CPP Career Group Analytics: Career related processing preferences

By Christoff Prinsloo and Maretha Prinsloo on December 18, 2019

© 腾龙 郭/ adobe.stock.com

 

Introduction

Cognadev’s CPP database which currently consists of approximately 400 000 sets of cognitive results, offers interesting insights into the intellectual functioning of various groups within the work environment, globally. The results span the cognitive preferences and complexity capabilities of candidates including their cognitive styles of thinking, information processing competencies (IPCs), units of information used and their learning potential. As such the insights gained from the data underline tendencies in cognitive functioning that are generally expected from certain age, ethnic, gender, educational level, educational field, employment category and other groupings. New insights are also offered with regards to the cognitive approaches of various career groups across regions. In this document, examples of the findings pivoting around a few interesting areas are reported.

A number of investigations on cognition have, over the years, focused on whether some commonly held beliefs about organisational roles are supported by CPP results. The aim therefore was to test whether the strengths and weaknesses traditionally associated with specific career paths held true, and whether new insights could be gained. This three-part blog series reports the results arising from an investigation of cognitive differences between various career categories such as engineers, marketers, accountants and administrative personnel, by means of three visualisation/analytics approaches:

  • In part 1 of this blog series, the processing preferences of various career groups were visualised using two-dimensional scatter plots to investigate common expectations of their cognitive functioning.
  • In part 2, the cognitive styles and information processing tendencies of top CPP performers across various career categories were analysed and visualised by means of tree structure representations to investigate the impact of career specific processing requirements and practice on cognitive functioning versus the impact of general cognitive competence on cognitive functioning.
  • In part 3, we report the results of an application of an Artificial Intelligence technique used to identify the cognitive style and information processing competencies as measured by the CPP, which best differentiate between various career groups. The findings were compared to results emerging from case study-based action research using empirical or quantitative as well as qualitative investigations.

 

Analytics Sample Information

For the purposes of this study, the following sample was selected as a target group for exploratory investigation.

  • Level-of-work: Only SST Level 3, alternatively referred to as Tactical Strategy (TS) or Alternative Paths (AP) level of work
  • Age at assessment: 35-54
  • Level of Education: Multiple Degrees

This yielded a sample size of 13,100 candidates, spread across a multitude of functional areas, disciplines of qualifications and sectoral involvement. Some examples of the many analyses that were conducted, are shown here.

 

Functional Area Analytics

Using the CPP-based Standardised T-scores, the aim of the visualisations below was to determine whether different functional areas consistently exhibited different information processing competency (IPC) profiles and differently ranked cognitive style preferences; and to what extent they mirrored expected cognitive characteristics. Results based on selected combinations of 56 IPC and 14 style scores were used. An example of the Metaphoric versus Analytical style scatter plot for various functional areas is shown below to illustrate cognitive processing characteristics of the various groups. The results represented in Figure 1 mirror our core understanding of typical job processing characteristics, with Engineering/Technical preferring an Analytical Thinking Style. Likewise, it is no surprise to see Customer Service and Teachers/Lecturers/Social scientists groups opting for a Metaphorical, verbally oriented, approach.

Figure 1: Functional groups: Metaphoric versus Analytical styles

 

The second example of the visualisations focusing on Employment Sectors depicts a two-dimensional graph or scatter plot regarding their information processing competency (IPC) scores.  The results shown in Figure 2 exhibits a plot of Judgement versus Analysis, showing IT dominating Judgement scores and again Legal, Pharmaceutical and Mining presenting top Analytical scores. The Retail and Food and Beverage as well as Consulting industries achieved lower average scores in terms of these two processing scores.

Figure 2: Sector of employment: Information processing competencies – Analytical versus Judgement IPC scores

 

Discipline of Education Analytics

The next sub-sample came in lieu of discipline of qualification. So, although an employee might have ended up in a managerial functional area, and in Retail, he/she might have had a scientific undergraduate qualification. This analysis was therefore done to investigate whether an employee’s fundamental academic training shaped their thinking, as opposed to the jobs they now fill.

Figure 3 shows an example of the scatter plot visualisations done. It again contrasts the Analytical and Metaphorical thinking styles, using discipline of qualification as the core dimension. The results indicate the clear Metaphorical thinking style for those from Marketing and Sales disciplines, while Military trained groups showed a clear Analytical thinking style preference. The long list of similar good analytical performers are the usual suspect of science and engineering. These results seem intuitively appealing.

Figure 3: Discipline of qualification: Metaphoric versus Analytical styles

 

Our final graph depicted here in Figure 4 shows Judgement versus Story telling Information Processing Competencies (IPCs). Not surprisingly, Marketers have found their niche, while Military personnel still know how make the tough calls objectively whilst the rest seem to cluster together and therefore show equally developed skills in this regard.

Figure 4: Discipline of qualification – Information processing skills of Judgement versus Story telling

 

The above findings to some extent exhibit the depth of insights that can be gained from Cognadev’s CPP database through the use of a reputable business intelligence platform. A number of interesting insights were revealed by the entire exercise. Most of these confirmed our understanding of the job market as it operates now, but the many different analyses also revealed a few surprises.

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