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Biometrics Section News for February

Wed, 02/01/2017 - 6:00am

Want to get more involved in JSM? Consider volunteering to chair a session. Chairing a session is an important responsibility and a great way to meet your colleagues. If you are interested, contact our section’s 2017 program chair, Barbara Engelhardt.

NIH-Funded Short Course Planned on Causal Inference in Behavioral Obesity Research

Wed, 02/01/2017 - 6:00am

The University of Alabama will host an NIH-funded short course, titled “Strengthening Causal Inference in Behavioral Obesity Research,” at the University of Alabama at Birmingham July 24–28.

Identifying causal relations among variables is fundamental to science. Obesity is a major problem for which much progress in understanding, treatment, and prevention remains to be made. Understanding which social and behavioral factors cause variations in adiposity and which other factors cause variations is vital to producing, evaluating, and selecting intervention and prevention strategies.

The nine course modules are designed to provide rigorous exposure to the fundamental principles underlying an array of techniques. In addition, through guided discussion using real examples in obesity research, participants will gain experience in applying the principles and techniques.

Limited travel scholarships are available to young investigators. The deadline for applications is March 31. Apply online.

Joint Program in Survey Methodology: Launching in Fall: New Opportunities for Contemporary Survey Methodologist

Wed, 02/01/2017 - 6:00am
Jody Derezinski Williams

The Joint Program in Survey Methodology (JPSM)—with faculty from the University of Maryland, University of Michigan, and Westat—is introducing new programs to respond to the ever-increasing use of Big Data and the corresponding need for methods of analysis and inference. Whether one is seeking a graduate degree in survey methodology or additional training through professional development programs, JPSM offers the necessary tools to address the fast-paced changes in survey research.

Onsite Degree-Seeking Program – New Data Science Track

The JPSM onsite graduate degree program at the University of Maryland comprises both a PhD and master’s degree program. In addition to the existing emphases in survey statistics and social science, students can choose a third emphasis in data science starting in fall 2017. This new track includes computational aspects of survey methodology, data visualization, management and analysis of large and complex data sets, human-computer interaction in survey research, and machine learning algorithms.

Online For-Credit Program – New MPS Degree

In 2015, JPSM started offering training online. Graduate certificates in both survey methodology and survey statistics can now be earned entirely online. In fall 2017, a master’s degree in professional studies in survey and data science will be added to the online offerings. This new 30-credit degree program has an applied focus and will offer shorter, modular courses in a web-based learning environment, providing the necessary flexibility for working professionals.

Professional Development – New Data Analytics Training Program

In a collaborative effort, JPSM and the University of Maryland College of Information Studies, Robert F. Wagner School of Public Service at New York University, and University of Chicago Harris School of Public Policy have created a first-of-its-kind nondegree training program in applied data analytics. This program provides professionals the opportunity to develop key computer science and data science skill sets to advance public policy. The goals of this include the following:

  • Provide training in rigorous and modern computational data analysis methods and tools for decision making
  • Develop new data products for government agencies
  • Create new integrated data to address cross-agency challenges
  • Establish new networks across agencies and geographies to address shared problems
Short Courses

For those who may be interested in taking one or two courses to boost their survey research understanding, nondegree-seeking options are also available. These short courses are taught by JPSM faculty and alumni, senior professionals in the field, members of the University of Maryland College of Information Studies, and a range of professors from international partner universities.

Future Generations of Survey Methodologists

This past summer, JPSM was a sponsor of the first-ever Data Detectives Summer Camp, conducted by the National Center for Health Statistics. This one-week STEM commuter day camp was designed for rising 6th–8th-grade students from DC-area middle schools, providing them the opportunity to learn about statistics through a variety of engaging, hands-on activities. This collaborative effort was supported by the American Statistical Association, University of Maryland School of Public Health, and the Centers for Disease Control and Prevention’s Disease Detectives Camp. With more than 200 applications for 30 spots, there are plans to continue offering this camp every summer.

In the spring of 2017, the third annual ASA DataFest will be held at Summit Headquarters in Washington, DC, and organized by JPSM and Summit Consulting. ASA DataFest is a data analytics competition for teams of undergraduate students from area colleges and universities. Over the course of a weekend, teams attack a large, complex, and surprise data set from a well-known company. The competition provides students the opportunity to develop and improve upon analysis and critical thinking skills with hands-on, applied experience. Past participants have found the event to have a direct effect on their success in landing jobs after graduation.

JPSM encourages undergraduates to consider survey methodology and survey statistics in various ways. Celebrating its 19th year, the JPSM Junior Fellows Program continues to be a highly competitive and sought-after summer internship opportunity. This national competition offers students a paid research assistantship in a federal statistical agency in the Washington, DC, area supplemented by a weekly seminar.

In addition, JPSM offers an undergraduate minor in survey methodology, which draws on students from across the University of Maryland campus. Upon graduation, many have found the skills acquired through this minor program have given them a competitive edge in both employment opportunities and graduate school applications.

Frauke Kreuter, director of the Joint Program in Survey Methodology, during her remarks at the 2016 JPSM commencement ceremony, said, “Just last week, the City of New York and Facebook asked if we (JPSM) had any recent graduates that could be hired. I am asked this question every year right around graduation by different agencies, and every year, my reply is the same … ‘Sorry, they have already been spoken for.’”

Alabama Chapter Hosts Mini-Conference

Wed, 02/01/2017 - 6:00am

ASA President Barry D. Nussbaum speaks during a mini-conference at the University of Alabama at Birmingham. The event was hosted by the Alabama Chapter of the ASA.

The Alabama Chapter hosted a mini-conference November 11 at the University of Alabama at Birmingham (UAB). Approximately 50 current and prospective chapter members attended.

The participants came from Alabama and Mississippi, and were primarily affiliated with UAB, the University of Alabama (Tuscaloosa), Mississippi State University, and the University of Mississippi.

The keynote speaker was ASA President Barry Nussbaum, who gave a talk, titled “It’s Not What We Say; It’s Not What They Heard; It’s What They Say They Heard.” His primary premise was that even when we as statisticians carefully present our results and conclusions to decision makers, the message the recipients (decision makers) receive may not be what we thought we delivered or what we thought they heard. He illustrated what one should and should not do through examples used in court cases, executive documents, and material presented for the president of the United States. Nussbaum’s talk, given with a mix of seriousness and humor, was well received.

Student presenters and their topics included the following:

  • Sheida Raihi, Mississippi State University, “Measuring and Testing Central Symmetry in Bivariate Settings”
  • Yuliang Liu, UAB, “Comparison of Single Event, Competing Risk, and Frailty-Based Models for Competing Risks Data”
  • Curry Hilton, University of Alabama, “Statistical Process Measurement (SPM) R Package”
  • Yan Li, UAB, “Sample Size Re-Estimation for Confirmatory Multi-Arm Trials with Normal Outcomes”

The chapter also held a business meeting, during which members discussed the governing structure of the chapter—particularly writing an up-to-date chapter constitution—proposed activities for 2017—including another mini-conference—and brainstormed ways to promote the chapter and participation in chapter activities.

Are You Ready to Vote in the ASA Election?

Wed, 02/01/2017 - 6:00am

The 2017 ASA election opens March 15 at 12:01 a.m. ET and closes May 1 at 11:59 p.m. PT.

Please take a moment to visit the Members Only area of the ASA website and check whether your membership records are up to date. The ASA uses this information to generate the ballots and send them to you via email.

On the ASA home page, click on the “Login” link and enter your username and password to sign in. Please check the following:

  • Membership expiration. If your membership expires at the time we are launching the election, you will not receive a ballot.
  • Section membership. To vote for section officers, you must be a member of that section. If you have an interest in a particular section, be sure you are a member.
  • Email address. All ballots are delivered via email. If the email address you have on file is incorrect, you will not receive your ballot.
  • Whitelisting. Add support@eballot.com to your safe list to ensure the proper delivery of key election information. Learn how to whitelist.
  • CSP 2017 Award Winners Named

    Wed, 02/01/2017 - 6:00am
    Sara Burns, winner of the John J. Bartko Scholarship; Jami Jackson Mulgrave, winner of the Lester R. Curtin Award; and Jinyuan Liu, winner of the Lingzi Lu Memorial Award, will receive registration and travel support to attend the ASA Conference on Statistical Practice.

    John Bartko Scholarship

    John Bartko scholarship winner Sara Burns earned a master’s in biostatistics from Washington University in St. Louis in 2016. She is now employed by the Department of Anesthesia, Critical Care, and Pain Medicine at Massachusetts General Hospital. Her projects range from designing randomized controlled trials for new medical devices to analyzing data from objective studies that aim to reduce pain medicine prescriptions in light of the current opioid epidemic.

    Burns is looking forward to attending lectures and taking a course at CSP to strengthen her R coding skills. She ultimately strives to become an expert in applying statistical techniques to answer meaningful questions in the medical field. She is passionate about improving the quality of research published in scientific articles, on which doctors base their clinical practice.

    Lester Curtin Award

    Jami Jackson Mulgrave earned a bachelor’s degree in psychology from Columbia University and a master’s degree in statistics (concentration in statistical genetics) from North Carolina State University. She is working toward a PhD, doing research on Bayesian nonparametric methods for graphical models of genetic data. She has a large set of programming and data visualization skills. Additionally, Mulgrave is involved in numerous student activities. She has been the NCSU chapter president of SACNAS and served as a science communicator for the North Carolina Museum of Natural Sciences. She is also involved in a program to introduce the STEM fields to sixth- to eighth-grade students from under-represented populations. Her long-term goal is to be a professor of biostatistics.

    Lingzi Lu Memorial Award

    Jinyuan Liu earned an MA from the department of biostatistics and computational biology at the University of Rochester. She is currently employed as a research assistant in the VA San Diego Healthcare System in California, where she is working on two methodological projects on causal inference and social network data analysis and their applications to drug surveillance and depression. She is also working on several collaborative projects, including meta-analysis, variable selection, and intra-class correlation. Her ultimate career goal is to develop new statistical models and conduct statistical inference in complex studies in an academic environment to help improve the understanding of human illness and health.

    Physical and Engineering Sciences Section News for February

    Wed, 02/01/2017 - 6:00am
    Greg Steeno, FTC Program Representative, and Greg Piepel, SPES Industrial Speakers Program Chair

    The 60th Fall Technical Conference (FTC), cosponsored by the ASA and American Society for Quality (ASQ), was held October 6–7 in Minneapolis, Minnesota. The conference was well attended, with sessions covering a range of topics in statistics and quality and providing ample opportunity to network with colleagues and peers.

    The event began the day before, with four day-long short courses. Educational opportunities included “Methods for Designing and Analyzing Mixture Experiments,” “Beyond Split-Plot Design and Analysis,” “Analysis of Big Data Using R,” and “Design and Analysis of Experiments with R.”

    The conference opened with a presentation by Lynne Hare, titled “Hahn Space.” The conference program invited sessions encompassed a variety of interesting and relevant topics. Selected presentations include the following:

    • Bayesian and Statistical Engineering (ASA-SPES)
    • Dimensional Data Analysis (ASQ-STAT)
    • Statistical Methods for Data Science (ASA-Q & P)
    • Case Studies: There Are No Answers in the Back of the Book (ASQ-CPID)

    In addition, the Technometrics invited session focused on reliability, the Journal of Quality Technology session theme covered CUSUM charts, and Quality Engineering showcased statisticians as innovation leaders.

    Contributed sessions topics ranged from advances in DOE/RSM to sequential experimentation to process control to case studies of industrial applications.

    Joanne Wendelberger from the Los Alamos National Laboratory gave the W. J. Youden Memorial Address, titled “Understanding Today’s Complex World,” while 2016 ASA President Jessica Utts gave a lunchtime talk, “Communicating the Value of What Statisticians Do.” The conference concluded with a SPES-sponsored wine and cheese reception and special session, titled “Leadership Perspectives: A Multifaceted Panel Discussion.”

    SPES is accepting papers for the 2017 Fall Technical Conference, to be held October 5–6 in Philadelphia. The conference theme is “Statistics: Powering a Revolution in Quality Improvement,” and the abstract submission deadline is February 28.

    The 2017 conference will be held in Philadelphia, Pennsylvania. Questions can be directed to Greg Steeno.

    SPES Marquardt Memorial Speakers Program

    The SPES Marquardt Memorial Speakers Program facilitates visits of experienced applied statisticians to colleges and universities to give a seminar and meet with students and professors. SPES reimburses the host institution up to $1,000 (previously $500) to cover the expenses of the speaker’s visit.

    Speakers provide information to students about (1) what an applied statistician does; (2) how applied statisticians solve problems in science, engineering, technology, and business; and (3) what nontechnical skills are required to be successful as an applied statistician.

    The Marquardt Industrial Speakers Program was established by SPES in the early 1990s to encourage careers in applied statistics. If you are an institution interested in having a speaker or a SPES member interested in being on the speakers list (or working directly with a local institution to set up a visit), contact Greg Piepel at (509) 375-6911.

    Nominations for Gertrude M. Cox and Roger Herriot Awards Sought

    Wed, 02/01/2017 - 6:00am
    Gertrude M. Cox Award

    The Gertrude M. Cox Award Committee is seeking nominations for the 2017 Gertrude M. Cox Award.

    The award, established in 2003 through a joint agreement between the Washington Statistical Society (WSS) and RTI International, annually recognizes a statistician in early to mid-career (fewer than 15 years after terminal degree) who has made significant contributions to one or more of the areas of applied statistics in which Gertrude Cox worked: survey methodology, experimental design, biostatistics, and statistical computing.

    The award is presented at the WSS Annual Dinner, usually held in June, with the recipient delivering a talk on a topic of general interest to the WSS membership before the dinner.

    The honoree is chosen by a six-person committee—three each from WSS and RTI. This year’s committee consists of Mike Larsen (co-chair), Chris Moriarity, and Linda Young from WSS and Jill Dever, Phil Kott, and Karol Krotki (co-chair) from RTI.

    Included in the award is a $1,000 honorarium, paid travel expenses to attend the WSS Annual Dinner, and a commemorative WSS plaque.

    Past recipients include Sharon Lohr, Alan Zaslavsky, Tom Belin, Vance Berger, Francesca Domenici, Thomas Lumley, Jean Opsomer, Michael Elliott, Nilanjan Chatterjee, Amy Herring, Frauke Kreuter, Jerome Reiter, Jae Kwang Kim, and Bhramar Mukherjee.

    Email nominations to Krotki by February 28 with a supporting statement and CV (or link). If you previously nominated a candidate and wish that nomination to be reconsidered, update the supporting materials.

    The award is in memory of Gertrude M. Cox (1900–1978). In 1945, Cox became director of the Institute of Statistics of the Consolidated University of North Carolina. In the 1950s, as head of the department of experimental statistics at North Carolina State College, she played a key role in establishing mathematical statistics and biostatistics departments at the University of North Carolina. Upon her retirement from North Carolina State University in 1960, Cox became the first head of the statistical research division at the newly founded RTI. She was a founding member of the International Biometric Society (IBS) and, in 1949, became the first woman elected into the International Statistical Institute. She served as president of both the American Statistical Association (1956) and IBS (1968–1969). In 1975, she was elected to the National Academy of Sciences.

    Roger Herriot Award

    Nominations are sought for the 2017 Roger Herriot Award for Innovation in Federal Statistics. The award is intended to reflect the characteristics that marked Roger Herriot’s career, including dedication to the issues of measurement, improvements in the efficiency of data collection programs, and improvements and use of statistical data for policy analysis.

    The award is not limited to senior members of an organization, nor is it to be considered as a culmination of a long period of service. Individuals or teams at all levels within federal statistical agencies, other government organizations, nonprofit organizations, the private sector, and the academic community may be nominated on the basis of their contributions. As innovation often requires or results from teamwork, team nominations are encouraged.

    The award consists of a $1,000 honorarium and a framed citation, which will be presented at a ceremony during the Joint Statistical Meetings in August 2017. The Washington Statistical Society may also host a seminar given by the winner on a subject of his or her choosing.

    The recipient of the 2017 award will be chosen by a committee comprising representatives of the ASA Social Statistics and Government Statistics sections and the Washington Statistical Society. Herriot was associated with, and strongly supportive of, these organizations during his career.

    Nominations should contain the following:

    • A cover letter from the nominator that includes references to specific examples of the nominee’s contributions to innovation in federal statistics. These contributions can be to methodology, procedure, organization, administration, or other areas of federal statistics and need not have been made by or while a federal employee.
    • Up to six more letters of support that show the innovativeness of each contribution.
    • A current vita for the nominee with contact information. For team nominations, the vitae of all team members should be included.

    The committee may consider nominations made for prior years, but it encourages resubmission of those nominations with updated information. Completed packages must be received by April 1. Electronic submissions sent to David Banks, chair of the 2017 Roger Herriot Award Committee, as Word or PDF files are strongly encouraged.

    Roger Herriot was the associate commissioner of statistical standards and methodology at the U.S. National Center for Education Statistics (NCES) when he died in 1994. Previously, he held several positions at the U.S. Census Bureau.

    For more information, contact Banks by email or phone at (919) 684-3743.

    ASA Sponsors DSAA2017

    Wed, 02/01/2017 - 6:00am
    Elevates Impact of Statisticians in Data Science
      Jill Talley, ASA Public Relations Manager

        Recognizing that statistics is one of three foundational areas of data science, the ASA is sponsoring the 4th IEEE International Conference on Data Science and Advanced Analytics (DSAA2017) October 19–21 in Tokyo, Japan. Founded in 2014 by the Institute of Electrical and Electronics Engineers (IEEE) Computational Intelligence Society and the Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery from Data, the conference provides a premier forum for researchers, industry practitioners, and Big Data users to exchange ideas and participate in top-level discussions about best practices of applications and the latest theoretical developments in data science and analytics.

        “Statistics, by nature, is interdisciplinary and—together with the expansive field of data science—can spur innovation and solve some of society’s most pressing challenges,” said Ron Wasserstein, executive director of the ASA. He continued, “The opportunity to collaborate with IEEE and ACM on DSAA2017 will drive discussion among some of the world’s foremost scientific leaders, business executives, and government officials, harnessing the power and possibilities of data-driven scientific discovery while showcasing and strengthening the expertise of statisticians in high demand all over the world.”

        In 2017, the ASA will play an active role in planning the conference program, including identifying session topics and speakers and assisting with overall conference promotion to key target markets. The ASA sponsored the conference in 2016 as well, marking the first time statistical and computing/information science societies teamed up to conduct a data science conference and promote disciplinary development in data science.

        “The healthy development of data science relies heavily on the effective dialogue between relevant disciplines, in particular, statistics, computing, management, and social science, as well as different domains and areas,” said Longbing Cao, chair of the DSAA Steering Committee. “DSAA is a unique global driver to enable and promote such transformative collaborations by initiating and continuously organizing strategic and high-profile activities, in particular, encouraging and underpinning the continuous engagement of ASA and statistics communities. Every year, DSAA supports prestigious keynote, tutorial, special sessions, and invited talks about data science trends by top leaders in statistics.”

        View photos from DSAA2016.

        San Antonio Chapter Mentors STEM Students

        Wed, 02/01/2017 - 6:00am


        The San Antonio Chapter worked with the University of Texas at San Antonio ASA student chapter to support the Saint Matthews Catholic School STEM Club in December. The students were in grades 6–8.

        The San Antonio Chapter, in collaboration with the department of management science and statistics and ASA student chapter at The University of Texas at San Antonio, promoted statistical literacy and supported the Saint Matthews Catholic School STEM club in December.

        The STEM club students (grades 6–8), along with their parents, were paired with statistics mentors to review their research project objectives and protocols and analyze the data. They learned about the fundamental statistical concepts in understanding any scientific phenomenon, as well as how to analyze quantitative data using proper statistical methods and present/interpret the results with statistical significance.

        The STEM club students will be competing in the upcoming Alamo Regional Science Fair.

        Call for JSM Late-Breaking Session Proposals

        Wed, 02/01/2017 - 6:00am
        Two will be selected from proposals received by the deadline

          Planning for the 2017 Joint Statistical Meetings (JSM) program started last July, and most technical sessions have already been organized. This advanced planning is required to organize such an expansive meeting as JSM, but may preclude scheduling sessions on contemporary topics of emerging interest as the year progresses. Two invited session slots have been set aside for such late-breaking topics, and any member of one of the JSM sponsoring organizations or partnering societies can propose such a session.

          A late-breaking session must cover one or more technical, scientific, or policy-related topics that have arisen during the one-year period prior to JSM 2017. Proposals for late-breaking sessions should be emailed to JSM 2017 program chair, Regina Y. Liu, with a copy to the ASA meetings department by April 14. The proposal must include the following:

          • Session description—including a title, summary of statistical and scientific content, and explanation of the subject’s timeliness and significance—and comments about the intended target audience
          • Format of the session (e.g., a chair and four panelists, 2–3 speakers and a discussant, etc.)
          • Names, affiliations, and contact information for the session organizer, chair, and all participants (speakers, panelists, discussants)
          • A title for each presentation in the session
          • Web links to relevant technical reports or news reports, if applicable

          Organizers should make sure participants have agreed to participate before the proposal is submitted. The JSM participation guidelines state that a speaker can give a main presentation and participate in a late-breaking session at the same meeting, so previous commitment to a regular session does not preclude participation.

          Two late-breaking sessions will be selected from the proposals received by the deadline (subject to approval by the ASA Committee on Meetings). Proposals will be judged on statistical and scientific quality, timeliness, significance and impact, potential audience appeal, and completeness. A description of the late-breaking sessions and other special sessions will appear in a future issue of Amstat News. Please submit your proposals to add exciting late-breaking sessions to the JSM 2017 program.

          Oscar Kempthorne (1919–2000)

          Tue, 01/31/2017 - 6:00am
          by Klaus Hinkelmann

          Kempthorne

          Born into a farm family on January 31, 1919, in St. Tudy, Cornwall, Oscar Kempthorne made up his mind very early that he wanted to get away from the backbreaking work on the farm. And he realized that this could only be accomplished through more education, or as he put it: “There was only one way to do it—brain power” (Des Moines Register, 11/25/90). As a consequence, he studied very hard on his own, but, fortunately, also some of the teachers in his rural school recognized his talents and supported him in his quest. He especially loved mathematics, and in secondary school his knowledge surpassed that of his teachers. So, he taught himself mathematics and, thus, prepared himself for a university education.

          In 1937 his hard work paid off as he won scholarships to Cambridge University. In his first two years there he took a great number of mathematics courses, but “got turned off from pure mathematics because it did not seem to be going anywhere” (Folks, 1995). Also a brief foray into mathematical physics did not catch his imagination. Then, finally, he had a first course in statistics from John Wishart, which seemed to interest him. Inspired by some of R. A. Fisher’s writings, the idea that mathematics should be useful and could be useful in agricultural research impressed him tremendously. He subsequently took a two-term statistics course from J. O. Irwin, which would complete his formal training in statistics. In 1940 he received a B.A. degree with Honours from Cambridge University and the M.S. degree in 1943.

          After working briefly for the British Ministry of Supply, he joined Rothamsted Experiment Station in 1941. Here he met R. A. Fisher and Frank Yates. These two men had contributed greatly to the prominence of Rothamsted through their groundbreaking work in experimental design, a field to which Kempthorne later also would make important contributions. During his time at Rothamsted, Kempthorne had actually very little contact with Fisher. As Kempthorne put it himself “He was there, but I was a boy; I didn’t know enough to ask him a question or to speak to him about anything” (Folks, 1995). Yet, he regarded Fisher as having the greatest influence in his professional life and he thought that Fisher was, by far, the greatest statistician that humanity has produced (Folks, 1995). It is, therefore, not surprising that Kempthorne became attracted to the areas of statistics that had been shaped in big measure by Fisher: experimental design, genetic statistics, and statistical inference.

          Kempthorne left Rothamsted when he was asked to participate, as a member of a technical statistical group, to observe and evaluate the Greek elections in 1946. This work brought him in contact with Ray Jessen, a survey sampling expert from Iowa State College and the leader of the group. This was at the time that a Statistics Department was being established at Iowa State College. Following the Greek mission, he was offered a position as Associate Professor at Iowa State in 1947. This was the beginning of an association that would last 42 years until his retirement in 1989. He was promoted to Full Professor in 1951 and was named Distinguished Professor in Sciences and Humanities in 1964.

          The Statistical Laboratory at Iowa State had achieved great prominence through the work of George W. Snedecor and the staff he had assembled between 1933 and 1947 (for a detailed account see David, 1984). The establishment of the Statistics Department would add greatly to the statistical activities at Iowa State, and Kempthorne would come to play an important role in expanding and promoting these activities through his teaching, research, writing, and consulting.

          His first great success came with the publication of his book Design and Analysis of Experiments published by Wiley in 1952. It was the first comprehensive treatment of experimental design at a “theoretical” level intended for graduate studies and research in this field. It built upon the foundations laid by Fisher and Yates, but Kempthorne was able to carry the development much further and thus paint a much broader and unified picture of this field. Fisher’s notion of randomization was cast in a more mathematical-probabilistic framework, which led to important consequences with regard to the development of linear models and modes of inference resulting from the analysis of variance. Based on randomization theory, he pointed out the asymmetry of blocking and treatment factors in the linear model in that one can test hypotheses about treatment factors, but not about blocking factors (these ideas were exposited more fully in the 1994 book Design and Analysis of Experiments. Vol. 1. Introduction to Experimental Design by Hinkelmann and Kempthorne). A comment by Folks (1984) puts this idea in the right perspective: “As a beginning doctoral student at Iowa State University, I made the comment to him [Kempthorne] that a two-factor experiment and a randomized block design resulted in the same model. I learned very quickly that my education was not complete—that there was a fundamental difference between these two situations resulting from the randomization”. In addition to his pioneering work on randomization, Kempthorne provided a unified treatment of the general sn factorial experiment with s being a prime or prime power. With a new parametrization for the observations from such an experiment, he laid a more mathematical framework for and brought new insights to the important concepts of systems of confounding and fractional factorials.

          Amazingly, only five years later Kempthorne published, again with Wiley, another groundbreaking book entitled An Introduction to Genetic Statistics. In the preface he writes: “My basic debt with regard to the whole book is to Sir Ronald A. Fisher. There is hardly a page, even less a chapter, which does not contain the results of Fisher and extensions of Fisher’s results” (Kempthorne, 1957). It also makes use of important contributions due to Sewall Wright, J. B. S. Haldane, and G. Malécot, but it is the extensions and applications of the results of these and other writers that make this book such an important addition to the genetics and statistics literature. Many of these extensions and new results represent Kempthorne’s own research, which was heavily stimulated by the environment in which he found himself at Iowa State and the interaction he had with plant and animal breeders, among them Jay L. Lush, one of the premier animal breeders at the time. Although, as he points out: “The present book is not a book on breeding theory and procedures”, it nevertheless provides the theoretical framework and underpinnings for such procedures with its detailed development and discussion of fundamental concepts in the context of both population genetics and quantitative inheritance. Among these concepts are selection theory, the theory of inbreeding, the notion of covariances between relatives for various types of genetic populations and mating types within populations, as well as the role of analysis of variance as it applies to quantitative inheritance and the estimation of genetic parameters, such as genetic variance components and combining abilities. It is no wonder that, with such a broad exposition, this book became a standard text and provided the tools for many developments in quantitative genetics research today.

          A third area to which Kempthorne contributed heavily, in particular in the latter part of his professional life, is that of the foundations and philosophy of statistical science. It is interesting to note that as a student he “did not really understand what philosophy was about or that there were such people as philosophers” to which he adds ” and I am not much better off now about that” (Folks, 1995). That, of course, is a typical Kempthorne understatement. He became an avid reader of the works of many of the important philosophers, like Kant, Schopenhauer, Nietzsche (as somebody who abhors philosophy I remember with some embarrassment my occasional failures to properly explain to him the meaning of some of the German words used by these philosophers) or philosophers of science, like Carnap, Peirce, Popper. He thought and wrote a great deal about how their ideas applied to the field of statistics and its foundations. Much of his own philosophy can be found in the book Probability, Statistics, and Data Analysis (Kempthorne and Folks, 1971), published by Iowa State University Press. He drew a sharp distinction between theoretical and applied statistics and felt that data analysis was at the heart of statistics. He instilled this view in his students and in the overall direction of the Statistics Department at Iowa State when he says: “What accounts for the success of the Stat Lab [at Iowa State]? I believe that it is because it was not driven by the mathematics, but by actual problems in biology, genetics, demography, economics, philosophy and so on. To be sure, the real problems give rise to abstract problems in statistical inference, which have a fascination of their own. However, for statistics to remain viable, statistical problems should have their genesis in real, data-related problems” (Folks, 1995).

          For all his broad and varied contributions Oscar Kempthorne was recognized by his peers and by the scientific community with numerous awards and honors, such as Fellow of the American Statistical Association, Fellow and President of the Institute of Mathematical Statistics, Fellow of the American Association for the Advancement of Science, Elected Member of the International Statistical Institute, Sc.D. degree from Cambridge University, Honorary Fellow of the Royal Statistical Society, and an Honorary Doctorate from the University of Ioannina (Greece).

          Kempthorne was a consummate teacher. He literally bombarded his students with handouts to supplement his lectures and expected them to work through every word and formula. He loved and was loved by (most of) his students and feared by them at the same time, because he demanded a lot. In a “Letter to Friends” on the occasion of his 65th birthday he wrote: “Give me arrogant but competent young people”. During his 42 years at Iowa State University he directed the work of 43 PhD students, and he always took great pride in their later accomplishments.

          In the same “Letter to Friends” he wrote: “I view myself as a person who is kindly, but sometimes harsh. My wife [Valda Marie, who was his strongest supporter] tells me so, and correctly. She says: ‘Go off and pursue your holy grail, and forget about the humans you should pay attention to.’ I find myself with friends, all over the world. How did this happen to a Cornish farm boy? Was it genes, environment or altruism? I do not know.” Surely, those of us who had the privilege of knowing Oscar Kempthorne, have an answer, and, it is hoped those who only read about him and read his work, will share in our admiration for him.

          References

          David, H. A. (1984). The Iowa State Statistical Laboratory: Antecedents and early years. Statistics—An Appraisal (H. A. David and H. T. David, eds.) 3–18. Iowa State Univ. Press, Ames.

          Folks, J. L. (1984). Use of Randomization in Experimental Research. Experimental Design, Statistical Models, and Genetic Statistics—Essays in Honor of Oscar Kempthorne (K. Hinkelmann, ed.) 17–32. Dekker, New York.

          Folks, J. L. (1995). A Conversation with Oscar Kempthorne. Statist. Science 10 321–336

          Gertrude M. Cox (1900–1978)

          Thu, 01/12/2017 - 11:49am
          From The American Statistician, May 1990, Vol. 44, No. 2.

          Cox

          Born January 13, 1900, in Dayton, Iowa, Gertrude M. Cox reflected the upbringing of the times and location. She was instilled with ethics, moral courage, and determination. This, combined with her grand dreams and the genius and tenacity to materialize them, resulted in legendary accomplishments and awed those who knew her. Her exceptional organizational ability and her realization that statistics needed to be made practical for those working in agricultural and biological research led to her bridging the gap between theoreticians and research workers.

          Initially, Cox prepared to become a deaconess in the Methodist Episcopal church, but she decided to pursue a more academic life, receiving her B.S. degree in mathematics from Iowa State College in 1929. She received the first master’s degree awarded in statistics from Iowa State College in 1931. From 1931 to 1933 she studied psychological statistics and was a graduate assistant at the University of California, Berkeley. She returned to Iowa in 1933 to assist George Snedecor by heading the newly created Statistical Laboratory, and she was made a research assistant professor in 1939. During this period she did graduate work in statistics and began her research on experimental design. She also assembled a series of notes on standard designs, which eventually led to the book Experimental Designs, cowritten by William G. Cochran and published in 1950.

          In 1940 she was appointed to organize and head a Department of Experimental Statistics in the School of Agriculture at North Carolina State College in Raleigh-the result of a footnote in a letter from Snedecor to North Carolina State College, in which he recommended five men. He added, “Of course if you would consider a woman for this position I would recommend Gertrude Cox of my staff.” In January 1941, the department was established with Cox as the first female full professor and first female department head at North Carolina State College, a propitious choice that changed the course of statistics in North Carolina.

          An Institute of Statistics was established at North Carolina State College in 1944, with Cox as its director. She continued as head of the department as well until 1949 and added many new faculty members, including Cochran. She recruited Harold Hotelling to head the new Department of Mathematical Statistics at the University of North Carolina at Chapel Hill in 1946. And in 1949, she helped to establish the Department of Biostatistics in the School of Public Health at the University of North Carolina at Chapel Hill, with Bernard G. Greenberg as head. All three departments flourished under her directorship of the institute, producing many of the statistical leaders and department heads of today. Her skill as an administrator was unsurpassed. She employed outstanding faculty and staff and left them to their teaching and research while she raised funds. In addition to her administrative duties, Cox continued to teach, drawing on her many years of consulting to produce practical real-life examples designed to illustrate experimental designs, all of which were flawlessly computed before the age of computers.

          Cox’s zeal at the institute led to many spin-off facilities in North Carolina. In the 1950s she was a moving force in planning for what is now the Research Triangle Institute (RTI). She retired from her faculty position at North Carolina State University and the institute in 1960 and organized and be-came the first head of the RTI’s Statistics Research Division. In 1965, she retired for the second time and served as a consultant to the RTI and governmental agencies. At this juncture, she also turned her energies to the international front, promoting statistical activities in Egypt and Thailand.

          She took 23 international trips and, during her “retirement,” received many U.S. and foreign visitors in Raleigh, North Carolina-people who came to visit her and the many facilities in the RTI area, which she helped to build.
          Cox’s contributions include active participation in statistical societies and organizations. In 1945, she became the first editor (for 11 years) of the Biometrics Bulletin of the Biometric Section of the American Statistical Association (later called Biometrics, the journal of the Biometric Society). In 1947 she founded the Biometric Society. She was president of ASA in 1956 and of the Biometric Society in 1968 and 1969.

          She received many honors in her lifetime, a few of which are mentioned here. In 1944, she was made a Fellow of the ASA and the Institute of Mathematical Statistics. In 1949 she became the first female elected into the International Statistical Institute. In 1957, she was made an honorary Fellow of the Royal Statistical Society, and in 1958 she was awarded an honorary doctor of science degree from Iowa State University. In 1970, Cox Hall, the current home of the Department of Statistics, was dedicated at North Carolina State University. In 1975 she was elected to the National Academy Of Sciences. In 1977 a $200,000 Gertrude M. Cox Fellowship fund was established in her honor at North Carolina State University. In 1987, the Gertrude M. Cox Scholarship fund was established by the Caucus for Women in Statistics and the COWIS. In 1989, at the ASA sesquicentennial meeting, the first awards were made to four recipients.

          In addition to her professional achievements, Cox was known for the personal interest she took in relatives, friends, and wives and children of faculty and staff. The memory books in the Department of Statistics at North Carolina State University hold many remembrances of her tenure there, from newspaper clippings of awards to the department to wedding invitations for staff and Christmas cards sent and received. Cox died of leukemia October 17, 1978. In a memorial article, three of her colleagues said, “To those of us who were fortunate to be with her through so many years, Raleigh will never be the same” (Anderson et al. 1979). Perhaps the statistical world will never be the same.

          Samuel W. Greenhouse (1918–2000)

          Thu, 01/12/2017 - 11:31am
          by John M. Lachin and Joel Greenhouse

          Greenhouse

          Samuel W. Greenhouse was born on January 13, 1918 in the Bronx, New York. Sam, as he was known to all, was one of the founding statisticians at the National Institutes of Health, who helped pioneer the use of statistical methods in epidemiological research, and was influential in the early development of the theory and practice of clinical trials. He was also a distinguished Professor of Statistics at the George Washington University.

          Sam received his B.S. in Mathematics from the City College of New York in 1938 and thereafter moved to Washington, DC to begin his career in the Bureau of Census with Edward Deming (1940–42). He served in the Army during World War II and afterwards worked with the United Nations Relief and Rehabilitation Agency (1945–48). In 1948 he was recruited by Harold Dorn, along with Jerome Cornfield, Jacob Lieberman, Nathan Mantel and Marvin Schneiderman, to create the first biometry group at the National Institutes of Health (NIH) in the National Cancer Institute (NCI). The May 1997 issue of Statistical Science presents a description of the activities of this group including interviews with several of the early NIH statisticians, and Sam’s own reminiscences and reflections on the development of statistics at the NIH. In 1954, Sam left the NCI to become Chief of the Theoretical Statistics and Mathematics Section in the National Institute of Mental Health. In 1966, he was appointed Chief of the Epidemiology and Biometry Branch of the National Institute of Child Health and Human Development, where he rose to the position of Associate Director for Epidemiology and Biometry (1970–74) and Acting Associate Director of the Office of Program Planning and Evaluation (1969–74). He was the first statistician to hold such a high administrative position at the NIH.

          While working full time at the NIH, Sam taught part-time and pursued his own graduate degrees under the direction of Solomon Kullback in the Department of Statistics at George Washington University (GWU). When Sam retired from government service in 1974, he began a full time academic career at GWU where he served as Chair of the Department of Statistics from 1976–69 and again in 1985–86. In 1988, he retired from the University faculty and was named Professor Emeritus. From 1988 until his death he served as the Associate Director for Research Development of the GWU Biostatistics Center.

          Sam articulated many times that the primary mission of the statisticians at the NIH was to collaborate and provide statistical support for the NIH scientists. Yet, it was always understood that these collaborations would lead to opportunities for statistical research in methodology and theory. It was not unusual to find Sam and the other early NIH statisticians co-authoring papers in subject matter journals and publishing corresponding theory and methods papers in statistics journals. This pattern was evident in his early papers on the evaluation of diagnostic tests. Although this work with Mantel (Biometrics, 1950) and Dunn (Public Health Reports) was rooted in the need to implement noninvasive methods for cancer screening, it also addressed methodological issues, such as deriving the estimated variance of sensitivity and specificity for the case when the diagnostic cut-point for a quantitative test was also estimated from the data. While at the NIMH, he helped design and analyze the first multi-disciplinary study of normal aging, and co-edited the resulting book Human Aging (1963). Recognizing the need for methods to analyze highly correlated psychological data from studies such as this one, led directly to new methodological work with Geisser (Psychometrika, 1959; Annals of Mathematical Statistics, 1958). They derived an estimate of the degree of departure from the assumption of compound symmetry in the test of within-subjects effects in ANOVA, and an adjustment to the degrees of freedom of the F-ratio when that assumption is violated. The Greenhouse-Geisser correction is now provided in virtually all computer packages for repeated measures analysis. Significantly, this work has been recognized as a Science and Social Science Citation Classic (July, 1982).

          Sam was also influential in the early development of the theory and practice of clinical trials and shared an interest with Cornfield in methods for the sequential analysis of emerging data in clinical trials. While at the NICHD, his collaborations focused more on observational data, e.g., assessing the safety of oral contraceptive use, and his interests returned to the development of methods for epidemiologic studies. His papers with Seigel (Journal of Chronic Disease, 1973; American Journal of Epidemiology, 1973), for example, showed that logistic regression could be applied to matched and unmatched case-control studies to obtain an adjusted estimate of the prospective odds ratio associated with a factor. At GWU in the late 1980’s, Sam and Joe Gastwirth recognized similarities between a class of problems arising in legal settings and in epidemiologic studies. A collaboration began that was deeply grounded in the practical experiences of their respective fields of application (see, e.g., JASA, 1987; Statistics in Medicine, 1995).

          Sam was passionate about statistics. He relished the opportunity to teach and engage colleagues and young statisticians in statistical discourse. Whether giving a seminar, making a site visit, or on sabbatical, Sam was always a popular and stimulating visitor. However, if one asked Sam about the truly important work he was doing, he would inevitably talk about his scientific collaborations. For it was through the practice of statistics, he believed, that statisticians made their biggest impact on science, and it was through scientific collaborations that the important statistical problems were identified. He felt that every biostatistician should spend time in the trenches, such as in a laboratory or a clinical trial data center, to obtain practical experience. He practiced what he preached. While at the Biostatistics Center he continued to collaborate extensively with investigators in the Division of Cardiology at the GWU School of Medicine; served as a coinvestigator of the Coordinating Center for the study of the Medical Treatment of Prostatic Symptoms; and served as a member of the data monitoring committee for the POSCH.

          Sam was a much sought after panel member and mentor. His reviews and advice were always fair, insightful, and expertly crafted. He worked tirelessly as an advisor and reviewer for a number of government agencies, including, the U.S. Public Health Service’s Accident Prevention Study Section (1958–62), the Federal Aviation Agency Council of Research Advisors, Office of Aviation Medicine (1959–65), and the Food and Drug Administration’s Biometric and Epidemiologic Methodology Committee (1967–72) which he chaired from 1969–72. He served on the Institute of Medicine’s committee investigating the health of Vietnam veterans, and co-authored a report for the White House on The Health Effects of Low-Frequency Electricity and Magnetic Fields. Sam was a member of numerous committees at the NIH, during and after his tenure there, including the Biostatistics Fellowship Review Panel (1961–69), the Statistics and Mathematics Study Section (1963–70), the NCI Epidemiology and Biometry Contract Review Committee (1967–73), the Computer Sciences and Biomathematics Study Section (1974–78), and the NHLBI Clinical Trials Review Committee (1983–86). In the 1980’s and 90’s, he frequently served as a member of the ad hoc NIH study section that reviewed statistical methods grant applications.

          Sam was a much loved presence in the profession. He attended the annual meetings of ENAR, the Society of Clinical Trials, the JSM and the AAAS without fail, and the ISI as often as he could. He was amazingly current and had strong opinions on all matters. He was not shy about asking questions of speakers, especially when he didn’t understand a point (or felt that they didn’t), and it would not be unusual for the discussion to continue in the hall or even later via email until he felt the issues were resolved. This was true whether the topic was statistics, literature, music, politics, religion or sports. His intellectual curiosity was voracious. In his 1997 Statistical Science article on his reminiscences of the NIH he wrote about how the group (Cornfield, Halperin, Mantel, he, and others) would often argue quite publicly over lunch about matters statistical and otherwise. Although one of Sam’s most endearing features was his personal warmth and smile, he could also be quite the provocateur. We fondly remember times in the late 1970’s and early 80’s, when Sam would visit Max Halperin or Nathan Mantel at the Biostatistics Center. Sam loved nothing more than a friendly spirited argument and Max and Nathan were always eager to comply. Sam was capable of arguing either side of an issue and often would, especially if it would get a rise out of Max or Nathan.

          It is a tribute to his energy and enthusiasm for statistics that Sam received many honors for his intellectual and professional contributions. The American Statistical Association in 1993 recognized him with their prestigious Founders Award, and in 1997 videotaped a discussion with Sam as part of the ASA series of conversations with distinguished statisticians. Sam was a Fellow of the American Statistical Association, the Institute of Mathematical Statistics, the American Association for the Advancement of Science, and the Royal Statistical Society, and an elected member of the International Statistical Institute. He was also a Fellow of the American College of Epidemiology and of the Council of Epidemiology of the American Heart Association. In 1969 he received the Superior Service Honor Award from the NIH and in 1976 was named a Johns Hopkins University Centennial Scholar. In December, 1999, Sam was recognized by the Harvard Institute of Psychiatric Epidemiology and Genetics for his lifetime contributions to psychiatric epidemiology and biostatistics. Unfortunately, because of his illness, he was unable to travel to deliver the lecture that he had prepared in honor of this occasion.

          Sam died of cancer on September 29, 2000 at the age of 82.

          Census Bureau Updates Statistics in Schools Program

          Sun, 01/01/2017 - 6:00am

          The U.S. Census Bureau unveiled its newly updated Statistics in Schools program for K–12 teachers and students in September. Using current and historical data, the Census Bureau provides teachers the tools to help students understand statistical concepts and improve their data analysis skills. The program offers free online activities and other resources in geography, history, math, and sociology.

          Over the past two years, Census Bureau subject-matter experts sought the expertise of teachers, education standards experts, and other professionals from across the country to help redesign the program to meet changing classroom needs. Launched initially for the 2000 Census as Census in Schools in partnership with Scholastic, the program aimed to help students better understand the once-a-decade census and the importance of being counted. The new evergreen program provides teachers with searchable activities by grade, school subject, and topic, each aimed at helping increase statistical literacy.

          “The Census Bureau is proud to have worked with educators from across the nation on activities that will help increase the statistical literacy of America’s youth,” Census Bureau Deputy Director and Chief Operating Officer Nancy Potok said. “Understanding the value behind the numbers that measure our changing society will help the future leaders of tomorrow learn how to make data-driven decisions that shape communities for generations to come.”

          The Census Bureau plans to add Statistics in Schools activities and resources throughout the summer, totaling more than 100 for the upcoming 2016–2017 school year. Activities include “The Progressives and the 1920 Census” for high-school history classes, “An Analysis of the Millennial Generation“ for high-school sociology classes, “Two-Way Tables—Walking and Bicycling to Work“ for middle-school math classes, and “Changes in My State” for elementary math classes.

          “These activities provide teachers with opportunities to teach statistical concepts and data analysis skills to students in various subjects—not just math,” said Roxy Peck, California Polytechnic State University professor emerita of statistics. Peck served as a subject-matter expert for the middle- and high-school math activities. “The need for statistically literate citizens continues to grow as we become a more data-driven society.”

          In addition to downloadable activities and games, teachers can access the following resources on the Statistics in Schools website:

          • Videos
          • Infographics and data visualizations
          • Information to help teachers explain 
Census Bureau data to students
          • Searchable data access tools

          For more information, visit the Statistics in Schools website.

          San Antonio Chapter Promotes Statistics at CORE4 STEM

          Sun, 01/01/2017 - 6:00am

          The ASA San Antonio Chapter in collaboration with the ASA Student Chapter at The University of Texas at San Antonio participated in CORE4 STEM Expo and Family Day November 5, 2016. The event, organized by the City of San Antonio and San Antonio Hispanic Chamber of Commerce, is the largest STEM education event in San Antonio, attended by more than 10,000 students.

          K–12 students and their families participate in the San Antonio Chapter’s family day.

          The chapters gave multiple presentations in a session titled “Statistics & Data Science: The Career of the 21st Century.”

          “San Antonio has an important gap to fill in the workforce areas of science, technology, engineering, and mathematics (STEM). Statistics is one of these areas, and the San Antonio Chapter has been working hard to help close this gap,” said David Han, president of the San Antonio Chapter.

          The presentations were attended by K–12 students and their families. While enjoying hands-on activities, they learned about the importance of statistics education as well as quantitative and analytical skills demanded in this data-rich era. They also learned about various career opportunities in the statistics and data science fields.

          Data Challenge on Tap for JSM2017

          Sun, 01/01/2017 - 6:00am
          Participants Will Analyze a Government Data Set Using Statistical and Visualization Tools and Methods Entering the Data Challenge 2017

          Contestants must do the following by February 1:

          Submit an abstract for a JSM 2017 speed poster session and specify the Government Statistics Section (GSS) as the main sponsor

          Forward the JSM abstract to Wendy Martinez

          The ASA Statistical Computing, Government Statistics, and Statistical Graphics sections will sponsor Data Challenge 2017 at JSM in Baltimore, Maryland. The contest is open to anyone interested in participating, including college students and professionals from the private or public sector.

          This contest challenges participants to analyze a government data set using statistical and visualization tools and methods. There will be two award categories: Professional (one level) and Student (three levels).

          Contestants will present their results in a speed poster session at JSM, so they must submit their abstracts to the JSM online system in the usual manner. Presenters are responsible for their own JSM registration and travel costs, as well as any other costs associated with JSM attendance. Group submissions are acceptable.

          To enter, contestants must do the following by February 1:

          • Submit an abstract for a JSM 2017 speed poster session and specify the Government Statistics Section (GSS) as the main sponsor
          • Forward the JSM abstract to Wendy Martinez

          The data set for the GSS Data Challenge 2017 will be the Consumer Expenditure Survey (CE). Public use data files and documentation (file structure, data dictionary, sample code, etc.), are available on the Bureau of Labor Statistics website. Contestants must use some portion of the CE data, but may combine other data sources in the analysis.

          Additionally, standard tables showing expenditures and related information for various demographic groups and an experimental table showing detailed average annual expenditures and other information for all consumer units (similar to a household or family) in the United States are available.

          The following examples of research using the CE data are also available:

          Contact Wendy Martinez with questions.

          Download the 2017 ASA Calendar and Poster

          Sun, 01/01/2017 - 6:00am

          Looking for the 2017 ASA Calendar and Poster?

          Download one or email Megan Murphy to receive a hard copy.

          Survey: Employers Struggling to Meet Demand for Data Analysts

          Sun, 01/01/2017 - 6:00am

          More and more public and private sector employers are adding statisticians and data analysts to their ranks, but are having a difficult time finding qualified candidates, according to a new national survey released by the Society for Human Resource Management (SHRM) and sponsored by the American Statistical Association.

          “Jobs of the Future: Data Analysis Skills” shows that—over the past five years—nearly two-thirds of organizations (65%) increased the number of positions requiring data analysis skills and more than half (59%) expect to increase the number of positions at their organizations over the next five years. Four out of five responding organizations (80%) have positions that require data analysis skills, and another 2% expected to create positions in 2016. Those employers who filled a data analysis position within the last 12 months faced a challenge, with 78% reporting they had difficulty recruiting qualified candidates.

          “Although data show that the number of students pursuing degrees in statistics is growing, and has been for more than 15 consecutive years, there is real concern that this growth may not be enough to satisfy the high demand for statisticians and other data analysts across all sectors of the economy,” says Ron Wasserstein, ASA executive director. “It is important that employers and the statistical community work together to encourage more students to study the statistical sciences so that supply begins to meet demand.”

          For the purposes of this research, data analysis skills are defined as the ability to gather, analyze, and draw practical conclusions from data, as well as communicate data findings to others. Examples of jobs that require data analysis skills include data analyst, data scientist, statistician, market research analyst, financial analyst, and research manager.

          Many organizations need professionals with these skills outside of the accounting and finance departments, where they are most commonly used. A significant number of responding employers hire individuals to analyze data in multiple areas, including information technology, marketing, advertising and sales, supply chain and operations, and customer service. The survey shows that one in two organizations use data analysis in the business and administration function, while more than half (54%) of human resources departments have at least one data analysis position. The vast majority of these positions (98%) are full time.

          The survey also found the following:

          • Organizations with 10,000 or more employees were more likely than smaller organizations to have data analysis positions in the human resources and supply chain and operations functions.
          • Publicly and privately owned for-profit organizations were more likely than government organizations to have data analysis positions in the marketing, advertising, and sales function.
          • Publicly owned for-profit organizations were more likely than nonprofit and government organizations to have positions requiring data analysis skills in the supply chain and operations function.

          As data increasingly play a role in a growing number of professional positions, these types of occupations are projected to grow faster than average in the coming decade. The Bureau of Labor Statistics projects employment of statisticians alone will grow 34% from 2014 to 2024, compared to 28% for mathematical science occupations and 7% for all occupations.

          In response to this demand, colleges and universities across the nation are expanding their statistics programs. The number of universities granting degrees in statistics increased 50% for bachelor’s degrees and 21% for master’s degrees from 2003 to 2015. From 2000 to 2015, bachelor’s, master’s, and doctoral degrees in statistics and biostatistics grew at 512%, 309%, and 133%, respectively.

          Access the complete findings of the SHRM/ASA survey.

          ASA Early Career Profiles: Bachelor’s-Level Graduates in Statistics and Data Science

          Sun, 01/01/2017 - 6:00am
          Organized by the ASA Section on Statistical Education
            What can I do with an undergraduate degree in statistics or data science? Take a look at what these individuals are doing. They are employed at early stages of a career after graduating from a bachelor’s degree program that included training in statistics or data science.
              Travis Britain Biography

              Undergraduate School: Duke University
              Graduation Year: 2015
              Position: Associate Consultant – Corporate Strategy
              Company: Liberty Mutual Group
              Sector: Consulting

              Background
              • BS in statistical science and economics
              • Bayes Impact Fellowship for nonprofit data science
              • Active in nonprofit and foundation impact evaluation
              Job Description
              • Provide internal strategy consulting services for Liberty Mutual Insurance Group, a Fortune 100 company, focused on high-impact problems in strategic planning; financial, competitive, and operational analysis; and capital investment
              • Sample projects include strategy design for Liberty Mutual Foundation, catastrophe claims operating model review, and Ireland profitability analysis and turnaround strategy
              Statistics and Data Science at Work

              My industry (insurance) is all about understanding risk. I may not be writing R script every day in my strategy job, but the mindset I developed from studying statistics is absolutely priceless. Strategy consulting—particularly in my industry—demands an acute appreciation of uncertainty, an ability to find elegance in complex systems, and—perhaps most importantly—the ability to synthesize a large volume of complex information and communicate the findings to a diverse (often nontechnical) audience in a way they can understand and act upon.

              Favorite Undergraduate Statistics Class

              My favorite undergraduate statistics class was, bar none, Statistics of Causal Studies, in which we learned the theory of and various practices for inferring causation from not only formal experimental studies (such as an RCT), but also observational studies using techniques such as propensity score matching. At the time, I was helping a nonprofit in Boston better understand how to understand their social impact, so I had a really unique opportunity to apply what I was learning in the classroom.

              Advice for Students

              It hurts my brain to think about how rapidly the world of data and information is evolving. By studying statistics, you’re already well ahead of the pack! The best advice I can give to you now is to seek out opportunities to apply statistical methods to a wide variety of disciplines to best position yourself for the future. In undergrad, I used statistics for applications in health care, traffic safety, public policy, finance and economics, law, engineering, and countless others. Each discipline provides new learnings and new techniques that reinforce the others, and by building a breadth of experiences as an undergraduate, you’ll be in a better place to reflect on what areas you enjoy the most and where you can add the most value to the world.

              Jieyu Gao Biography

              Undergraduate School: Purdue University
              Graduation Year: 2016
              Position: Emerging IT Leaders
              Company: Purdue University
              Sector: Science/Technology

              Background
              • Undergraduate researcher in Alex Chubykins lab
              • Data analysis using Jupyter Notebook and R
              Job Description
              • Research computing TACC STATS project
              • Analyze the data to show the visualization of the performance of clusters
              • Python, Jupyter Notebook, Linux
              • Faculty project
              • Providing suggestions on data analysis of different fields of research projects
              Statistics and Data Science at Work

              Data visualization tools, including bar graph, histogram, and error bar graphs; scientist test: ANOVA, F-test, independent t test

              Favorite Undergraduate Statistics Class

              Experimental Design was a very practical and useful class, especially introducing how to design experiments to gather the most useful data at the designing stage.

              Brittany Cohen Biography

              Undergraduate School: Duke University
              Graduation Year: 2014
              Position: Quality Assurance Engineer
              Company: Applied Predictive Technologies
              Sector: Science/Technology

              Background
              • BS in statistics, minor in computer science, graduated cum laude
              • Internships with Publishers Clearing House, Bureau of Economic Analysis, and Applied Predictive Technologies (turned into full-time opportunity)
              Job Description
              • Validate front-end software and back-end analytics
              • Derive new analytics to be implemented in our software platform
              • Work with product managers and client users to understand business use cases
              Statistics and Data Science at Work

              At APT, we develop a software platform that allows major companies to make data-driven decisions. We are constantly looking for new analytic methodologies to implement and for ways to improve our existing methodologies. I have been able to get involved with the team that uses statistics to develop new significance formulas for our complex analyses. I have been able to apply learnings from my probability course, among others, to take the variance of unintuitive expressions.

              Favorite Undergraduate Statistics Class

              My favorite undergraduate course was Statistical Consulting, which is a course I took as an elective. In this course, we helped researchers and organizations on campus that were in the middle of doing research and had statistical questions. This course was really exciting to me because it allowed me to understand the importance of statistics in a variety of fields. It was exciting to see that, even in the middle of my college education, I was able to make an impact in research in multiple industries.

              Advice for Students

              I think one of the most advantageous things I did during my college career was to combine statistics with computer science. The two go hand-in-hand, and I was fortunate enough to find a job that allows me to continue using my statistics knowledge!

              Ariana Montes Biography

              Undergraduate School: California Polytechnic University, San Luis Obispo
              Graduation Year: 2014
              Position: Configuration Engineer
              Company: Apttus
              Sector: Science/Technology

              Background
              • BS in statistics
              • Summer research 
intern at Cal Poly
              Job Description
              • Responsible for the design and implementation of CPQ for a reputable Fortune 500 company
              • Advanced knowledge of product and pricing architecture
              • Involved in identifying and delivering complex requirements
              • Work closely with project team to deliver an advanced design
              • Develop excellent working relationships with clients
              Statistics and Data Science at Work

              My current role is not heavy in statistics, but I have been able to utilize a lot of knowledge from my undergraduate education related to consulting. I am on the phone with clients gathering requirements and come up with practical solutions to solve their business needs.

              Favorite Undergraduate Statistics Class

              Statistical Consulting and Analysis of Cross-Classified Data

              Advice for Students

              Network as much as you possibly can! Try to attend conferences in the industry you’re interested in, introduce yourself to as many people as possible, and connect with everyone on LinkedIn. You’ll be surprised how important networking is in your future career.

              Emily Hadley Biography

              Undergraduate School: Duke University
              Graduation Year: 2015
              Position: College Adviser
              Company: College Advising Corps
              Sector: Government/Education

              Background
              • BS in statistical sciences and BA in public policy studies from Duke University
              • Internships with the New Hampshire Governor’s Office of Citizen Services and The Education Trust, both giving me the opportunity to apply my statistics skills in the policy realm
              • Statistics senior project and public policy senior thesis that focused on predicting and addressing high-school dropout in rural North Carolina
              Job Description
              • AmeriCorps position looking to increase college access for students from all backgrounds by placing recent college graduates in high-need schools
              • Advise 120 seniors at a low-income, rural high school in North Carolina on post-secondary opportunities
              • Organize college access events, including financial aid sessions and college representative visits for all 540 students
              • Track data to measure impact of programming
              Statistics and Data Science at Work

              Though my job title may not indicate a data focus, data is a crucial part of my work. On the job, I am always collecting data about the students I work with, from demographic information to standardized test scores to counting one-on-one interactions with individual students. I use this data to understand my strengths and weaknesses and set goals. For example, my data show that female students are far more likely to seek out my help repeatedly while male students are likely to come once, so I have developed programming to re-engage my male students. The national office uses adviser data to tell a larger story of the importance of College Advising Corps. I also serve as a College Adviser Corps data and policy fellow, where I am on a team of researchers investigating how College Advising Corps can use both quantitative and qualitative data to inform its work, particularly as it relates to underclassmen.

              The community I serve also has a dearth of statisticians, so I have been called upon to do pro bono data analysis for the school board, the local community college, and other institutions. This has included working in development of data tracking systems and survey methods, as well as analysis of existing data sets such as regression and model building.

              Favorite Undergraduate Statistics Class

              Statistical Decision Analysis, as it applied Bayesian theory to logical decision making, and Statistical Consulting, as it helped develop both analysis and communication skills as they related to statistical analysis of community projects

              Advice for Students

              I believe one of the greatest powers of data and statistics is to shine a spotlight on issues that are often neglected, particularly in the policy world. So my advice is to not be afraid of following a path that is not traditional in the realm of statistics. Once a community knows you are a statistician, they will often seek you out for a wide variety of projects and your statistical expertise will grow in surprising, relevant ways.

              Trevor Smith Biography

              Undergraduate School: Amherst College
              Graduation Year: 2016
              Position: Analyst
              Company: Hillary for America
              Sector: Government/Education

              Background
              • Statistics and political science majors
              • Internship with 
Capital One
              • Competed at UMASS DataFest
              Job Description
              • Analysis of digital ad campaigns
              • Constructing and publishing performance reports
              • Running tests
              Statistics and Data Science at Work

              I use stats on a daily basis for my job. I use R to run analyses on different ad campaigns. I also use my statistical background to help identify potential sources of bias in various experiments that we create and run.

              Favorite Undergraduate Statistics Class

              Advanced Data Analysis—I liked the broad coding experience and the ways that we connected coding to theoretical statistics and real-world examples.

              Advice for Students

              It is really important to get a solid understanding of the theory behind statistics while studying it. Most of the coding can be learned quickly on the job.

              Corinne Idzorek Biography

              Undergraduate School: 
St. Olaf College
              Graduation Year: 2015
              Position: Business Intelligence Analyst
              Company: 
Thrivent Financial
              Sector: Financial/Banking

              Background
              • Economics and mathematics double-major, with a concentration in statistics
              • Summer internship as a marketing analytics intern at Thrivent Financial
              • Courses in statistical modeling, advanced statistical modeling, probability theory, statistical theory, algorithms for decision making, and econometrics
              Job Description
              • Create predictive models from customer data that determine which products are marketed to whom
              • Explore Big Data opportunities to bring in new, publicly available data for prospecting
              • Gather and manipulate internal data based on requests to help evaluate current business and influence change
              Statistics and Data Science at Work

              In my position, we use statistics every day. We are constantly evaluating and cleaning data, utilizing multiple exploratory data analysis techniques to find important variables, and building predictive models. We test everything from logistic regression models to neural networks and ensemble models for every project. Ultimately, we have to be able to communicate our methods, decisions, and results to others within the organization.

              Favorite Undergraduate Statistics Class

              Statistical Modeling and Advanced Statistical Modeling were my favorite classes because we learned so many useful techniques for cleaning, exploring, analyzing, and modeling real data that I use every day at work. We also learned how to communicate techniques and results so they were accessible to everyone, which was so important to learn from the start.

              Advice for Students

              Every department at pretty much every company wants someone who can take their data and find meaning in it. Regardless of what sector or area 
of business you’re interested in, there are 
opportunities. With Big Data becoming the norm, everyone wants to get their hands on more information and be able to get something (statistically) significant from it.

              Jonathan Jordan Biography

              Undergraduate School: Amherst College
              Graduation Year: 2015
              Position: Investment Banking Summer Analyst
              Company: 
Nymex Capital
              Sector: Financial/Banking

              Background
              • BA in economics and statistics
              • Statistics fellow
              • Investment banking 
summer analyst
              Job Description
              • Build operating models with DCF, M&A comps, and public comps
              • Conduct research on industry, competitors, expected synergies, and historical prices
              • Prepare pitch books that include industry overview, model, and analysis
              Statistics and Data Science at Work

              At Nymex, I mostly use data to understand the stories of particular industries and high-level trends. Organizing and understanding data regarding growth rates, prices, and volume are the most common ways I use my statistics background.

              Favorite Undergraduate Statistics Class

              Intermediate Statistics—great introduction to regression and my first real experience diving into a data set to tell an interesting story

              Advice for Students

              Take a statistics class as early as you can!

              Dana Udwin Biography

              Undergraduate School: Smith College
              Graduation Year: 2014
              Position: Data Analytics Consultant
              Company: Massachusetts Mutual Life Insurance Company
              Sector: Insurance/Actuarial

              Background
              • Mathematics major with a concentration in statistics
              • East Asian languages and literature minor
              • Summer undergraduate research fellow at the National Institute of Standards and Technology (2013) researching contributing factors to performance of face recognition technology on video
              • Developing statistical activities in R for the classroom with Nicholas Horton (Amherst College)
              • Ben Baumer’s (Smith College) undergraduate data science course
              • Mathematical statistics in the UMass graduate-level statistics catalogue and machine learning in the UMass graduate-level computer science catalogue
              • Supporting the Smith College Mathematics and Statistics Department as a teaching assistant and grader for students of introductory statistics (and related classes)
              Job Description
              • Analyze both internally and externally sourced data using Python, R, and other computational implements of statistical inquiry to create tactical and strategic value for MassMutual
              • Visualize complex data using a suite of web programming tools (e.g., HTML, Twitter Bootstrap, JavaScript, and associated libraries for data manipulation such as Crossfilter and dc.js)
              • Attend graduate-level data science courses in the statistics and computer science departments at the University of Massachusetts, Amherst to supplement project-based learning
              Statistics and Data Science at Work

              We are constantly using statistics both to serve MassMutual’s broader mission of selling policies and to streamline or improve internal processes. For example, one project utilized k-means clustering to identify subpopulations within the MassMutual consumer base. Even when building dashboards to visualize large, messy data, we are thoughtful and methodical in choosing what metrics are valuable and how to calculate and portray these metrics in a clear, accurate way. We built such a visualization to track enterprise-wide spend, an example of supporting internal processes.

              Favorite Undergraduate Statistics Class

              Ben Baumer’s undergraduate data science course at Smith College was a great crash course in all things data science: databases, large-scale analyses, and exploiting outside sources to discover knowledge with efficacy and panache (look up Mark Hansen to see Big Data and art collide).

              Advice for Students

              Take the coursework that interests you. Become a tutor to help you review, refresh, and test your own understanding. A couple of programming classes will come in handy later on. Look for research opportunities in any department at your school or domain out in the workforce; there are interesting problems in unexpected places that can be solved with data. And statistics is awesome! We need statistical prowess (YOU) to make valuable the massive and expanding wealth of data in our world.

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