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SA Journal of Industrial Psychology

versão On-line ISSN 2071-0763
versão impressa ISSN 0258-5200

SA j. ind. Psychol. vol.43 no.1 Johannesburg  2017 



Secondary education as a predictor of aptitude: Implications for selection in the automotive sector



Juliet I. PuchertI; Nicole DoddII; Kim L. ViljoenI

IDepartment of Business Management, University of Fort Hare, South Africa
IIIndustrial Psychology (Mil), Faculty of Military Science, Stellenbosch University, South Africa





ORIENTATION: Details of applicants' secondary education (incorporating subject choice) could be a useful screening tool when processing large applicant pools. Here, the relationships between secondary education (incorporating subject choice) and the reasoning and visual perceptual speed components of the Differential Aptitude Test are explored.
RESEARCH PURPOSE: The objective of the study was to determine whether type of secondary education (incorporating subject choice) could be used as a substitute for reasoning (verbal and non-verbal) and/or visual perceptual speed aptitudes in the selection of operators for an automotive plant in South Africa.
Motivation for the study: The motivation for this study arose from the evident gap in academic literature as well as the selection needs of the automotive industry.
RESEARCH DESIGN, APPROACH AND METHOD: This research adopted a quantitative approach. It involved a non-probability convenience quota sample of 2463 work-seeking applicants for an automotive operator position in South Africa. Participants completed a biographical questionnaire and three subtests from the Differential Aptitude Test battery. The Chi-square test was used to determine the relationship between type of secondary education (incorporating subject choice) and selected cognitive aptitudes.
MAIN FINDINGS: The study's findings revealed statistically and practically significant relationships between type of secondary education (incorporating subject choice), verbal reasoning, non-verbal reasoning and visual perceptual speed. Broad performance levels in the three aptitude subtests employed in this study were significantly associated with the type of matriculation certificate held by applicants. The findings specifically indicated that the secondary education types that included the subjects mathematics or both mathematics and science were associated with higher levels of performance in the three aptitudes. This had consequences for these applicants' success in the screening process which could lead to enhanced chances of employability
PRACTICAL AND MANAGERIAL IMPLICATIONS: Applicants' type of secondary education (incorporating subject choice) could be regarded as a key criterion in human resource selection and be instructive in the screening process. This could reduce the candidate pool prior to more costly psychometric assessments.
CONTRIBUTION OR VALUE-ADD: The findings are specifically relevant to the South African automotive industry in terms of their human resource selection practices. The insights gained from the findings may also be used as a guide to human resource practitioners in the selection of similar level employees in other working contexts. The study makes a case for a multiple-hurdle approach to selection.




More than any other area, the measurement of intelligence is one of psychology's most significant achievements (Deary, Strand, Smith & Fernandes, 2007; Foxcroft & Roodt, 2013; Furnham, 2008; Nisbett, 2013). General mental ability (GMA), or intelligence, is furthermore regarded as the most validated individual differentiating construct in psychology (Bertua, Anderson & Salgado, 2005; Giberson & Miklos, 2012; Schmidt, 2002). GMA is known to be the common aspect underpinning achievement on all mental ability tests (Brown, Le & Schmidt, 2006; Carretta & Ree, 2000).

In comparison to GMA, specific cognitive aptitudes, often simply called aptitudes, are narrower in focus and aim to provide a unitary assessment of a specific aptitude (Foxcroft & Roodt, 2013; Schmidt, 2002). Aptitude is broadly defined as the potential within a person to obtain a specific level of skill or ability, following a certain amount of training and/or practice (Byars & Rue, 2011; Coetzee & Volsoo, 2000). Cognitive ability tests (CATs) measure specific mental abilities such as verbal skills, quantitative or numerical skills and reasoning ability (Foxcroft & Roodt, 2013; Noe, Hollenbeck, Gerhart & Wright, 2011). Perceptual speed and spatial ability tests are also commonly incorporated in aptitude tests. In addition, finger dexterity, wrist-finger speed and manual dexterity are examples of the abilities analysed in psychomotor aptitude instruments (Byars & Rue, 2011). Whilst often tested individually, it is noteworthy that a combination of two or more specific aptitudes is in actual fact a measure of general cognitive ability (GCA) (Brown et al., 2006; Domino, 2002; Foxcroft & Roodt, 2013; Schmidt, 2002).

Decades of empirical research has consistently revealed the significance of CATs in predicting, amongst others, academic success, work success and significant life events (Heaven & Ciarrochi, 2012; Kuncel & Hezlett, 2010; Kuncel, Ones & Sackett, 2010; Luo, Thompson & Detterman, 2003; Macpherson & Stanovich, 2007). This proliferation of support for the use of standardised CATs has also had the unfortunate result of developing a pervasive array of flawed ideas and beliefs (Kuncel & Hezlett, 2010; Nisbett, 2013). One of these erroneous conclusions is that cognitive ability is mostly because of genetics, with the environment having little effect on it (Foxcroft & Roodt, 2013; Nisbett, 2013). Incremental theorists take a different stance and view it as malleable. Through exerting more effort, working and learning from mistakes, cognitive ability can be influenced (Malmberg, Wanner & Little, 2008). Based on this paradigm, an individual's cognitive ability is not dictated by genetic inheritance per se but rather the genetic inheritance may exert a considerable influence on how the individual's cognitive ability develops in different environments (Hunt, 2014). Cognitive test scores are hence the complex reflection of a sum of talents, learned knowledge and skills and other environmental factors such as past experience, education and training (Kuncel & Hezlett, 2010; Wicherts, Dolan & Van der Maas, 2010). These environmental factors, including secondary education, could then shape CATs, such as the three aptitudes used in this study.

Some researchers have taken the nurture viewpoint to the extreme, indicating that one of these environmental factors, namely, education, has a causal relationship with cognitive ability. The significant correlation between cognitive ability and length or quantity (i.e. the highest grade successfully completed) of schooling has been well documented. Researchers have insisted that there is a stable and robust relationship between schooling and the enhancement of cognitive skills (Ceci, 1991; Hunt, 2014; Jacobs et al., 2003; Watkins, Lei & Canivez, 2007). Studies have confirmed this relationship through the investigation of particular educational reforms, namely increased compulsory schooling. These studies have provided consistent evidence for the long-term impact of increased schooling years on cognitive ability (Brinch & Galloway, 2011; Schneeweis, Skirbekk & Winter-Ebmer, 2014). Given this, it stands to reason that education could be used as a predictor of aptitude and as a proxy for psychometric assessment.

It certainly cannot be disputed that in educational settings, cognitive ability plays an essential role in learning and academic performance as the two variables are significantly associated with one another (Soares, Lemos, Primi & Almeida, 2015). However, the direction and scope of this potential causal relationship between education and CAT scores remains elusive and drawing clear conclusions thereon is highly controversial (Deary et al., 2007; Lynn & Mikk, 2007; Rohde & Thompson, 2007). When prior academic achievement and cognitive ability are simultaneously considered, researchers have concluded that the only meaningful predictor of final academic achievement is prior academic achievement (Brinch & Galloway, 2011; Soares et al., 2015).

Nevertheless, research into the relationship between educational factors and CAT scores reveals that something is happening but they are not yet able to specify just how it is happening (Hunt, 2014). Scores on cognitive tests and schooling are positively associated, but it is difficult to determine if the relationship is unilaterally causal or if a reverse influence exists (Carlsson, Dahl, Öckert & Rooth, 2015). It is plausible that the association between schooling and cognitive ability involves reciprocal causation. One postulated reason for this reciprocal relationship is the adaptive plasticity of the developing brain (Ariёs, Groot & Van den Brink, 2015; Baker et al., 2015; Baker, Salinas & Eslinger, 2012; Howard-Jones, Washbrook & Meadows, 2012; Stevens & Bavelier, 2012). Other researchers have indicated that because of increased environmental complexity and the significant emphasis on extended education at secondary level, there have been enhanced levels of academic aptitude (Barber, 2005; Howell & Wolff, 1991).

The tipping point in this highly emotive topic could be the distinction between quantity and quality of education. Even when samples have been matched in terms of educational level or quantity, research has highlighted quality of education as an important factor in CAT test scores (Barro & Lee, 2001; Donnelly, 2001). South African research has also confirmed the significant effect of the quality of education on intelligence scores (Nell, 1999; Shuttleworth-Edwards et al., 2004; Van Tonder, 2007). One of the current challenges facing the South African educational system is the improvement of the quality of education in all schools (Mayer et al., 2011; Ramdass, 2009; Smith, 2011; Spaull, 2013). This is a fundamental prerequisite in order for the educational system to effectively fulfil its primary task of preparing youth for the world of work as well as optimising knowledge and skills production within these students (Van de Werfhorst, 2014).

It is incontestable that this world of work cannot effectively function without human resources. Through the consistent application of their knowledge, skills and wisdom, the workforce increases the quality and quantity of labour output. Human resources are essential in achieving overall strategic business objectives (Breaugh, 2013; Naude & O'Neill, 2011; Shatouri, Omar & Igusa, 2012). The selection of this workforce is therefore a fundamental aspect of an organisation's strategic planning initiatives. Organisations need to tactically engineer a methodology to match talent supply with the current and future talent demand (Grobler, Wärnich, Carrell, Elbert & Hatfield, 2011; Mehok, 2009; Nel et al., 2011; Noe et al., 2011). There is specifically a need for human resource selection processes to indicate the incumbents that have a realistic possibility to be successful. Failure to do so can have dire consequences for the organisation. Various methods and techniques are thus employed in human resource selection to increase the organisation's productivity and competitiveness (Grobler et al., 2011; Nel et al., 2011; Noe et al., 2011). Education and skills development is a priority in South Africa where unskilled, inexperienced jobseekers are viewed as a risky investment in a faltering local economy (Mahembe, 2012; Nzimande & Patel, 2012; Peo, 2013). In light of this, it is noteworthy that research papers on the range of selection techniques employed in South African organisations have indicated the increasing trend towards using psychological assessments (see Louw, 2013; Van der Merwe, 2002).

The background for this study has elucidated an important issue for consideration within the South African human resource management field. That is, understanding the relationship between type of secondary education (incorporating subject choice), reasoning and visual perceptual aptitudes in the selection of personnel.

Research purpose and objectives

Cognitive ability is the result of interaction between genetic endowment and the environment. Current research highlights that education can be a central factor in the environment's influence on cognitive ability (Baker et al., 2012, 2015; Barber, 2005; Downey, von Hippel & Broh, 2004; Ostrosky, Ardilia, Rosselli, Lopez-Arango & Uriel-Mendoza, 1998). Regardless of the specific content area being taught, what happens at school has an impact on neurocognitive development. Learning basic literacy, numeracy and other academic subjects, even only for a few years under basic conditions, leads to a number of cognitive enhancements resulting in schooled children thinking and reasoning in a significantly different manner in comparison to unschooled children. Immersed in an environment that prioritises cognitive abilities, whilst learning a specific set of skills at school, children's scope and depth of cognition and aspects of their executive functioning are also fundamentally enhanced (Baker et al., 2012; Downey et al., 2004; Howard-Jones et al., 2012; Ostrosky et al., 1998; Schneeweis et al., 2014; Stevens & Bavelier, 2012).

Within this dynamic, studies have identified the impact of educational quality on cognitive ability and have specified that this aspect needs additional investigation (Donnelly, 2001; Luo et al., 2003). Furthermore, it is evident that there is significant investigation and debate surrounding educational attainment level and cognitive ability, but scant exploration on the relationship between type of secondary education (incorporating subject choice) accomplished and level of GCA (Kuncel et al., 2010). There is specifically a lack of research into the reasoning skills of adolescents and the role of subject-based content at the secondary educational level on the youth's reasoning achievements (Ariёs et al., 2015). A need has been identified for research into what it is about prior learning at school that enhances subsequent cognitive abilities. Specifically, there is a call to investigate whether syllabi or different subjects employed at schools have a distinguishing impact on neurocognitive development, specifically the executive intelligence functions (Baker et al., 2012). Researchers have called for their findings into the relationship between mathematical achievement and GCA in young adults should be replicated in a large more diverse sample to further explore the extent of this association (Rohde & Thompson, 2007).

Taking into consideration the background and motivation for this study, there were four primary research questions underpinning the project:

  • Is there a significant relationship between the type of secondary education (incorporating subject choice) obtained and verbal reasoning ability?

  • Is there a significant relationship between the type of secondary education (incorporating subject choice) obtained and non-verbal reasoning ability?

  • Is there a significant relationship between the type of secondary education (incorporating subject choice) obtained and visual perceptual speed?

  • Is there a significant relationship between applicants' type of secondary education (incorporating subject choice) and selection outcomes?

One of the most critical management decisions is the appropriate selection of a candidate for a vacant position (Azar, Sebt, Ahmadi & Rajaeian, 2013; Grobler et al., 2011). Recruitment and selection are, however, costly human resource management exercises and errors in judgement can be extensive in terms of time, energy and money (Grobler et al., 2011; Lough & Ryan, 2010; Moore, 2006; Paterson & Uys, 2005). There is no set, typical and/or generally accepted human resource selection process and no two organisations conduct selection in the same manner (Louw, 2013; Van der Merwe, 2002). A widely used technique, the multiple-hurdle (or successive-hurdle) approach, results in the candidate pool becoming increasingly smaller after each stage in the selection process (Grobler et al., 2011; Nel et al., 2011; Noe et al., 2011). To assist in this decision-making process, various tools are used to assist recruitment and selection practitioners, including the initial screening of candidates' curricula vitae, reviewing application forms, conducting interviews, carrying out assessment and testing, as well as doing medical and reference checks (Chan & Kuok, 2011; Grobler et al., 2011; Louw, 2013).

General cognitive ability is regarded as a valid predictor of both educational and vocational performance, providing valuable appraisals of creativity and career potential (Kuncel, Hezlett & Ones, 2004; Ng & Feldman, 2010). The reason for the first broad test of cognitive ability, developed by Binet in 1905, was to be able to predict individual differences in educational outcomes and research continues to underpin a moderate to strong association between cognitive ability and educational achievement (Deary et al., 2007). The use of psychometric tests as educational and occupational selection tools is hence justified on the basis of firm evidence that their scores accurately predict real-world success and have considerable value at both practical and theoretical levels (Deary et al., 2007; Foxcroft & Roodt, 2013). Given their usefulness in making employment decisions, GCA tests have been used in human resource selection for over 80 years (Outtz, 2002). Aptitude testing can denote the future performance of job applicants accurately. Psychological tests are considered as valuable instruments for two reasons. They can predict what the person is able to do currently as they reflect existing skills and knowledge, plus they are able to pr