most significantly related to Terminal-trainee 

performance. 

Equation 2A has the most distinctive and complex 

composite. Even though it includes all of the "core" 

tests except Inference, it is the only retained equation 

embodying two or more other variables. It includes: the 

two CPI personality scales of Achievement and Socialization; the highly speeded RPM Perceptual Test, and 

the Sum of ATC Experience. 

TABLE III: COMPARISON OF THE THREE BEST PREVIOUSLY DEVELOPED EQUATIONS 

WITH THE FOUR EQUATIONS BASED ON SUMMARILY-DERIVED COMPROMISE WEIGHTS 

Predictor Vars. 

Aptitude: 

DAT Spatial 

DAT Number 

DAT Abst. Reas. 

Air Traf. Prbs. I 

CTMM-Analogies 

CTMM-Inference 

CTMM-NQ-Coins 

CTMM-NQ-Arith. 

Personality: 

CPI-Ai, Achiev. 

CPI-So, Social. 

Speed Tests: 

RPM-PS, Perc. Spd. 

Background : 

Sum of ATC Exp. 

Criterion V ars 

Academic Grd. 

Laboratory Grd. 

Acad.-Lab. Grd. 

Pass-Fail 

Avg. Supv. Ratg. 

Academic Grd. 

Laboratory Grd. 

Acad.-Lab. Grd. 

Pass-Fail 

Avg. Supv. Ratg. 

Academic Grd. 

Laboratory Grd. 

Acad.-Lab. Grd. 

.flass-Fail 

Avg. Supv. Ratg. 

Digital Factor Weights Of Predictor Variables 1/ SD 

Three Most Practical 

Of Previously 

Developed Equations 

2A 

1 

4 

2 

3A 4A 

4 

Four Additional & Newly Wts. of 

Developed Equations With Original 

Summarily~Derived Weights t Enroute 

15 16 17 18 Equation 

2 

3 

2 

4 

2 

3 

4 

2 

.06 

.13 

.14 

.18 

.49 

Experi. Sample: Correlation Of Predicted V s. Actual Values :t: 

.63 .54 .53 .53 .48 .63 .63 .46 

.48 --:36 A3 .39 .48 .43 .44 .23 

.65 .53 .57 .55 .57 .63 .64 .42 

.48 .39 .42 .42 .40 .50 .52 .42 

.21 .12 .09 .10 .08 .22 .22 -.01 

Valida. Sample: Correlation Of Predicted V s. Actual Values t 

.42 .51 .51 .52 .50 .46 .44 .53 

T4 T6 --:42 .36 .45 .39 .37 .25 .37 .44 .45 .44 .46 .42 .41 .39 

.31 .28 .31 .29 .32 .35 .35 .27 

.29 .19 .21 .17 .22 .14 .12 .16 

Combined Samples : Correlation Of Predicted V s. Actual Values :t: 

.51 .52 .52 .52 .49 .53 .52 .49 

-:37 --:35 AI .36 .46 .39 ,38 .23 

.44 .45 .47 .45 .48 .46 .46 .38 

.37 .32 .36 .34 .36 .41 .42 .33 

.27 .17 .16 .15 .15 .19 .18 .09 

t Weights were assigned variables of the four additional equations, 15-18u under an arbitrarily imposed 

restriction that none would exceed 4, yet be somewhat proportionately related to the most effective of 

previously used sets of weights. 

*All r's are based on maximum N's. Most r's for Supv. Rating are based on N's of 90 for Exp., ·74 for Val., 

& 164 for combined, with most r's for other criteria based on 106, 106, and 212. The minimum r's for the 

respective significance levels of .05 and .01 are: For N == 90, ==== .20 & ~ .27; N = 74, ~ .23 & ==== .30; N = 

164, ~ .16 & ~ .21; N == 106, ~ .19 & ~ .25, and N= 212, ::=: .13 & ~ .18. 

1024 Aerospace Medicine • November 1964 
PROBLEMS IN AIR TRAFFIC MANAGEMENT -COBB 

The four "core" tests are included, but weighted differently, in the six remaining equations. These four tests 

alone comprise the predictor composites for equations 

3A and 15; are supplemented only by the CPI-Socialization scale to yield composites for 17 and 18, and are 

supplemented only by Sum of ATC Experience to form 

the predictor groups of 4A and 16. 

The correlations of actual criterion values with predicted composite scores (are shown in the lower portion of Table III and) reflect the highly comparable 

degree with which each of the equations may be used 

to forecast multiple criteria. However, a comparative 

study reveals certain differences relative to selection of 

the most practical equation. 

When correlations involving Supervisory Rating 

Average are excluded, the data indicate slightly superior predictive efficiency for equations 3A, 4A, 15 and 

16. All of these equations are remarkably similar in 

several respects. Based on a comparison of all arrays of 

correlations for the Validation Sample with those of the 

Experimental Sample, the differences are smaller for 

these equations than for the remaining four. This exhibition of less shrinkage in correlation .from sample to 

sample is indicative of greater consistency-of-measurement for these four equations. It should also be noted 

that, even though different ·patterns of weights are 

utilized, the predictor composite of 3A corresponds to 

that of 15 and the composite of 4A. matches that of 16. 

However, the summarily-derived weights for equations 

15 and 16 yield slightly better validation results. 

Final Selection of Two Terminal Eq~ations:-Equations 15 and 16 may therefore be designated as the most 

useful of all Terminal equations for the prediction of 

multiple criteria. Equation 15 is based on scores of only 

the four "core" tests. Except for the inclusion and 

weighting of ATC Experience, equation 16 is identical 

to number 15 in every respect and the two equations 

yield highly comparable results for the Validation 

Sample. (see Table III.) Composite scores based on the 

four test variables of equation 15 correlate slightly 

higher with Academic Grade than do the scores predicted by number 16. For the remaining criteria, the 

correlations favor the latter. However, the only appreciable contribution of ATC Experience as a supplementary variable appears to be in the prediction of the 

Laboratory Grade Average. 

Differences Between Terminal and Enroute Equations:-Even though a comparison of all corresponding 

data indicates the superior effectiveness with which the 

four "core" tests of the Terminal composite may be used 

for the prediction of multiple criteria, the correlations 

based on application of the Enroute equation are very 

substantial and, in some instances, approximate those 

obtained with the specially developed Terminal equations. In view of previous research, wherein the predictive efficiency of the Enroute equation has been 

repeatedly demonstrated for Terminal classes, its validation on the present samples was anticipated. The 

emergence and validation of a predictor composite so 

contrastingly different from that of the Enroute equation had not been anticipated. With exception of the 

Air Traffic Problems Test, the predictor variables for 

the two types of equations are indeed different. Despite 

these apparent differences, however, a recent unpublished analytic study has illustrated the high degree of 

similarity between the two groups of tests in terms of 

factorial content. 

A non-verbal abstract reasoning, or induction factor, 

and number facility are heavily represented in the measures provided by either composite. Factorially, the 

Abstract Reasoning and Number Ability tests of the 

Enroute composite are characterized by less complexity 

and, as their names might imply, exemplify these two 

factors. In contrast, all tests selected for the Terminal 

composite are factorially more complex and, in addition to the two factors most pertinent to Enroute prediction, embody a third factor which is presumably 

responsible for its superior predictive efficiency for 

Terminal trainees. This additional factor seems to be a 

syllogistic reasoning, or secondary induction, factor inTABLE IV. COMPARATIVE PREDICTIVE EFFICIENCY OF EQUATIONS 15 AND 16 

AND THE PREVIOUSLY DEVELOPED ENROUTE EQUATIONTerminal Equation 15 

Based On Scores Of Only 

Four Aptitude Tests 

.Terminal Equation 16 

Based On Four Aptitude Tests 

And .Sum Of ATC Experience 

Experimental Sample 

Enroute Equation 

Based On Five Aptitude Tests 

Selected By Previous Research 

Per Cent of Students In Each Approximate Fourth Of The Predicted-Score Distribution Who Failed,· Marginally Passed, or Non-Marginally Passed The 

Terminal AT C Training Course 

Score % % % Score % % % Score. % % % 

Range Fail Marg. Pass N Range Fail Marg. Pass N Range Fail Marg. Pass N 

""'182 11 89 27 ""'175 4 96 26 ""'1895 11 89 27 

115-131 4 20 79 25 156-174 4 24 72 25 1695-1894 16 80 25 

98-114 4 24 72 25 139-155 26 74 27 1415-1694 8 32 60 25 

=:: 97 18 30 52 27 ""'138 23 31 46 26 "'1414 15 26 59 27 

Validation Sample 

Per Cent Of Students In Each Score Range Grouping (Based On The Experimental Sample) Who Failed, Marginally Passed, or Non-marginally Passed 

The Terminal ATC Training Course 

Score % % % Score % % % Score % % % 

Range Fail Marg. Pass N Range Fail Marg. Pass N Range Fail Marg. Pass N 

""'132 3 9 88 34 ""'175 4 18 78 28 ""'1895 3 17 80 40 

115-131 6 28 66 32 156-174 4 25 71 24 1695-1894 5 21 74 19 

98-114 8 23 69 13 139-155 11 89 18 1415-1694 18 9 73 22 

"'97 23 35 42 26 "'138 23 31 46 35 "'1414 17 46 37 24 

Aerospace Medicine • November 1964 1025 
PROBLEMS IN AIR TRAFFIC MANAGEMENT-COBB 

volving verbal abstract reasoning. It is most highly represented in .the CTMM-Inference Test. The question of 

why verbal abstract reasoning is of more importance 

in Terminal training performance than for Enroute performance remains a topic for additional research. 

Comparison of Equations in Terms of Predictive Effectiveness:-The data of Table IV illustrate the effectiveness with which the composite scores of Terminal 

equations 15 and 16, and of the Enroute equation, may 

be used to forecast performance in the Terminal ATC 

Training Course. Based on data of the Experimental 

Sample, cutting scores (or score range groupings) were 

established to represent the upper, two intermediate, 

and lower fourths of the distribution of composite 

scores derived by each equation. For each quarter of 

the composite score distribution, the percentages of 

failing, "marginal," and "non-marginal" passing cases 

are shown. "Marginal" trainees are those who passed 

the course but with grades so low that they constitute 

the bottom 23 per cent of the distribution of Combined 

Academic-Laboratory Grade Averages after exclusion of 

training-course failures. For validation of each equation, the cutting scores (established on the Experimental 

Sample) were imposed, without modification, on the 

distribution of composite scores computed for cases of 

the second sample. Results for this Validation Sample, 

which are presented in the lower portion of Table IV, 

illustrate the effectiveness with which these score range 

groupings may be used to forecast actual performance 

for the training course. 

A comparison of results based on this particular type 

of validation procedure indicates slightly superior potential for equation 15 over 16, and that both Terminal 

equations are better than the Enroute equation for 

forecasting purposes. Of the trainees having the higher 

scores predicted by the four-test composite of equation 

15, only 3 per cent were fails and only 9 per cent were 

"marginals." The remaining 88 per cent were passes. 

In contrast, 23 per cent of the cases comprising the 

lowest category of aptitude scores were failures; 35 per 

cent were "marginals," and only 42 per cent were "nonmarginal" passes. In other words, only 12 per cent of 

the trainees represented in the top category failed to 

attain satisfactory "non-marginal" status for the training course, whereas 58 per cent of those in the lowest 

aptitude index grouping were unable to do so. 

The three graphs of Figure 1, which are based on a 

second and more liberal validation procedure, permit 

a further comparison of the equations in terms of performance-forecasting efficiency. By this method, the 

cutting scores for each equation were established by 

simply dividing the predicted score distribution. of the 

Validation Sample into approximate fourths, independently of Experimental Sample data. The prediction results shown in the top graph are based on the composite 

of four aptitude tests and the ATC Experience variable 

used in equation 16; the next indicates the highly comparable effectiveness of equation 15, which excluded 

ATC Experience, and the lower graph presents results 

obtained with the Enroute equation. The correlation of 

II FAIL ~MARGINAL D PASS 

SCORE RANGE BY FOURTHS 

~ 176 

156- 175 

134 ._ 155 

:5 133 

SCORE RANGE BY FOURTHS 

!: 136 

120 - 135 

98- 119 

:5 97 

SCORE RANGE BY FOURTHS 

~ 2008 

1776 - 2007 

1450- 1775 

s 1449 

.. ~~~~~~P~R~ED~I~CT~IO~N~B~Y~F~O~U~R~A~P~TI~TU~D~E~T~E~ST~S~A~N~D~S=UM~O=F-P~R~E~V~IO~U~S~AT~C~E~X~P~ER~I~EN~C~E-----------------, ~ 

~~~~~~~~~~~------------8-~~------------------------------------~27 

~ 

27 

27 

25 

26 

Fig. 1. Comparative Effectiveness of the three equations in predicting Terminal ATC course pertormance for cases of the second 

sample only. 

1026 Aero~ace Medicine • November 1964 
PROBLEMS IN AIR TRAFFIC MANAGEMENT-COBB 

each equation's predicted scores versus the Combined 

Academic-Laboratory Grade Average for this sample 

may be found in Ta,ble III. They were: .46 for equation 

16; .44 for numberi15, and .39 for the Enroute. A comparison of these correlations and of the graphically illustrated results in Figure 1 (for this latter and more 

liberal type of validation procedure), indicates that the 

five tests of the Enroute equation are somewhat less 

effective than the four "core" tests embodied in the two 

Terminal equations. The graphically presented data 

also illustrate that the inclusion of specific types of experience, such as represented in the ATC Experience 

variable of equation 16, contribute toward a slight improvement over the prediction accuracy achieved with 

only the four "core" tests of equation 15. 

DISCUSSION AND CONCLUSIONS 

The four aptitude tests, which the analyses have 

identified as most predictive of Terminal ATC Training Course performance measures, are different from, 

and are factorially more complex than, the five tests 

which previous research demonstrated as most predictive of Enroute training. The primary difference in test 

content appears to be the presence of a factor related 

to verbal abstract reasoning in the composite of tests 

selected for the Terminal trainees. 

At the present time a battery of Civil Service Commission (esc) tests is being used operationally to 

select applicants for all types of air traffic control specialist training. It is known that this group of tests 

incorporates those aptitude areas previously found to be 

of importance for Enroute trainees and it has also been 

shown that composite scores based on the battery are 

highly valid for the prediction of performance for 

Terminal trainees. 2 • 5• 7 However, the degree to which 

the additional aptitude area of verbal abstract reasoning is represented in the operational screening battery 

and whether or not it is of sufficient significance in discriminating between Enroute and Terminal trainees to 

warrant a possible change in selection standards are 

questions which would require additional research and 

a study of monetary feasibility. 

For the present study, it should be emphasized that 

the predictor composites and their indices of predictive 

potential have been derived on performance data' of 

trainees who had been recruited under former esc 

selection standards. Qualification and selection was 

based solely on medical and pre-employment experience 

requirements, with the latter including a consideration 

of not only those types of experience directly related to 

air traffic control, but also of various types of communications work and of pilot experience. In this sense, 

the samples represent a pre-screened input of trainees 

and thus contribute toward additional significance of 

predictive results obtained with composite scores based 

only on aptitude measures. 

However, these screening procedures have also 

tended to prohibit a true evaluation of the Sum of ATC 

Experience variable. In view of the fact that the present analyses were based on data of only those applicants who qualified for training on the basis of previous 

experience, and inasmuch as the Sum of ATC Experience variable incorporated a major portion of those 

types and amounts of experience assessed for qualification purposes, the data for this variable are undoubtedly 

attenuated. The correlations and other indices are probably of lesser magnitude than would have been obtained with non-restricted samples. In other words, if 

the preceding hypotheses are correct, the results obtained in the present study are not indicative of the 

true value with which ATC Experience could be used 

to predict performance measures for a non-screened 

trainee input. 

The contribution of A TC Experience in the prediction of training course performance is recognized in the 

newly .established CSC standards for selection of ATCS 

trainees. Under the new standards, although everyone 

applying for training must achieve a qualifying score 

on the test battery, that score which is considered qualifying is lower for those applicants having this type of 

ATC experience. 

REFERENCES 

l. BoTTENBERG, R. A. : An iterative multiple regression procedure using the IBM 650. WCLL-TM-60-3, Personnel 

Laboratory, WADD, USAF, Lackland, AFB, Texas, 1959. 

2. CoBB, B. B. : Problems in air traffic management: II. Prediction of success in air traffic controller school. Aerospace Med., 33:702, 1962. 

3. GREENBERGER, M. H., and WARD, J. H., JR.: An iterative 

technique for multiple correlation analysis. IBM Tech. 

Newsletter No. 12, International Business Machines Corporation, New York, N. Y., 1956. 

4. TRITES, D. K.: Problems in air traffic management: I. Longitudinal prediction of effectiveness of air traffic controllers. 

Aeros,Jace Med., 32:1112, 1961. 

5. TRITES, D . .K.: Ground Support Personnel, Aerospace Med., 

34:359, ' 1963. 

6. TruTEs, D. K., and CoBB, B. B.: Problems in air traffic management: III. Implications of age for training and job 

pedormanqe of air traffic controllers. Aerospace Med., 35: 

336, 1964.1

' I 

7. TRITES, D. K., and CoBB, B. B.: CARl research on air traffic 

control specialists: age, aptitude, and experience as predictors of pedormance. A non-technical report, Civil 

Aeromedical Research Institute, FAA, Oklahoma City, 

Oklalroma, 1964. 

Aerospace Medicine • November 1964 1027