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Research Manual 5th Edition – Profession Analysis

Published 15 August 2018


Research Methodology
Research
TMP Team Management Profile

TMS Research Paper — Mapping work preferences across 295 professions worldwide

295 Professions in Database 135 Professions Analysed (100+ respondents)

Profession Analysis

RM5 contains a worldwide profession database of 295 professions in the Team Management Profile Questionnaire sample. The distinction between coded professions and non-professions depends on whether a specific qualification must be obtained for a person to hold that position, i.e., doctor, lawyer/solicitor, forensic scientist, fire-fighter, or boilermaker. Analyses cover 135 professions with 100 or more respondents.

Key Findings

  • The profession samples with the highest mean Extrovert score are Media/Public Relations Specialist (7.4) and Fundraiser (6.8).
  • The profession samples with the highest mean Introvert score are Draftspersons (4.4) and Geographic Information Systems Specialists (3.6).
  • The highest mean score for Creative information-gathering is 5.2 for Architects, followed by Psychologists and Multimedia Designers/Artists which both recorded 4.9.
  • The profession samples with the highest mean score for Practical information-gathering are Paralegals (7.9) and Personal/Executive Assistants (7.5).
  • The highest mean scores for Analytical decision-making are Geoscientists and Geophysicists (16.5).
  • The highest mean score for Beliefs decision-making is 0.34 for Priests/Ministers of Religion/Pastors/Chaplains.
  • The profession sample with the highest mean Structured score is 8.8 for Engineers: Industrial, very closely followed by Hospitality Workers at 8.6.
  • The profession samples with the lowest mean Flexible score are Guidance/Career Counsellors (0.1) and Psychologists (0.2).

Key Insight: Profession data reveals striking variation: Geoscientists score highest on Analytical decision-making (16.5) while Priests/Ministers show the strongest Beliefs orientation — evidence that profession and work preferences are closely aligned.

Percentile norms allow cross-profession comparisons. For instance, when comparing Geoscientists (mean Analytical score: 16.5) to Priests/Ministers using those same percentile norms, 94% of clergy score lower on the Analytical scale.

Role Preference Distribution Comparisons

The data presents comparative major role preference distributions across several professions:

  • Armed Forces – NCO (n=138) vs Arts & Media Professionals (n=360): The Armed Forces sample shows 7% representation in the Adviser–Explorer sectors, while 37% of the Arts and Media sample have major role preferences in the Adviser–Explorer part of the Wheel.
  • Paralegal (n=119) vs Aged/Disability Care Worker (n=113): The Upholder–Maintainer sector is 11 times higher in the Care Worker sample. Of the 135 Profession samples reported, the paralegal sample has the highest representation of Concluder–Producers (39%).
  • Accountant (n=12,153): Shows strong representation in Thruster–Organiser and Concluder–Producer sectors.
  • R&D Managers at Hewlett Packard Laboratories (n=132): 23% of respondents were in the Creator–Innovator sector compared with only 17% in the Concluder–Producer sector. Many displayed split Wheel preferences between Creator–Innovator and Thruster–Organiser sectors.
TMP major role distribution for Armed Forces NCO
Armed Forces – NCO (n=138)
TMP major role distribution for Arts and Media Professionals
Arts & Media Professionals (n=360)
TMP major role distribution for Paralegals
Paralegal (n=119)
TMP major role distribution for Aged/Disability Care Workers
Aged/Disability Care Worker (n=113)
TMP major role distribution for Accountants
Accountant (n=12,153)
TMP major role distribution for HP R&D Managers
R&D Managers at HP Labs (n=132)

Interpretation and Validity

Profession data analyses provide evidence for criterion-related validity studies. Most of the 135 Profession data sets show a relationship between role preference and the Type of Work which is important in carrying out that job function. Two mechanisms operate: people are attracted to roles matching their preferences, and the job demands can have a substantial influence in changing people’s work preference, particularly as they develop new skills and grow in the job. A two-thirds overlap between preferences and work seems to be an ideal goal. McCann, D.J., & Mead, N.H.S., (Eds.), (2018), Team Management Systems Research Manual: 5th Edition, Team Management Systems, Brisbane, and York.