Tuesday, January 05, 2016

Engineering Professors: Gender Gatekeepers

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



INTRODUCTION
Professors are engineering’s gatekeepers. They set the qualifications for entry. They choose the method to educate those admitted. Their stance can unconsciously  bias the profession toward or against any particular group.

Far fewer women pursue engineering degrees than pursue educational achievement in other professions (see Footnote #1 for reference citations). The most recent data cites the percentage of women earning an engineering degree in 2012 as 19.2% (see Footnote #2 for reference). This represents a very substantial opportunity cost for society. It is a loss of invention, creativity and productivity that could be used to magnify engineering’s contribution to the world. 

This study investigates whether the information processing elections of engineering professors plays a role in this process as applied to women in engineering. 

Click to see Video
A companion video both expands and abbreviates this research.  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.


ENGINEERING PROFESSORSHIPS
Earlier research has shown tenure track positions
(full, associate and assistant professors) systematically differ from the other faculty (adjunct, lecturer, reader, instructor, etc.-see Footnote #3 for reference citation). Tenure track positions set the standards for admission and govern educational progress.

Table 1 shows the sample of tenured and tenure track engineering professors. The sample is large enough to produce meaningful statistics. The number of universities and specialties represented is sufficient to be accepted as a meaningful representation of the profession as a whole (see Footnote #4 for engineering specialty listing).
Table 1
SAMPLE CHARACTERISTICS
TENURE TRACK UNIVERSITY PROFESSORS OF ENGINEERING
The sample includes both male and female engineering professors. Table 2 shows no significant difference between these men and women engineering professors. Both are likely to understand issues in the same manner and approach their resolution with the same character of response. Table 2 suggests that we can safely ignore professor gender differences for many aspects of this study.
Table 2
PROBABILITY OF SIGNIFICANT DIFFERENCE BETWEEN
MEN vs. WOMEN ENGINEERING PROFESSORS


Table 3 below summarizes the sample of non-engineering professorships. This sample was used for this test whether engineering professors differed from their non-engineering counterparts. The sample was drawn from 26 unique non-engineering professions ranging from accounting to zoology (see Footnote #5 for non-engineering area listing). The sample appears to be reasonably representative of disciplines which have proven to be more attractive to women.  
Table 3
SAMPLE CHARACTERISTICS
NON-ENGINEERING TENURE TRACK PROFESSORS


SHORT-RUN DECISION MAKING
“I Opt” strategic styles are a person’s short-run decision strategies. When situations do not send a clear signal on the most appropriate resolution people choose a strategy based on their predefined preferences. These preferences can be measured with “I Opt” technology. A comparison of engineering versus non-engineering style preferences of university professors are shown in Table 4 (See Footnote #6 for to summary explanation and reference to style content).
Table 4
UNIVERSITY PROFESSOR STYLE COMPARISON
ENGINEERING vs. OTHER NON-ENGINEERING ACADEMIC DISCIPLINES


The difference between engineering and other areas is modest in magnitude (see blue Percent Difference column in Table 4). Only the analytical HA style registers statistical significance (see yellow highlight in Table 4). However Chart 1(below) suggests that the differences may be more pronounced than they first appear.
Chart 1
STYLE COMPARISON
ENGINEERING vs. NON-ENGINEERING PROFESSORS

Engineering professors differ significantly (p<.05) from their peers (yellow highlighted graphic in Chart 1). They are more reliant on thought-based, structured (i.e., pre-patterned) input—the Hypothetical Analyzer (HA) style. And that difference appears to be focused on a specific cadre of professors.

Most engineering professors are moderate in their approach (“medium” level on Chart 1 scale). But a significant proportion registers on the extreme low (see “A”) and high levels (see “B”). The “average” 10% HA difference noted on Table 4 may be behaviorally deceptive. Highly committed people can affect disproportionate influence on issues in which they take an interest.
 Chart 2
HYPOTHETICAL ANALYZER (HA) PROFILE COMPARISON
ENGINEERING PROFESSORS BY ACADEMIC RANK


Chart 2 compares the professor ranks on the HA scale. The highly committed people seem to cluster at the higher ranks. The disjoint is a formula for a bit of natural tension. This is not necessarily dysfunctional. People holding these more extreme positions can serve a vital function in sorting out reliable from untrustworthy knowledge. (See Footnote 7 for reference to similar “disjoints” in academia). 

“I Opt” Styles like the HA shown above matter to gender because of their behavioral effects. Style effects are seen in the immediate response to unexpected situations. Reliance on the HA style produces behavior that is more careful in approach, measured in pace and thorough in response than will be seen in professors in other academic fields. The image of the “average” engineering professor created by this style election is of a reasonable progressive thinker. 
 

LONG-RUN DECISION MAKING COMPARISON
“I Opt” patterns measure the long-run decision strategies. Most objectives require a sequence of decisions. “I Opt” patterns define and measure the combination of styles deployed in any such sequence. They are more fully explained in the 8 minute video “I Opt” Strategic Styles and Patterns (see Footnote 8 for reference).

Typically (but not always) two styles are sufficient to navigate most of the conditions likely to be encountered. The choice of these styles comes to characterize both the individual person and/or the group (e.g., engineering professors in general).
 
Table 5 (below) compares engineering and non-engineering professors on the four basic “I Opt” patterns (i.e., combinations of styles that are named for easy reference). The single significant difference occurs in the Changer pattern. This pattern can be characterized as new ideas experimentally applied. It is a fast and adaptive strategy but which typically carries a risk of failure.

Table 5
COMPARISON OF PATTERN SIGNIFICANCE BETWEEN RANKS
ENGINEERING vs. NON-ENGINEERING PROFESSORS



The 17% Changer shortfall (in yellow above) suggests that engineering professors may have problems in attracting students who are not already pre-committed. Higher levels of the Changer pattern are typically associated with charismatic leaders. These leaders make more use of emotion than the engineer’s favored Conservator and Perfector patterns (i.e., +13% and +4% above in blue highlight above). Emotion is the tool that generates the excitement (e.g., enthusiasm, anticipation, etc.) which better motivates action and commitment. 

Table 5 is deceptive. It masks the dynamics of what is actually occurring. The real effects can be seen in Table 6 which dis-aggregates the engineering professor ranks.
Table 6
ENGINEERING COMPARED TO NON-ENGINEERING PROFESSORS
SIGNIFICANCE AND MAGNITUDE OF DIFFERENCE


The marginally significant Changer pattern difference in the in Table 5 (p=.0422) suddenly becomes highly significant (p=.004) for full professors in Table 6. The unemotional Conservator and Perfector patterns rise from insignificant to a marginal but real significance (both at ~p=.04). It is the full professors who are the more reserved, measured, cautious and intellectual than are their peers in other professions.

The size of the difference with non-engineers is also striking. The variances at lower professorial levels are literally “wiggles” rather than differences. In contrast, full professors register 40% less commitment to the Changer pattern and a much higher commitment to optimal outcomes (Perfector: 23% higher).  They also put much more value on certainty of outcome through a higher reliance on proven methods (Conservator: 15% higher). While not a formula for attracting uncommitted students—male and female—it does serve a purpose. That purpose can be seen when field engineers are brought into the picture.

 
THE PROFESSIONAL CONUNDRUM
The very nature of engineering requires that the university maintain close ties with its operating counterpart. To be relevant, university engineering has to able to contact and relate to field engineers who have access to the expensive equipment, exotic materials and complex situations that fuel engineering professors. Chart 3 tells the story.
 Chart 3
ENGINEERING PROFESSORS vs. FIELD ENGINEERS


Engineering professors are more risk taking (red arrow labeled “A”) and emotionally capable (red arrow labeled “B”) than are their professional counterparts. If they were to adopt the stance of the non-engineering professor (blue dotted line) the spread in communication strategies would widen. This could compromise the relevance of the university to practicing engineers.

The picture painted by Chart 3 is of a university structure well attuned to its professional counterpart. The societal mission of engineering is being well-served by the current profiles (see Footnote #9 for reference). This creates a bit of a conundrum for gender-based initiatives. Any gender-oriented changes must preserve the benefits now being enjoyed. An intellectual scalpel rather than sledgehammer would appear to be the preferred tool of choice.


ANALYSIS OF CURRENT GENDER CONDITION
Engineering has been a male domain for centuries. Engineering professors have developed teaching strategies to serve that community. These strategies work so long as the subject of those strategies remains stable. But instability arose with the freeing of women’s talent beginning in the 1960’s and continuing through today.

The change in women’s roles created an unused talent pool. The problem is that the “material” of that pool is a bit different. Both male and female “pools” can produce engineers. But the tools, techniques and processes suitable to one are not necessarily ideal for the other. An authoritative hard science source defines the issue (See Footnote #10 for reference): 

“…Women embody…structural, cellular, and molecular sex differences in the brain that can be described as true dimorphisms, defined as the occurrence of two forms in the same species(Emphasis mine).

the brain strives to achieve equally optimal performance in cognitive functions, but this has to be attained in very different hormonal and genetic environments” (Emphasis mine).
 
The implication of this objectively verifiable condition is that strategies that work in all male classes may not be optimal for a mixed gender class. The engineering problem for engineering professors is to devise ways of utilizing the latent females talent and put it in service of the profession. 

ATTRACTION TO ENGINEERING
The first step for capturing this latent resource is to get women into engineering classes. The thousands of STEM initiatives—often university supported—are alerting women to engineering opportunities. It is in the introductory engineering classes that this exposure can be translated into a commitment.

Currently the curriculum of introductory courses is likely (but not always) designed around analytically structured transmission of knowledge. This is highly effective in conveying knowledge to those already interested. It is less effective in inspiring a continuing interest among the uncertain. Rationality informs, emotion engages.

Both men and women are affected by emotion. The history of male dominion suggests that male emotional requirements are probably “built into” engineering education. Attracting more women will likely require that their interests be similarly accommodated. How to go about doing that is the engineering issue.

We know that men and women are different. We do not know the behavioral specifics of that difference. The absence of knowledge of behavioral relationships reduces the value of analysis as a tool. Experimentation is the logical option. Assistant and Associate engineering professors are as well-equipped (on average) as is any other profession to deploy this approach. However, they are likely to need the acquiescence of the full professors. This may be a bit problematic.

Full professors are the people who sit on the important committees—including tenure. They are the ones whose advice is sought out on important matters. They are also the ones most highly committed to optimization and certainty of outcome (Perfector and Conservator patterns). In other words, they are those least likely to use experimentation as a tool.

One strategy is to frame gender equity as an engineering problem. Engineering analysis needs objective data to work. Data on the specifics of gender differences is unavailable. Tracking the results of multiple gender-based experimental strategies can provide operational data needed for analysis. Once data has been accumulated optimization and codification could be used to cement in the gains. Effectively, emotional force is captured using a prototyping strategy. Analysis can then be applied to extend the positive effects across profession. This strategy has the merit of using the entire range of the university’s engineering assets.


TEACHING ENGINEERING
Physics does not care about gender. Math is indifferent to biology. The subject matter of engineering is not amenable to compromise in the pursuit of gender equity. And it need not be.

The intellectual content of engineering is not an issue (see Footnote #11 for reference). The way that content is conveyed may be. For example, stress is a strategy sometimes employed in engineering education. This has a differential effect on men and women. Used improperly it can unnecessarily limit educational achievement but in different ways.

Stress has a biologic as well as social basis. Men deploy the hormones cortisol and epinephrine in response to stress. This is what produces the “fight or flight” reaction. The “fight” aspect can direct energy toward increased effort. Women also deploy these hormones but in smaller amounts and counteract it with increased levels of oxytocin. The net effect is more of a tendency toward conciliation rather than “fight or flight.” Unfortunately physics is not amenable to conciliation. The result can be frustration rather than motivation (See footnote #12 for source reference).

Artificial stress does have a role in engineering education. Meltdown simulations in nuclear engineering may be one of them. However, the gratuitous use of the technique simply for motivational purposes will systematically disadvantage women. Motivation can be achieved in other ways without imposing a gender penalty. For example, merely aligning the knowledge to be conveyed with a student’s self-interest can produce unstoppable motivation.

Educational formats are another area where gender penalties can be unwittingly imposed. Many engineering courses employ “teamwork” as a component. This typically involves assigning a common objective to a group—and nothing more. Without guidance on matters such as leader selection, management methods, followership responsibilities and the like students are likely to see brute force is a reasonable tool. The net result is that testosterone tends to control—to the disadvantage of the women in the group (i.e., men generate 7 to 8 times more testosterone than do women).

Skills in team management are a necessary educational component in modern engineering. Teams are the context within which the tools of engineering will ultimately be applied. Both men and women would benefit from competent instruction in group processes. As a bonus many gender based deficiencies would be automatically offset by the knowledge gained.

“Soft science” tools that compatible with the “hard science” preferences of engineering are available (See footnote #13 for elaboration). What is necessary is the recognition of the issue and investing in the necessary knowledge. The investment required is modest and the return on that investment will be substantial.


ENGINEERING RETENTION
Attraction initiatives and educational adjustments will go far toward increasing engineering’s gender equity. It will also strengthen engineering’s societal reach and relevance. But the current depleted state of engineering’s gender equity position requires a transitional effort. Fortunately, it is one which engineering professors are well-equipped to provide.

The high levels of analytical HA style and the Perfector patterns characteristic of engineering professors are inherently “reasonable.” Both short (style) and long-term (pattern) strategies value new input and subject it to rational evaluation. This makes engineering professors ideal counselors. And counsel is what women will need when establishing a beachhead in the predominantly male domain of engineering.

The lower professorial levels are structurally better suited to teaching experimentation than are full professors. This is a favorable condition since it is the lower levels that typically teach the lower level courses.

But the situation flips on counseling efforts. Full professors are likely to see deeper into any situation (they have higher HA). They have more extensive experience. They can access a wider range of well-placed contacts. Being tenured they are not driven by “perish” element of the “publish or perish” culture. Of course convincing them to undertake this responsibility may require incentives.  However, the currently depleted gender equity condition is transitional and any such incentives need not be permanent.


CONCLUSION
This research has shown that professors of engineering are a unique breed among their academic peers. They are equal to their peers in idea generation (RI style) but put more emphasis on complete understanding before acting (HA style). These professors were born into and developed their teaching methods an era of male dominion. These methods have proven able to produce results in field settings that are the envy of many of their counterpart professions. Engineering professors have much to be proud of.

Gender equity offers engineering an opportunity to expand the success that it already enjoys. There is a pool of unused assets waiting to be deployed. The very information processing postures responsible for engineering's success are now acting to retard its ability to take advantage of this latent opportunity. All that is needed to remedy the situation is to redirect—not change. The engineering professor’s analytical HA style and Perfector pattern preferences need only recognize the differences in the “materials” (i.e., women students) and accept the issue as a challenge. Their natural “problem solving” nature will do the rest.


FOOTNOTES AND BIBLIOGRAPHY


1.    Listing of prior studies in the engineering series:
 Salton, Gary J., 2014. The Engineering Personality research blog.
Salton, Gary J., 2015. Women in Engineering research blog.
Salton, Gary J., 2015. Engineering Insights research blog.
Salton, Gary J., 2015. Women in Engineering Leadership research blog.
 2. Table 5-2: Bachelor’s Degree: Degrees Awarded: by Sex (Updated). National Science Foundation: National Center for Science and Engineering Statistics. 2013. Referenced November 7, 2015:
3. Salton, Gary J., 2011. The Professors research blog
4. Academic areas of sampled engineering professors
Aerospace        Electrical            Naval
Agricultural       Environmental      Nuclear
Biological         Industrial             Physics
Civil                  Materials             Transport
Computers        Mechanical        
5. Academic areas of sampled non-engineering professors
Accounting    Education       Meteorology   Psychology                  
Agriculture     Engineering    Nursing     Public Health                
Archaeology  English           Oceanography     Sociology                     
Biology          Finance          Pharmacology        Theology                      
Chemistry      Management  Philosophy Women’s Studies         
Ecology         Mathematics  Political Science     Zoology                        
Economics     Medicine       Organizational Behavior                                 
6. "I Opt" strategic styles represent various combinations of input-output elections.  These combinations define possible behaviors. For example, omitting detail precludes precise action. The degree of commitment to various “I Opt” styles defines the probability that a particular style will be exhibited in actual behavior.  A more complete explanation can be found by viewing the 8 minute YouTube video "I Opt" Strategic Styles and Patterns at :https://www.youtube.com/watch?v=KVOyznCCWB8
7. Earlier research as shown that professors can be divided into two groups. One tends to stress development of new ideas. The other focuses on testing them. The interplay of these two groups is what produces reliable knowledge. Footnote 3 provides reference to a more extensive textual and video explanation.
8. Salton, Gary J., 2008. ”I OPT” Strategic Styles and Patterns.
9. Salton, Gary J., 2015. Engineering Insights research blog.  See the video segment from 5 minutes 19 seconds to about 8 min for an analysis defining the engineering mission.

10. Gillies, Glenda E. and McArthur, Simon. 2010. Estrogen Actions in the Brain and the Basis for Differential Action in Men and Women: A Case for Sex-Specific Medicines. Pharmacological Reviews. Pharmacol Rev. 2010 Jun; 62(2): 155–198. 
11. The first few minutes of the Women in Engineering video provides the data and outlines the logic for concluding that there is no gender based intellectual limit on engineering content. See:
12. WebMD using Robert Sapolsky, PhD, professor of neurobiology at Stanford University as an authorative source. See Page 1 of 3. Accessed November 15, 2015.
13. What follows is self-serving but nonetheless valid. "I Opt" technology has offered a full range of quantitatively anchored teambuilding and leadership tools since 1991. Unlike other "theories" I Opt is evidence-based and firmly grounded in the systems analysis/software engineer's familiar Input-Process-Output model. "I Opt" tools are proven in use by a variety of universities, non-profits and commercial enterprises for over 20 years primarily for management training. See "Tools/Reports" menu item on www.iopt.com for more information on the analysis offered. See the scroll-down "I Opt" Research Blog menu or the "Coffee Break Videos" link on the home page for reference to the extensive evidence-based research.










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