Gender and Thought Diversity in Chemistry - American Chemical Society

Chapter 8

Gender and Thought Diversity in Chemistry

Downloaded by UNIV OF FLORIDA on December 29, 2017 | http://pubs.acs.org Publication Date (Web): October 26, 2017 | doi: 10.1021/bk-2017-1255.ch008

Gary J. Salton*,1 and Shannon Nelson2 Professional Communications Inc., 101 Nickels Arcade, Ann Arbor, Michigan 48014, United States *E-mail: [email protected] 1Chief: Research and Development 2Chief Executive Officer

Gender diversity is an important issue for society, science and the economy. We have used a proven engineering-based methodology (I Opt) to analyze the root cause of gender imbalance in science and engineering. “I Opt” uses exact measurement to group people into four basic strategic styles: Reactor Stimulator (RS), Logical Processor (LP), Hypothetical Analyzer (HA), and Relational Innovator (RI). Our studies reveal that women consistently put more emphasis than men on RS and LP styles. This election generates behavior that is a key reason for gender bias. There are undoubtedly other sources of gender bias, but the structural divergence identified here can be used to define and direct remedial strategies. This can include attracting and retaining the different kinds of women needed for all of the niches in the chemistry profession.

The Basic Mechanism Everyone has a preferred decision strategy. Life would be intolerable if every one of the thousands of decisions made every day required an assessment. People adopt strategies that work in their environments. Since people live 24 hours a day, those strategies include both work and non-work components. Different families, neighborhoods, work circumstances and other similar factors produce many different “environments.” As a result, people use different strategies as a means of navigating life. On an individual basis no strategy is any better or worse than any other. If it produces an acceptable outcome, it is a “good” strategy.

© 2017 American Chemical Society Nelson and Cheng; Diversity in the Scientific Community Volume 1: Quantifying Diversity and Formulating Success ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Equally “good” strategies interact in groups. Those exchanges can yield positive or negative results. Engineering has a tool for assessing this situation. Their classic input-process-output model is universally applicable. It applies to personal decisions. It equally applies to multiple people focused on a common issue. It is a good tool for the job at hand. An example may help illustrate its operation in a group situation. A person favoring input specificity will likely be “put off” by a person focused on generalities. Similarly, someone inclined toward action output may find another‘s interest in time-consuming planning to be annoying. Finally, linking the different input and output options require the use of different processes (i.e., “ reasoning”). Divergences in this “reasoning” can make rational reconciliation difficult. Reasoning that “makes sense” to one party can be seen as flawed by the other. The above describes a one-to-one situation (a dyad). Real world situations typically involve more than two people. Their interactions are simultaneous as well as sequential. Divergent positions have to be reconciled on a group level before common action can be taken. And there is no assurance that thought diversity will produce a better outcome. The costs are certain. The benefits—if any—are contingent. Thought diversity is consistently attractive only in situations where the methods of achieving the desired result are unknown or uncertain. The engineering model is capable of assessing the described situations. It is a necessary but insufficient component in the evaluation of group behavior. The context within which that tool is applied must also be considered.

The Context Engineering’s classic model is always applied in a context. The “process” box dynamically adjusts to this context. It can change the salience of the input elements and the value of the output options. For a decision that has inconsequential impact a default strategy favoring complete knowledge may be relaxed. A strategy favoring planning may be dismissed in favor of immediate action if the potential gain from detailed assessment is small. Context guides the operation classic model. Weighting is not the only factor affected by context. Structural circumstances also play a role. Standards can arise with regular interaction. These are a group’s way of ensuring group efficiency and effectiveness. They also can amplify or suppress any particular behavioral expression. For example, requiring completed plans forecloses the possibility of spontaneous response. Many other structural factors exist. Even group decision strategies can come into play. Consensus can cause people to modify their preferences in favor of some kind of least common denominator. A majority strategy relaxes this imperative. A hierarchical strategy focuses attention on the preferences of a single individual. In every case the personal preferences of individuals can be modified by the responsive orientation of the “process” box of the model. In all of the above cases psychological variables have a minimal group impact. The neural connections represented by psychological variables are real and do 206 Nelson and Cheng; Diversity in the Scientific Community Volume 1: Quantifying Diversity and Formulating Success ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


influence the operation of the classical model. However, they tend to be distant and indirect. And even when they are on display their influence can be tempered by group processes. For example, stress may be generated by a particular practice. However, it is only relevant to the group if it is visibly expressed. Even then, if confined to one or a few individuals it is likely to be dismissed by a group. Gender bias has to do with the relationships between people, not the psychological condition of any particular person. Psychology may be a relevant template in some situations but a more immediate model with more manipulable variables could better serve the group interests addressed in this paper. Diversity is by definition a social phenomenon. It always involves groups. The engineering model has no difficulty in addressing this level of reality. It simply multiplies the classical model expression to every one of the actual or potential interactions involved. What is needed is a tool that can evaluate the operation of these multiple classical models any structural context. Sociology is that tool. It is the field focused on the study of the “development, structure and functioning of human society” (1). The psychological variables of the people involved are replaced by structural conditions which guide the expression of the behavior. Behavior is the only thing that can affect a group. The relevance of behavior to group functioning is beyond question. Engineering has provided the transmission mechanism. Sociology provides the contextual variables that guide the operation of that engineering model. What remains is to define a tool that links engineering’s mechanism with sociology’s context. That tool is “I Opt” technology.

The Instrumentation “I Opt” is an acronym for input-output-process template. It is a proprietary technology. It was launched in 1991 by Professional Communications Inc., a forprofit (but not profit maximizing) private corporation. The firm is “a research and development firm … (whose) … interest centers on the behavior of groups of people functioning in organized, goal directed environments” (2). The mission fits the issue at hand. “I Opt” technology rests on a firm theoretical base. It is validated on all eight validity dimensions (3). Standard test-retest protocols over periods as long as 18 years (4, 5) have demonstrated its reliability. Further evidence of its validity is its acceptance as the subject for a doctoral dissertation (6). Its regular use in the business curriculums at multiple universities adds more indirect support. It is a tool that has earned substantial credibility over the years. In addition to its academic credentials the tool has also been proven in the real world. It is being used on a world-wide basis by corporations, non-profits and universities on a daily basis. Its operational validity is demonstrated by the acceptance of its guidance by experienced high and low level executives confronting serious concrete issues in volatile environments. The tool works in the real as well as academic world. 207 Nelson and Cheng; Diversity in the Scientific Community Volume 1: Quantifying Diversity and Formulating Success ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


The primary (but not exclusive) use of “I Opt” has been in improving the functioning of operating groups. The quality that makes the technology appropriate for this use is ratio measurement. The strength of a quality (i.e., the likelihood of it being expressed in behavior) can be computed. This means that the strength of the qualities of multiple individuals can be added, subtracted, multiplied and divided. Percentage changes can be calculated. Averages can be computed. Differences can be enumerated. Ratio measurement eliminates the need for categories.The nature of the issue being addressed defines the degree of importance assigned to a particular measurement. This contrasts with instrumentation which relies on nominal (i.e. classification) or ordinal (i.e., rank order) calibration that cannot use the full capacities of arithmetic.

The Issue The focus of this article is gender diversity. The current viewpoint is that diversity is “good.” Exactly why diversity is good is seldom mentioned. The obvious reason is that denying access on the basis of an irrelevant quality limits the talent pool. A wide scale loss of talent ensures a performance shortfall. In other words, diversity does not ensure improvement in any specific instance. But the systematic denial of access ensures the suboptimal performance of a profession–including the profession of Chemistry. The interest of this article is women working in the chemically related professions. The fact that there is some kind of disjoint is obvious. Women make up 47% of the overall workforce (7). The proportion of women in chemistry and material science is 34%. That is a 28% shortfall. The percent of women in chemical engineering is even less at 17%. A 64% shortfall from the overall average is an unmistakable signal of a problem (8). An explanation for the general shortfall of women is beyond the reach of this paper. However, the difference between chemists and chemical engineers is not. The intellectual capacities involved are about the same. The educational demands are roughly equivalent. The general character of work is reasonably comparable. Yet twice as many women choose chemical science over engineering in spite of being paid 31% less (9, 10). Obviously, pay is not the only reason for entering a profession. However, when combined with high student debt and the magnitude of the shortfall (i.e., 64%) it does tend to reinforce the judgement that the signal of a problem is valid. Whatever the reason for the disjoint it is not to be found in general cultural conditions and variables (e.g., “pigheaded” men). Rather it must lie in somewhere within the structure of the two closely allied disciplines. This is an important observation. Changing the minds of tens of thousands of “pigheaded men” is hard. Changing structural conditions is much easier.

Structural Relevance Anything that directs behavior along preset lines can be seen as a structure. These can be intentionally developed or be a simple consequence of the activity 208 Nelson and Cheng; Diversity in the Scientific Community Volume 1: Quantifying Diversity and Formulating Success ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


being performed. It is reasonable to assume that—at least initially—these devices performed a useful function. However, there are conditions under which relevance can be eroded. If practices are stable for long periods they can evolve into norms. Over time norms can evolve into beliefs. These beliefs can then order themselves into prioritized values (i.e., values are the rank order of beliefs). Language and symbols adjust to reflect the underlying mental structure which they express. At this point all of the components of culture are in place. So what starts out as a useful tool can evolve into a rigid practice that needs no justification beyond its current existence. A structural difference separating science and engineering is relevant to chemistry. Science uses experimentation as an investigatory tool. Failure is accepted as a learning device. This makes some sense. The consequences of failure are typically minor. The loss of time, some material and perhaps a few pieces of damaged equipment is typical. This loss is offset by incremental gain in knowledge—the ultimate goal of any science. These gains are typically realized without exposing the general population to risk. This mitigates any consequences of failure. Under these conditions science can tolerate a wide range of approaches driven by different information processing strategies. Engineering faces a different situation. Buildings can fall, chemicals can damage whole environments and poorly designed circuits can cripple entire networks. Engineers can be fired or even sued for failure. Effectively, the difference between science and engineering is not the nature of the work. Rather it lies in the consequence of failure (11). That consequence effect is structural. The difference created by consequence is reflected in favored information processing strategies. “I Opt” styles are short term information processing strategies. Style defines the most likely behavioral election on a particular decision. However, not every decision will yield to the favored stategy. When this happens people revert to their next most favored strategy. The combination of the primary (most favored) and secondary (next most favored) strategy is termed a strategic pattern. For most (but not all) people this combination of behavioral elections is enough to navigate a majority of life’s decisions. The difference between science and engineering lies in the pattern rather than style of behavioral elections. Both science and engineering favor the “I Opt” style of Hypothetical Analyzer (HA) as their dominant approach. This thoughtbased style uses structured input (i.e., input that follows some predefined scheme) and thought output (i.e., a plan, assessment, etc.). This commonality creates the mistaken impression of equivalence. The difference between science and engineering lies in the secondary styles. If the shared primary style (HA) does not apply to a situation engineers tend to revert to a methodical action-oriented Logical Processor stance (i.e., structured input and action output). Scientists tend to favor a more exploratory Relational Innovator (RI) strategy (unpatterned input and thought output). The behavioral effect is that engineers tend to be more rigid in their approach while scientists are more adventurous. This result is “built into” the nature of the work (12). The norms, beliefs and values that arise from the differences dictated by mission affect gender diversity. The more “adventurous” nature of science creates a greater ability to accept variation—including in gender. Engineers see more 209 Nelson and Cheng; Diversity in the Scientific Community Volume 1: Quantifying Diversity and Formulating Success ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


risk in any deviation—including the risks associated with gender variation. There are no “bad” or “good” guys. The higher barriers in engineering are simply the result of people trying to protect the integrity of the profession using a proven well-established culture as a guide. This is a profession to which they are committed. It is also a profession which reliably provides products of immediate benefit to us all. The effectiveness and dependability generated within this culture is not something we would want to lose. Derivative factors act to reinforce the structural bias created by the mission. The women who enter and stay in engineering tend to favor more structured input than do the men (i.e., use of structured input in both their primary and secondary styles). The guidance provided by structure results in a strong focus. This focus partially insulates them from social pressures. Satisfaction is derived from successful accomplishments. The action output of their secondary LP style gives a visible form of achievement. It provides a psychological return for the work done. The women’s strategy is optimal given the culture within which it is being applied. But the above personal strategy has secondary group effects. It can serve to reinforce the hostile elements of the culture. The strategy tends to make women appear more rigid than men in their approach (women engineers have a higher commitment to the LP strategy than do men). This rigidity becomes apparent in behavior and acts to reinforce the judgement that women are “different.” The difference in secondary styles also creates irregular instances of conflict which further reinforce the judgement of difference (13). These visible differences can act as evidence to support an otherwise irrational bias. Other factors also affect the acceptance of women. An earlier study argued that the culture of engineering education was created in a male dominant world. The resultant educational practices evolved into “norms” that are still in play. They do not recognize the biological differences in brain structure, chemistry and functioning of the genders. These norms both discourage women from entering and penalize those that persist (14). The structural barriers even extend to the uniformity of the profession. The Engineering Personality study identified 89 specialty areas (15). However these boundaries in engineering are permeable. It is common to find engineers schooled in one area (e.g., mechanical) working in another (e.g. process). This permeability is a formula for a uniform culture. A gender-based disadvantage in one area is likely to reappear in another. Contrast the above condition with science. Aside of a commitment to the scientific method, the various elements of science do not share a common culture. Some are field based. Others work out of offices while still others work in laboratories. Instrumentation varies from petri dishes to the Large Hadron Collider at CERN. The list could go on. These environments differ widely and this variation provides many more windows of opportunity for women. The point to be made is that gender bias is real (16, 17). However, it is (for the most part) founded on structure rather than psychological predispositions. The current sledgehammer tactics will ultimately work. But wielding sledgehammers takes a lot of effort and is accompanied by a lot of breakage. 210 Nelson and Cheng; Diversity in the Scientific Community Volume 1: Quantifying Diversity and Formulating Success ACS Symposium Series; American Chemical Society: Washington, DC, 2017.


Root Cause Analysis for Gender Bias in Engineering The above analysis draws on a 2016 study of 5,130 degreed working engineers including 505 chemical engineers (13). The purpose of the study was an attempt to locate systematic gender-based differences. The items of interest were those that could generate behavioral patterns that might act to create or support visible gender bias. The study’s patterns of interest were those which would systematically disadvantage one or another of the two genders. The underlying idea is that being systematically disadvantaged can provide a motive to create tools that might temper or disable the offending behavioral pattern. Gender bias can be one such tool. Figure 1 tells the story of the research findings. The peak in the Hypothetical Analyzer (HA) style shows that both men and women engineers favor highly rational, reflective and measured approaches. Both genders also put the spontaneous RS style at the bottom of their preferences. This commonality (denoted by the label “Men & Women”) masks a structural difference that can serve to foster and/or maintain gender bias.

Figure 1. Strategic profiles of women and men: Average style strength by gender That difference resides in secondary styles—the fallback preferences used when the primary approaches do not apply. The average woman is 9.6% more committed to the LP style than is the average man (p

Gender and Thought Diversity in Chemistry - American Chemical Society

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