The University of Northampton has 10,942 students of which 449 are reading Psychology – either as their major subject or as part of a joint degree (Student Records, 2018). Psychology is a broad discipline, that attracts personalities from all walks of life. This study investigates the personality profile of the psychology student and asks if there is a correlation between the way they think and the type of psychology modules they are most interested in and ultimately the area of psychology they may wish to specialise in. This is important because working in an occupation that you have the skill and aptitude for, will enhance psychological wellbeing and lead to greater career success (Healy & Morton, 1985). Additionally, increasing a client’s understanding of which work environment is more closely aligned to their skills will result in a better fit (Holland, 1959). This research will therefore be useful to career counsellors who have clients wishing to understand what field of psychology they may be most suited to.
Previous studies have identified a moderate, positive effect size between an individual’s personality and career choice (Holland, 1966; Fuller, Holland & Johnston, 1999 as cited in Chartrand et al, 2002; Nauta, 2004; Webb, Lubinski and Benbow, 2007) and have been able to demonstrate predictive validity that certain personality characteristics are drawn to and more suited towards careers in the sciences and mathematical vocations (Webb, Lubinski & Benbow, 2007; Toker & Ackerman, 2012; Passler, Beinicke & Hell, 2015). Lowman’s (2010) definition of “interest”, suggests individuals who profile as the same type, are fairly consistent in expressing a preference for related occupational or leisure activities and that these could be related to personality (Holland, 1959). Empirical meta-data evidence has supported the notion that the type of work you feel empowered by is indicative of your personality type and therefore should display longitudinal traits such as stability (Low, Yoon, Roberts & Rounds, 2005 as cited in Nauta, 2010). Notwithstanding, this consistency enables a degree of prediction for performance and academic excellence (Passler, Beinicke & Hell, 2015).
One of the most well-known theories linking career choice to interests and personality and used by career counsellors is Holland’s (1997) RIASEC hexagonal model (Offer, 1999). It has been cited repeatedly in peer review journals such as the Journal of Career Assessment and Career Development Quarterly and is a key framework for many career interest inventories (Nauta, 2010), although there is criticism of gender-bias occupational stereotypes (Offer, 1999). The model categorises occupational activities into six types. These types are Realistic, Investigative, Artistic, Social, Enterprising and Conventional, displayed adjacent to each other in a hexagon.
Types closest to each other are highly correlated and least correlated with the type diametrically opposed to them in the hexagon (Webb, Lubinski & Benbow, 2007). They all have leisure interests, skills and abilities associated with them and are applicable to both development in academic and occupational environments (Holland, 1966). Hence, an individual preferring work that involves the use of tools or machines is likely to profile as a “Realistic” type (Passler, Beinicke & Hell, 2015). Similarly, individuals that express a greater affinity towards science subjects at school are likely to profile as an “Investigative” type (Passler, Beinicke & Hell, 2015;
Wai, Lubinski & Benbow, 2009). However, Hansen, Dik and Zhou (2008) found only a partial covariance between leisure interests and RIASEC categories. Holland’s (1966) work was further developed by Chartrand, Borgen, Betz and Donnay (2002) who looked at science, technology, engineering and maths (STEM) vocations and noted that STEM vocations such as engineering have either RI (Realistic, Investigative) or IR (Investigative, Realistic) types as the first two letters in their Holland code. However, there are less data to link personality with R and I types and further research is needed to help identify variables (Armstrong, Day, McVay & Rounds, 2008). A Holland code is made up of three types– the first denoting the highest scoring category, followed by two sub classes. Any three letter combination containing either an I (Investigative) or R (Realistic), would indicate an interest in a STEM discipline.
Critics of Holland’s RIASEC model argue that it is an “overly simplistic picture of the world of work” (Armstrong & Rounds, 2010 p.149); that the interest types lack detail; and that there is often more than one category in a client’s profile (Deng, Armstrong & Rounds, (2007 p. 149) as cited in Armstrong & Rounds, 2010). Such clients have difficulty reconciling their personality with their career choice, giving career counsellors an indication that further work is required to ensure best fit (Armstrong & Rounds, 2010). Although Holland (1997) recognised the possibility of there being more than six types, he believed adding more would detract from the simplicity of the model, making it less user friendly.
Indeed, Rottinghaus, Falk and Park (2017) argue that Holland’s RIASEC model measures characteristics for STEM vocations too broadly, emphasising tasks and learning theories but have failed to keep pace with advances in scientific research and related STEM skill sets. However, Holland’s work has found wide ranging empirical support and validation (Nauta, 2004) and will be used here to investigate the personality profile of the psychology student with the aim of determining if there is a correlation between personality and the type of Psychology discipline they are most interested in studying.
Holland’s RIASEC model of vocational personality and environment has practical application in the workplace because it aligns people with environments and provides clients with an easily understood way of assessing and articulating self-knowledge and ability. It helps raise awareness of different types of work environment and barriers or opportunities for pursuing career interests (Nauta, 2010). It can assist in identifying best fit for individuals looking to obtain job satisfaction, by providing an opportunity for them to assess whether their abilities, competence and values are reflected in the type of work environment and career they wish to pursue. Equally, its use by career counsellors, helps individuals clarify their vocational identity (Nauta, 2010). This is important because doing satisfying work for which you are personally engaged and suited to, leads to higher levels of productivity, psychological well-being and a greater sense of identity. However, critics of the model question how well suited it might be to the changing nature of the workplace that has given rise to ‘portfolio careers’ and unpredictable career paths (Offer, 1999).
Most psychologists would agree that Psychology is a science (Bieschke, 2006) and as such it is recognised as a STEM discipline (Rottinghaus, Falk & Park, 2017). Further support came from 22 psychology bodies at an APPIC conference in November 2002, however the degree to which this applies is still debated with questions on its non-universality and inability to replicate (Suedfeld, 2016). Suedfeld (2016) suggests that “identity psychology” threatens psychology’s progress towards “STEMness”, maintaining that this is a form of prejudice that assumes demographic groups have the same values, experiences and goals and that individuals within these groups share the same psychological characteristics.
Despite Psychology being a STEM subject, it is nevertheless a broad discipline, encompassing both quantitative and more recently, qualitative methodology. Gough and Lyons (2015) argue that qualitative research has been part of psychology’s tool kit since Wundt and that its methodology is even today being performed by experimental scientists (Harre, 2004 as cited in Gough & Lyons, 2015). Qualitative research is bound by positivist remits of validity, reliability and objectivity. Its epistemology theory is concerned with finding “facts” by remaining objective and carrying out well constructed research (Sprague, 2005 as cited in Else-Quest & Hyde, 2016). Remaining unbiased though is problematic for psychologists who look for factors to compare (Else-Quest & Hyde, 2016).
The alternative argument proposes a more socially constructed view of reality embedded in historic, linguistic and cultural practices. Investigation is shaped by the scientist’s values and beliefs which influence their research design. Given these two alternative approaches, it is conceivable that the discipline of psychology attracts students whose personalities are more suited to facts and numbers as well as those who are more drawn to emotion and meaning. Even within the different schools of thought, there are fundamental differences in the way psychology explains human behaviour. The psychobiological school considers the internal physiology of the organism, whereas behaviourism looks to faulty conditioning. Cognitivists on the other hand, argue for schemata and irrational thinking caused through emotive action; whereas the psychodynamic school focuses on the past and childhood distortions and the humanists are more future orientated – giving credence to autonomy, choice and responsibility of the organism (Henry, 1999).
Such is the diversity within the field. It can therefore be argued that there are fields of psychology more akin to the hard sciences by virtue of the experimental methodology and statistical analysis they employ, as well as fields that lean more towards the social sciences (so called ‘soft’ sciences). People who choose to study psychology may therefore have different profiles and these may be dependent on their preference for either the “hard” or “soft” science disciplines. Hard subjects in science are known as physical sciences. Physical science can be defined as a branch of science that studies non-living systems (Merriam-Webster online dictionary) and includes astronomy, astrophysics, chemistry, geology, meteorology, mathematics, statistics and physics (www.timeshighereducation.com).
As Psychology is a STEM discipline, it is likely that Holland’s model would predict that Psychology students should reveal high Investigative and Realistic interest scores (Gottfredson & Holland, 1996 as cited in Toker & Ackerman, 2012 p.525), however, the broad nature of the discipline, leads me to propose that this may not be so.
Previous studies have supported high spatial ability relative to verbal ability in determining performance success in STEM occupations (Muniz, Rueda & Nery, 2013; Buckley, Seery & Canty, 2018; Kell & Lubinski, 2013). Spatial ability, defined as “the ability to generate, retain, retrieve and transform well-structured visual images” (Lohman, 1994a, p1000 as cited in Wai, Lubinski & Benbow, 2009) is thought to be a key skill for understanding the more complex science and technical information that separate the truly gifted contributors to their fields.
Criticism for the predictive nature of spatial ability is levelled at whether such an ability is innate or learned (Sorby, 1999) as this would mean targeted interventions could be developed to improve ability. Equally there is disagreement as to what constitutes spatial ability as there are many cognitive factors at play – factors such as perception, visual processing and short-term memory (Buckley, Seery & Canty, 2018) with evidence to suggest that mental manipulation of shape orientation (such as seen in the Cube Test) is temporal processing rather than spatial (Kyllonen & Chaiken, 2003). However, Muniz, Rueda and Nery’s (2014) research to establish the validity of the cube test found otherwise.
A study by Rottinghaus, Lindley, Green and Borgen (2002) demonstrated that individuals with high levels of spatial ability are drawn to careers in STEM fields and that there appears to be a relationship between spatial ability and STEM disciplines, although the reason for why this may be, remains unknown (Buckley, Seery & Canty, 2018). Spatial ability as an indicator of performance, is not restricted to the STEM disciplines, there is support for other vocations such as Architecture and the Arts (Gardener, 1993 as cited in Webb, Lubinski & Benbow, 2007). Webb, Lubinski and Benbow (2007) advocate research into the part spatial ability plays in vocations other than STEM. Current research on the part spatial ability plays relative to performance has focused on broad occupational fields such as education, business, arts, social science, humanities, biological science, mathematics, computer science, physical science and engineering (Wai, Lubinski & Benbow, 2009), however the field of psychology was not examined and this informed this Study’s rationale. Longitudinal data was used by Wai et al, (2009) to research the differences in spatial ability in relation to verbal and mathematical ability in these broad occupational fields and results were plotted for Bachelor, Masters and Doctorate degree students.
They found that the STEM fields displayed a different ability pattern to the other fields. Armstrong and Vogel (2010) suggested extending the study to include personality traits, to see whether they had an effect on predicting academic and career related behaviour. A broad search of the literature revealed that there appeared to be no other studies specifically focused on identifying similar correlations between personality, career choice and spatial ability relative to verbal ability, specifically within the various psychology disciplines. Added to which, there are an increasing number of occupations that identify as a STEM field, with calls to expand the traditional four areas to includes the arts (STEAM) and even medicine (STEMM) (Rottinghaus, Falk & Park, 2017).
This study will investigate the particular cognitive and personality characteristics of psychology students and compare them to the different psychology disciplines to analyse the characteristics required.
Given the rationale, I was interested in exploring whether undergraduate students of psychology would display a similar spatial to verbal ability pattern as that linked to STEM fields and if their personality profiles indicated a similar preference.
The focus of this study is to determine the correlation between the scientific personality of the undergraduate psychology student and the way they think as indicated by their spatial, verbal and mathematical ability pattern. Wai et al’s (2009) study found a different pattern for the STEM fields of Maths/Computer Science, Physical Science and Engineering. This pattern showed spatial ability to be higher than verbal ability.
I am interested in finding out whether those students who express a liking and academic ability for the more scientific psychology modules (as indicated by their degree of “STEMness”) display a similar spatial, verbal and mathematical (SVM) ability pattern to those found by Wai et al’s (2009) study. This study investigates whether the pattern differs depending on the psychology module content contains more or less scientific factors that indicate its level of “STEMness”. If it is shown that there is a correlation, I can therefore imply that such profiles can be used by career counsellors to help students who have elected to study psychology but are undecided about which career path to pursue and to which they would be most suited.
Participants completed a 10-item demographic sheet with questions on their age, gender, favourite school subjects, name of psychology degree, academic year, elected psychology module choices for each year of study and projected choices for future years of study, interest, competence, ability and performance in five core psychology modules, aspirational degree level and leisure time activities.
Justification for each of the demographic questions were based on previous studies.
Age has a bearing on verbal and spatial ability in the mid-20’s, showing positive correlations for humanities and high verbal ability and negative correlations between verbal ability and engineering and mechanical preferences (Webb et al, (2007) as cited in Lubinski, 2010).
Support for difference in gender ability for maths is also demonstrated, with cross cultural studies using comparable data showing an inverse relation between the sexes in mathematics and reading results. (Stoet & Geary, 2013; Holland, 1966; Lubinski & Benbow, 2006).
Webb et al, (2007) also found that high correlations between education/career preferences and high scoring SAT scores were good indicators for determining favourite high school subjects and leisure pursuits that indicated a STEM relevance. Lubinski and Benbow (2006) study found that students whose favourite high school subjects were in the social sciences and humanities displayed higher verbal ability compared to mathematical and spatial ability and students whose favourite high school subjects were in engineering, mathematics or computer science, displayed higher levels of mathematical and spatial abilities compared to verbal ability.
Holland (1997) hypothesized that high scores on the RIASEC Investigative and Artistic categories would aspire to higher degree qualifications. Humphreys, Lubinski and Yao (1993) study, using data from Project Talent, found high spatial ability scores were indicative of superior, committed performance in STEM careers and were important for success in more of the creative arts occupations.
An individual’s scoring on the RIASEC personality categories, will indicate a corresponding interest in leisure pursuits (Holland,1959; Rottinghaus, Lindley, Green & Borgen, 2002; Nauta, 2010). Lowman, (2010) as cited in Passler, Beinicke and Hell (2015, p.31) noted that leisure preferences tended to group together around personality types and remain mostly consistent.
The choice of psychology degree and elected psychology module choices (both current and future choices) were given a “STEMness” rating by assessing key phrases and words found within the module course content descriptions (Table XX). Each corresponding key term was awarded a point, with the total number of points equating to the STEMness rating for the module (Appendix XX). These were reordered to show modules with the least number of points equalling little evident of STEMness to the most number of points equalling excellent evidence of “STEMness”.
Modules with least evidence of “STEMness” are considered less universal in their nature as research has historically been based on Western culture; they have an inability to replicate results, even whilst using the same procedures and they rely more on the observation and interpretation. Such psychology disciplines are likely to fail the test of being a science if the physics model was applied (Suedfeld, 2016). Examples of such disciplines include modules such as Positive Psychology, Counselling, Social Psychology. For a full list see Appendix XX. Modules with the most evidence of “STEMness” contain more of the key terminology as detailed in Table XX below, examples of which include Neuroscience of the mind, Research Methods and Statistics, Mental Health.
The five core psychology modules, with a brief description of their content taken from the module guides were Research Methods and Statistics, Cognitive Psychology, Biological Psychology, Social Psychology and Developmental Psychology (Appendix XX). Participants will be asked to rate their interest, competence, ability and performance using a 1-5 Likert Scale where 1 represents “not at all/NA” and 5 represents “Extremely”. Answers will be scored from 0-20 on each of the five modules and I DON’T KNOW HOW I’M GOING TO ANALYSE THEM!!! WHAT AM I LOOKING FOR??
A 10-item multichoice test that asks participants to select the option that cannot be made into the 3-Dimensional target shape. Correct answers will inform results. The free test was downloaded from the 123test.com website. 123.com bases its tests on theoretical knowledge and uses representative sampling. Validity for the 3-Dimensional cube testing visuospatial reasoning was .80 Cronbach’s alpha (Rueda & Muniz, 2012).
The SAT Mathematical (SAT-M) and SAT Verbal (SAT-V) tests are designed to test the mathematical and verbal strengths of 17 year’ olds, entering higher education. Lubinski & Benbow’s (2006) study of high achieving students, who took these tests at age 12/13 were followed over a thirty-five year’ period. Their study showed that it was possible to predict that students scoring highly (700 points) in the tests were fifty percent more likely to achieve a Doctorate or PhD level degrees.
SAT reliability and validity in previous studies have shown incremental validity for the tests (Lubinski & Benbow, 2006; Wai, Lubinski & Benbow, 2009; Lubinski, 2010).
SAT-M and the SAT-V test items were selected from the free practice test questions obtained from the website https://collegereadiness.collegeboard.org/sat-subject-tests/subjects. An SAT exam contains 60 questions, but participant fatigue was likely to be confounding variable. I will therefore select ten questions as representatives of each of the mathematical, verbal and spatial skills. Wai et al (2009), created similar SAT tests for his participants using composite questions. Replicating their methodology, question items for this study were chosen using systematic heterogeneity (Humphreys, 1985), representing different skill sets. Table XX shows rationale and example items from the questionnaire.
Although there were some limitations with Holland’s (1966) study (small sample sizes, how the participants were chosen and the result showing differences in gender codings) (Chartrand, Borgen, Betz & Donnay, 2002), this Study will use the same methodology and use the Mean scores, divided by psychology discipline to identify the 3 number coding.
One study was identified and used to base this study upon. It used data from two other studies :- Project Talent and the Study of Mathematically Precocious Youth (SMPY).
Two further studies were identified and were used to inform this study. They were Project Talent and the Study of Mathematically Precocious Youth (SMPY) (Lubinski & Benbow, 2006). Both studies involved young people aged 13 to 17 years and were longitudinal and took place over a period of 20 years.
Project Talent, dating from 1960, is a longitudinal study that followed 400,000 14-17 year olds with the aim of identifying and developing STEM talent. Participants were assessed on cognitive abilities, information tests and personality traits.
Using instruments such as the Strong Interest Inventory (SII) (Strong, Donnay, Morris, Schaubhut & Thompson, 2008), an individual’s preference for a particular type of career environment can be identified and plotted against two dichotomies – dealing with data versus ideas or dealing with things versus people (Prediger, 1982; Holland, 1982). Prediger (1982), superimposed these dichotomies onto Holland’s hexagonal RIASEC model, thus interests are quartered in a structure that identifies dimensions of people-things and data-ideas (fig. 1).
It is therefore logical to hypothesize that the profile of a student attracted to psychology as it applies to the social science subjects will have a different personality/ occupation and spatial ability pattern to that of a psychology student more attracted to neuroscience, for instance.