By Christoff Prinsloo & Maretha Prinsloo on December 13, 2019
© Sergejson / adobe.stock.com
“… one cannot really tell if a successful person has skills, or if a person with skills will succeed – but we can pretty much predict the negative, that a person devoid of skills, will eventually fail.” (Nassim Taleb, Antifragility, p 303)
Here, we follow up on the previous blog/article on cognitive stylistic preference versus cognitive power to explore Taleb’s statement which represents a subtractive epistemology. This is done by analysing the cognitive styles of various age and career groups to determine which cognitive styles best differentiate between groups and whether we should rather avoid psychometric profiles which could potentially hold risk. Or as Taleb puts it: “just work on removing the pebble in your shoe” as knowledge grows by subtraction much more so than by addition.
Could a simple method of forecasting in psychometrics work better than complex approaches and how could this contribute to our understanding of people which is a prerequisite for effective talent management?
In this investigation, we looked at how preferred cognitive styles vary over different job families and age-groups, equated on their education (possessing a single degree). A sample of the most recently acquired 60,572 cases of CPP data were used, subdivided into four age groups (20-29, 30-39, 40-49, 50 and above) and only those cases who possessed a single degree qualification. We computed the median ranked style for each of the 14 CPP cognitive styles, within each age-group, across 10 job families.
To represent the graphs of the average stylistic preferences of all the various age and career groups here may be counter-productive. Interesting differences between groups may be obscured by the equilibrium forces characteristic of statistical methods in psychometrics. Given the fact that the research aims and the subject matter remain key in applying a most appropriate research method, the cognitive styles of specific groups will be analysed here to get an idea of possible outliers which may differentiate between groups.
The most prudent way of encapsulating the stylistic differences between groups is to identify substantively varying medians across both job families and age groups. The five ranked styles showing most changes in median preferred ranked style across ages highlighted by the shaded areas:
Random, Metaphoric, Explorative, Memory, and Impulsive.
We can now look in detail at each of these substantively varying ranked styles.
Figure 1: The Random preferred cognitive style rank across job family and age group
The trend here is that for all job families except Technical-Engineering-Research and Manufacturing-Construction, as individuals age, so do they show a preference for a less systematic/rigorous approach to working with information rather than systematically analysing, structuring or reasoning about issues.
Figure 2: The Metaphoric preferred cognitive style rank across job family and age group
There is less variability with age for this style, except for the older group of Teachers/Trainees who show an increasing preference for capitalising on auditive modes of processing and viewing cognitive challenges from abstract, creative and/or symbolic angles. As individuals in this particular job family age, it seems their preference for conveying information and aligning the perceptions of others is increasingly achieved by adopting the use of powerful metaphors. However, we must be cautious in our interpretation here as the sample size for this specific job family 50-and-above age group is just 17 cases.
Figure 3: The Explorative preferred cognitive style rank across job family and age group
An explorative style is preferred by someone who thoroughly investigates different types of information but may get confused by over-exploring and checking too much, resulting in repeatedly revisiting the same information without moving forward. Again, we have to be cautious here in over-interpreting the 50-and-above age group data as sample sizes for Manufacturing-Construction and Creative-Media groups are 18, and 11 respectively. However, for the Technical-Engineering-Research group, the sample size is 76. On balance, there are few systematic age-related general trends for this style.
Interesting hypotheses can, however, be inferred here, for example that older individuals in creative and technical career fields may rely more on previous experience and personal insights than on exploring unfamiliar external sources of information. The 20 – 29 year old accountants, of whom many are trainees or interns, may not explore additional and unfamiliar sources of information as widely as their older counterparts in accounting do. The most explorative in the 50 – 59 year age group seem to be those in accounting, marketing and teaching; in the 40 – 49 year age group, those in manufacturing; in the 30 – 39 year age group those in creative and marketing careers; and the 20 – 29 year age group those in human resources, teaching and marketing. For most age groups the more creative career fields are thus associated with an explorative approach whereas for those in the construction or manufacturing fields, characterised by practical risks, exploration is as important.
Figure 4: The Memory preferred cognitive style rank across job family and age group
The two most obvious changes in style preference with age are within the Human Resources group (n=75) and Creative-Media group (n=11), with the aged 50 and above group showing an increased preference for using memory strategies to process information/formulate solutions. A preference for a memory style of working within an individual is exemplified by a reliance on past experience and a knowledge base, internalisation and integration of information while processing it, and a tendency to use memory strategies such as confirmation of hypotheses, external reminders, visualisations and associations. As we grow older, experience is embedded in our memories, and we tend to employ more of that stored information resource in our decision-making.
Figure 5: The Impulsive/Reactive preferred cognitive style rank across job family and age group
Similar in some respects to the data in Figure 3, for the Random style. Older age groups within certain job families show an increasing preference for an impulsive or reactive cognitive style. An individual showing a preference for this style of cognition may respond to problems emotionally rather than rationally, favouring quick solutions over taking longer but being more accurate. An element of impatience is associated with this stylistic preference.
Others may observe that the impulsive individual may not spend sufficient time on complex cognitive challenges, preferring instead to make quick decisions under conditions of uncertainty. The two job families where the least median preference is shown over all ages are the Technical-Engineering-Research and Manufacturing-Construction groups. This tendency may be related to the risk associated with judgement errors in these career fields.
The five cognitive styles which best differentiate between the age and career group are the Explorative, Memory, Metaphoric, Random / Trial-and-error, Impulsive / Reactive approaches.
In addition, the CPP ranked preferred cognitive styles show some interesting and intuitively appealing trends across age and job families. The ‘standout’ finding is that a preference for a more undirected action, reliance on previous experience (memory), and impulsive (quick closure) approach to decision-making is more prevalent among older-aged groups within job families which do not incorporate a substantive technical component (such as Engineering and Manufacturing).
This further substantiates the findings of a number of previous studies on both strategic effectiveness and on the predictive validity of the CPP, where these cognitive styles have indicated a somewhat operational and less effective information processing approach than that associated with the Logical, Integrative, Holistic, Learning and Quick Insight styles.
If we heed Taleb’s advice to predict performance in terms of negative as opposed to positive indicators, these somewhat less effective cognitive styles, which also best differentiate between age and career groups, may also best differentiate between individuals within groups, and may flag potential risk for HR decision on selection, placement and development.
Cognadev Technical Report #11 provides the detailed sample information and descriptive statistics from which this blog article has been compiled.