Mentors - Accidental and Other Types

Renee Miller, 2005 Social Statistics Program Chair

From: http://www.amstat.org/publications/amsn/2005/highlights08-1.pdf

Excerpts from “Remarks upon Receiving the 2005 Jeanne E. Griffith Mentoring Award”

I’ve come to realize there are different types of mentors and different types of mentoring. Following what Albert Madansky said in “ASA Needs More Askers,” (Amstat News, March 2005), one reason I’m writing this is that ASA President Fritz Scheuren asked me to. Fritz, himself, is an example of an “accidental” mentor, someone who transfers knowledge without necessarily realizing he is doing anything out of the ordinary. I noted that one could learn things from Fritz just by chatting with him in an elevator.

Other examples of “accidental” mentors were my supervisors at the U.S. Census Bureau in the 1970s: the late Arno Winard and Roger Herriot. At the time, I didn’t consider them mentors. While it’s not possible to know for sure, I think they would have thought that way, too. I remember them saying how important it was to get the poverty data out on a timely basis and to make sure the data weren’t being misused. I think they would have considered that to be their focus, rather than mentoring. But, in looking back, I learned a lot from them. They shared with me their time and knowledge. It was in thinking about this situation that I began to wonder, “Can one be an accidental mentor?” I think so. In fact, I think I was. In speaking to Beth Kilss (recipient of the 2004 Jeanne E. Griffith Award), she commented that she probably was one, too, that she realized some people could benefit from nurturing and [she] just did it.

Mentors also can be interpreters. For example, Mollie Orshansky, developer of the poverty index, helped explain tome what was actually going on when I worked in the Poverty Statistics Branch of the U.S. Census Bureau. I recall we had to count how many times we were using the word “poverty” compared with “low-income” in our reports and that the poverty report took a long time to make its way through the U.S. Census Bureau’s clearance process. As an eager young person right out of school, these practices seemed strange and somewhat disturbing. Mollie provided me with background on these developments from her vast experience and helped put them into perspective.

Other types of mentors include mentors on a particular project, mentors on institutional aspects of an agency (such as its background and practices), and mentors who help someone make their way around the agency. Mentors on a particular project might focus on technical aspects, or perhaps how to write it up. For example, Doug Hale of the Energy Information Administration (EIA) was, at one point, my writing mentor. Mentors on institutional aspects might focus on administrative practices. For the third category, mentors who help someone make their way around the agency, Mark Gielecki (a member of EIA’s mentoring committee), coined the phrase “procedural mentors.” He and I thought this category differed from administrative mentoring and that it was more about how to get work done. There also are what I think of as “all around mentors” who provide all kinds of support and encouragement over the years. My current supervisor, Nancy Kirkendall, director of the Statistics and Methods Group of the Energy Information Administration, falls into this category. She, herself—as many of you know—is a mentor, a teacher, and someone who leads by example.

Before Nancy, Lynda Carlson, now of the National Science Foundation, was our office director. When Lynda came, we were the Office of Statistical Standards and considered the cops, or enforcers. This was an important function when EIA was a new agency, but, over time, I think it became a less effective way of doing business. Lynda transitioned us to being the Statistics and Methods Group, where we act more as statistical consultants than enforcers. Lynda showed me what a mentor could do.

And then, there is my mother. A lifelong educator—having taught in New York City elementary schools for more than 30 years and, until a few years ago, a docent at the Central Park Zoo—she, too, is a leader by example.

At the 1996 Statistical Policy conference (put on by the Council of Professional Associations on Federal Statistics), there was a session on training statisticians for the future. Jeanne Griffith talked about the skills the statistical workforce of the future would need. She talked about what she would like to see happen if she could wave her magic wand. We’re of a profession that talks in terms of minimizing, optimizing, and detecting data anomalies (to give some examples)—not generally in terms of magic wands. But Jeanne was so convincing that day that I felt a magic wand could actually be waved.

I thought about the magic wand recently. When people congratulated me on receiving this award, a few were a little less than serious. They were longtime employees who told me they needed mentoring, but it sounded more like they were hoping I could instantaneously transform them and solve all their problems. I thought, “I don’t have a magic wand.” And then I thought about Jeanne.

Maybe mentoring is the way to activate the magic wand to which Jeanne referred in order to obtain the trained statistical workforce of the future. I know there are many among you who are mentors, would-be mentors, accidental mentors, and other types of mentors. We need all of you to realize Jeanne’s vision. �

Renee Miller was awarded the 2005 Jeanne E. Griffith Mentoring Award on June 8, 2005, at a ceremony held in Washington, DC. Miller is a supervisory mathematical statistician in the Statistics and Methods Group of the Energy Information Administration. For more about Miller and her mentoring accomplishments, see the June 2005 issue of Amstat News, Page 40.

Government Statistics Section pages prepared by:     Bill Wong.
Last updated:     July 26, 2007.