Tuesday, April 22, 2014

The Engineering Personality

By: Gary J. Salton, Ph.D.
Chief: Research and Development
Professional Communications, Inc.

“Personality” describes behavioral traits that set expectations. These expectations can affect decisions.  These decisions can in turn affect performance as well as personal well-being. Personality is a matter of substance that merits some attention. 

A 1954 article in The Bent of Tau Beta Pi, a publication of the engineering honor society, declared “there exists an engineer personality” (see footnote #1 for link).  In the years that followed thousands of publications describing one or another “qualities” characteristic of engineers have appeared (see footnote #2 for search results).  The common thread running through these declarations is their narrative character.

“I Opt” technology offers an alternative. It has a quantifiable foundation that uses information processing rather than psychology as a lens (the name is an acronym for Input Output Processing Technology). Its categories express unique information processing conditions (see footnote #3 for detail on the technology). 
Click to link to YouTube

A companion video leverages the ability of animation and visual descriptors to greatly expand the explanations and analysis offered here. You can access this YouTube video from our website at www.iopt.com or by clicking the icon on the right to go directly to the YouTube video.

Graphic 1 shows the “I Opt” style strengths of 2,385 professional, non-supervisory engineers. It compares their “I Opt” style strengths to eight other professional staffs (n=8,011) commonly found in organizations (see footnote #4 for a description sample used). The colloquial annotations under each category are only intended to facilitate understanding. They are not definitions of the categories.

Graphic 1  

The scale used in Graphic 1 measures styles as a percent of a maximum possible commitment. Thus 17% RS means that the strategy of spontaneous action is used about 17% of the time. The sum of the four styles total 100%.  This creates an information processing profile along the dimensions measured. 

The blue dotted line in Graphic 1 is the engineering profile. Two qualities stand out.  The strength of the thought-oriented analytical HA style (see “A” on Graphic 1) is the highest of all of the professions measured.  And the strength of spontaneous, instant-action RS style is the lowest (see “B” on Graphic 1).  This condition is enough to identify engineering as unique.

But there is more.  Only Finance/Accounting and Logistics exceed the engineer’s disciplined action LP style (see “C” on Graphic 1). And only they are lower in the spontaneous idea generating RI style (see “D” on Graphic 1). Even in its secondary tendencies engineering stands out.  

It is the pronounced nature of engineering’s commitment to particular information processing styles—both high and low—that gives rise to the Engineer Personality. No one talks about an “Accounting Personality” or an “HR Personality.” The “Engineer Personality” exists because engineers stand apart. And this difference is not confined to external comparisons.
Graphic 2 

Graphic 2 shows the strength distribution of the engineer’s dominant Hypothetical Analyzer (HA) style is skewed (see footnote #5 for detail).  A total of 50.1% of engineers register higher than the medium level of HA strength.  Engineering is attracting people highly committed to full understanding using logical analytical methods.
Graphic 3 
Graphic 3 shows that the engineer’s secondary Logical Processor (LP) style also displays a skew—but less pronounced. This amplifies the unique nature of engineering. Not only is engineering unusual when compare to other professions but it is also internally unusual. Skewed distributions within a profession are rare.

The importance of the foregoing is that there is a solid foundation for Goshem’s declaration that “there exists a engineer personality.”  It is born not only in the nature of the job but also in the kinds of people that are attracted to it.  The “unusual” gets attention and engineering has a lot of “unusual.”

The strength of the average commitment to particular styles explains the existence of the engineer personality.  It does not explain the content of that personality. To discover that we need to examine how the basic Input-Process-Output model is applied in the real world.

“I Opt” technology’s predictive ability rests on a simple observation. Behaviors are not independent events. They occur in sequence. Preceding behaviors can facilitate or frustrate subsequent behavioral options.

For example, you cannot react to what you do not notice. A focus on general aspects of a situation automatically precludes detail. Without detail precision is impossible. Similarly a spontaneous, opportunistic approach focusing on direct action purchases speed at the cost of thought.  There is not much use to “thinking” about something that has already been done. Various combinations of the “I Opt” styles are thus able to produce predictable behaviors. They also are the source of the specific qualities attributed to engineers.

The “I Opt” Behavioral Cascade is the key tool used in the quest to identify behaviorally based personality qualities. Graphic 4 shows a simplified version of the cascade for someone using the engineer’s favored Hypothetical Analyzer (HA) style. 

The left hand side of Graphic 4 describes the information processing choice sequence likely to be used.  The right hand side describes the probable attributions that a naive observer is likely to make as they view that behavior.
Graphic 4 

The sequence of information processing elections on the left side of Graphic 4 automatically produces logically related behaviors.  These create themes that lend itself to generalization.

The right hand side of Graphic 4 shows the internal processes likely to be “inferred” by a naive server. For example, the careful selection of an approach is likely suggest that the subject has a built-in “thoughtful introspective” mind. They do not “see” that the behavior is born of the mental work required by the task being undertaken. Qualitative attributions like “perfectionism” and an”intellectual” orientation are similarly discerned and declared (see footnote #6 for more detailed information on the cascade). 

Each of the four basic “I Opt” styles produces a unique cascade.  People alternate between the styles according to their degree of commitment and the subject being addressed. A naive observer witnessing a sequence of different cascades (e.g., the engineer’s 17% RS) would likely attempt to generalize. Generalization is based on commonality.  In the engineer’s case that commonality is likely to be found in the use of structured methods (i.e., some type of predefined pattern is being followed) common to both of the engineer’s dominant styles (HA and LP). Structure lends a degree of rigidity to behavior. For the HA it is in the realm of thought. For the LP it falls in the realm of action. Upon seeing this stringency the likely inference for our naive observer is that the person is a certain “obstinate” quality.

At this point an exposure becomes visible. Attempting to change the “qualities” of a personality will have consequence.  For example, attempt to relax the engineer’s measured work pace in favor of a “sense of urgency” is likely to compromise the quality of the work being done. “One size fits all” management training imported from other professional areas run the risk of converting these exposures into threats—for both the individual and organization.

A line chart is useful when comparing categories in different populations. However, the internal interrelationships of the categories are obscured.  Personality judgments will typically consider all of the “I Opt” information processing styles simultaneously.  “I Opt” technology captures these interrelationships by using its ratio measurement capabilities to create an “I Opt” profile.

An “I Opt” profile is a radar chart.  The four axes represent the basic styles.  The surface area of the right triangles connecting the axes measure “I Opt” patterns.  Patterns are the information processing dimensions common between adjoining styles. In the engineer’s case, one common element is the use of structure as input screening criteria.

Since everyone is measured on the same ratio scale, individual profiles can be consolidated to get representations of groups as well as individuals. Graphic 5 shows examples of both individual and group based profiles.
Graphic 5 
The profile alone is enough to provide a great deal of information on the probable behavioral characteristics of an individual or group.  For example, the surface area in the lower right quadrant of Graphic 5A immediately tells the viewer that this person favors structured methods as their principal strategy. The distance between any two centroids (i.e., single point representations of entire profile) in Graphic 5C defines the likely difference in the “personalities” of the individuals in a group. The difference in the areas of consensus (white) and majority (grey) areas in Graphic 5B provides an indication of the cost (i.e., time, effort, emotional energy, etc.) of obtaining complete agreement through consensus.

The “I Opt” human information processing model is a constant. But the subject to which it is being applied can call forth different psychological qualities. For example, if the interest is in corporate culture the psychological qualities of concern might be things like loyalty, profit orientation or vision—things not mentioned in Goshen’s 1954 article. But these qualities can be important if considering a merger of organizations. Differences in corporate psychology and/or culture can doom or smooth organizational integration. 

A practical tool for considering personality effect in different areas is to create an “I Opt” Snowflake (named because if it’s geometric resemblance). This is a radar chart with descriptions attached to the styles (vertical axes) and patterns (diagonal areas).  Graphic 6 is an example of the use of the snowflake applied to an individual.  It assesses the likely emotions that the average engineer is likely to elicit in other people.
Graphic 6 
Graphic 6 is constructed by superimposing the profile of an average engineer on the emotional impact snowflakes. The more the profile extends on a particular axis, the more visible will be the attributes cited on that axis.  The greater the surface area in a particular quadrant, the more the attributes cited on the associated diagonal will be considered representative.

Graphic 6 highlights an important aspect of any qualitative “personality” assessment. It depends heavily on the person doing the assessment. If the person doing the evaluating is positively disposed toward the subject they are likely to judge our engineer as calm, astute, patient and attentive.  If they are negatively disposed exactly the same behavior is likely to be seen as slow, timid, trifling and tortuous.

Qualitative personality assessments (versus those based on exact measurement) offer an opportunity for an image management. Different words can be used to define a particular condition (e.g., patient vs slow).  For example, superfluous reassessments can be redefined as quality assurance. This is something to keep in mind if confronting a hostile evaluation based on subjective judgments.

Snowflakes can be constructed for any area affected by information processing. The subject area determines the psychological attributes of interest. The “I Opt” profile determines the likely direction and magnitude that the attribute will assume.  The behavioral cascade translates the information processing choices into the descriptive characterizations of psychology (see footnote #7 for links to additional snowflakes).

Goshem was right. There is an engineer personality. However it is not as solid as he infers. The information processing elections that create the engineering personality are pronounced but not universal.

The different approaches to information processing are perhaps best seen by looking at the dominant “I Opt” styles of some of the engineering specialties. Table 1 collapses the 89 engineering specialties cited by respondents into 25 groups. The percentages represent the proportion of the sample that subscribes to each of the strategic styles as their “default” or dominant approach.  The dominant style is the strongest (the highest percentage in each row) in the engineer’s repertoire.
Table 1 
The first thing that might be noticed is that the dominant information processing approach in all areas of engineering is the analytical HA strategic style.  This consistency is the basic source of the engineer personality.

The next thing to notice is that engineers who do not subscribe to the most popular Hypothetical Analyzer (HA) approach are most likely to instead favor the disciplined action LP strategy. This happens in 20 of the 25 grouped categories.

But there are five areas where this secondary strategy favors the speculative idea-generating RI style (these are boxed on the table). In three of these five cases this makes intuitive sense. These three areas are all involved with the more creative engineering specialties (R+D #16, Design #6 and Development #7).  Explanations for the two remaining areas (Software #29 and Reservoir #18) are not transparent to the author.

A final note is the fact that each of the four styles is dominant in some proportion of the sampled population.  These are all working, professional engineers. This fact suggests that there is a niche in every area of engineering for people using different strategies.  This is the source of the exceptions that prove the rule.

This mini-analysis demonstrates that engineering is a “Big Tent.” It is able to use people favoring all strategies.  It just may take some people a little longer to find a “seat” than others.

A final pedagogical observation is worth noting. The skewed distribution of both dominant styles (HA and LP) has teaching implications. Engineering demands discipline and instruction and must rely on rigorous curricula. However, a skewed distribution will shift the mean of a curve. Professors grading on the curve may be setting an artificially high standard that favors logical “in the box” thinking. Students favoring the more adventurous styles will be disadvantaged.  This is something worth thinking about.

(1)  The Engineer Personality, Charles E. Goshen, The Bent of Tau Beta Pi, December, 1954, pp 15-16. http://www.ctgclean.com/tech-blog/wp-content/uploads/Engineer-Personality001.pdf

(2)  A Google search term “engineering personality traits” produced 1,740,000 results. Confining the search to educational institutions (i.e., edu) reduced the results to a still substantial 76,500.  Google search was conducted on March 7, 2014.

(3)  The necessary brevity of this research blog precludes full elaboration of the theory underlying “I OPT” technology.  A general orientation to the dynamics associated with information processing theory can be found in the first 5 minutes of the YouTube video Team Tension—Causes and Management (http://www.youtube.com/watch?v=xQ_5b4BUUB0&feature=youtu.be). 

The YouTube video I Opt” Strategic Styles and Patterns" provides a more detailed operational specification of the basic process

A complete operational understanding of the theory, methods and mechanisms requires taking an “I Opt” certification course.  Information on this option can be obtained by emailing Shannon Nelson at shannon@iopt.com.

(4)  Sampled population included 2,385 professional engineers drawn from 178 unique organizations (subsidiaries were consolidated into parent and not counted as separate entities). A majority of the sampled engineers were located in the United States but included significant representation from 30 other countries including:

The titles used by engineers included in the sample indicate that they are working in 89 unique engineering specialties including:

(5)  An analysis 108,198 people from all professions confirm that the distributions of the strength categories roughly correspond to a normal curve. The skew in the engineering data thus reflects a real difference between engineering and other professions.

(6)  The necessary brevity of this research blog precludes a detailed specification of the Behavioral Cascade. A more detailed example of how the Behavioral Cascade works is available on the companion video at https://www.youtube.com/watch?v=jM1yf_7RIfY&feature=youtu.be beginning approximately 4 minutes into the video.

(7)  A battery of I Opt “snowflakes” that are predictive of observable qualities and which cover subjects like general behavior, learning, communication, emotional impact, corporate culture and general culture are available free of charge at http://www.iopt.com/support-materials.html.

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