Content uploaded by Markus Sommer

Author content

All content in this area was uploaded by Markus Sommer

Content may be subject to copyright.

VALIDATION OF THE DUTCH AIRFORCE TEST BATTERY USING

ARTIFICIAL NEURAL NETWORKS

Markus Sommer, Joachim Häusler, Koning, A. J. and Martin Arendasy

Dr. G. Schuhfried GmbH

Hyrtlstr. 45

2340 Mödling

Austria

sommer@schuhfried.at

THEORETICAL INTRODUCTION

The main selection criteria for individual tests and test batteries used to select military

pilot applicants are the construct and criterion validity, the overall cost of testing and the time

requirements. Naturally, the derivation of decisions from a test battery requires a sufficiently high

correlation between the tests and the criterion variable. However, recent metaanalysis (cf. Burke,

Hobson & Linsky, 1997; Hunter & Burke, 1994; Martinussen, 1996) indicates that the correlation

coefficients between a single test and the criterion measure do not exceed an absolute value of

.30. There are a variety of causes for this, ranging from a lower reliability of the criterion or

predictor variables (Lienert & Raatz, 1998; Goeters, 1998), an attenuation of the variance in the

predictor variables due to selection (Lienert & Raatz, 1998; Goeters, 1998) to the lack of

symmetry between the generality of the predictor variables and the generality of the criterion

variable. With regard to the later cause Wittmann and Süß (1997), Ajzen (1987) and Ree and

Carretta (1996) pointed out that for more general and global criteria such as successful

performance in a flight-simulator or an educational program, aggregate measures such as general

ability (“g”) are better suited for prediction than more specific predictors. Thus one way to handle

this problem is to combine the available information about an applicant to generate a prediction

about his success. In general, one can resort to various methods of statistical judgment formation

in order to do so. But classical methods of statistical judgment formation such as discriminant

analysis or regression analysis are vulnerable to violations of their statistical assumptions and

often lack stability in cross-validation in practical applications (cf. Bortz, 1999; Brown &

Wickers, 2000). A promising alternative is the use of artificial neural networks. This statistical

method has few requirements with respect to data characteristics and has proven to be a robust

procedure for pattern recognition tasks (Bishop, 1995; Kinnebrock, 1992; Mielke, 2001; Rojas,

2000; Warner & Misra, 1996). In a previous study Griffin (1998) evaluated artificial neural

networks with regard to their ability to predict naval aviator flight grades in their primary phase

of flight training using a test battery which primarily consisted of psychomotor tests. Griffin’s

results indicated that artificial neural networks resulted in a higher validity coefficient compared

to the multiple linear regression analysis. However the difference did not reach statistical

significance. In line with the current literature on neural networks (Bishop, 1995), the author

attributed this result to the lack of non-linear relations between the chosen predictor variables and

the criterion variable. Based on this result the aim of the present study is to compare linear

discriminant analysis and a neural network with respect to classification rate and generalizability.

METHOD

The first stage of the selection procedure for pilot applicants involved the use of

psychological tests. The tests for the psychological dimensions mentioned in the JAR-FCL3 were

selected based on their theoretical foundation and construct validity. The test battery consisted of

the following subtests taken from the Intelligence Structure Battery S2 (INSBAT: Arendasy,

Hornke, Sommer, Häusler, Wagner-Menghin, Gittler, Bognar & Wenzl, 2005): Numerical-

inductive reasoning (NID), Figural-inductive reasoning (FID), Arithmetical competence (AK),

Computational estimation (ASF), Numerical flexibility (NF), Inspection time (BZ) and Decision

quality and speed (EF). The first two subtests measure different aspects of the second stratum

factor fluide intelligence (Gf), while the third, fourth and fifth subtests assess individual

differences in different facets of the stratum two factor quantitative reasoning (Gq). The subtests

Inspection time (BZ) and Decision quality and speed (EF) measure the aspects of decision speed

(Gds). Furthermore, the Adaptive Three-Dimensional Cube Test S2 (A3DW: Gittler, 1998) was

used to measure spatial rotation, while Cognitrone S4 (COG: Wagner & Karner, 2003) was used

for measuring selective attention. In order to measure perceptual speed the Tachistoscopic Traffic

Perception Test S1 (TAVT: Biehl, 2002) was also administered.

In the case of the INSBAT subtests as well as A3DW and TAVT the person parameters in

accordance with the Rasch model (Rasch, 1980) obtained by the respondents in the respective test

were included in the analysis. In the case of COG the main variables “sum of correct reactions”

was used as the predictor variable.

A second selection phase involved global assessments of the subjects’ performance in a

standardized flight simulator. The global assessment of the trainees’ performance in the simulator

served as a criterion variable. On the basis of the global assessment of their performance in the

flight simulator the respondents were subdivided into a group of successful and not successful

military pilot applicants.

Sample

The sample encompasses 150 pilot applicants for the Dutch Airforce. The complete data

of 99 pilot applicants are provided. The remaining pilot applicants did not complete the entire test

battery and were thus excluded in the multivariate analysis. The remaining sample consists of 98

(99.00%) male pilot applicants and one (1.00%) female pilot applicant. All the candidates are

between 16 and 25 years of age, with a mean age of 18.84 years and a standard deviation of 2.04

years. A total of 61 (61.6%) military pilot applicants received a negative global evaluation of

their performance in the standardized flight simulator.

.

RESULTS

Results obtained with non-linear methods:

The artificial neural network was calculated using the program NN Predict (Häusler,

2004). The type of network used consisted of a multi-layer perceptron with one functional

intermediate layer and full feed-forward connection. As a transformation function the activation

function Softmax was used, which represents in essence a "multiple logistical" function, the

result of which can be interpreted as a posteriori probability. According to Bridle (1990), this

activation function is particularly suitable for use with categorical criterion variables. QuickProp

(Fahlmann, 1988) was used as the learning algorithm. The number of iterations was 5000.

Following a suggestion of Häusler and Sommer (2006), the number of predictor variables and

intermediate layer elements was determined by comparing different network architectures with

varying numbers of intermediate layer elements on the basis of their adjusted validity coefficient

(adj. R²) and economy (BIC). The results are provided in table 1.

Table1: Predictor variables, number of hidden layer units, BIC and adj. R² for different artificial neural network

architectures.

Predictor variables

hidden layer elements

BIC

adj R²

NID, AK, BZ, EF, FID

3

348.5

.462

NID, ASF, AK, EF, NF, A3DW

2

351.7

.256

NID, AK, BZ, EF, FID, NF, TAVT

3

345.22

.599

As can be seen in table 1 the total optimization resulted in an optimum number of three

hidden layer elements and a total of seven predictor variables taken from the subtests Numerical-

inductive reasoning (NID), Arithmetical competence (AK), Figural-inductive Reasoning (FID),

Numerical flexibility (NF), Inspection time (BZ), Decision quality and speed (EF) and the main

variable overview of the Tachistoscopic Traffic Perception Test (TAVT-UEB).

Using the empirically derived number of hidden layer elements and the empirically

derived predictor variables, the predictive validity of this optimized test battery was investigated.

Table 2 summarizes the validity coefficients and classification rates for the simple classification

and for the jackknife validation.

Table 2: Validity (R), adjusted explained variance of the criterion (adj. R²), classification rate (CR), sensitivity (1-β)

and specificity (1-α) of the prediction after simple training of the artificial neural network and according to

the jackknife method for the optimized test battery, based on the total sample. The validity was calculated as

the correlation between true value and predicted value.

Simple prediction

Jackknife prediction

R

adj. R²

CR

1-β

1-α

R

adj. R²

KR

1-β

1-α

.84

.61

92.9

84.2

98.4

.83

.59

92.9

84.2

98.4

As can be seen from Table 2, the results regarding the predictive validity of the optimized

test battery are confirmed by the jackknife method. The validity coefficients and classification

rates, both for the simple prediction and in the jackknife validation, are high, with a reasonably

balanced relationship between sensitivity and specificity. The correlation between the

classification probabilities of the simple prediction and the jackknife validation is R=.99. The

results therefore reveal a substantial correspondence between the simple prediction and the

jackknife validation, which indicates that the stability of the results is high. In order to ensure the

stability of the results we further validated the model by means of an internal bootstrap. This

involved setting up the model using the complete data set and then testing it on 1000 bootstrap

samples. The validity coefficient and the classification rate were calculated for each bootstrap

sample. For the validity coefficient a confidence interval of [.74; .94] was obtained, while the

confidence interval for the classification rate was [88.2 %; 97.7 %]. In summary it can be said

that the results of both the bootstrap and the jackknife validations indicate that the network

architecture used in this study provides a stable result.

The next step involved calculating the incremental validity and the relative relevance of

the individual test variables of the optimized test battery. The results are presented in table 3.

Table 3: Incremental validity and relative relevance of the main variables from the optimized test battery

Predictors

Incremental validity

Relative relevance

Numerical-inductive reasoning (NID)

.157

13.4 %

Arithmetical competence (AK)

.205

16.9 %

Inspection time (BZ)

.195

16.2 %

Decision quality and speed (EF)

.167

14.1 %

Figural-inductive reasoning (FID)

.324

24.6 %

Numerical flexibility (NF)

.105

4.6 %

Overview (TAVT)

.117

10.2 %

As can be seen in Table 3 the two subtests Numerical-inductive reasoning (NID) and

Figural-inductive Reasoning (FID) contribute the most to the predictive validity of the optimized

test battery. This result argues for the importance of fluide intelligence (Gf) in predicting the

success of pilot applicants. Arithmetical competence (AK), Inspection time (BZ), Decision

quality and speed (EF) and the main variable overview from the Tachistoscopic Traffic

Perception Test (TAVT-UEB) also contribute substantially to the predictive validity indicating

the importance of quantitative reasoning (Gq) and mental speed (Gs) in selecting pilot applicants.

Numerical flexibility (NF) contributes less than the other predictors but nevertheless proved to

contribute significantly to the predictive validity of the optimized test battery. The result is in

accordance with the relevance attributed to quantitative reasoning in the JAR-FCL. Contrary to

our prior assumptions the Adaptive Three-Dimensional Cube Test (A3DW) and Cognitrone

(COG) did not significantly contribute to the predictive validity of the test battery above and

beyond the predictor variables included in the optimized test battery.

Where practical applicability is concerned, the level of certainty of the classifications on

the individual subject level is also of importance. This can be investigated using the distribution

of the classification probabilities calculated with the aid of the artificial neural network. Figure 1

shows the distribution of the estimated person-specific probability of being classified as passing

the flight simulator test. The x-axis shows the success probability. For the sake of clarity the

person-related probabilities were summarized in ten groups. The y-axis represents the proportion

of individuals who actually received a positive (white bar) or negative (black bar) evaluation.

0

10

20

30

40

50

60

70

80

90

0.00 - 0.10 0.11 - 0.20 0 .21 - 0.30 0.31 - 0.40 0.41 - 0.50 0.51 - 0.60 0.61 - 0.70 0.71 - 0.80 0.81 - 0.90 0 .91 - 1.00

Probability to pass the flight simulator test

Percent suited / unsuited candidates

fail pass

Figure 1: Classification of a trained artificial neural network according to the jackknife method on the basis of the

result in the optimized test battery. The x-axis shows the probability of passing the flight simulator test;

the probabilities are divided into ten groups. The bars indicate the percentage in each group of subjects

who actually failed (black bar) or passed (white bar) the flight simulator test.

In summary it can therefore be said that even in the case of the assessment of individual

cases the optimized test battery achieves a good discrimination between successful and less

successful pilot applicants.

Results obtained with linear methods

The calculation of the discriminant analysis was carried out with SPSS 14. The results

indicate that the discriminant analysis is unable to separate successful and less successful pilot

applicants based on their test scores (Wilks-Lambda=.927, df=7, p=.418; Box-M: F=1.421,

p=.069). Using the empirically derived predictor variables by means of the artificial neural

network, the predictive validity of the optimized test battery was investigated. Table 4

summarizes the validity coefficients and classification rates for the simple classification and for

the jackknife validation.

Table 4: Validity (R), adjusted explained variance of the criterion (adj. R²), classification rate (CR), sensitivity (1-β)

and specificity (1-α) of the prediction for the entire data set and according to the jackknife validation of the

discriminant analysis. The validity was calculated as the correlation between true value and predicted value.

Simple prediction

Jackknife prediction

R

adj. R²

CR

1-β

1-α

R

adj. R²

KR

1-β

1-α

.264

.070

58.6

57.4

60.5

.205

.042

48.5

47.5

50.0

As can be seen in Table 4, the results regarding the predictive validity of the optimized

test battery using a linear method are considerably lower than the ones obtained with artificial

neural networks. Furthermore, the correlation between classification probabilities of the simple

prediction and the jackknife validation amounts to R=.61. It can thus be concluded that the results

obtained with the discriminant analysis are less stable than the results obtained with artificial

neural networks. The results were further validated means of an internal bootstrap. This involved

the estimation of the model parameters of the discriminant analysis in the whole data set and then

testing it on 1000 bootstrap samples. The validity coefficient and the classification rate were

calculated for each bootstrap sample. For the validity coefficient a confidence interval of [.12;

.52] was obtained, while the confidence interval for the classification rate was [53.0 %; 73.4 %].

Compared to the results obtained with artificial neural networks the confidence intervals for both

the classification rate and the validity coefficient are quite large. Taken together the results

indicate that the solution obtained by means of a linear discriminant analysis proved to be far less

stable than the results obtained by means of an artificial neural network.

Figure 2 shows the distribution of the estimated person-specific probability of being

classified as passing the flight simulator test according to the discriminant analysis.

0

10

20

30

40

50

60

70

80

90

100

0.00 - 0.10 0.11 - 0.20 0 .21 - 0.30 0.3 1 - 0.40 0 .41 - 0.50 0.51 - 0.60 0.6 1 - 0.70 0 .71 - 0.80 0.81 - 0.90 0.91 - 1.00

Probability to pass the flight simulator test

Percent suited / unsuited candidates

fail pass

Figure 2: Classification based on the jackknife validation of the results obtained with a discriminant analysis on

the basis of the result in the optimized test battery. The x-axis shows the probability of passing the flight

simulator test; the probabilities are divided into ten groups. The bars indicate the percentage in each

group of subjects who actually failed (black bar) or passed (white bar) the flight simulator test.

As can be seen in Figure 2 the individual classification probabilities are close to a chance

rate of .50. This reflects the inability to separate able and less able pilot applicants based on test

scores when using linear classification algorithms such as a discriminant analysis.

DISCUSSION

The results demonstrate that artificial neural networks outperform classical methods of

statistical judgment formation with respect to classification rate, magnitude of the validity

coefficient and separability of correctly and incorrectly classified pilot applicants. Furthermore,

the results obtained with an artificial neural network were stable in a jackknife as well as a

bootstrap validation. In summary it can be said that these results support the criterion validity of

the test battery used in this study. However, with regard to practical applications objections are

often raised to the use of artificial neural networks in psychological assessment on the grounds

that it involves a “black box”, from which the relevance of the individual predictor variables does

not follow (cf. Kinnebrock, 1992; DeTienne, DeTienne & Joshi, 2003). This article has shown,

however, that this argument does not apply in such general terms. By comparing models with

varying number of predictor variables but otherwise identical network architecture it is possible

to calculate at least the incremental validity or the relative variance which the various test

variables contribute to the predictive model. The weightings themselves, however, remain

difficult to interpret. Nevertheless, in practical applications the predictive model described in this

article enables empirically validated prediction of pilot applicants’ success in a standardized

flight simulator at a rather high level of accuracy and can be used to reduce training costs by

selecting the most promising candidates for further military pilot training. However, the authors

acknowledge that the results reported in this paper should be cross-validated using a different

sample of military or even civil pilot applicants from various countries in order to further

investigate the generalizability of the results.

REFERENCES

Ajzen, I. (1987). Attitudes, traits and actions: Dispositional prediction of behavior in personality

and social psychology. In L. Berkowitz (Ed,), Advances in experimental social psychology (Vol.

20, pp. 1-63). New York: Academic Press.

Arendasy, M., Hornke, L.-F., Sommer, M., Häusler, J., Wagner-Menghin, M., Gittler, G.,

Bognar, B., & Wenzl, M. (2005). Manual Intelligence-Structure-Battery (INSBAT). Mödling:

Schuhfried GmbH.

Biehl, B. (1996). Manual Tachistoscopic Traffic Perception Test (TAVTMB). Mödling:

Schuhfried GmbH.

Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford: Oxford University Press.

Bortz, J. (1999). Statistik für Sozialwissenschaftler [Statistics for social scientists]. Berlin:

Springer.

Brown, M. T., & Wicker, L. R. (2000). Discriminant analysis. In H. E. A. Tinsley & S. D. Brown

(Eds.), Handbook of applied multivariate statistics and mathematical modeling (pp.209-234). San

Diego, CA: Academic Press.

Burke, E., Hobson, C. & Linksy, C. (1997). Large sample validations of three general predictors

of pilot training success. International Journal of Aviation Psychology, 7, 225-234.

DeTienne, K. B., DeTienne, D. H. & Joshi, S. A. (2003). Neural networks as statistical tools for

business researchers. Organisational Research Methods, 6, 236-265.

Fahlman, S. E. (1988). Faster-learning variations on back-propagation: an empirical study.

Proceedings of the Connectionist Models Summer School. Los Altos: Morgan-Kaufmann.

Gittler, G. (1998). Manual Adaptive Three-Dimensional Cube Test (A3DW). Mödling:

Schuhfried GmbH.

Goeters, K.-M. (1998). General standards of selection: Validity and utility analysis. In K.-M.

Goeters (Ed.), Aviation Psychology: A science and a profession (pp.103-112). Aldershot:

Ashgate.

Griffin, R. B. (1998). Predicting Naval Aviator Flight training Performance using multiple

regression and artificial neural networks. International Journal of Aviation Psychology, 8, 121-

135.

Häusler, J. (2004). Software NN Predict. Vienna: self-published

Häusler, J. & Sommer, M. (2006). Neuronale Netze: Nichtlineare Methoden der statistischen

Urteilsbildung in der psychologischen Eignungsdiagnostik [Neural networks: Non-linear methods

of statistical judgment formation in personnel selection]. Zeitschrift für Personalpsychologie, 5

(1), 4-15.

Hunter, D. R. & Burke, E. F. (1994). Predicting aircraft pilot-training success: A meta-analysis of

published research. International Journal of Aviation Psychology, 4, 297-313.

Kinnebrock, W. (1992). Neuronale Netze [Neural networks]. München: Oldenburg Verlag.

Lienert, G. A. & Raatz, U. (1998). Testaufbau und Testanalyse (6. Auflage) [Test construction

and application (6th edition)]. Weinheim: Psychologie Verlags Union.

Martinussen, M. (1996). Psychological measures as predictors of pilot performance: A meta-

analysis. International Journal of Aviation Psychology, 6, 1-20.

Mielke, A. (2001). Neuronale Netze [Neural networks]. [Online] URL: http://www.andreas-

mielke.de/nn.html [01.10.2001].

Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests. Chicago: The

University of Chicago Press.

Ree. M. J. & Carretta, T. R. (1996). Central role of g in military pilot selection. International

Journal of Aviation Psychology, 6, 111-123.

Rojas, R. (2000). Neuronal Networks. A systematic introduction. Heidelberg: Springer.

Wagner, M. & Karner, T. (2003). Manual Cognitrone (COG). Mödling: Schuhfried GmbH.

Warner, B., & Misra, M. (1996). Understanding neural networks as statistical tools. The

American Statistician, 50, 284-293.

Wittmann, W., & Süß, H.-M. (1997). Challenging G-mania in intelligence research: answers not

given, due to questions not asked. Paper presented at the International Meeting of the

International Society for the study of individual differences, 19-23 July, Aarhus, Denmark.