I
recently attended a training session for Malcolm Baldrige National Quality Award examiners. Last year, after a nine-year absence, I rejoined the board of examiners as an alumni examiner. The training stressed the necessity of writing feedback reports in plain English. Examiners had demonstrated a tendency to fall quickly into "Baldrigese" when talking to each
other and preparing score books during evaluations. I believe that one of our worst habits as quality practitioners is our use of jargon. I've often heard from my friends in management,
engineering and other areas that quality
is a three-letter word, because such people outside the quality field continuously struggle to understand our increasingly dense jargon and acronyms. Just for fun, during the
training course I started putting together a list of the three-letter acronyms we use constantly in quality. The list is impressive, and I'm sure I've missed quite a few. The list of four-letter
acronyms for quality terms, although not as long, is also quite impressive. It's no wonder we have trouble communicating with those outside the quality profession. We seem to be more intent on
talking with each other than in explaining clearly to others what we mean. In the past several years I've taught many Black Belt courses with my colleagues at the Juran
Institute. The participants in these courses, especially those from non-English speaking countries, quickly requested a glossary of terms. In putting the glossary together, I was amazed, and
somewhat dismayed, to see how many adopted or created terms we regularly use in each class. The combination of statistical terms and quality jargon makes understanding our meaning far more
difficult than is necessary. Early one morning I read two of the chapters we'd assigned as pre-reading for a Master Black Belt training session on categorical data analysis.
The terms introduced in these two chapters are enough to complicate any learning experience. They included nominal, ordinal and interval variables; logit and loglinear models; population
association; joint distributions; marginal distributions; conditional distributions; cohort, prospective, case-control, and retrospective studies; Pearson correlation; concordant; discordant;
monotonicity; Kendall's tau-b; Somer's d; nominal association; multiplicative invariance property; concentration coefficient; uncertainty coefficient; Gini concentration; entropy; proportional
prediction rule; kappa; tetrachoric correlation; and contingency coefficient. To make matters even more difficult for the student, we usually have special meanings
in quality and statistical methods for words found in common English, such as independence, entropy, nominal, concentration, association, observational, density and distribution.
Even when we write in English, we make sure no one understands. In his book Categorical Data Analysis
(John Wiley and Sons, 1990), Alan Agresti repeats a passage from Goodman and Kruskal's 1959 paper where the authors are quoting from an 1887 M. H. Doolittle paper. Doolittle attempts to clarify the meaning of
association for his contemporaries: "Having given the number of instances respectively in which things are both thus and so, in which they are thus but not so, in which
they are so but not thus, and in which they are neither thus nor so, it is required to eliminate the general quantitative relativity inhering in the mere thingness of the things, and to determine
the special quantitative relativity subsisting between the thusness and the so-ness of the things." Fortunately, there is hope. Many of the exercises in the Baldrige Award
examiners' training sessions are designed to improve examiners' writing skills for the item and category comments, the key themes and key factors, and site-visit items to minimize confusion among
examiners, judges, staff and eventually the applicant. Well-written sentences in the early stages lead to consensus during scoring, well-planned site visits and well-written feedback reports to
the applicants. Some years ago, while working in Bell Labs, a colleague and I were preparing a presentation for the president of AT&T. The presentation was full of
statistical, reliability and quality jargon. During a pilot-run presentation with members of the president's staff, we learned that the president's background was in the operating telephone
companies, so he wouldn't understand any of our statistical methods or terminology. The staff members told us to remove all of the jargon and try to present our work in normal business English.
So we spent a long night rewriting each sentence almost word by word. By the next morning the presentation was entirely different. But the president understood immediately. In
fact, he asked so many questions and stayed for so long that he ruined his carefully planned schedule for the rest of the day. We had learned an invaluable lesson: By putting our presentation
into the language of our audience, we had actually communicated with them--and won long-term support for our work. This is a lesson we seem to need to learn over and over
again: When we speak clearly, we often find that someone listens. About the author A.
Blanton Godfrey is chairman and CEO of Juran Institute Inc., a leading international research, consulting and training company focused on quality management. He is also the co-editor in chief of
the fifth edition of Juran's Quality Handbook. Contact him by e-mail at agodfrey@qualitydigest.com . |
