This report discusses problems of data aggregation in personnel selection using data from two recent studies in the field of aviation psychology and traffic psychology. In the first study 99 military pilot applicants were tested using a comprehensive test battery. In order to determine the predictive validity of the chosen test battery artificial neural networks, linear discriminant analysis and logistic regression analysis are used as methods of statistical judgment formation. A global evaluation of the applicants’ performance in a standardized flight simulator served as criterion measure. The results of this study demonstrate that artificial neural networks outperformed classical methods of statistical judgment formation with regard to classification rate and validity coefficient. In the second study a comprehensive test battery measuring driving related abilities was administered to 222 respondents. A global evaluation of the respondents’ performance in a standardized driving test served as criterion measure. Similar to the results obtained in study 1 artificial neural networks outperformed classical approaches to statistical judgment formation with regard to classification rate, validity coefficient and separability of safe and less safe drivers on the individual level. Based on these results it can be concluded, that artificial neural networks are a useful method in personnel selection which increases the objectivity and validity of judgments derived from standardized test batteries.
It is becoming increasingly more common for psychological tests or entire test batteries to be used in the selection of suitable candidates (see Schuler, 2000). The main criteria for the tests used to select suitable job applicants are (1) the criterion validity of the individual tests or the whole test battery, (2) the overall costs of testing, and (3) time requirements. Generally, within the context of personnel selection, efforts are made to form an opinion on the suitability of applicants based on the applicants’ results in a standardized test battery. However, the derivation of such decisions requires a sufficiently high level of agreement between the tests used and criterion measure.
In many fields of applied psychology such as aviation psychology and traffic psychology one often finds low correlation coefficients between individual test results and the chosen criterion variable which hardly exceed .40 (cf. Bukasa, Wenninger & Brandstätter, 1990; Bukasa, Christ, Ponocny-Seliger, Smuc & Wenninger, 2003; Damos, 1996; Goeters, 1998; Karner & Neuwirth, 2000). This means that an individual test can hardly account for more than sixteen percent of the variance in the criterion. There are a variety of causes for this, ranking from a lower reliability of the criterion- or predictor variables (Lienert & Raatz, 1998), an attenuation of the variance in the predictor variables due to selection (Lienert & Raatz, 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 aviation educational program, aggregate measures such as general ability (“g”) are more suitable for prediction than are more specific predictors. Thus, one way to handle this problem consists in combining the available information on an applicant to generate a prediction about her or his success. In general one can resort to a clinical judgment formation or a statistical judgment formation in order to do so.
In contrast to clinical judgment formation the various methods of statistical judgment formation resort to mathematical algorithms to summarize the existing information from the individual to a global evaluation of a respondent’s traffic safety. Methods of statistical judgment formation are thus more objective.
However, classical methods of statistical judgment formation, such as discriminant analysis or regression analysis, are vulnerable to violations of their statistical prerequisites (Bortz, 1999; Brown & Wicker, 2000; Venter & Maxwell, 2000) and often lack stability in cross-validation studies (Jahnke, 1982). Furthermore, these classical methods only model linear or logit correlations and are thus unable to illustrate more complex curvilinear correlations. Likewise by the application of these classical methods of statistical judgment formation a linear additive model is assumed, whose content wise theoretical foundation cannot be examined in the context of these approaches. Consequently reciprocal effects or total compensatory effects between the predictors therefore remain unconsidered in recent validation studies.
In order to counteract these problems and to produce ecologically valid predictions, artificial neural nets (McCulloch & Pitts, 1943; Hebb, 1949; Anderson & Rosenfeld, 1988) can be used as a method of the statistical judgment formation. Artificial neural nets can be understood as robust methods for pattern recognition tasks with little prerequisites to data characteristics (Anderson & Rosenfeld, 1988; Bishop, 1995; Kinnebrock, 1992; Rojas, 2000; Warner & Misra, 1996). In general an artificial neural net consists of several interconnected units, which are structured into three layers (see figure 1).
The input layer consists of a number of units equal to the number of predictor variables, while the output layer represents the criterion measure. In between these two layers one or more so called “hidden layer” can be positioned. This general structure is often referred to as multi-layer perceptrone (Anderson & Rosenfeld, 1988; Bishop, 1995; Kinnebrock, 1992; Rojas, 2000; Warner & Misra, 1996). According to Kinnebrock (1992) one single hidden layer often suffices in practical applications. The number of units in the hidden layer is optional and determines to a great extent the complexity and generalizability of the artificial neural net. Mielke (2001) already pointed out, that a higher number of “hidden” layer units enable the artificial neural net to adapt more closely to the data at hand. However, this comes with an increased risk of over-generalization, which can hinder the generalization of the newly constructed artificial neural net to different sets of data. Therefore the determination of the number of hidden layer units is of high importance (Mielke, 2001). As can be seen in figure 1 an artificial neural net features connections between the units of the three layers. The individual units within a layer can be connected with all units of the adjacent layer which is called a complete feed-forward connection. Thus each unit transmits its information to all units of the adjacent layer. The information transmitted to a unit of the adjacent layer is weighted. The main aim of the construction of an artificial neural net resides in the iteratively optimization of these weights. As became apparent the procedure for the construction of an artificial neural net differs clearly from the estimation of a classical linear model such as the discriminant analysis or regression analysis. Artificial neural nets are generally under-specified, which is due to the comparably higher amount of path coefficients. They thus have negative degrees of freedom and therefore the individual path coefficients cannot be clearly estimated, since more than one solution for the weights of the individual paths is conceivable. Instead of an estimate algorithm artificial neural nets thus use so-called learning algorithms, which accomplish an optimization of the weights in an iterative process, by strengthening beneficial paths and weakening other paths. There is a range of learning algorithms, which can be used to train a newly constructed artificial neural net, back-propagation being one of the well known. Thus artificial neural nets are to be understood as diagnostic heuristic.
Based on these theoretical considerations artificial neural nets should be at least as effective – or even better – for the identification of suitable job applicants. This presumption is in line with the results current study in the field of traffic psychology, aviation psychology and personnel selection (Collins & Clark, 1993; Griffin, 1998; Häusler & Sommer, 2006; Sommer, Arendasy, Olbrich & Schuhfried, 2004; Sommer, Arendasy, Schuhfried & Litzenberger, 2005; Sommer & Häusler, 2006) which compared artificial neural nets with classical methods of statistical judgment formation. In the present paper it thus is to be examined whether artificial neural networks would also be useful tools to detect safe and less safe drivers as well as suitable and less suitable military pilot applicants. These two fields of applications were selected since they seem to be most relevant to military psychological assessment given the need for military pilots as well as drivers.
Study 1: Aviation Psychology
In a first phase of the selection process all pilot applicants have been administered a comprehensive standardized test battery. The test battery covered the areas of inductive thinking (AMT: Hornke, Etzel & Rettig, 2003), spatial perception (A3DW: Gittler, 2002) and attention (COG: Wagner & Karner, 2003). Moreover, the tests also measured the candidates’ reactive stress tolerance (DT: Schuhfried, 1998), verbal memory (VERGED: Etzel & Hornke, 2003a), visual memory (VISGED: Etzel & Hornke, 2003b) and psychomotor coordination (SMK: Bauer, Guttmann, Leodolter & Leodolter, 2002). The statistical analyses always take into account the main variables of the respective test. In the AMT, A3DW, VISGED and VERGED, for instance, these are the person parameters according to the Rasch Model. In Cognitrone, the variable mean time correct rejection is used. In the Determination Test the number of correct responses was used. Psychomotor coordination is covered with the test scores mean angle deviation and time in ideal range of the SMK. In a second selection phase, data were collected about the general performance on the flight simulator. On the basis of these data, the candidates were subdivided into the two groups of suited and less suited candidates. In the following analysis, those classified with D = less suitable were referred to the group of the unsuitable, as this group would otherwise have only included four candidates and an evaluation of the psychological test battery would not have been possibly of this categorization. 53.54 percent of the pilot applicants received a positive global evaluation and are thus considered to be successful.
The sample encompasses 104 members of the German Federal Army who are in the course of a pilot training. The complete data of 99 candidates are provided. All the candidates are men between 16 and 25 years of age, with and average age of 20.4 years and a standard deviation of 1.85 years. One of them (1%) had completed just 9 years of school but no vocational training, while 19 candidates (19.2%) had completed a vocational school. 74 candidates altogether (74.7%) provided a high-school leaving certificate with university entrance permission, and five candidates (5.1%) graduated from university or college.
Result obtained with classical methods
The calculation of the discriminant analysis was carried out with the program SPSS 10.0. The results show that the prerequisites of homogeneity of the variances and co-variances were met (Box-M: F=1.363, p=.072). The outcome of the analysis was a discriminant straight that cannot divide significantly between the two groups (Wilks-Lambda=.851, df=8, p=.059). In this analysis a total of 69.7% of the sample is correctly classified. This includes 81.1% of the suitable pilot candidates and 56.5% of the unsuitable pilot applicants. The chance rate amounts to 53.5%. This results into a validity coefficient of r=.390.
In order to ensure the stability of this result a jackknife validation is carried out. Jackknife validations are a commonly used procedure to examine the stability of results in case there is no second independent data set at hand (Brown & Wicker, 2000; Hagemeister, Scholz, & Westhoff, 2002). In the jackknife validation the classification rate amounts to 54.50 percent, with a chance rate of 53.50 percent. This equals a validity coefficient of r=.348. Figure 2 shows the distribution of the probability to receive a positive evaluation of one’s performance in the flight simulator according to the jackknife validation of the discriminant analysis.
Figure 2: Classification of the discriminant analysis according to the jackknife method. The x-axis shows the estimated probability of a positive evaluation in the flight simulator; the probabilities are divided into ten groups. The bars indicate the relative frequency (as a percentage) in each group of subjects who actually received a negative (black bar) or positive evaluation (white bar) of their performance in the flight simulator.
If only classification probabilities > .70 or < .30 are taken into account, 26.30 percent of the pilot applicants can be classified with a high degree of certainty. Respondents with a classification probability < .70 and > .30 are considered as not clearly classifiable. In other words, a confidence interval has been implemented to exclude more insecure classifications. The classification rate increases to 73.0%. From an inspection of the distribution of the classification probabilities in figure 1 it becomes apparent, that the result obtained with the discriminant analysis does not lend itself to a practical application due to the low amount of clearly classifiable respondents.
Result obtained with artificial neural nets
Calculation of the neural network was realized with the program Matlab 6 (Nabney, 2002). The neural network at hand is a multi-layer perceptrone with one hidden layer of five units. The number of “hidden” layer units was determined on the basis of a comparison of various network architectures using the criterion outlined by Häusler and Sommer (2006). The input layer encompassed eight units representing the individual test scores. The output layer represents the criterion variable. The neural network is equipped with a complete feed-forward connection. The transformation function used is Softmax, which is an activation function that is especially suited for categorical data (Bridle, 1990). Basically, it is a multiple, logistical function the result of which can be interpreted in the sense of a posteriori probability. The training algorithm used here is the back propagation algorithm “scaled conjugate gradient”. This algorithm is recommended in particular for non-linear optimization tasks with a higher number of weights (Masters, 1995). Altogether 500 iterations were used in the training phase. Using this artificial neural net a total 79.8% of the sample is classified correctly. The chance rate amounts to 53.5%. 83.0% of the suitable pilot applicants and 76.1% of the unsuitable pilot applicants are classified correctly. The validity coefficient amounts to r=.650.
In order to examine the stability of this result, a jackknife validation is realized (Dorffner, 1991; Michie, Spiegelthaler & Taylor, 1994). The classification rate in the jackknife validation amounts to 73.7%. This equals a validity coefficient of r=.600. Figure 3 shows the distribution of the probability to receive a positive evaluation of one’s performance in the flight simulator according to the jackknife validation of the artificial neural net.
Figure 3: Classification of a trained artificial neural network according to the jackknife method. The x-axis shows the estimated probability of a positive evaluation in the flight simulator; the probabilities are divided into ten groups. The bars indicate the relative frequency (as a percentage) in each group of subjects who actually received a negative (black bar) or positive evaluation (white bar) of their performance in the flight simulator.
If only those classifications are taken into consideration that have been made with <0.25 or >0.75, 61.6% candidates can be classified. In this case, the classification rate is situated at 88.5%. The majority of correct classifications are thus made with high probability, while incorrect classifications were made with a rather low probability.
Study 2: Traffic Psychology
The variables used as predictors of driving behavior were Gaining an Overview from the Tachistoscopic Traffic Perception Test (Biehl, 1996), General Intelligence from the Adaptive Matrices Test Form S2 (Hornke, Etzel & Rettig, 2003), Correct Responses from the Determination Test Form S1 (Schuhfried, 1998), Motor Time and Reaction Time from the Reaction Test Form S3 (Schuhfried & Prieler, 1997), the Mean Time for Correct Rejection in the Cognitrone Form S1 (Wagner & Karner, 2003) and the Field of View and Tracking Deviation from the Peripheral Perception Test (Schuhfried, Prieler & Bauer, 2002). In the following sessions this test battery will be referred to as test battery PLUS.
In addition to completing the above tests, each subject also took a standardized driving test. The driving test took place over a previously defined route and lasted approximately 45 minutes. The driving test used in Vienna was the “Vienna Driving Test” (Risser & Brandstätter, 1985); while in Bad Tölz the “Bad Tölz Driving Test” (Burgard, 2004) was used. The measure of driving behavior in road traffic was the mean of the global assessments of two independent observers using an a priori five-point scale. An average global assessment of 3.33 was defined as the cut-off value. The dichotomized global assessment of driving behavior in the standardized driving test served as the criterion variable in the subsequent analysis. Using this cut-off value, the driving behavior of 60.4% of the sample received a positive assessment.
The sample consisted of 164 (74%) men and 58 (26%) women aged 19 – 91 with an average age of 59 and a standard deviation of 18. The median age was 64. Many of the subjects were therefore middle-aged or elderly. The age variable did not, however, give rise to any incremental validity in the subsequent analysis. Some of the subjects were drivers who had already committed traffic offences. Participation in the study was, however, voluntary. A total of 39 people (18%) had completed compulsory schooling or basic secondary school but without completing vocational training (EU educational level 2), 96 people (43%) had completed vocational training or a course at a technical college (EU educational level 3), 35 people (16%) had a school-leaving qualification at university entrance level or a qualification from a technical university (EU educational level 4) and 52 people (23%) had a university degree (EU educational level 5).
Result obtained with classical methods
The calculation of the logistic regression was carried out with the program SPSS 10.0. Using the method “Enter” the analysis resulted in a -2 log likelihood value of 191.46, Chi²=37.60, p<.001. Altogether 72.9% of the respondents were classified in accordance with their global evaluation of their performance in the standardized driving test, which results into a validity coefficient of r=.350. The chance rate amounts to 60.4%. Among those correctly classified are 85.8% of the respondents with positive global evaluations and 53.4% of the respondents with a negative global evaluation of their driving performance. Thus the sensitivity can be regarded as rather high while the specifity is rather low resulting into an imbalance between sensitivity and specifity of the predictions made by applying this classical method of statistical judgment formation.
In order to ensure the stability of these results a jackknife validation is carried out. The classification rate in the jackknife validation amounts to 69.8% with a random rate of 60.4%. This equals a validity coefficient of r=.340. Among those correctly classified according to the jackknife validation are 85.8% the respondents with positive global evaluations and 45.5% of the respondents with a negative global evaluation of their driving performance in the standardized driving test. Figure 4 shows the distribution of the probability to receive a positive evaluation of one’s driving performance according to the jackknife validation of the logistic regression.
Figure 4: Classification of the logistic regression according to the jackknife method. The x-axis shows the estimated probability of a positive global assessment of driving performance in the standardized driving test; the probabilities are divided into ten groups. The bars indicate the relative frequency (as a percentage) in each group of subjects who actually received a negative (black bar) or positive global evaluation (white bar) in the standardized driving test.
If only those classifications are taken into consideration that have been made with a probability of < .30 or > .70, the test battery merely enables 45.9% of respondents to be classified with a high degree of certainty. Thus the majority of the classifications made are done with a low level of certainty. In this case, the classification rate amounts to 75.5%. An inspection of the distribution of the classification probabilities in figure 4 indicates that the results do not particularly lend themselves to be used in practical personnel selection settings due to the low amount of respondents classifiable with a higher degree of certainty.
Result obtained with artificial neural nets
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; the results of this can be interpreted as a posteriori probability. QuickProp (Fahlmann, 1988) was used as the learning algorithm. The number of iterations was 10,000. Using this artificial neural net a total 86.5% of the sample is classified correctly. The chance rate amounts to 60.4%. 97.0% of respondents with a positive global evaluation and 81.8% of the respondents with a negative global evaluation of their driving performance are classified correctly. The validity coefficient amounts to r=.780.
In order to examine the stability of this result, a jackknife validation is realized. The classification rate in the jackknife validation amounts to 83.8%. This equals a validity coefficient of r=.770. Among those correctly classified are 81.8% of the respondents with a positive global evaluation and 77.6% of the respondents with a negative global evaluation of their driving performance. Figure 5 shows the distribution of the probability to receive a positive evaluation of one’s driving performance according to the jackknife validation of the artificial neural net.
Figure 5: Classification of a trained artificial neural network according to the jackknife method. The x-axis shows the estimated probability of a positive global assessment of driving performance in the standardized driving test; the probabilities are divided into ten groups. The bars indicate the relative frequency (as a percentage) in each group of subjects who actually received a negative (black bar) or positive global evaluation (white bar) in the standardized driving test.
If only those classifications are taken into consideration that have been made with a probability of < .30 or > .70, the test battery enables 77.9% of respondents to be classified with a high degree of certainty. In this case, the classification rate amounts to 92.5%.
The results obtained in the two studies presented above demonstrate, that artificial neural networks can outperform classical methods of statistical judgment formation with respect to classification rate, validity coefficient as well as a more clear separability of suited and unsuited applicants based on the classification probabilities of the individual respondents. The results obtained with the artificial neural networks in both studies also featured a satisfying generalizability as demonstrated in a jackknife validation. Based on these results and previous studies we can thus conclude that artificial neural networks are a valuable and applicable alternative to classic algorithms of statistical judgment formation which can be used to considerably increase the precision of diagnostic decisions derived from test batteries.
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