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Critical Review of Quantitative and Qualitative Research in Intelligence Quotient (I.Q.) and Cognitive Ability. 



The assessment of a person’s psychological aspects such as knowledge, skills and abilities has become a central component of Psychometrics (Gavin, 2008).  Early research within the psychometrics domain had as it main aim to measure intelligence (Gavin, 2008).  However, subsequent developments within psychometrics have led to implementation of other measurements such as personality, academic achievement, beliefs, attitudes and quality of life (Petrides & Furnham, 2001; Gavin, 2008; Raykov & Marcoulides, 2011).  Psychometric tests usually conform to a certain type of frequency distribution commonly known as normal or Gaussian distribution (Gavin, 2008).  Given that researchers within the psychometric testing domain are interested in measures of central tendency (i.e. mean), this makes standard deviation an equally important measure, as it shows how data is dispersed.


Psychometric research adopting quantitative methods make use of hypothesis testing in order to measure differences between scores (Cohen, 1992).  First of all researchers must find an operational definition of variables such as intelligence and then create a suitable instrument for measuring it.  In this way, through hypothesis testing, a causal relationship can be established between say IQ scores and for example academic achievement (Bellinger et al, 1992).  In this particular scenario IQ scores would be considered the independent variable and academic achievement the dependent one ( Gavin, 2008).Hypothesis testing looks at the probability of an event occurring within a selected population that is beyond mere chance (Gavin, 2008).  For instance, when using the example of high IQ causing high academic achievement, it would imply that high academic achievement is caused by IQ and no other variable.  Here the null hypothesis would be that there is no relationship between the two variables and the alternative hypothesis would postulate that there is indeed a causal relationship between the two aforementioned variables.

It is also interesting to recognise  that in hypothesis testing criterion α (alpha) level for statistical significance is normally set at p≤ 0.05 (Cohen, 1995).  This is thought to be the level at which the null hypothesis is rejected (5 or fewer times out of 100).  This level is also described as the percentage 5%, or sometimes as the proportion 0.05.  It should be noted that, although this value was historically an arbitrary choice, it has been accepted and established as a reasonably good choice (Cohen, 1990).This approach to Intelligence also paved the way for the computer/information-processing paradigm in cognitive psychology, and cognitive neuroscience and associated disciplines (Boake, 2002).  Proponents of this approach postulate that the mind, brain, and cognitive ability are composed of basic quantitative structures and operations which form part of the inborn of core cognition (Plomin et al, 1994; Rowe, 1997; Nisbett et al, 2012).

However there are some fundamental flaws with the hypothesis testing as one of the most common quantitative methods for establishing causal inferences.  For instance sometimes an association between two variables does not necessarily  imply a causal relationship (Cohen, 1995).  Moreover there can also be concurrent variables which can help explain IQ scores among individuals.  For instance early childhood relational traumas are thought to hinder brain and cognitive development (Chong, 2015).   More specifically early relational traumas act as yet another variable which could provide an alternative explanation for the apparent association between IQ scores and academic achievement (Nelson, 2015).  Another issue could be the fact that criterion α (alpha) level 0.05 may not always be an indicative probability of an event repeating itself (Cohen, 1995).  It can also indicate the probability of finding the exact same population in which the studied event could repeat itself (Cohen, 1990).


Criticisms of the Quantitative Approach to IQ Research

It has been argued that psychometrics definitions and quantification of unobservable phenomena such as knowledge, skills, abilities and personality is particularly problematic ( Gavin, 2008).  For instance proponents defending a qualitative approach to studying intelligence argue that its quantification can be rather problematic (Cernovsky, 1994). Researchers favouring qualitative methods to study intelligence may also regard intelligence as socially constructed as opposed to possessing a ‘real’ ontological status (Parker, 1994). This epistemological stance informs the adoption of a very common qualitative method known as Discourse Analysis (Winter et al,2015).

Discourse Analysis is a methodology which falls within the postmodern tradition (Gavin, 2008).  It is thought that this method implicitly subscribes to a relativist ontology, whilst its epistemological underpinnings are social constructionist (Pereira, 2008).This method would pay careful attention to language and power relations and  more specifically to the ways in which language operates to legitimise scientific discourses and empower a particular social group (Gavin, 2008).  Thus, when conceptualising IQ through the lens of Discourse Analysis, qualitative researchers ( Pereira, 2008; Fox, Prilleltensky & Austin, 2009) would argue that IQ as a concept is socially constructed and that its construction is rooted in language.  More specifically careful analysis of language through deconstructing discourses and uncovering hidden and inexplicit meaning in text, it appears that intelligence as a concept rests heavily on social consensus.

In a study conducted by Pereira (2008) Discourse Analysis was used as a method for ‘dissecting’ hidden power relations that favour men as ‘cleverer’ than women.  In this respect Pereira (2008) analysed an article from the BBC news website which reported a study from The British Journal of Psychology.  This  article used key language and reference to authoritative figures in organizational psychology.  When language and argument is used in such ways it simply puts forward discourses which legitimise a male dominated society.  Here, Pereira pays careful attention to hidden and inexplicit discourses concerned with power relations that inevitably end up justifying unequal pay and job opportunity within organizational settings.  In his paper Pereira concluded by arguing that so long as psychometric science disregards the moral and socio-political repercussions, its own scientific credibility will remain questionable.

However qualitative and constructionist approaches such as discourse analysis have also been criticised for culminating in a nihilistic relativism (Raskin, 2001), given that it provides little or no practical application to real life social situations.  In contrast there are some advantages of conceptualising cognitive ability through an objectively measurable concept such as IQ.  For instance, IQ measures foster the understanding of cognitive performance associated with normal and abnormal neurological development (Kline, 2013).



In conclusion, it should be noted that both quantitative and qualitative approaches to study IQ provide useful insights into understanding cognitive ability.  Measuring IQ can be useful in determining fitness for carrying out certain roles within working and organizational settings.  It can also enable government, health professionals, policy makers and key stakeholders to identify individuals who may require key support due to cognitive deficits or other cognitive-based disabilities which may affect quality of life.  However qualitative methods such as Discourse Analysis provide useful insights into understanding how some scientific discourses are legitimised more through argument than credible evidence.





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