Shannon C. Nelson, CEO
Professional Communications, Inc.
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.
.
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.
