8Pelser.qxd


There are two main reasons why time and effort should be
invested in the pursuit of valid personnel selection procedures,
namely to remain within the ambits of the law, referred to here
as the legal incentive (Lopes, Roodt & Mauer, 2001; Mauer,
2000a, 2000b) and secondly, to maximise the probability of
selecting the potentially most productive candidate(s), referred
to in this paper as the economic incentive (Hunter & Hunter,
1984: Schmidt & Hunter, 1981).

The legal incentive involves industrial relations, ethical and
cultural issues, and the social utility of fairness considerations
(Lopes et al., 2001; Mauer, 2000a, 2000b; Wheeler, 1993). The
economic incentive encompasses issues such as shortages of
skills, the impact of globalisation as experienced in the South
African economy and the need for increased competitiveness
(Wheeler, 1993). These two incentives, however, provide a useful
categorisation of the benefits for using scientific selection
procedures in South African organisations.

The validit y of psychological tests and other assessments 
used by organisat ions to make decisions affect ing an
individual’s career stat us is a legal requirement as per the
Employment Equit y Act (55 of 1998). There is currently no
other option for organisations than validating the tests 
and other assessments that are used to make career 
decisions about their employees or potential employees. Only
in this way can there be scientific proof of compliance 
with the requirements set by the Employment Equit y Act,
thereby making selection decisions based on these measures
legally defensible.

Beyond the legal requirements, there are distinct economic gains
to be made by using selection procedures that are valid. Accurate
predictions of applicants’ performance on the job and
subsequent selection of the most able candidates, should
translate into increases in the productivity and performance
level of the organisation (Anastasi & Urbina, 1997; Hunter &
Hunter, 1984; Schmidt & Hunter, 1981, 1998). This conception
forms the foundation of studies concerning the utility of
selection procedures. 

Schmidt and Hunter (1998) concluded that, in contrast to
random selection, the utility of selection measures is directly
proportional to the validity thereof and that the potential
economic gains to be derived by replacing less valid predictors
of performance with measures that have higher validity, are
quite large in financial terms. The higher the validity coefficient
of a selection measure, the greater is the economic utility for the
period that the selected candidates work for the organisation,
and vice versa. It thus makes economic sense for organisations to
investigate the validity of the various selection methods that may
be used in their specific context.  In the present study, the
context was the need for a valid selection procedure for haul
truck operators in an open-pit platinum mine. 

Minerals are South Africa’s major source of foreign exchange
earnings and it is anticipated that “mining will dictate the pace
of economic development for many years to come” (Chamber of
Mines, 2002, p.1). South Africa holds 94% of the world’s known
Platinum Group Metals (PGM’s), the only other major supplier
being Russia (mostly from stockpiles). Since 1999 there have
been significant increases in the demand for platinum, which is
expected to be sustained in the short to medium term (Anglo
American Platinum Corporation Limited, 2000).

MARIKIE PELSER
Z C BERGH

DELÉNE VISSER
Department of Industrial and Organisational Psychology

University of South Africa

ABSTRACT
The purpose of the study was to evaluate the concurrent validity of learning potential and psychomotor ability
measures for the prediction of haul truck operator performance in an open-pit mine. Additional goals were to
determine the nature of the relationship between learning potential and psychomotor abilities and to assess the
relative contributions of these variables as predictors. The predictors were the TRAM 1 Learning Potential test and
Vienna Test System subtests that were administered to 128 experienced haul truck operators. The job performance
criteria used were spotting in time, corrected tons hauled and supervisor ranking. The concurrent validity of the
learning potential and psychomotor ability measures was partially supported. An exploratory factor analysis
provided relatively convincing evidence for a general cognitive ability factor (g) underlying performance on the
learning potential and several psychomotor measures. The existence of a general psychomotor factor was not
substantiated in the current study. Suggestions were made for improving design and criterion shortcomings.

OPSOMMING
Die doel van die ondersoek was om die saamvallende geldigheid van leerpotensiaal en psigomotoriese metings vir
die voorspelling van die werkprestasie van sleepvragwabestuurders in ’n oopgroefmyn te beoordeel. Addisionele
doelwitte was om die aard van die verwantskap tussen leerpotensiaal en psigomotoriese vermoëns te gepaal en
om die relatiewe bydraes van hierdie veranderlikes as voorspellers te bepaal. Die voorspellers was die TR AM 1
Leerpotensiaaltoets en die Vienna Test System subtoetse wat toegepas is op 128 ervare sleepvragwabestuurders.
Die werkprestasiekriteria was inteikentyd, gekorrigeerde tonmaat gesleep en toesighouerbeoordeling. Die
saamvallende geldigheid van die leerpotensiaal en psigomotoriese metings is gedeeltelik ondersteun. ’n
Ondersoekende faktorontleding het die bestaan van ’n algemene kognitiewe vermoëfaktor (g), wat onderliggend
aan die leerpotensiaal en verskeie psigomotoriese metings is, redelik oortuigend gestaaf. ’n Algemene
psigomotoriese faktor is nie in die huidige ondersoek gevind nie. Voorstelle is gemaak om die ontwerp- en
kriteriumtekortkominge te verbeter.

THE CONCURRENT VALIDITY OF LEARNING POTENTIAL AND

PSYCHOMOTOR ABILITY MEASURES FOR THE SELECTION OF

HAUL TRUCK OPERATORS IN AN OPEN-PIT MINE

Requests for copies should be addressed to: D Visser, jdv@rau.ac.za

58

SA Journal of Industrial Psychology, 2005, 31 (2), 58-70

SA Tydskrif vir Bedryfsielkunde, 2005, 31 (2), 58-70



Due to the overall shortfall in supply and the strong demand
fundamentals for PGM’s, there is growing competition between
various platinum producers (both nationally and globally) to
supply this shortfall at the largest profit margins possible. This
means that productivity at existing operations has to be
increased. It also implies that expansion projects (with the same
focus on low cost and high productivity rates) have to be
undertaken. These demands point to the need for valid selection
procedures for existing operations and for expansion projects to
ensure that employees are skilled and productive and can add
value to the operations.

In an open-pit platinum mine, the haul truck operator plays a
major role in the overall productivity of the mine. At the
platinum mine in question, haul truck operators make up the
largest contingent of operators (128 out of a total of 200) on the
mine. Compared to other types of production equipment at the
operation, the fleet of haul trucks is by far the largest investment
in terms of capital outlay and running costs. A further
consideration is that, relatively speaking, the safety risks
regarding haul trucks are high, because they are mobile and
capable of higher speeds than any of the other mobile
production equipment in the pit. 

The production process for haul truck operators consists of
the following components: loading, transport ing and 
tipping. Haul truck operators are responsible for transporting
material (ore or waste) from the loading face to various
tipping areas (crusher, stockpile or waste dumps) depending
on the nat ure of the material to be tipped. They then tip the
material into the crusher or onto a stockpile depending on
the stat us of the crusher. 

At the loading face, it is the responsibility of haul truck operators
to manoeuvre and place the truck into a position where it is ideal
for the face shovel or front end loader to load the material. This
procedure is referred to as “spotting in”. In the case of the
shovel, the haul truck must be positioned so that the shovel has
no need to over-swing or overextend the “arm” of the shovel.
(Overextending the “arm” leads to losses in productivity,
because the time taken to swing is longer and it also puts strain
on the machine, resulting in potential damage to the shovel). In
the case of the front end loader (which is on wheels), the ideal
placement of the truck for loading has productivity and safety
implications. If the haul truck operator does not place the haul
truck optimally, the loader needs to reposition itself to tip with
a full load in the bucket. This may result in the machine over-
balancing with resultant injuries and property damage. As
highlighted earlier, the material is then transported to the
crusher, the waste dump or the ore dump (stockpile). 

At the crusher the operators have to reverse the truck into the
ideal position to tip. At waste and ore dumps they need to
reverse towards a safety berm in order to tip over the side of the
dump. A safety berm is a heap of rock/sand of 1.5 metres high
on the edge of the dump. It has two main uses. Firstly, haul truck
operators use it to “spot into” the dump in terms of location and
direction, and secondly, it signals to haul truck operators when
they have reversed too far, which may result in the truck falling
down the side of a 30-40 metre dump resulting in injury or
death and property damage. The process of manoeuvring and
placing the truck in the ideal position to tip is also referred to as
“spotting in”. Therefore “spotting in” is required for the loading
and the tipping components of the production process.

The technology on haul trucks is advanced. On-board computers
are linked to a dispatch system that monitors performance and
indicates where and when operators should load, dump, refuel
and report for service. The operators interact with the dispatch
system throughout the shift (by pressing various options on the
communication touch screen in the haul truck) to indicate their
current status so that the dispatch system can allocate trucks to
the various loading points optimally. 

Consequently, training involves the actual operation of the
truck, the mechanics of the dispatch system, and it also focuses
on safe and efficient “spotting in” under various environmental
conditions, safety policies and procedures. Training periods are
thus typically long and relatively expensive.

Bearing in mind the high costs of haul trucks, the potential for
accidents, and the high training costs, it is suggested that (as in
the aviation industry) “even small improvements in identif ying
potential wash-outs could yield large cost avoidance savings”
(Ree & Carretta, 1998, p. 82). Ree and Carretta’s (1998)
viewpoints highlight three possible criteria against which the
effectiveness of selection procedures can be measured, namely
productivity, safety, and length of time taken to complete the
training programme. The only criterion that was used in this
study, was productivity. 

Unstructured interviews were traditionally used to select haul
truck operators at the open-pit mine as has also been done
internationally in other kinds of operations (Campion, Pursell &
Brown, 1988; Robertson, Gratton & Rout, 1990), despite the fact
that such interviews yield low criterion validities (Schmidt &
Hunter, 1998). Earlier research already supported the validity of
cognitive and psychomotor ability as predictors of training
success and of job performance criteria for positions requiring
the operation and maneuvering of mobile machinery (Bouwer,
1985; Carretta, 1989; Duke & Ree, 1996; Gutenberg, Arvey,
Osburn, & Jeanneret, 1983; Hunter & Hunter, 1984; Jensen, 1982,
1986). 

Research findings generally indicate encouraging to strong
correlations for cognitive (org) and psychomotor abilities as
predictors of job performance and training success in jobs
requiring operating skills. In two meta-analyses of aviation
studies by Hunter & Burke (1994) and Martinussen (1996)
modest correlations for various measures of cognitive ability,
ranging between 0,13 and 0,22, were found. These findings were
questioned by Carretta and Ree (2000) as under-reported, due to
possible statistical artifacts (such as range restrictions and
unreliability). In contrast, a vast amount of research indicate
strong correlations, ranging from 0,36 to 0,70, between
cognitive abilities and job performance and training criteria
(Carretta & Ree, 1996b, 1997, 2000; Hunter, 1986; Hunter &
Hunter, 1984; Jensen, 1986; Schmidt & Hunter, 1998). Similar
findings in earlier and more recent studies are reported for
psychomotor abilities as predictors of job performance and
training success (Duke & Ree, 1996; Mashburn as cited in Griffin
& Koonce, 1996). In psychomotor assessments in which
computer technology replaced many apparatus tests, strong
correlations have also been reported (Cox, 1988; Fleishman,
1988; Griffin & Koonce, 1998; Ree & Carretta, 1998; Schoeman,
1995; Wheeler & Ree, 1997). A number of studies also refer to
general cognitive ability or g as an additional predictor, thus
pointing to the interaction between cognitive and psychomotor
abilities in the prediction of job performance (Carretta, 1989,
1992; Carretta & Ree, 1994, 1997; Chaiken, Kyllone & Tirre, 2000;
Duke & Ree, 1996; Hunter & Burke, 1994; Martinussen, 1996;
Tirre & Raouf, 1998). Studies which indicate the influence of
moderator variables, such as socioeconomic status (job status or
occupational level) and age (Humphreys, 1984; Jensen, 1986;
Kantor & Carretta, 1988) on the correlation between cognitive
abilities and psychomotor abilities and job performance, are
particularly relevant for South Africa.

Learning potential measures fall within the framework of the
learning/dynamic theories of intelligence or cognitive ability.
The distinguishing characteristic of learning potential tests is
that respondents learn a new skill or competency while doing
the tasks set out in the test. Some individuals become more
competent than others and the differences in competency are
captured in the test scores (Taylor, 1999). When learning
potential tests are presented primarily in non-verbal
diagrammatical format (except for the instructions), cultural

VALIDITY OF LEARNING POTENTIAL 59



bias that would be prevalent if candidates were to be required to
respond to items in a second or third language, is limited to a
degree (Lopes et al., 2001; Taylor, 1999). Validity studies have
been conducted for the use of learning potential measures in the
South African mining industry, but the criterion measures have
always been academic outcomes (Taylor, 1999). Learning
potential measures have not been validated against job
performance criteria, as was the case in this study.

The psychomotor abilities utilised by haul truck operators at
loading and tipping sites are very specific. It was therefore
important to select appropriate job-related predictors that would
capture candidates’ psychomotor abilities to perform their jobs.
For the present study it was decided to use the Vienna Test System
which is a diagnostic instrument developed for the assessment of
driving ability in Austria and that is now utilised world-wide for
the same purpose (Schuhfried, 1996). It is made up of a battery of
primarily psychomotor tests, but it also includes computerised
versions of intelligence and personality tests. The system requires
no prior driving experience to yield predictions of driving ability
and therefore it is ideal for use in the selection of potential haul
truck operators. Various validity studies had been conducted in
Europe for the different sub-tests of the Vienna Test System
(Bukasa, Wenninger & Brandstätter, as cited in Schuhfried,
2000a; Cale, as cited in Schufried, 2000a), but none have been
carried out at an open-pit mine in South Africa. 

Bearing in mind the significant role that haul truck operators
play in the productivity of an open-pit platinum mine,
combined with the legal incentive for the use of valid selection
procedures in the South African context and the economic
realities of the importance of productivity to the expanding
platinum mining industry, the current study was an attempt to
find valid predictors that may be used to select haul truck
operators. In view of the research findings mentioned above, it
was decided to investigate whether a cognitive measure of
learning potential and psychomotor ability tests are valid
predictors of the job performance of haul truck operators in an
open-pit mine so that these predictors might in the future be
used for the selection of operators.

Moderator variables often have an impact on the relationships
between predictor and criterion variables in criterion validity
studies. For the current study it was therefore also necessary to
investigate the effect of potential moderators that have been
found to operate in similar contexts in earlier research. The final
aim of the study was to investigate the relationships between the
predictors to determine whether general cognitive ability (g)
underlies performance in all the predictors (cognitive and
psychomotor predictors), or whether two underlying factors
would be found, namely g and general psychomotor ability.

The hypotheses were stated as follows:
� Learning potential and psychomotor abilities are valid

predictors of haul truck operator performance in an open-
pit mine.

� Moderator variables (specifically age, years of education, and
years of operating experience) influence the relationship
between learning potential, psychomotor abilities and haul
truck operator performance.

� Learning potential and psychomotor abilities are positively
correlated.

� Two factors underlie learning potential and psychomotor
abilities, namely a general cognitive ability (g) factor and a
general psychomotor factor.

METHOD

Respondents

All of the haul truck operators at an open-pit mine (N = 128)
were included in the sample for the present study. There were
125 males as against only three females and all of the

respondents were Black. Their mean age was 41,56, their mean
number of years of education was 8,23 and they had a mean of
15,13 years of operating experience. 

Measuring instruments

Three categories of variables had to be assessed for the study.
These included the moderator variables (age, number of years of
education and number of years of operating experience), the
predictor variables and the criterion variables. 

Moderator variables

It is common practice in applied psychological research to
investigate whether biographical variables such as age, sex and
education moderate the relationships between predictor and
criterion variables (Cascio, 1998). Anastasi and Urbina (1997)
stressed the need to include only those variables for which 
there is evidence of their moderating effects in multiple
regression analyses. It was therefore decided to investigate age,
education and experience as potential moderators in the
predictor–criterion relationships, because the results of several
earlier studies indicated that these variables may act as
moderators in the present study. 

For instance, Oehlschlagel and Moosbrugger (cited in
Schuhfried, 1996) and Wagner (cited in Schuhfried, 1996) found
that as age increases, performance in psychomotor tests
decreases. With regard to educational level, Hunter and Hunter
(1984), Martinussen (1996) and Schmidt and Hunter (1998)
found that correlations of years of education with job
performance and training outcomes were relatively small (in the
0,10 to 0,20 range). It was nevertheless decided to include years
of education as a potential moderator of job performance,
because it was not certain whether the mentioned validity
coefficients were relevant to the South African context, where
the quality of schooling in many schools had been of a
questionable standard and access to the schooling system, for
the greater part of the population, had been problematic. It was
anticipated that the number of years of schooling that the
candidates claimed to have completed, would give a very rough
indication of their current literacy and numeracy levels. This
might have affected the ease with which they acquired the
knowledge and skills to perform the job, a relevant issue due to
the considerable variation among the candidates in this study
who reported education levels varying from illiterate to higher
than Grade 12.

Studies by Martinussen (1996), Schmidt and Hunter (1998),
Schmidt, Hunter, Outerbridge and Goff (1988) and Shinar (1978)
hinted that experience may be a moderating variable in a validity
study such as this one. Years of operating experience was
therefore also included as a potential moderator variable. It was
defined as experience in operating any mobile machinery such
as cars, trucks and forklifts.

Predictor variables

Two measuring instruments were used to assess the predictor
variables. The first was a cognitive test to measure the
respondents’ overall learning potential, namely the TRAM 1
Learning Potential Test Battery (Taylor, 1999). Secondly, several
subtests of the Vienna Test System (Schuhfried, 1996, 2000a,
2000b, 2000c), namely the Cognitrone, Determination Unit,
Two-hand Coordination and Zeit Bewegung Abschätzung (ZBA)
sub-tests, were used to assess psychomotor ability. 

TRAM 1 Learning Potential Test Battery

General cognitive ability, or g, has consistently been found to be
a valid indicator of job performance when compared with other
potential predictors (Gutenberg, et al.,1983; Hartigan & Wigdor,
1989; Hunter, 1986; Hunter & Hunter, 1984; Jensen, 1986; Levine,
Spector, Menon, Narayanan & Cannon-Bowers, 1996; Schmidt &
Hunter, 1981, 1998; Schmidt et al., 1988; Schmidt & Hunter,
1998). Furthermore, culture-fairness in the assessment of
cognitive potential is a particularly important issue in the 

PELSER, BERGH, VISSER60



South African context (Taylor, 1994). The TRAM 1 Learning
Potential Test Battery (Taylor, 1999) was therefore selected as a
predictor in this study. 

The TR AM 1 is a learning potential assessment instrument 
for candidates who fall in the illiterate and semi-literate 
ranges or who have not had formal schooling up to Grade 10
(Taylor, 1999). It was included in this study as a culture-fair
measure of learning potential, which should also give an
indication of fluid intelligence (gf) and general cognitive
ability or g (Taylor, 1994).

The test requires candidates to translate symbols into other
symbols, using a dictionary. The symbols are pictorial or
quasi-geometric. The symbols are translated using some
underlying rule (such as opposites – sun/moon; or the symbols
being used together – such as teacup/teapot). In Phase A1
candidates first complete the translation process by themselves.
Thereafter they are given a lesson to explain the underlying
rules, followed by the completion of Phase A2. Then they are
given another test book and another dictionary to assess the
transfer of skills. The final step is the completion of a memory
test (Taylor, 1999).

Scores are provided on the following dimensions: Automatisation,
Transfer, Memory and Understanding, Speed and Accuracy.
Composite scores of respondents’ overall performance are also
generated (Taylor, 1999). Only the overall assessment rating was
used as a predictor in this study. Taylor (1999) reported reliability
coefficients ranging from 0,62 to 0,95 for the various dimensions.
In terms of validity, he found that composite scores on the TRAM
1 correlated significantly (r = 0,59) with academic performance in
an ABET course and also with academic performance (r = 0,51) in
N1 studies (NQF level 2, or grade 10).

Vienna Test System

In the past decade the Vienna Battery has been used relatively
widely in South Africa. De Jager and Van der Walt (1993)
reported on the reliability and validity of the stress tolerance test
for operators of moving equipment at a diamond mine. Local
predictive validity studies of the Vienna Test System for
predicting the job performance of truck drivers (De Jager, 1997)
and of operators of moving equipment in a coal mine (De Jager
& Van der Walt (1997) have also been performed.

The subtests of the Vienna Test System that were used as
predictors, are discussed below. 
� Cognitrone: This subtest of the Vienna Test System assesses

candidates’ ability to concentrate and to adjust their work
tempo to different stimuli patterns. It was included because
of its logical conceptual link with haul truck operator
performance. Haul truck operators are required to
demonstrate sustained concentration throughout the shift,
taking into consideration the demands which the continually
changing operating environment places on them.
The test is based on Reulecke’s concentration theory, which
post ulates that concentration is made up of three 
variables, namely energy (concentration consumes energy),
function (different actions require different levels of
concentration) and precision (the qualit y of task
completion). Individuals involved in tasks requiring
concentration must continually regulate the energ y,
function and precision of their actions. This can be
exhausting and cannot be maintained on a continuous
basis (Reulecke as cited in Schuhfried, 2000a). 
In the subtest, candidates are required to indicate as fast and
as accurate as possible whether any of four figures presented
on a computer screen is similar to the figure in the test
question. The test yields various options in terms of
differentiated results. In this study “sum correct reactions”
was utilised. It gives an indication of performance quality
and, to some extent, also provides data on processing speed
(Schuhfried, 2000a). 

Schuhfried (2000a) reported a split-half reliability of 0,95
for the subtest. In criterion-related validit y st udies
significant correlations between test results and safety
criteria, such as accident frequency and driver errors were
obtained (Bukasa, Wenninger & Brandstätter as cited in
Schuhfried, 2000a; Cale as cited in Schuhfried, 2000a).
However, no criterion-related studies with the Cognitrone as
the predictor, and operator or driver performance as the
criterion, could be found. A correlation of 0,48 with the
Determination Unit subtest was reported (Wagner as cited
in Schuhfried, 2000a). This substantial correlation may
have been due to both tests tapping similar needs for
sustained concentration, efficient information processing
and quick reaction time. Negative correlations with age
were reported in two studies (Oehlschlagel & Moosbrugger,
Wagner as cited in Schuhfried, 2000a). 

� Determination Unit: This subtest assesses candidates’ reaction
speed, reactive stress tolerance and ability to demonstrate
sustained multiple-choice reactions to rapidly changing
stimuli (Schuhfried, 1996). Like the Cognitrone, this test was
included because of its conceptual links with haul truck
operator performance requirements. Its focus is on the
operators’ appropriate and fast responses in rapidly changing
environments that may involve stress. This is particularly
relevant in the mining environment where the operators of
moving machinery need to cooperate closely, with very little
margin for error, to achieve production objectives. 
The Determination Unit requires the discrimination of
colours and acoustic signals, memorisation of the relevant
characteristics of stimulus configurations and response
buttons, and also memorisation and application of
assignment rules. Individuals have to react to differently
coloured visual stimuli as well as acoustic stimuli that
require either finger or foot responses. The test starts off
slowly, gains speed to a very fast response requirement
(approximating high stress situations, such as accident or
near-accident situations) and then slows down marginally
(approximating the period just after the accident/near-
accident). Bearing in mind that the reactions required are not
simple, a certain amount of overlap bet ween general
cognitive ability (g) and performance in this test should be
expected (Jensen, 1993). The score used in this research was
“overall results correct”, which reflected the total number of
appropriate timely and delayed responses for the entire test. 
Schuhfried (1996) reported an internal consistency
reliability of 0,99 for the Determination Unit. In various
criterion-related validity studies significant correlations
between results on the Determination Unit and driving
performance criteria were obtained, for instance in a study
with driving behaviour during a test drive as well as results
of a driving test as the criteria (Klebelsberg & Kallina; Karner
& Neuwirth as cited in Schuhfried, 1996). In another
example driving safety criteria, frequency of accidents and
driver errors, were used (Cale; Wenninger & Brandstätter as
cited in Schuhfried, 1996).

� Two-hand Coordination Speed and Accuracy: This subtest
assesses hand-eye and hand-hand coordination. It was
included as a predictor in this study because of the two-
hand coordination requirements of haul truck operating
activities.
Candidates are required to move a cursor on a given track
with the aid of two joysticks, one that can move forward and
backward and one that can move right and left. Candidates
must therefore use both hands in a coordinated way to move
the cursor along the track within acceptable accuracy limits.
The track consists of three sections varying in the demands
made on the left and right hands. The scores yielded are “total
mean duration” (the speed dimension) and “total percentage
error duration” (the accuracy dimension). 
Internal consistency reliabilities of the measures varied from
0,85 to 0,97 (Schuhfried, 2000c). No criterion-related validity
studies were found. 

VALIDITY OF LEARNING POTENTIAL 61



� Zeit Bewegung Abschätzung (ZBA): This subtest of the Vienna
Test System assesses candidates’ ability to correctly estimate
and anticipate motion and distance pertaining to moving
objects. It was included in this study because of its perceived
relevance to “spotting in” activities. If haul truck operators
can estimate both distance and direction, it will arguably be
easier for them to “spot into” the loading equipment
correctly and quickly on their first attempt. 
In the test a slow-moving dot moves across the screen. At a
stage it disappears and candidates are required to indicate
both where and when it will hit a line. The test is very similar
to the LAMP tests developed in World War II, in which an
aeroplane appeared on the left side of the screen, travelled
across the screen and then disappeared behind a cloud.
Candidates were required to estimate when the aeroplane
would reappear on the right hand side of the cloud (Ree &
Carretta, 1998).
For the present study only linear progressions were included.
Sine wave progressions were omitted, because they relate to
operating activities more complex than the operating
activities required for the operation of haul trucks. The scores
used were “median of deviation time during a linear
progression” (time estimation), which was measured in
seconds, and “median of direction deviation during a 
linear progression” (motion estimation), which was
measured in pixels. 
Schuhfried (2000b) reported internal consistency reliabilities
of 0,92 for the ZBA time estimation measure and 0.69 for the
ZBA motion estimation measure. No validity studies on the
current version of the subtest could be found, nor have
studies on the relative effects of age or any other potential
moderator variable been reported.

Criterion variables

In an attempt to balance the shortcomings of objective (Cascio,
1998; Deary, 2001) and subjective criteria (Anastasi & Urbina,
1997; Cascio, 1998; Guion, 1987; Thorndike, Cunningham,
Thorndike & Hagen, 1991), both objective and subjective
criterion measures were used in the study. No attempt was made
to determine the reliability of the various criteria, due to
difficulties in obtaining the data required to determine 
such reliabilities. 

Two objective criterion variables, namely spotting time into
loading equipment and tons per work hour corrected for
kilometres hauled and gradient, were assessed. The subjective
criterion variable measured was supervisor ranking.

Scheepers (1973) pointed out that an acceptable driving criterion
could only be achieved by registering the movements of the
vehicle on a continuous basis in much the same way as a flight
recorder registers the movements of an aircraft in flight. At the
open-pit mine in question, every truck was constantly monitored
by a Geographical Positioning System (GPS) and the computerised
dispatch centre had continuous data on every truck/operator
combination in terms of location, tons loaded, status (such as
service/breakdown) and other variables relevant to the monitoring
of the performance of every truck/operator combination. This
data was used to compile the objective criterion data.

Spotting time into loading equipment 

This criterion variable was included, because it involves the two
most complex operating skills required from haul truck
operators. Firstly, the ability to manoeuvre and place the truck
in an optimal position for the shovel or front end loader to be
able to load, without having to swing further than necessary or
to overextend the “arm” of the shovel, is referred to as
“spotting in”. The second skill involves reversing into the ideal
position to tip into the crusher or to “spot into” the safety berm
in order to tip at the waste or ore dumps. It was argued that the
more difficult operators found these two skills, the longer it
would take them to load or tip, and thus the less productive
they would be. 

The dispatch system provided data on these variables per
truck/operator combination on a continuous basis. “Spotting
in” was calculated by determining the time between the “full
action of the previous truck” and the “first bucket action of the
new truck”. This criterion was calculated in “overtrucked”
situations, that is, when there was at least one truck waiting 
to be loaded.

Spotting time into the dumping or tipping sites was omitted as
a criterion, because the measuring equipment at the various
tipping/dumping sites lacked accuracy. Therefore, only time
taken to “spot in” under the bucket was used as the spotting
time criterion measure. To remove the effect of the different
loading equipment (shovel versus front end loaders), only
“spotting in” times for the shovel were included. Spotting time
was averaged over a three-month period.

Tons per work hour corrected for kilometres hauled and gradient

The operators drove different routes to the various pieces of
loading equipment and places of loading material or loading
areas, and therefore they did not travel the same distance to
transport their loads (ore or waste). The routes that they travelled
also differed substantially in terms of road gradients. It was thus
not possible to use tons hauled per hour as an indicator of
productivity. Some correction was required for kilometres
hauled and gradient of the road.

The following calculation was performed to correct the data
extracted from the computerised dispatch system:

The tons hauled were divided by the hours worked (WH). WH
referred to haul truck operating time, excluding time spent in
the queue waiting for loading equipment availability (WH =
operating time – queue time). WH was then divided by the
kilometres travelled yielding a “tons corrected for distance
hauled” (TCDH) measure. Road gradient was incorporated by
multiplying the linear distance travelled by a correction factor
calculated by the dispatch system from the road network data
that is captured on the system to yield an “effective flat haul
rate” (EFH). Finally, TCDH was corrected to a “tons per work
hour corrected for kilometres hauled and gradient” (TPKH) by
multiplying TCDH with EFH (TPKH = TCDH * EFH). This
variable, TPKH, was used as the second criterion and the results
were also averaged over a three-month period.

Super visor ranking

Supervisor rankings were included as an additional criterion
variable. The rankings were obtained in a relatively rudimentary
fashion with the aim of corroborating the findings of the
objective criteria. The supervisors of every shift were asked to
rank the operators on their shifts, using the paired comparisons
method (Cascio, 1998). The supervisors were instructed to
decide which operator in every pair of operators they would
select if the loading conditions were particularly difficult (due
to congestion in the loading area or wet road conditions) with
number 1 being the best operator, number 2 the second best
operator and so forth.

No attempt was made to compare operators between shifts,
because of the unreliability that could potentially be caused by
different supervisors not being familiar with the performance of
all the operators on all the shifts. Care was taken to ensure that
this criterion was not contaminated by supervisors having access
to either predictor or objective criterion scores. 

Procedure

The respondents were assessed in groups with the TR AM 1,
depending on their availabilit y. They were also requested 
to complete the various subtests of the Vienna Test System
that were used as predictors in the present st udy. The
criterion measures were obtained during the same time
period that the assessments were done. The Spotting Time
and Corrected Tons Hauled variables were obtained from the

PELSER, BERGH, VISSER62



computerised dispatch system in operation at the mine and
averaged per operator over a period of three months. The
Supervisor Ranking variable for the operators was obtained
from their supervisors. 

Due to various practical considerations, some of the data were
not available for a number of members of the sample. The
numbers of valid cases are reported in Table 1. Missing data
occurred mostly for the criterion measures, but there appeared
to be no logical reason why the missing data would result in a
biased sample. 

RESULTS

The first step in the analysis of the data entailed examining the
distributions of the variables. The descriptive statistics for the
moderator, predictor and criterion variables are given in Table 1.
These include means, standard deviations, and indices of
skewness and kurtosis. Rather than reporting the conventional
skewness and kurtosis statistics computed by the SPSS package,
these statistics were converted to z values as suggested by Field
(2000) to increase the interpretability of the obtained values. As
a rule of thumb, absolute values of z that are larger than 2, are
considered indicative of deviation from normality.

Furthermore, the so-called Kolmogorov-Smirnov test of the
normality of a distribution was performed and the results 
are also reported in Table 1. If the p value of this test 
statistic is smaller than 0,05, the distribution deviates in 
some way from normality. When this is the case, the skewness
and kurtosis values, or a histogram of the distribution, may
give more information about the shape of the distribution
(Field, 2000).

TABLE 1

DESCRIPTIVE STATISTICS FOR THE MODERATOR, 

PREDICTOR AND CRITERION VARIABLES

N Min Max M SD Skew- Kurto Kolm- p

ness z sis z Sm*

Age 128 25,00 60,00 41,56 8,12 0,95 1,16 0,07 0,200

Years of 127 0,00 13,00 8,23 2,79 -2,39 0,70 0,10 0,023
education

Years of 118 1,00 35,00 15,13 8,07 1,41 0,92 0,07 0,200
operating 
experience

TRAM 1  121 31,96 86,72 62,64 11,97 0,08 1,48 0,12 0,004
Learning 
potential

Cognitrone 127 83,00 696,00 426,63 117,04 -2,01 0,34 0,09 0,093

Determin- 127 167,00 519,00 411,96 72,16 -3,90 1,17 0,08 0,200
ation unit

Two-hand  127 23,47 123,63 62,33 22,59 2,70 0,89 0,09 0,200
coordination 
speed

Two-hand  127 0,00 8,78 1,07 1,51 12,61 4,51 0,25 0,000
coordination 
accuracy

ZBA time  127 0,19 4,01 1,10 0,80 7,47 2,41 0,18 0,000
estimation

ZBA motion  127 5,00 155,00 28,13 20,15 14,14 5,78 0,20 0,000
estimation

Spotting time 110 62,00 108,00 79,07 7,58 3,76 1,73 0,10 0,036
in seconds

Corrected 94 2042,11 2922,39 2469,94 161,78 0,91 1,31 0,07 0,200
tons hauled

Supervisor 110 1,00 30,00 14,74 8,21 0,10 1,61 0,09 0,200
rankings

Kolmogorov-Smirnov test statistic for the normality of a distribution

In the case of Two-hand Coordination Accuracy, ZBA Time
Estimation and ZBA Motion Estimation the skewness was severe
and positive (see Table 1). Because low scores indicated good
performance on these subtests, the positive skewness indicated
that most of the respondents performed well on these subtests.

This may also mean that the test did not allow for an adequate
degree of variability amongst candidates. As expected, all the
variables that were skewed or leptokurtic, also deviated from
normality (see the Kolmogorov-Smirnov test statistics in Table 1).
In addition, the distribution of the TRAM 1 deviated significantly
from normality, despite demonstrating no significant skewness
or kurtosis. Examination of the histogram of the TRAM 1
distribution revealed a bipolar tendency in the scores. 

To test the hypothesis that learning potential and psychomotor
abilities can predict haul truck operator performance, the second
step in the data analysis consisted of computing the product
moment correlation coefficients between the predictors and the
criteria, and also the correlations among the criteria. These
correlations are reported in Table 2. 

TABLE 2

CORRELATIONS OF THE MODERATORS AND PREDICTORS WITH

THE CRITERIA, AND INTERCORRELATIONS OF THE CRITERIA

Spotting  Corrected Supervisor 

time tons hauled rankings

Age Pearson r -0,02 -0,13 0,01
Sig. (2-tailed) 0,847 0,218 0,961
N 110 94 110

Years of education Pearson r -0,02 0,19 -0,06
Sig. (2-tailed) 0,875 0,075 0,527
N 110 94 109

Years of operating Pearson r -0,07 -0,06 0,04
experience

Sig. (2-tailed) 0,497 0,611 0,720
N 102 89 104

TRAM 1 Learning Pearson r -0.13 0.14 -0.24**
potential

Sig. (1-tailed) 0,092 0,089 0,007
N 104 90 104

Cognitrone Pearson r -0,20* -0,02 -0,15
Sig. (1-tailed) 0,018 0,427 0,064
N 110 94 110

Determination  Pearson r -0,08 -0,07 -0,03
unit Sig. (1-tailed) 0,212 0,256 ,383

N 110 94 110

Two-hand  Pearson r 0,13 -0,04 0,05
coordination speed

Sig. (1-tailed) 0,096 0,367 0,304
N 110 94 110

Two-hand  Pearson r -0,04 0,02 -0.05
coordination 
accuracy

Sig. (1-tailed) 0,325 0,422 0,295
N 110 94 110

ZBA time  Pearson r 0,02 0,09 0,12
estimation

Sig. (1-tailed) 0,418 0,193 0,099
N 110 94 110

ZBA motion Pearson r 0,03 0,00 0,03
estimation

Sig. (1-tailed) 0,385 0,499 0,382
N 110 94 110

Spotting time Pearson r 1,00
Sig. (2-tailed) -
N 110

Corrected tons Pearson r -0,03 1,00
hauled

Sig. (2-tailed) 0,793 –
N 94 94

Supervisor rankings Pearson r 0,41** -0,02 1,00
Sig. (2-tailed) 0,000 0,883 –
N 99 85 110

** Correlation is significant at the 0,01 level
* Correlation is significant at the 0,05 level

N smaller than 128 is the result of incomplete pair-wise data

VALIDITY OF LEARNING POTENTIAL 63



The intercorrelations among the job performance criteria were
examined first (see Table 2). There was a significant and strong
positive correlation (r = 0,41, p < 0,001) between Spotting
time (an objective criterion) and Supervisor rankings (the
subjective criterion). This correlation may be regarded as an
indication that these t wo criteria measured the same
dimension relatively reliably, thereby providing evidence for
the construct validity of these job performance criteria. This
result is noteworthy, because it occurred despite the trend for
correlations bet ween objective criterion measures and
subjective criterion ratings to be low (Cascio, 1998). A certain
degree of overlap bet ween these t wo criteria had been
anticipated due to the brief that the supervisors were given
when asked to rank their candidates by using the paired
comparisons method. They were asked to indicate which
operator in every pair of operators they, the supervisors,
would select if the loading conditions were particularly
difficult (such as congestion in the loading area, or wet road
conditions). The supervisor ranking criterion was therefore
expected to measure, at least to some degree, the same
dimension as the spotting time criterion. 

The reliability and validity of the Corrected tons hauled
criterion, however, was not supported. An explanation for the
low correlations between this criterion and the other criteria was
that the road network data on the dispatch system was not
entirely updated at the time of the study, and that the flat haul
rate correction factor that was implemented using this road

network data as foundation, was thus not totally correct. This
criterion was possibly not reliable enough to be used, because
low reliabilities of criterion measures place a ceiling on the
validity coefficients that are attainable. This may lead to a Type
2 error, namely in this case, missing a significant validity
coefficient that was present (Carretta & Ree, 2000; Cascio, 1998;
Schmidt, Hunter & Urry, 1976). It was not possible to determine
the exact extent of this unreliability and the consequential effect
size on the validity of the criterion. 

The spotting times of each operator into the various pieces of
loading equipment was readily observable, more so than the tons
hauled per operator, which was inclined to get lost in the
realities of different operators driving different haul distances
and road gradients, and production being reported per day per
shift rather than per shift per operator. It therefore made sense
that Spotting time was a better objective criterion than
Corrected tons hauled.

The correlations of the predictors with the criteria are also
presented in Table 2. One-tailed significance tests were
performed on these correlations, because the correlations were
expected to be in a specific direction. For the Spotting time
criterion only one statistically significant correlation was found,
namely a negative correlation (r = -0,20, p = 0,018) with
Cognitrone efficiency. The negative correlation provided partial
support for the hypothesis that psychomotor measures can
predict job performance of haul truck operators. 

PELSER, BERGH, VISSER64

TABLE 3

CORRELATIONS OF THE MODERATORS WITH THE PREDICTORS

AND INTERCORRELATIONS OF THE PREDICTORS

TRAM 1  Cognitrone Determination  Two-hand  Two-hand ZBA time  ZBA motion 

Learning unit coordination coordination estimation estimation

potential speed accuracy

Age Pearson r -0,34** -0,16 -0,34** 0,03 0,29** -0,03 0,12
Sig. (2-tailed) 0,000 0,072 0,000 0,782 0,001 0,782 0,171
N 121 127 127 127 127 127 127

Years of education Pearson r 0,39** 0,09 0,33** -0,12 -0,03 -0,07 -0,01
Sig. (2-tailed) 0,000 0,314 0,000 0,174 0,746 0,452 0,879
N 120 126 126 126 126 126 126

Years of operating experience Pearson r -0,28** 0,00 -0,28** -0,19* 0,31** -0,03 0,13
Sig. (2-tailed) 0,002 0,986 0,002 0,043 0,001 0,735 0,159
N 117 117 117 117 117 117 117

TRAM 1 Learning potential Pearson r 1,00
Sig. (2-tailed)
N 121

Cognitrone Pearson r 0,29** 1,00
Sig. (2-tailed) 0,002
N 120 127

Determination unit Pearson r 0,43** 0,29** 1,00
Sig. (2-tailed) 0,000 0,001
N 120 127 127

Two-hand coordination speed Pearson r -0,10 -0,17 0,04 1,00
Sig. (2-tailed) 0,298 0,062 0,669
N 120 127 127 127

Two-hand coordination accuracy Pearson r -0,24** -0,09 -0,30** -0,23** 1.00
Sig. (2-tailed) 0,009 0,301 0,001 0,010
N 120 127 127 127 127

ZBA time estimation Pearson r -0,08 -0,11 -0,17* 0,00 -0,10 1,00
Sig. (2-tailed) 0,412 0,239 0,050 0,996 0,277
N 120 127 127 127 127 127

ZBA motion estimation Pearson r -0,15 -0,08 -0,14 -0,01 0,15 0,08 1,00
Sig. (2-tailed) 0,099 0,403 0,114 0,910 0,092 0,374
N 120 127 127 127 127 127 127

** Correlation is significant at the 0,01 level
* Correlation is significant at the 0,05 level
N smaller than 128 is the result of incomplete pair-wise data



Similarly, a single statistically significant correlation was
obtained for the Supervisor ranking criterion, namely a
negative correlation (r = -0,24, p = 0,007) with Learning
potential. A low score on the Supervisor ranking criterion
indicated good performance of the haul truck operator (the
number 1 candidate was the best performer on every shift),
and therefore this correlation meant that candidates who
performed well on the learning potential measure, tended to
be ranked as good operators by their supervisors. This
correlation provided evidence in support of the hypothesis
that learning potential can predict job performance of haul
truck operators.

No significant correlations were found between the predictors
and the Corrected tons hauled criterion. This result may 
be due to the problems relating to this criterion referred 
to earlier.

In the third step of the data analysis, the correlations of 
the anticipated moderators with the rest of the variables 
were examined. The purpose of these analyses was to
determine whether age, years of education, and years of
operat ing experience are moderator variables that 
inf luence the relat ionship bet ween learning potent ial,
psychomotor abilit ies and haul truck operator 
performance. Statistically significant correlations at the 
0,01 level were obtained bet ween the three potent ial
moderator variables. There was a strong negat ive 
correlation bet ween age and years of education (r = -0,50, 
p < 0,001). Similarly, there was a negative correlation
bet ween years of educat ion and years of operat ing 
experience (r = -0,35, p < 0,001). As may be expected, the
correlation bet ween age and years of operating experience
was strong and positive (r = 0,66, p < 0,001). 

In Table 2 the correlat ions bet ween the ant icipated
moderators and the criteria are reported. None of these
correlations were statistically significant, which implies that
evidence for the hypothesised influence of the possible
moderator variables was not found in this part of the results.
The correlations of the moderator variables with the predictor
variables were studied next (see Table 3). There were several
statistically significant correlations bet ween the moderator
variables and some of the predictors. It was therefore feasible
that the moderators inf luenced the predictor-criterion
relationship via their relationships with the predictors. 
Only in the cases of Cognitrone efficiency, ZBA 
t ime est imat ion or ZBA mot ion est imat ion were no
significant correlations obtained bet ween the proposed
moderators and the predictors.

More particularly, the TR AM 1 Learning potential 
measure correlated statistically significantly with all the
potential moderators. This variable correlated negatively 
with age (r = -0,34, p < 0,001), positively with years 
of education (r = 0,39, p < 0,001) and negatively with years 
of operating experience (r = -0,28, p = 0,002). The first 
two correlations were to be expected, but the reason for 
the negative correlation bet ween years of operating
experience and the TR AM 1 should be sought in the 
strong positive correlation bet ween age and years of 
operating experience. 

Similarly, the Determinat ion unit efficiency measure 
also correlated stat ist ically significantly with all the 
potential moderators. It correlated negatively with age 
(r = -0,34, p< 0,001), positively with years of education 
(r = 0,33, p < 0,001) and negatively with years of operating
experience (r = -0,28, p < 0,002) The near-ident ical
correlations of the TR AM 1 and the Determination unit
measure with the moderators was at least to some degree due
to the significant overlap in terms of the dimension assessed
by these predictors (see Table 3, r = 0,43). 

There were also statistically significant positive correlations
of Two-hand coordination accuracy with age (r = 0,29, 
p = 0,001) and with years of operating experience (r = 0,31, 
p = 0,001). In other words, the older the operators were 
and the more years’ experience they had as operators, 
the more likely they were to make errors on the Two-
hand coordination test.

Two-hand coordination speed and years of operating
experience correlated statistically significantly at the 0.05 level
(r = -0,19, p = 0,043). This negative correlation means that
candidates with more experience, were likely to perform
weaker on the measure than those with less years of experience.
In the present study the negative correlation between age and
performance on the Cognitrone as reported by Oehlschlagel
and Moosbrugger, and Wagner (as cited in Schuhfried, 2000a),
was not replicated. 

The next step in the assessment of the role of the 
anticipated moderators involved the statistical control of 
the effects of these variables. In Table 4 the partial 
correlations of the predictors with the criteria, controlling 
for age, years of education and years of operating experience,
are presented. 

TABLE 4

PARTIAL CORRELATIONS OF THE PREDICTORS WITH THE CRITERIA

Spotting Corrected Supervisor 

time tons hauled rankings

TRAM 1 Learning Pearson r -0,18 0,05 -0,26*
potential

Sig. (1-tailed) 0,057 0,324 0,012
N 76 76 76

Cognitrone Pearson r -0.21* -0.04 -0.18
Sig. (1-tailed) 0,032 0,355 0,061
N 76 76 76

Determination unit Pearson r -0.08 -0.11 0.03
Sig. (1-tailed) 0,233 0,164 0,407
N 76 76 76

Two-hand  Pearson r 0,03 -0,04 0,01
coordination speed

Sig. (1-tailed) 0,398 0,375 0,465
N 76 76 76

Two-hand  Pearson r -0,03 0,12 -0,04
coordination 
accuracy

Sig. (1-tailed) 0,400 0,154 0,354
N 76 76 76

ZBA time estimation Pearson r -0,10 0,11 0,03
Sig. (1-tailed) 0,194 0,162 0,385
N 76 76 76

ZBA motion Pearson r 0,07 0,02 0,01
estimation

Sig. (1-tailed) 0,284 0,436 0,474
N 76 76 76

* Correlation is significant at the 0,05 level

From Table 4 it is clear that the effects of the correlations of
the moderator variables with one another and with the various
predictors were negligible, because the moderator variables
did not affect the predictor-criterion relationships
significantly. The partial correlations reflected in Table 4 are
only marginally higher for the two significant correlations (r
= -0,21 and 
r = -0,26) than those reflected in Table 2 (r = -0,20 and 
r = -0,24) for which the moderator variables were not taken
into account. However, in the case of the correlation between
the TR AM 1 and Spotting time, the moderators did appear to
have a minor effect, because the partial correlation was close
to being statistically significant (r = -0.18, p = 0.057). This

VALIDITY OF LEARNING POTENTIAL 65



relationship shows promise for further research using learning
potential as predictor. 

In the fourth and final step of the data analysis, the
intercorrelations bet ween the predictors were st udied. 
The purpose of these analyses was to test the hypotheses 
that learning potential and psychomotor abilities are
positively correlated and that t wo factors underlie the
predictors used in the present st udy, namely a general
cognitive abilit y (g) factor and a general psychomotor 
factor. The intercorrelations bet ween the predictors are
presented in Table 3.

The TR AM 1 Learning potential correlated statistically
significantly with three of the predictors intended to assess
psychomotor abilit y, namely Cognitrone efficiency,
Determination unit efficiency, and Two-hand coordination
accuracy. All of these correlations were in the expected
direction, implying that high learning potential scores tended to
be associated with good performance on the psychomotor tests,
and vice versa. Learning potential was correlated positively with
Cognitrone efficiency (r = 0,29, p = 0,002) and Determination
Unit efficiency (r = 0,43, p < 0,001), and negatively with 
Two-hand coordination accuracy (r = -0,24, p = 0,009) that
reflected the percentage of error time. The hypothesis that
learning potential and psychomotor abilities are correlated, was
therefore supported. 

With respect to statistically significant correlations between the
psychomotor predictors, the Determination unit correlated
positively with the Cognitrone (r = 0,29, p = 0,001), and
negatively with Two-hand coordination accuracy (r = -0,30, p =
0.001) and ZBA time estimation accuracy (r = -0,17, p = 0,05).
There was also a statistically significant negative correlation
bet ween Two-hand coordination speed and Two-hand
coordination accuracy (r = -0,23, p = 0,01). All of these
correlations were in the expected direction. The positive
correlation between Cognitrone efficiency and Determination
unit efficiency obtained in this study supported Wagner’s (as
cited in Schuhfried, 2000a) findings of a correlation of 0.48
between the Determination unit and the Cognitrone.

An exploratory factor analysis was subsequently carried out 
on the intercorrelations of the TR AM 1 and the Vienna 
Test System subtests used in this st udy to explore the
relationships between the predictors in greater detail. As a
first step, the correlation matrix was tested for sampling
adequacy and the null hypothesis of an identity matrix 
was also tested. The Kaiser-Meyer-Olkin measure of 
sampling adequacy was equal to 0,63 and Bartlett's test of
sphericity was statistically highly significant [÷2 (21) = 73,68,
p < 0,001]. These results were acceptable and it was thus
decided to proceed with the factor analysis. 

The principal axis method of factor analysis was performed
on the data, but the results did not yield an unambiguous
solution with respect to the appropriate number of factors to
be extracted. Various strategies are available to assist the
researcher in deciding on how many factors to extract and
t wo of these were implemented in this study. The first
strateg y used was the Kaiser-Guttman criterion, which
specifies that the number of factors to extract is determined
by the number of factors with eigen values larger than unit y
(Stevens, 1992). The second strateg y used was the so-called
“scree” test (Cattell, 1979). 

The eigen values of the various factors (which indicated the
relative importance of the factors in terms of the overall
variability in the data accounted for by each factor) were plotted
against the number of factors that could be extracted. Two
observations were made from the plot, namely, there were three
factors with eigen values larger than unity and the line appeared
more or less straight from Factor 2 onwards. The Kaiser-Guttman

criterion therefore indicated that three factors should be
extracted, whereas the “scree” started at Factor 2, indicating that
only one factor should be extracted. To complicate matters
further, the existence of two factors were hypothesised, namely
g and psychomotor ability. It was therefore decided to extract
single-factor, t wo-factor and three-factor solutions in
anticipation that the most appropriate solution would be
suggested by the pattern of results. 

For the two-factor and three-factor solutions the factor axes
were rotated using the direct oblimin method of oblique
rotation. The delta parameter was set at zero (the default
setting of the SPSS programme) to ensure that the correlations
allowed between the factors were not large. The factor pattern
matrices containing the factor loadings (standardised
regression coefficients in the case of the two- and three-factor
solutions) of these three solutions are reported in Table 5. The
single-factor solution is reported in the second column, the
two-factor solution in the third and fourth columns, and the
three-factor solution in the fifth to seventh columns. Factor
loadings that are equal to or larger than 0,30 are indicated in
bold type. 

TABLE 5

PRINCIPAL AXIS FACTOR SOLUTIONS OF

THE CORRELATIONS BETWEEN THE PREDICTORS

Single Factor 1 Factor 2 Factor 1 Factor 2 Factor 3

factor

TRAM 1 Learning  0,61 0,66 0,01 0,64 0,15 0,00
potential

Cognitrone 0,38 0,47 0,16 0,42 0,22 -0,06

Determination 0,72 0,63 -0,24 0,67 -0,10 -0,04
unit

Two-hand  0,03 -0,13 -0,51 -0,03 -0,66 -0,07
coordination speed

Two-hand  -0,41 -0,32 0,52 -0,48 0,33 -0,30
coordination 
accuracy

ZBA time  -0,11 -0,12 -0,06 -0,08 0,07 0,54
estimation

ZBA motion  -0,24 -0,22 0,10 -0,23 0,05 0,04
estimation

Factor loadings larger than 0,30 are indicated in bold

DISCUSSION

The primary goals of the study were to investigate whether a
learning potential test and psychomotor ability tests were valid
predictors of the job performance of haul truck operators, to
determine the impact of potential moderator variables on the
relationships between the predictor and criterion variables, and
to establish whether performance in the predictors could be
ascribed to two underlying factors, namely g and general
psychomotor ability.

The distributions of the variables were examined in view of
the negative effects that some properties of variables may
have in a validation study. For instance, if there is restriction
of range or if the shapes of the predictor and criterion
variables are dissimilar (such as one being positively skewed
and the other being negatively skewed), the range of the
correlations possible in a set of data is affected (Hays, 1994).
Predictably, the three Vienna subtests that exhibited the
severest degrees of skewness, were also severely leptokurtic
and deviated from normalit y. These observations may be a
reflection of the presence of range restriction which is a
t ypical obstacle in concurrent validit y studies. Since all the
respondents were experienced operators, the scores on the
above-mentioned variables grouped together at the end of the
scale that ref lected good performance. The bipolar

PELSER, BERGH, VISSER66



distribution of the TR AM 1 may possibly be explained by the
nat ure of a learning potential measure, where “catching on”
(Jensen, 1986) and automatisation (Sternberg, 1984) or
“improvement with practice” (Jensen, 1986) play a significant
role. The t wo peaks of the distribution may very well reflect
the results of the candidates who “caught on” to the learning
tasks and automatised or improved with practice, and those
who did not.

In the present study, the variables that were expected to act as
moderators did not notably affect the predictor-criterion
relationships. Furthermore, the hypothesis that learning
potential and psychomotor ability measures would predict the
criteria used in the present study, was only partially verified.
This result contrasted with Bouwer’s (1984) findings of no
significant correlations between psychomotor measures and job
performance for heavy duty truck drivers in a South African
study. Two statistically significant correlations were obtained,
namely between the TRAM 1 and the Supervisor ranking
criterion and between Cognitrone efficiency and the Spotting
time criterion. 

It was expected that earlier findings by Carretta (1989), Carretta
and Ree (1994, 1997), Gibb and Dolgin (1989), Hunter and
Hunter (1984), McHenry, Hough, Toquam, Hanson, & Ashworth
(1990) and Ree and Carretta (1994) of psychomotor abilities
providing significant, albeit small, incremental validity beyond
general cognitive ability (or learning potential), would be
replicated in the present study. Initially stepwise multiple
regression analyses were planned for regressing each of the three
job performance variables on the predictor and potential
moderator variables to ascertain the extent to which each of the
predictors explained the various criteria. This plan was
abandoned, because there were too few significant correlations
or partial correlations (controlling for the moderator variables)
between the predictor and job performance variables. 

The haul truck operator position should be classified as a low
complexity job requiring only very basic cognitive skills. This
fact may explain the lack of high correlations between learning
potential and performance. It has generally been found that
general cognitive ability or g tends to correlate better with job
performance in more complex jobs (Levine et al., 1996;
Gutenberg et al., 1983; Hunter, 1986; Jensen, 1986). Nonetheless,
various studies have indicated that the validity of cognitive
ability varies across jobs, but that it never approaches zero
(Hartigan & Wigdor, 1989; Hunter, 1986; Hunter & Hunter, 1984;
Schmidt & Hunter, 1981; Schmidt et al., 1988). 

The hypothesis that learning potential and psychomotor abilities
are correlated, was supported in the current study. This finding
also corroborated earlier findings indicating that general
cognitive ability, g, and psychomotor ability are positively
correlated (Carretta & Ree, 1997, 2000; Chaiken, et al., 2000;
Hunter & Hunter, 1984; McHenry, et al., 1990; Rabbit, Banerji &
Szymanski, 1989; Ree & Carretta, 1994; Tirre & Raouf, 1998).
Moreover, this result contained new information, because the
measure of general cognitive ability used in this study was a
learning potential measure.

The shared variance bet ween the TR AM 1 and three of the
psychomotor measures was possibly due to a common factor
of cognitive abilit y or g. It appeared that these four measures
formed a cluster of substantial intercorrelations. Similarly,
the intercorrelations bet ween the psychomotor predictors
could point to the presence of a higher order psychomotor
factor as found in earlier studies (Carretta & Ree, 1997;
Chaiken et al., 2000; Ree & Carretta, 1994; Vernon, as cited in
Walsh & Betz, 1990). To test the research hypothesis that the
combined use of the TR AM 1 and the Vienna Test System
measures applied in this st udy would yield a general
cognitive abilit y or g factor and a general psychomotor
factor, a factor analysis was indicated. 

The most striking feature of the factor matrices presented in
Table 5 is that the same four measures loaded highly on the first
factor for all three of the solutions. The magnitudes of the
loadings on the first factor also corresponded rather closely
between the three solutions. The four predictors in question
were the TRAM 1 and the three psychomotor variables that
correlated significantly with the learning potential measure (see
Table 3). These subtests of the Vienna Test System were
Cognitrone efficiency, Determination Unit efficiency and Two-
hand coordination accuracy. 

Furthermore, the remaining factors of the two-factor and three-
factor solutions could not be interpreted unambiguously,
because only two substantial factor loadings were obtained per
factor. This means that these factors were not adequately
determined (Harman, 1976; Huysamen, 1989) and had to be
disregarded. When there are less than three variables loading on
a factor, there is probably not enough evidence to make valid
interpretations about the factor in question. A cautious
interpretation of the data of the current study therefore shows
that the factor analytic findings did not constitute sufficient
evidence for the existence of a general psychomotor factor. The
hypothesis relating to a general psychomotor factor was thus not
substantiated. The single-factor solution that was indicated by the
Cattell “scree test” therefore appeared to be the most acceptable
explanation of the variability in the predictor variables. 

The hypothesis that general cognitive ability, g, underlies
performance on the TRAM 1 learning potential measure and
psychomotor measures, was accepted, because a single factor
that had high loadings on the cognitive and some psychomotor
measures proved to be the only viable solution. This finding also
corresponded with earlier research showing that cognitive tests
and psychomotor tests are inclined to load on general cognitive
ability or g (Carretta & Ree, 1997; Chaiken et al., 2000; Hunter
& Hunter, 1984; McHenry et al., 1990; Rabbit et al., 1989; Ree &
Carretta, 1994; Tirre & Raouf, 1998). The high loading of the
Determination unit efficiency variable on the postulated g
factor, could possibly be ascribed to the fact that the responses
required for this test were complex (Jensen, 1993).

The fact that some of the psychomotor measures loaded on the
general cognitive factor (g) rather than on the postulated
psychomotor factor, may constitute evidence that reaction time
is a measure of general cognitive ability as postulated by the
information processing school of thought on intelligence
(Jensen, 1982, 1993; Vernon as cited in Jensen, 1986). The
research of Jensen (1982, 1986, 1993), Kranzler and Jensen (1991),
Kyllonen and Christal (1990), and Miller and Vernon (1992) that
indicated that the correlation bet ween cognitive and
psychomotor abilities might be due to cognitive abilities such as
information processing speed, working memory capacity, and
reaction time that typically also underlie good performance in
many psychomotor tests, also found some indirect support in
the results of the present study. 

Another explanation for the finding that the psychomotor
measures loaded on g, may be the view that the measurement of
g is unavoidable in all measures of ability, because a degree of
reasoning is unavoidable when responding to test material,
regardless of whether the test requires a psychomotor response,
specialized knowledge or verbal skills (Carretta & Ree, 1996a,
1996b). According to Jensen (1980), most tests would be reduced
to practical uselessness once g has been partialled out. 

In future research it should nevertheless be kept in mind that two-
factor or three-factor solutions may be viable theoretically, because
there is ample evidence for solutions including a general
psychomotor factor (Carretta & Ree, 1997; Chaiken et al., 2000; Ree
& Carretta, 1994; Vernon as cited in Walsh & Betz, 1990). When
further studies are planned, the results of the present study may be
used to plan which additional psychomotor tests should be
included in an exploratory factor analysis. For instance, in the two-

VALIDITY OF LEARNING POTENTIAL 67



factor and three-factor solutions Factor 2 consisted of the two Two-
hand coordination tests which, compared to the other
psychomotor tests, made stringent demands on fine motor
coordination. The respondents were required to move a cursor on
a given track with the aid of two joysticks, one which can move
forward and backward only, and one that can move right and left
only. Hence candidates had to use both hands in a coordinated way
to move the cursor through the track within acceptable accuracy
limits. The track consisted of three sections varying in the demands
made on the left and right hands. These results suggested that the
Two-hand coordination tests defined a general psychomotor factor.
Similarly, the combination of the Two-hand coordination accuracy
and the ZBA time estimation variables in Factor 3 may indicate the
presence of a psychomotor precision factor, because both measures
required precision accuracy. The ZBA time estimation variable
involved the accurate prediction of the speed at which an object
was moving and deviations were measured in milliseconds,
whereas the Two-hand coordination accuracy variable involved the
assessment of the accuracy of small, precise movements. The
possibility of the existence of such psychomotor factors may be
worthy of investigation in future studies.

In conclusion, evidence for the concurrent validity of the
cognitive measure, learning potential, for predicting the job
performance of haul truck operators, was obtained for one of the
criteria, namely supervisor ranking. This result corresponded
with findings of other researchers that cognitive measures can
predict job performance and training criteria (Gutenberg et al.,
1983; Hartigan & Wigdor, 1989; Hunter, 1986; Hunter & Hunter,
1984; Jensen, 1986; Levine et al., 1996; Schmidt & Hunter, 1981,
1998; Schmidt et al., 1988). In this study limited evidence was
found for the concurrent validity of psychomotor ability in a low
level entry job requiring operating skills as was found by other
researchers (Carretta, 1989, 1992; Carretta & Ree, 1994; Duke &
Ree, 1996; Hartigan & Wigdor, 1989; Hunter & Burke, 1994;
Hunter & Hunter, 1984; Levine et al., 1996; Martinussen, 1996;
Schoeman, 1995; Wheeler & Ree, 1997). Only Cognitrone
efficiency correlated significantly with one of the criteria,
namely spotting time. 

A possible reason for the paucity of significant predictors in this
study is range restriction in the predictors for which no
correction was made. This was a concurrent validity study and
all the subjects were experienced operators. It can therefore be
argued that limited variability in the performance on the
psychomotor measures was to be anticipated. The severe
kurtosis present in some of the psychomotor predictors, notably
in the ZBA motion estimation accuracy, ZBA time estimation
accuracy and Two-hand coordination accuracy measures,
supported the notion of the operation of range restriction in
this study. A cross-validation using a predictive design is
recommended, because this would minimise the range
restriction which is typical of concurrent designs. Range
restriction was expected to be active in the current study,
particularly due to the levels of experience of the candidates
which influenced their scores on the predictors and hence
confounding the relations between predictors and criteria. The
presence of range restriction might have led to the validity
estimates reported being much lower than they were in reality
(Anastasi & Urbina, 1997; Cascio, 1998). 

An important limitation of this study was that no attempt was
made to control for the motivational aspects of operator
performance, a typical limitation of concurrent validity designs
(Anastasi & Urbina, 1997; Cascio, 1998). For the objective
criteria, Spotting time and Corrected tons hauled, operator
performance was assessed over a three-month period based on
performance records captured on a routinely basis by the
computerised dispatch system. The operators were therefore not
aware of their performance being assessed. It is thus conceivable
that the predictor variables related to driving ability, whereas
the criterion variables related to driving behaviour. Driving
behaviour could have been affected by a myriad of

motivational factors that may have impacted on the correlations
found in the study (Cascio, 1998, Deary, 2001), thereby
depressing the validit y coefficients obtained. It is
recommended that all participants in fut ure st udies be
informed that there will be systematic monitoring of their
operating performance over a three-month period. Although
this will not remove all motivational effects, it may have the
effect of limiting their impact.

The criterion problem which plagues most validity studies, was
also a relevant factor in the current study. Because the road
network data and the consequent correction factor that was
built in to correct for road gradient (the “effective flat haul
rate”) were suspect, criterion unreliability was suspected for
the criterion, Corrected tons hauled. There was insufficient
data available to attempt to estimate its effect and hence
correction for attenuation of the validity coefficients could
also not be attempted. Professional technical auditing of the
computerised dispatch system should be undertaken before
any further cross-validation studies are attempted to ensure
that the objective criteria are reliable. Furthermore, the
subjective criterion, namely Supervisor ranking, was obtained
in a relatively rudimentary fashion using the paired
comparisons ranking method (Cascio, 1998), the focus being
on attempting to corroborate the findings of the objective
criteria. Although the strong correlation between performance
on the Spotting time criterion and the Supervisor ranking
criterion appeared to indicate good reliability and validity of
the Supervisor ranking criterion, it is advisable that a more
robust performance measure such as a behaviourally anchored
rating scale be used (Cascio, 1998; Latham & Saari, 1984) to
improve the psychometric properties of the criterion and also
to provide a better understanding of the performance levels of
the various operators. 

There are three criteria against which the effectiveness of
selection procedures may be measured, namely productivity,
safety and length of time taken to complete a training
programme. Due to the limited scope of this study, productivity
was the only criterion used. No attempt was made to link the
predictors to safety or training criteria, both of which hold
promise for further research.

Safety is a crucial consideration in the mining industry (Mines
Health and Safety Act 29 of 1996), and arguably enjoys even
more focus than productivity. It makes sense that the mining
industry should be interested in identif ying potential
operators who display the least risk from a safety perspective.
Despite its importance, validity studies relating to safety
criteria are rare. This may be due to the difficulty of obtaining
safet y criteria that are reliable (accidents are, generally
speaking, relatively infrequent events and near-misses are
seldom reliably reported).

It can also be argued that the Vienna Test System subtests relate
better to safety than to productivity measures. For instance, the
Cognitrone yields data in terms of the candidates’ ability to
concentrate and to adjust their work tempo to different stimuli
patterns (Schuhfried, 2000a). The Determination unit focuses on
operators’ capacity to make appropriate and fast responses in
rapidly changing environments that may involve stress
Schuhfried, 1996). The test starts off slowly, gains speed to a very
fast response requirement (approximating high stress situations,
e.g. accident or near-accident situations) and then slows down
marginally (approximating the period just after the
accident/near-accident). The Two-hand coordination subtest
focuses on candidates’ hand-eye and hand-hand coordination,
which is conceptually related to safety because of small
movements that need to be made during the spotting in process
in tight loading conditions (Schuhfried, 2000c). The Distance
estimation time and motion measures attempt to identif y those
candidates who are least likely to underestimate distance and
hence stop too late, or cut in front of moving machinery when

PELSER, BERGH, VISSER68



it is not safe to do so (Schuhfried, 2000b). It is clear that the
Vienna Test System subtests have a strong conceptual link to
safety and therefore it is recommended that the safety variables
pertinent to haul truck operator performance should be
measured using a simulator. Scores on the predictor variables
can then be correlated with the safety variables, such as the
number of times during the simulation exercise that the
operator stopped too late, underestimated the speed of
approaching vehicles, or displayed risk behaviour (such as
driving too fast or overtaking on an incline). 

Bearing in mind the limitations of the present study that were
pointed out above, and the fact that only partial evidence for the
concurrent validity of the TRAM 1 Learning potential measure
and the Vienna Test System in predicting job performance of
haul truck operators was found, it is recommended that further
research be conducted to correct these limitations. In particular,
attention should be paid to using safety variables as job
performance criteria.

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