The Einstein Challenge: American Chemical Society Article Restated

By: Gary J. Salton, PhD, Chief: Research and Development

      Shannon C. Nelson, CEO
      Professional Communications, Inc.

Albert Einstein is said to have held that “If you can’t explain it to a six year old, you don’t understand it yourself.” The following blog tests the authors understanding of their recent American Chemical Society book chapter titled “Gender and Thought Diversity in Chemistry.” We have restated that scholarly article into a form that we believe could be understood by a child. The reader is left to judge whether we have fully met Einstein’s challenge. The paper was published in the Fall 2019 edition of Organization Development Review (Volume 51, Number 4)

THE BIG PICTURE

There is always something wrong in the world. There is a good reason. Culture moves slowly. Culture is the way we treat each other. It is slow to adjust because millions of people have to decide to do about the same things at about the same time.

Technology moves fast. Technology does not have to wait for agreement. It can be deployed as soon as it is discovered or created. If the technology affects how we relate to each other, we can get a problem. For example, it used to take all day to wash a family’s clothes. It took hours to prepare a meal and that had to be done 3 times a day.  There were no vacuum cleaners; it took hours just to sweep and dust. In this world, it made sense to divide up the work—economists call that division of labor—between the husband and wife. One person made the money. The other created a home for everybody to live in.

Then technology changed the world. Suddenly there were lights, washing machines, packaged foods, medicines, cars, airplanes, adding machines and on and on and on. The division of labor that worked well in earlier times no longer made sense. People had to figure out some new way of treating each other. 

 

It takes a lot of time to adjust a culture. Millions and millions of people have to sit around kitchen tables, desks and classrooms and talk. They have to decide what works for them. They also have to think about how what they decide will affect others—their neighbors, work mates and even other citizens. Then somehow all of these individual decisions have to be reconciled—you know, made to fit together.

 

This whole process can take lots of time.  There are millions of people involved. If all you did at school was to count to one million would take you about a month. For you a year is a long time. For culture 50 years is a blink of an eye.  This long process is what is happening in science and engineering.  The change has started but it is far from being done. You and your classmates are the ones who will make the new and better world happen.

THE PROBLEM

Girls are half (50%) of the world population. But only about 13% of engineers are women.  Neither science nor engineering depends on physical ability so differences in strength don’t matter. Boys and girls have about the same brainpower. Both genders can do the job. So why are not there more girls in engineering?

People are pretty good at inventing reasons that something is happening. Girls might say that the boys are keeping them out because they are “pigheaded clouts.”  The boys may say that the girls do not like to do engineering jobs and are not very good at it.  And both the boys and girls can link all of their “reasons” together to create stories. All of these stories, those of both boys and girls, sound like they might be true. The problem is that no one really knows for sure. And figuring out what is really happening can get even harder.

Sometimes if you believe a story you can make it come true. Once upon a time people believed that girls were not good at math. So people discouraged girls from going into it. Fewer girls went into math. That meant that there were fewer girl mathematicians. People then looked at the field of math, saw few women and said “see, girls are not good at math and that is why they are not there.”

 

What happened was that girls faced a “stacked deck” in math. A stacked deck happens when you arrange the cards in a deck so that the chance of a player winning is low. The chance is not zero. Sometimes a player will win. It just will not happen as often as it should. That is what happened in math.

There were a few brave girls who beat the odds. They put up with a lot of bullying from the boys who did not think they should be there. When those brave girls graduated they had a harder time getting a job. When they did get a job it usually was not as good as the ones the boys got. This gave the boys more evidence that “girls were not as good at math.”

What everyone believes is not necessarily true. And there was always evidence that girls were just as good as boys in math. For example, a woman named Julia Robinson solved Hilbert’s Tenth problem—one of the hardest problems in mathematics. And it was important. The future of computer programing depended on it. Julia solved it. She was later elected President of the American Academy of Arts and Sciences as a result. That is one of the highest honors a scientist can get. Girls are just as good at math as are boys.

 And there were other women doing very hard things. A woman named Grace Murray Hopper invented one of the first computer languages—COBOL.  The reason you call a glitch in a computer program a “bug” is because that is what she named it. We all owe a lot to Grace Hopper.

So you can beat a stacked deck. But it is hard. And a lot of girls who could solve hard problems never went into math because of the stories people invented. The fact that the girls were not in math hurt boys, girls, men, women, moms, dads, babies—everyone.

The girls who could have gone into math but did not could have made Artificial Intelligence better. They could have helped nanotechnology make advances faster. Space exploration might be easier. Medical imaging might get better.  Nobody wins when you are playing with a stacked cultural deck. All you are doing is keeping talent away from the problems that it could fix.

We thought that the same thing that happened in math might be happening in Engineering. Lots of people were making up lots of stories about why there were so few women in engineering. Making up stories is easy. We wanted to let the data--facts, statistics, factual input—create a story.  If we did the job right we could be pretty sure that whatever we discovered would be true.

THE KIND OF DATA WE USED

The first thing you have to figure out when you are letting data build a true story is to decide what you are going to collect data on. You have lots of options. You could try to measure emotions—you know; how people feel. Or you might look at the kinds of companies people work for. Or you could try to get data on the family situations of women. The list is endless.  And all these different ways of investigating are legitimate. The issue is whether they will produce a story that we can use to make the world a better place.

We decided to use information processing as our tool for looking at engineering. We figured that whatever was causing the boy-girl discrepancy had to involve how people understood the world that they lived in. And the only way anyone can understand the world is by processing the information that the world provides.

Information processing is pretty simple. People have invented a 3-box model for showing how it works. The model involves identifying the kind of information you look for to address the issue—your input. The kind of thing you are trying to get done—your output. And how you go about connecting your input with your output target—the process you are using.  Here is a picture of the model.

The reason that the model is important is that it sets the kinds of things you can and will do. For example, if you do not notice something you cannot take action based on it. It does not matter how you feel about it. You simply don’t have the input information needed to do anything. 

 

The model can even set how you do something. If you choose to collect detailed input you can be precise. If you gloss over the detail you will not have the information that precision usually requires. Lots of other things are also set by the kind of information you choose to search out for and accept.

What you intend to do with your input information—your output—also affects how you do things. You might choose to plan a course of action before doing anything. Or you might just choose to act based on what you think might work. If you choose to plan, it will take time and effort.  If you choose to act without a plan you will probably be faster but the result will be less certain.

 

When you got up this morning you chose the clothes you are wearing. How did you do that? When you got to school you might have chosen to go right to your seat instead of talking to someone. Why?  These are decision we make all of the time. You make thousands of these kinds of decisions every day. If we had to think about every one of them we would never get anywhere.  We get around this problem by figuring out standard ways of doing things. We reuse ways of doing things that have worked for us in the past. This is our “process” box working. It is just the pattern we use to connect our input and output.

The information processing model works for you as an individual. But it also affects how other people see you and treat you. For example, if you choose to use your input to plan other people will probably would not see a lot of action. Planning is mostly a thinking kind activity.

People watching you cannot see your thinking. They would not see much happening. It could be easy for them to think you are disinterested. The same kind of thing could happen if you choose to use your input for action. People watching you might judge you to be careless or reckless even if all that you were trying to do was to fix the issue quickly.

The reason people watch what you are doing is that it affects them. What you do can help or hinder this other persons goals. They watch what you do and make attributions on what you are likely to do in the future. Attributions are just little stories that we use to figure out what people are likely to do in the future. They might be true or might not. No one really knows. But we use them anyhow because we have to. What we do affects other people. What they do affects us. It is only smart to be able to know what to expect. Attributions do not have to be right all of the time. They just have to be right more than they are wrong most of the time.

So, we knew what kind of information we needed to try to answer the question of why there are so few girls in engineering. The next question was to figure out how we could measure things in order to get that information.

HOW WE USED “I OPT” TO MEASURE THINGS

We have developed a tool that can measure your information processing choices. It is called “I Opt”. Using it we can figure out how you are likely to approach new issues you face. We can also figure out what other people are likely to think about you as you make those choices.

We do not know exactly what you or anyone else will actually do. What we do know is just how you are likely to go about making that choice. That is enough to tell us a lot about what is going to happen in a particular situation. Engineering is a situation where people have to work together to get something done. Knowing how people are likely to work together can help us figure out what is going on in that situation.

 “I Opt” only works for grown-ups.  At your age you can choose to be anything you want to be. But sometime between now and when you are fully grown you will probably choose an information processing approach. You will probably choose an approach that works most of the time in the kind of job you choose to do. A fireman has to pay attention to different things than does a brain surgeon. There are lots of different kinds of jobs. They need lots of different approaches to do them right. There is no one right way to do things.

Our “I Opt” tool works to measure these different approaches. We had to make sure that it measured things right. We wanted to be sure that we were not using a rubber ruler. We did this through a process called validation. A lot of smart people in statistics figured out eight tests to be sure that a ruler really works. “I Opt” passed all of the tests. It is kind of like testing your knowledge of mathematics.  If you get all of the answers right the teacher can be pretty sure you know the subject.

Next we needed to figure out how we were going to apply our “I Opt” measuring tool.  It is impossible to talk to everybody. So we used what is called a sample. A sample is just a piece of the thing you are trying to measure.  It is kind of like when you go to the ice cream shop. They might give you a taste of a particular flavor. That is a sample. You use it to figure out if you want a whole ice cream cone made out of it.

We did the same kind of thing with our measurements.  But our sample was a lot bigger.  We got thousands of people to tell us how they process information. To do that we used a survey.  Our survey is just a bunch of questions. We use the answers to measure input and output and then we figure out how people connect them—the process. 

 

This way of doing things gave us a pretty big sample—kind of like getting a big scoop of ice cream to taste instead of just a little spoonful. That made us pretty certain that it would tell us what the whole thing—engineering in our case—was really like. 

 

So, we had a question—why aren’t there more girls in engineering. We had a measuring tool—“I Opt” technology. And we had a bunch of people—both men and women—who could act as a sample.  The next thing to do is to actually apply our tools in the real world of actual people.

WHAT WE FOUND OUT

What we discovered is that the question of why there are so few girls in engineering is not easy to answer. The first thing we had to do was to figure out what was involved in doing engineering. We found out that doing engineering is pretty much a thinking kind of job. Engineers spend a lot of time figuring out all the things that might happen. Then they figure out the kinds to things that they can do if those things really do happen.

 

We have a name for the kind of approach you have to use to do that kind of job. We call it “Hypothetical Analyzer.” The things that might happen are called hypothesis in the grown up world. Figuring out what to do with them is called analysis. That is how we came up with the name. In fact, that is how most names in science are created. They describe what you are talking about.

So at the end we knew what engineers did. We then had to figure out if there were any differences between men and women –boys and girls when they were your age—who work in engineering. We found that both men and women depended on thinking through things through before acting. Men and women engineers do engineering in about the same way most of the time. This main way of doing engineering cannot be the cause of why there are so few women in engineering. The same cause—scientists call this a variable—cannot explain a difference in a result.

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We call the way people do things most of the time their primary style. But nothing works all of the time for everything. We all have the ability to use all of the different approaches to a problem—we all can plan, act decisively, be creative or follow procedures exactly. But we also tend to favor one or the other of these approaches as our second choice. We named this fallback option our secondary style—a clever name, don’t you think?

When we looked at the secondary styles in our sample we found a difference between men and women engineers. Not a big difference but one what would be noticed as men and women worked together solving engineering problems. The difference was about 10%.  In other words, men and women would disagree on the right secondary approach one time in every ten.

 

More men than women would choose an experimental “let’s give it a try” approach.  More women than men in engineering would prefer to solve the problem using a way that they already knew would probably work. The men’s way might work better but it has a greater risk of failure. The women’s way is more certain but does not have the possibility of a big improvement.

Who is right? Which way is better? It is a silly question. It’s like asking if apples are better than peaches. Both are good. Both are fruit. You can be equal and still be different. 

 

The choice of the “best” fruit to eat has no consequence beyond the eating. If you choose an apple this time you can still choose a peach the next. In the case of engineering the story is different. The small differences that occasionally happen can affect the future. These disagreements can cause people to make attributions. You remember—attributions are just little stories about why something happens.

 

The men engineers want to try new things—not all of the men, just some of them. The women engineers want to use what they know already work—not all of them, just some. The men who want to try new things “attribute” the choice of the women who disagree with them as “because” they are less creative. The women are rejecting the creative option because they believe they have a better one. But the fact that it is not true does not matter to the attribution. The ten percent of the women involved really did reject the creative option. This gives the men involved “evidence” that the attribution is true.

Just as happened in math, if you believe a story you can make it come true. There are about 87 men for every 13 women in engineering. The few men who have been affected by the disagreement have a lot of other men to talk to about women’s creativity. If only a few of these other men believe the story those men can talk to still other men. It is kind of like a relay race. Every time the story is passed more people come to believe it. Sooner or later “everyone” comes to accept the story. The attribution has been made to “come true.”

The attribution has an effect. Women trying to enter engineering are treated as if they were inferior—less able to do the job. A tone of condescension—a kind of snobbery—is adopted by the men. Women are tolerated but not welcomed. Women looking at this judge the condition as not good for them. They start looking elsewhere for jobs. Fewer women go into engineering as a career. The men look at the situation as “proving” that women are not as good as men in engineering. The same thing as happened in math could be happening today in engineering.

Engineering ended up at the bottom of the pile in its share of women. Almost every other profession has more women than does engineering. Those other jobs can be just as tough as engineering. It is not the engineering job keeping women out.  It has something to do with the way women are being treated within engineering. Our study suggests that that “something” is the false attributions—those little stories—that men engineers tell each other.

TESTING OUR CONCLUSIONS

Our initial study made some sense. However, we also knew that sometimes things that seem to make sense turn out to be wrong. So we decided to test our thinking by comparing chemical engineering to chemical science. Choosing to compare chemical engineering to chemical science cancels out a lot of “reasons” used to support any negative attributions being made in chemical engineering.

Both chemical science and engineering use the same kind of knowledge. They both require about the same kind of education. They both work with the same kinds of tools. The only difference is that engineers tend to produce “things” while scientists produce knowledge—the how and why things work as they do.

But there is another big difference. Chemical science has a lot more women. There are about 34% of women in the chemical science workforce.  Chemical engineering has only 17% women—more than for engineering as a whole but still very low. Engineering is still at the bottom of the pile.  We compared the women in both science and engineering and found that they were identical in the way they processed information. Whatever was causing the difference in participation we knew that it was not in the kind of women who were going into the two fields.

Then we compared the men. The men in both chemical engineering and chemical science had the same primary style. They both used the thinking Hypothetical Analyzer approach.  But when their primary thinking approach did not work a difference appeared. The men scientists tended to rely more on an experimental “let’s give this a try” as their secondary approach. The men engineers tended to rely more on trusted methods that worked in the past. And this can make a big difference in the kind of attributions people make.

The greater reliance on the experimental approach makes it easier for men scientists to accept differences in opinion on what should be done. For the chemical scientist the women’s greater use of trusted methods is just another option. Not right or wrong, just different. There is no need for the men to make attributions to help defend their position. A stream of different ideas and options are just the ordinary way things get done science.

The acceptance of different approaches as being equally valuable means that the atmosphere in science is more favorable to women. As a result twice as many women choose chemical science rather than chemical engineering. This finding tends to confirm the major part of our study of engineering. It is not engineering that is driving women away. It is probably the false narrative—the untrue story—that is doing the job.

CONCLUSION

Does our study “prove” women are staying out of engineering because of false attributions? No but we can be pretty sure that at least some women are not going into engineering because of the attitudes of the men in the field. Those attitudes are at least in part due to the attributions that have been made in the past about women’s engineering abilities.

Our study also tells what is likely to happen in the future. As more and more young women enter engineering the attribution stories the older men are telling each other will start falling apart. Just as happened with Julia Robinson in math, it is hard to ignore talent when it has been given a chance to be displayed. The more women that enter engineering, the more women will get a chance to show what they can do.

What we old guys can do is to try to make it as easy as we can for young women to get into engineering. This study is part of that effort. Our comparison of chemical science and engineering has shown that women can be as successful in engineering as in any other field. We have also shown how little differences can be used to build an attribution that can then be made to “come true” simply by being believed.  That attribution will fall apart as more and more women enter engineering. They can do the job just as well as men. That talent will show. As it does the attribution stories will crumble—fast.

The story told by this study is a good one. The problems found were not structural. They are an artifact built on a weak foundation. The ball is already rolling to fix the issue of low participation of women in engineering. Women, men, boys, girls—everybody—are  going to be better off in the future as this issue moves from a current problem to a footnote of history.

Women in Engineering Leadership

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



SUMMARY OF CURRENT FINDINGS 
This study finds that women are poorly represented in engineering management. However, it does not appear to be due to gender selection bias.  Rather the pool of women at the professional engineering level consists of substantial portions who are not well-aligned with culturally mandated demands of engineering leadership. The engineering culture has proven highly effective in meeting engineering goals. It is very unlikely that the this culture can be radically adjusted to accommodate women without damage to the core function. A rational strategy for increasing the management representation of women will involve attracting and retaining women whose information processing profile more closely reflects the role responsibilities of the various managerial levels.

PRIOR FINDINGS

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A study on the Engineering Personality (Salton, 2014 see Footnote #1 for reference) was able to trace characteristic engineering behaviors to the nature of the work done. It was also able to establish that engineering exceeded other professions in their cultural commitment. 

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The study of Women in Engineering (Salton, 2015 see Footnote #2 for reference) found that women have the capabilities necessary for success in engineering.  Their low participation rate appears to be due to the engineering culture identified in the Engineering Personality study.

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The study Engineering Insights (Salton, 2015 see Footnote #3 for reference) extended the research to consider the hierarchy of engineering management. It found that all levels in engineering are sensitive to the same variables, weight them in about the same manner and target the same kind of output. This creates a strong culture without the “relief valves” typically found in other professions.

CURRENT RESEARCH

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This study looks at the status of women in engineering leadership positions. The goal is to see if women in leadership positions differed in any significant way from their male leader counterparts. A video summary of this research is available on YouTube by clicking the icon on the right.

The low representation of women in engineering limits the number of women who could rise to a leadership position. Table 1 shows the number of women in our database at each level of management. Levels labeled “Marginal Testability” do not have a sufficient number of women to do a definitive analysis (Footnote #4 for explanation).

Table 1

ENGINEERING SAMPLE CHARACTERISTICS

SHORT-TERM DECISION MAKING

“I Opt” styles are used to measure the probability that a person will choose a particular kind of response on the next decision. The “I Opt” style” measures the kind of input a person seeks/accepts, the probable character of the intended response and the mechanism for getting from input to output. Information availability sets the range of behaviors that a person can issue. (Footnote #5 for “style” explanation reference). 

For example, attention to detail necessarily limits the decision horizon since detail expands exponentially with time. Focusing on general aspects (i.e., ignoring detail) lengthens the horizon at the cost of a loss of precision. Obviously, different decision horizons can lead to markedly different choices (e.g., long vs short term profit maximization). A host of other behaviors can be accurately predicted from style preferences (see Footnote #6 for reference to a non-exhaustive listing of predictable behaviors).

Table 2 shows the percent difference between men and women in their average “I Opt” style strength as well as the probability that the difference is just random variation  (Footnote #7 for explanation). It should be kept in mind that these are women actually working in engineering and not women taking engineering degrees. The number and “I Opt” style character of female engineers who have chosen not to enter or to leave engineering is unknown. However, leaders are drawn from the pool of working engineers and thus the number of working female engineers are what is relevant to this study.

Table 2

MALE versus FEMALE STYLE DIFFERENCES BY LEVEL

“I Opt” styles represent the first choice a person is likely to make when confronting an unfamiliar situation. They set the initial tone of a relationship. They are what get a person noticed (or ignored) by those making promotional decisions.  Styles influence career advancement.

Table 2 shows that women differ significantly from men at the professional level (see highlighted yellow boxes).  This suggests that a significant proportion of the women in these categories differ from men at a style strength sufficient to move the averages. These women are more highly committed to methodical action (i.e., LP style) and less attentive to more speculative idea generation (i.e., RI style). As noted in the study on Women in Engineering (see Footnote #2) this kind of difference can create an adverse environment.

The managerial levels of engineering show no significant difference between men and women in their approach to decision making. They share the same decision horizon, have roughly the same risk profile, favor the same character of response and so on.  In other words, men and women are being promoted to higher levels based on the same decision making criteria.

This same kind of male-female decision making consistency was found in an earlier Gender at the Executive Level study (Salton, 2006. See Footnote #8 for reference). The various levels of management appear to demand that incumbents adopt an information processing perspective suited to the kind of issues that the level typically addresses. In other words, no special allowances are being made to increase managerial participation along this dimension (See Footnote #9 for a cautionary footnote).

LONG-TERM DECISION MAKING 

No single information processing strategy applies to every situation. When a favored strategy does not apply people shift to their next most favored strategy.  The specific rank ordering of favored styles establishes a behavioral theme that readily visible to all involved.  “I Opt” terms these characteristic long term behavioral themes as “patterns.”

Styles (short-term decision strategies) characterize an individual among those who have only occasional or intermittent exposure to their decision making.  Patterns (long-term decision strategies) temper these initial judgements among those who are able to witness repeated decisions.  Since time is required to witness repeated exposures “I Opt” patterns are deemed to be long-term strategies. 

In terms of hierarchical promotion, styles typically cause a person to be noticed as adept in a particular function. Patterns are usually used to assess that person’s likely performance in a particular role within that function. Table 3 shows the pattern differences between men and women at the various hierarchical levels..

Table 3

MALE versus FEMALE PATTERN DIFFERENCES BY LEVEL

At the managerial levels a majority of the categories register an overall insignificant level of difference between men and women. There are two instances where a marginal level of significant difference is visible. Both of these differences can be questioned.

The Project Management difference in the “Performer” category (i.e., a short-term horizon focused on immediate results) rests on a sample size of only 20 women.  On its own, sample size would not be a “killer.” However, the nature of the job is also highly variable.  Project manager can involve responsibility for a billion dollar infrastructure project or the design of a critical component part.  The combination of small size, widely discrepant job demands and marginal levels of significance call into question the “reality” of the difference found. Refined categorization and larger sample size could well cause the difference to disappear.

The difference in Mid-Level management’s “Perfector” category (i.e., longer-term horizon focused on complete understanding of new items) is different. It has a more substantial sample size of 84 women. Categorization remains an issue. Mid-Level management consists of jobs with a wide range of responsibility. For example, if just the General Manager (n=17 all males) were removed from the comparison the probability of no male-female difference would jump from 1.4 to 3.2 out of 100. Shifting seventeen people can more than double the uncertainty.

A detailed examination of the data underlying the pattern calculation was conducted. A significant “Perfector” difference persisted even when the comparison was confined to managers or directors.  The mid-level manager difference appears to be real. The difference is likely attributable to the information processing strategies used by the pool of women from which mid-level managers are drawn—the Professional Level.

Table 3 indicates that Professional level women and men differ on all four “I Opt” patterns.  In 3 of the 4 patterns that difference reaches “highly significant” levels.  This reinforces the position that the raw number of women engineers dramatically overstates the number actually available for promotion. Many of the professional level women engineers do not have the information processing patterns that match the culturally prescribed patterns suitable for higher levels. 

The result of this condition is that it will appear that women are being systematically discriminated against in promotional opportunities. In reality, those women who use strategies compatible with the demands of these higher level positions are being promoted and “fit in” with some precision (see footnote #10 for an analysis of the mid-level manager anomaly). 

This analysis points to at least some of the causes underlying the issues on condition of women in engineering. The women electing to stay in engineering after graduation are systematically different than their male counterparts. In addition to stereotype exposures (see “Women in Engineering” study) this difference methodically disadvantages them in career advancement. The cause (i.e., information processing election) is invisible. Women considering engineering are likely to attribute this to gender bias. This discourages others from entering the field. The raw number of women in the field is further reduced.  It is a bit of a vicious circle.
  

ASSESSMENT 

The Bureau of Labor Statistics registered 7.6% female managers in Architecture and Engineering while they made up 15.4% of the workforce. In contrast women made up 38.6% of management versus a total workforce of 43.7% across other professional occupations (Bureau of Labor Statistics, 2014; see Footnote #4 for reference).  There appears to be a serious shortfall of women engineering managers whether considered in absolute or relative terms.

This analysis indicates that the managerial shortfall has two interrelated causes. The first is due to the relatively few women who choose to enter and stay in engineering. This can be addressed in a variety of ways. The study of Women in Engineering (footnote #2) suggested that things like the compensation structure could be adjusted to better recognize the role responsibilities of women while still retaining gender equity. Attention to the physical work environment might also be adjusted to lessen the exposure to potentially gender hostile occurrences. These kinds of things in combination with the current promotional efforts would do much to attract and retain women engineers. 

The second issue involves the kind of the women being attracted and retained. Information processing styles and patterns are strategies used continuously in all areas of life. While strategies for temporarily adjusting them can be taught (see footnote #12 for reference and explanation) they tend to be very “sticky” and difficult to change. This means that attracting and retaining women with information processing strategy aligned with engineering management needs is key. The numbers tell us that engineering will promote women who meet its cultural mandates. Engineering is not likely to sacrifice its mission objectives or cultural mandates to meet abstract societal concepts of gender equality. 

What is needed is to attract a mix of women engineers whose way of viewing the world matched the demands of the profession. All styles should be welcomed—there is a role for everyone within engineering. However, a bit extra effort focused on women with strong analytical (i.e., HA) and idea generating (i.e., RI) capacities would help fill that portion of the pool from which management is drawn. This will probably require efforts by all involved—academicians, professional associations and current engineering management. 

The Engineering Insights (see footnote #3) study argued that engineering’s cultural norms are near ideal and rooted in the demands of the profession. That means adjusting cultural norms and standards is probably ill-advised. Rather strategies have to be devised that attract, retain and develop women with the requisite characteristics.  These strategies are not likely to come from the management structure of Chief Engineer on down. These levels all share a common information processing structure. This creates a common view and limits the range strategies which might be deployed. 

The Engineering Insights study does offer a potential avenue for remedying the condition. The Vice Presidential level is populated by individuals with a strong Relational Innovator (i.e., RI) component. These people have the predisposition to generate options and ideas which can create a favorable environment while maintaining the highly successful core engineering culture.

The strategy suggested by this research is for the Vice Presidential level to reach down and adopt the development of Professional Level talent as a major responsibility. This is likely to be a stretch for the VP. Their natural interactions will be with the various levels of engineering management. Adopting this responsibility will require them to extend that interaction down to a far more populous professional level (see footnote #13 for elaboration). 

The delegation of this responsibility to the VP makes sense. In addition to the ideas and options, the VP also has the authority and resources to actually effect the changes that they deem warranted. The specific strategies are likely to be unique to each organization. A “cookbook” prescription is probably not needed. The strong idea-generating RI capacities of the engineering VP found in the Engineering Insights study suggest that there will be no shortage of potential remedies.

FOOTNOTES AND BIBLIOGRAPHY

<1> Salton, Gary (April 2014). “The Engineering Personality.”

       A textual version can be found in our research blog at:
       http://garysalton.blogspot.com/2012/10/organizational-rank-and-strategic-styles_22.html
       A video describing the research can be found on YouTube at
       http://www.youtube.com/watch?v=sqeGLvjU2Oc&feature=youtu.be.

       It is also available in the Coffee Break Videos section of 
       www.iopt.com.

<2> Salton, Gary (January 2015). “Women in Engineering.”

       A textual version can be found on our Research Blog at
       http://garysalton.blogspot.com/2015/01/women-in-engineering.html
       A video describing the research can be found on YouTube at
       http://www.youtube.com/watch?v=sqeGLvjU2Oc&feature=youtu.be.

       It is also available in the Coffee Break Videos section of 
       www.iopt.com.

<3> Salton, Gary (July 2015). “Engineering Insights.”

       A textual version can be found on our Research Blog at

       http://garysalton.blogspot.com/2015/07/engineering-insights.html

       A video describing the research can be found on YouTube at

       https://www.youtube.com/watch?v=40cZB_ngGSQ

       It is also available in the Coffee Break Videos section of
       www.iopt.com.

<4> The Student’s-t test was used to estimate significance. 
Technically there is no minimum sample size when using
this statistic. The primary effect of low sample size is the 
loss of statistical power. Power is the probability of saying 
that there is no effect when one is actually present (i.e., a Type II error).
The low sample size among senior Engineering Executive (n=9) 
and 1st Level Executives (n=6) suggests that the finding of no 
significant different in these categories should be taken as a tentative 
indication rather than a definitive finding.

<5> Salton, Gary (February 2008). "I Opt" Strategic Styles and Patterns.
https://www.youtube.com/watch?v=KVOyznCCWB8. This video provides an orientational overview of how styles are used to represent information processing postures. A complete explanation of the theory and application of the technology is available in a ~17 hour online/telephone counseling certification program conducted on demand by Shannon Nelson (shannon@iopt.com).

 

<6> 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

<7> The “p=” value in italics below the significance text is the decimal probability that the difference discovered between males and females is just a random variation in scores. The academic standard for marginal significance is p=.05 or a 5% chance that the difference is due to chance variation rather than a causal difference. In the case of “I  Opt” styles the differences in the LP and RI styles would qualify as “highly significant.”  

<8> Salton, Gary (November, 2006). Gender at the Executive Level.
A 9 minute video is available on YouTube at https://www.youtube.com/watch?v=C1L5_kuwHEI.
A textual summary of the research can be found on the Google Research blog at https://www.blogger.com/blogger.g?blogID=10944318#editor/target=post;postID=116370873786509038;onPublishedMenu=posts;onClosedMenu=posts;postNum=31;src=postname

<9> It is worth noting that the sample proportions in this study cannot be used to infer causes of differentials in female versus male participation rates between various engineering levels. The data is roughly random within categories but not between them. There is no assurance that the categories are comparable in terms of migration from one level to another.  However, legitimate inferences can be made between males and females within a category.

<10> The marginal significance of the Perfector pattern discrepancy in mid-level management is due to a combination of style differences.  Women have less commitment than do men in both the analytical HA and idea-generating RI styles (short-term decision strategies).  Neither of these reaches statistical significance at the style level.  However, patterns multiplicatively combine styles. This combinatorial process is what elevates the “Perfector” pattern to significance at mid-level.  Practically, this illustrates the importance of small differences.  Women will be considered for promotion based on style. But they may tend to lose out to men who are likely to be seen as being better equipped to handle the variety of decisions required by the higher level job (i.e., a better aligned pattern strategy).

<11> Bureau of Labor Statistics, 2014; “HOUSEHOLD DATA ANNUAL AVERAGES; 11. Employed persons by detailed occupation, sex, race, and Hispanic or Latino ethnicity. Accessed: June 29, 1015: www.bls.gov/cps/cpsaat11.pdf

<12> Everyone has access to all information processing styles. People can elect to use one or another to facilitate particular transactions.  Booklets describing how this can be done are available in the “So You’re a …” series which can be seen at http://www.iopt.com/so-you-are-a.html.  However, people make thousands of unique decisions every day and it is impossible to guide each one through rational selection. There is no choice but to rely on a generalized strategy as the guide for the conduct of life. Since all aspects of life are involved adjusting the global strategy is a major undertaking that takes much time and effort to effect. 

Reliability studies have determined that changes in strategy are infrequent and when they do occur they tend to follow a predictable pattern that may not be aligned with a rationally selected target (e.g., preparation for engineering leadership). These studies can be seen on YouTube at:

“I Opt” Style Reliability Stress Test: https://www.youtube.com/watch?v=Vs6eoIsqVkc

“I Opt” Pattern Reliability Stress Test: https://www.youtube.com/watch?v=0SLg28BhNHU

Textual versions of these studies can be found on the “I Opt” Research Blog at:

“I Opt” Style Reliability Stress Test:

http://garysalton.blogspot.com/2011/03/i-opt-style-reliability-stress-test.html

“I Opt” Pattern Reliability Stress Test:

http://garysalton.blogspot.com/2011/03/i-opt-pattern-reliability-stress-test.html

      

<13> This interaction can take many forms.  Periodic luncheons with a rotating sample of younger professional engineers might be one course.  MBWA (Management by Walking Around) might be another. The basic idea is for the VP to become aware of and involved with the personal circumstances of the individuals at the professional level.  The technical elements of development are already tended to through on the job training and various Engineering Leadership Development Programs. The VP’s role is to make the cultural tweaks that cause women to want to stay long enough to use those technical abilities.