Yang: Embracing Quantitative AND Qualitative …

Los Alamos
Embracing Quantitative AND Qualitative: Free from the bondage of either/or
I liked math; solving equations was kind of fun, not so much the struggle to figure it out but rather the high when I got it.
I am talking about relatively unsophisticated math and statistics.
I enjoyed statistics throughout graduate school courses for my advanced degrees. But for my PhD dissertation, I went all qualitative. Not only did I learn the beauty of qualitative methods, I came to respect their nuances and challenges.
There is a widespread assumption that quantitative descriptions are more rigorous and “hard” than are qualitative descriptions, which are often referred to as “soft.”
Amusingly, qualitative descriptions are seldom referred to as “easy,” and many of us have come to realize that understanding math may be easier than grasping the moving targets of human intentions, emotions, motivations, and behaviors.
The bottom line is that understanding life requires both quantitative and qualitative analysis.
But organizations are obsessed with “hard” numbers: quantifiable indices, scales, measurements, volumes, etc. Organizations pay less attention to the “soft” aspects of organizational behavior: relationships, emotions, nurturing, empathy, sympathy, understanding, non-verbal communication, thinking, reflections, etc.
It is as if the “soft” aspects are dismissed as “easy” by organizations, despite our individual realization that they are not and that managers-at-large keep messing up in these aspects (thereby keeping Dilbert in syndication).
One of the valuable principles of social statistics is that while the aggregate information is useful and important, it can not help us predict an individual behavior.
We can predict that at 5PM on a 5-lane highway in California, there is a very high probability of heavy traffic or a traffic jam, and that most people would be impatient and annoyed.
But we cannot tell how each of them will react to this highly probable event. Many will simply be resigned to the inevitable, some may be swearing and trying to weave in and out of lanes, some may switch radio stations constantly, and one or two may zen into contemplation of life.
So, numbers can inform but they only paint a limited picture, and it’s a picture of probability.
Of course, organizations do need numbers for guidelines, for aspects such as employees’ performance, promotions, and salaries.
But even here, numbers do not help a manager deal with frustrations over budget shortfalls or when one of her employees can only get a promotion if there is no salary increase.
Similarly frustrating is an institutional cap on promotions to, say, about 1% of the workforce per year, while at the same time the institution drives its workers to develop themselves according to about 3 promotions per 30-year career. (Yes, the math is easy; at this developmental rate 10% of the workforce should be promoted per year. Every year.)
And if promotion caps weren’t enough to destroy organizational credibility, many institutions go on to adapt the forced curve in evaluating performance, thereby setting up an unhealthy competitive environment and pitting against each other when people who should be, and would like to be, colleagues.
(Though recently there have been a few organizations abandoning such rigorous/rigid practice, the number of such organizations has not begun to even tip the scale.) I absolutely detested this practice when I was teaching.
Some semesters, I felt that more than half of my students were performing below average expectations and I had to bump up the grades for some in order to meet the curve requirement.
One semester, I rebelled, as I had an unusually diligent and creative group of students; it would have been unconscionable for me to artificially lower some students’ grades just to meet the idiotic forced-curve requirement.
Fortunately my supervisor was able to back me up.
Numbers alone carry little meaning; not only do we need to learn how to interpret the social meaning attached to the numbers, but more importantly, we need to be mindful about whether the questions behind these numbers are pertinent, relevant, central, and adequate.
“Communications problem” is a lovely catch-all label for all kinds of organizational ills. Most of the time, the so-called communications “problem” is just a surface manifestation of deeper and knottier problems.
An organization survey asks employees if they understand certain policies, without (probably deliberately) giving the employees opportunity to assess the quality of these policies.
It is fairly typical of top management to assume that policy making is strictly their purview; they then convey these policies downward. When encountering some resistance, their first reaction is usually “we need to better communicate these policies,” and usually by reiterating these same policies, LOUDER and more slowly.
A colleague of mine once asked some engineers “what do these specifications mean?” To which the lead engineer literally read back the specifications, very s-l-o-w-l-y! My exasperated colleague responded, “I know what they say, but what do they mean?”
Only then could the real conversation begin.
Most employee surveys for feedback convey what, not the why or how…indeed, “communications problem.”
As I mentioned, these surveys rarely allow employees to respond to the validity, quality or effectiveness of the specific policies.
This is colossally screwed-up logic. It’s as if organizations are saying, “We are going to hire only people who don’t want to think for themselves.” Yet, ALL organizations claim to want to hire the brightest.
So, when the management encounters push-back on new decisions, rules, or policies, they assume it’s because employees haven’t understood the policies or rules. And then, they’ll “explain” these offending new rules with new training that repeats them, slowly and loudly.
It’s maddening; it’s insulting; it’s imbecilic.
Our organizations, and their senior managers, seem to have confused authority for authenticity. The playwright, Robert Bolt, wrote eloquently in “A Man For All Seasons,” in the voice of Sir Thomas More (formerly the Lord Chancellor of England for Henry VIII),
“Some men think the Earth is round, others think it flat; it is a matter capable of question. But if it is flat, will the King’s command make it round? And if it is round, will the King’s command flatten it?”
Numbers can be useful and powerful, but without a sense of context, or sensible judgment, we can easily get mislead by authorities who may speak loudly but with little authenticity and no credibility.
Finally, zero data or the absence of data can carry considerable meaning. Zero tax paid by a giant conglomerate speaks volumes about our system. Sherlock Holmes famously drew great significance from the observation that “the dog didn’t bark at night.”
Context, context. Lewis Thomas said in “The Lives of a Cell,”“…a good way to tell how the [scientific] work is going is to listen in the corridors. If you hear the word, ‘impossible!’ spoken as an expletive, followed by laughter, you will know that someone’s orderly research plan is coming along nicely.”
Challenge to senior managers of R&D organizations: Quantify this! And think of the potential calamity if the same word “impossible” was uttered on the stock exchange floor or in the courtroom.
Until we realize and materialize more possibilities, 
Staying Sane and Charging Ahead.

Direct Contact: taso100@gmail.com

Editor’s note: Dr. Yang has a PhD in Management from the Wharton Business School of the University of Pennsylvania. She taught at Wharton for a number of years, and consulted for small groups and small organizations and on cross-cultural issues. Her professional worldview comprises three pillars: 1. All organizations are social systems in which elements are inter-related. 2. To improve organizations, the focus should be on the positive dimensions on which to build. This philosophical foundation is Appreciative Inquiry. 3. Yang subscribes to the methodological perspective that she is part of the instrument from which to gain quality data from respondents, and with which to compare and contrast with others’ realities.