Statistical Mentoring at Early Training and Career Stages

At Los Alamos National Laboratory (LANL), statistical scientists develop solutions for a variety of national security challenges through scientific excellence, typically as members of interdisciplinary teams. At LANL, mentoring is actively encouraged and practiced to develop statistical skills and positive career-building behaviors. Mentoring activities targeted at different career phases from student to junior staff are an important catalyst for both short and long term career development. This article discusses mentoring strategies for undergraduate and graduate students through internships as well as for postdoctoral research associates and junior staff. Topics addressed include project selection, progress, and outcome; intellectual and social activities that complement the student internship experience; key skills/knowledge not typically obtained in academic training; and the impact of such internships on students’ careers. Experiences and strategies from a number of successful mentorships are presented. Feedback from former mentees obtained via a questionnaire is incorporated. These responses address some of the benefits the respondents received from mentoring, helpful contributions and advice from their mentors, key skills learned, and how mentoring impacted their later careers.     

The Big Tent for Statistics: Mentoring Required

Research supports the positive impact of mentoring on both job and career satisfaction. Recognizing this, the American Statistical Association (ASA) has started a new mission-centered focus on mentoring. This article describes the development and implementation of meeting-based mentoring programs at four ASA conferences in 2014 and 2015. We present results of the feedback evaluations from program participants and use them to motivate recommendations for creating and running conference mentoring programs and overcoming common challenges. These recommendations are applicable to creating and running conference mentoring programs in any field. We conclude with a discussion of the opportunities for the ASA to augment its mentoring programs in support of the professional development of its members.     

Mentoring to Achieve Diversity in Graduate Programs

The discipline of statistics has a celebrated, diverse, and colorful past. With a definite international flavor, we continue to make great strides in keeping our discipline relevant and accessible for addressing significant societal concerns. Unfortunately, we lag behind many other disciplines when it comes to fully tapping into the potential of all demographic groups within the United States. Mentoring provides one of many opportunities to change this narrative. This article looks at hard numbers related to diversity, points to some existing successful mentoring programs, and is a reflection of lessons learned through personal experiences.     

Sample Selection and Treatment Effect Estimation of Lender of Last Resort Policies

This article develops a framework for estimating multivariate treatment effect models in the presence of sample selection. The methodology deals with several important issues prevalent in policy and program evaluation, including application and approval stages, nonrandom treatment assignment, endogeneity, and discrete outcomes. This article presents a computationally efficient estimation algorithm and techniques for model comparison and treatment effects. The framework is applied to evaluate the effectiveness of bank recapitalization programs and their ability to resuscitate the financial system. The analysis of lender of last resort (LOLR) policies is not only complicated due to econometric challenges, but also because regulator data are not easily obtainable. Motivated by these difficulties, this article constructs a novel bank-level dataset and employs the new methodology to jointly model a bank’s decision to apply for assistance, the LOLR’s decision to approve or decline the assistance, and the bank’s performance following the disbursements. The article offers practical estimation tools to unveil new answers to important regulatory and policy questions.     

Quality Management: Development of a Framework for a Statistical Agency

Over the last 20 years there has been an increasing focus on quality management (QM) as a distinct and necessary element of any competitive business organization. Although quality has always been a concern of national statistical agencies, quality improvements within such organizations have been achieved through a variety of different independent initiatives rather than through concerted effort under the umbrella of a formal “QM program.” This article addresses the application of QM to a national statistical agency. The essential elements of QM are reviewed from the perspective of a statistical agency. Ongoing quality initiatives at Statistics Canada and at other statistical agencies are described. The article concludes by identifying some of the major considerations in developing a QM strategy for Statistics Canada.     

Ethical Numbers: Ethics Training in U.S. Graduate Statistics Programs, 2013–2014

As important members of research teams, statisticians bear an ethical responsibility to analyze, interpret, and report data honestly and objectively. One way of reinforcing ethical responsibilities is through required courses covering a variety of ethics-related topics at the graduate level. We assessed ethics requirements for graduate-level statistics training programs in the United States for the 2013–2014 academic year using the websites of 88 universities, examining 103 biostatistics programs, and 136 statistics degree programs. We categorized programs’ ethics training requirements as required or not required. Thirty-one (35.1%) universities required an ethics course for at least some degree students. Sixty-two (25.5%) degree programs required an ethics course for at least some students. The majority (77.4%) of required courses were worth 0 or 1 credit. Of the 177 programs without an ethics requirement, 19 (10.7%) listed an ethics elective. Although a single ethics course is insufficient for instilling an ethical approach to science, degree programs that model expectations through coursework point to the value of ethics in science. More training programs should prepare statisticians to consider the ethical dimensions of their work through required coursework. Supplementary materials for this article are available online.     

基于胜任力的管理辅导效能实验研究

研究通过在国内一家知名企业实施的纵向现场追踪实验发现：中国情景下适应性绩效是个多维结构，包括“组织与文化适应性”、“创造性地解决问题？等五个因素。而且，基于胜任力的辅导模式能更有效地提高被辅导人的适应性绩效水平。这个结果包括两层意思：其一，同未能得到辅导支持的成员相比，被辅导人具有更高的适应性绩效水平；其二，接受“胜任力辅导模式”者比接受”传统辅导模式”者具有更高的适应性绩效水平。另外，统计结果还表明，职位因素在辅导行为对适应性绩效的贡献上并未有显著影响。     

What is Mentoring?

What is mentoring? Is it just a buzz word or is this really valuable? How can mentoring help one to grow and advance personally and professionally? How and where does one even begin? Many of us have these questions. In this article, I will share my perspective and provide some reflections on these questions based on my own personal and professional journey.     

Toolkits for Equity: An Antiracist Framework for Scholarly Publishing

Abstract

Fueled by a recent groundswell of support, scholarly publishing organizations are formalizing their focus on equity and inclusion, yet there is still a lack of effective programs and solutions actually in place. A cross-organizational working group is developing antiracism toolkits (for organizations, for allies, and for Black, Indigenous, and People of Color) to transform scholarly publishing workplaces and organizational cultures. The resources, which will be hosted at c4disc.org, provide a common framework for analysis, a shared vocabulary, best practices, and training materials to guide individuals and organizations as they address systemic inequities specific to the scholarly publishing community. Originally planned as a presentation at the NC Serials Conference in March 2020, this article introduces the toolkits and explains why an antiracist framework is essential to transforming scholarly publishing.     

The Challenges in Developing an Online Applied Statistics Program: Lessons Learned at Penn State University

Numerous professional fields have an increasing need for individuals trained in statistics and other quantitative analysis techniques. Today there exists great potential to fulfill this need by providing opportunities through online learning. However, to provide a high-quality education for returning adult professionals seeking advanced degrees in applied statistics online, many challenges need to be overcome. Based on our experience developing Penn State University’s online program in applied statistics, we discuss the evolution of the program’s curriculum, recruitment and development of online faculty, and meeting the requirements of students as important areas that require consideration in the development of an online program. We also highlight program evaluation strategies employed to ensure innovation and improvement in online education as cornerstones to a program’s success.     

Cross-Consortial Collaboration: Expanding Capacity for Law Libraries

ABSTRACT

In our digital information era, libraries have increasingly joined forces to act cooperatively through library consortia. Through membership in a consortium, a library can capitalize on programs like advocacy, joint purchasing, e-resource licensing, shared discovery platforms, etc. Resources saved through such efforts, including staff time and money, can then be deployed back into the library for other, mission-critical activities. Following the economic collapse of 2008, libraries needed more than ever to maximize exceedingly scarce budgets. In response, consortia began to push the envelope, exploring even deeper collaborations in an effort to minimize redundancies and maximize efficiencies. This article will look at some examples of cross-consortial collaboration and focus specifically on the ongoing efforts among the Legal Information Preservation Alliance (LIPA), the Mid-America Law Library Consortium (MALLCO), and the NELLCO Law Library Consortium (NELLCO) to achieve more robust cooperation across the three organizations. The authors are the three executive directors of the named organizations.     

Genetic programming as a means for programming computers by natural selection

Many seemingly different problems in machine learning, artificial intelligence, and symbolic processing can be viewed as requiring the discovery of a computer program that produces some desired output for particular inputs. When viewed in this way, the process of solving these problems becomes equivalent to searching a space of possible computer programs for a highly fit individual computer program. The recently developed genetic programming paradigm described herein provides a way to search the space of possible computer programs for a highly fit individual computer program to solve (or approximately solve) a surprising variety of different problems from different fields. In genetic programming, populations of computer programs are genetically bred using the Darwinian principle of survival of the fittest and using a genetic crossover (sexual recombination) operator appropriate for genetically mating computer programs. Genetic programming is illustrated via an example of machine learning of the Boolean 11-multiplexer function and symbolic regression of the econometric exchange equation from noisy empirical data.Hierarchical automatic function definition enables genetic programming to define potentially useful functions automatically and dynamically during a run, much as a human programmer writing a complex computer program creates subroutines (procedures, functions) to perform groups of steps which must be performed with different instantiations of the dummy variables (formal parameters) in more than one place in the main program. Hierarchical automatic function definition is illustrated via the machine learning of the Boolean 11-parity function.     

关于综合运用Benford 法则和面板模型检测统计数据质量的研究

 本文介绍了如何利用Benford 法则来检测统计数据质量的一般方法。在此基础上,进一步探讨了如何将其与面板模型相结合从而找出可能存在质量问题的具体地区和时间序列数据的方法。并利用上述方法对我国多个国家级开发区的主要经济指标的数据质量进行了实证分析。     

Scholarly Communications Training: Professional Development for the Next Generation of Scholars

Abstract

Many campuses are seeing increased needs for support in scholarly communications areas such as open access, research identity management, scholarship metrics, and related topics. This article discusses a professional development program that addresses scholarly communications needs for online and in-person graduate students and faculty at UNC Greensboro (UNCG), through a collaborative, interdepartmental effort that brings together librarians from the departments of Research, Outreach, and Instruction (ROI) and Technical Services. The authors provide a brief overview of the literature related to scholarly communications needs and training in academic libraries and discuss the UNCG program’s inception, modules, format, assessment, and future directions.     

Re-Defining the Who,When, and Where of Mentoring for Professional Statisticians

Organizations tailor their mentoring strategies to accommodate internal resources and preferences, producing different approaches in academic, government, and corporate environments. Across these settings, three common barriers impede effective mentoring of statisticians: overspecialization, time constraints, and geographic dispersion. The authors share mentoring strategies that have emerged at their organization, Mathematica Policy Research, to overcome these obstacles. Practices include creating a methodology working group to unite researchers with diverse backgrounds, integrating mentoring into existing workflows, and harnessing modern technological infrastructure to facilitate virtual mentoring. Although these strategies emerged within a specific professional context, they suggest opportunities for statisticians to expand the channels through which mentorship can occur.     

A Welcome to the International Statistical Conferences

Criteria for assessing the effectiveness of a medical screening program are difficult to define; medical knowledge and screening procedures change rapidly, and self-selection at medical screens is unavoidable. This article discusses these and other basic issues in evaluation of medical screening programs with particular reference to results from the HIP breast cancer study. In addition, the article reviews various statistical models that describe the processes of disease and screening. The models are shown to be statistically indistinguishable in practice because of the small sample sizes typically available in medical screening trials. Finally the article suggests incorporating knowledge from clinical trials and from studies of robustness into statistical models designed to identify reasonable strategies for screening.     

Extending the Barnard’s test to non-inferiority

In 1945, George Alfred Barnard presented an unconditional exact test to compare two independent proportions. Critical regions for this test, by construction accomplish the very useful property of being Barnard convex sets. Besides, there are empirical findings suggesting that Barnard’s test is the most generally powerful. For Barnard’s test, calculation of critical regions is complicated due that they are constructed in an iterative form until is obtained a test size, as close as possible to the nominal significance level and less than or equal to it. In this article we present an extension to non-inferiority of this very leading test. This extension was contructed for any dissimilarity measure and tables were constructed for the difference between proportions. Also we calculate the critical regions for this extended test for sample sizes less or equal than 30, nominal significance level 0.01, 0.025, 0.05, and 0.10 and for non-inferiority margins 0.05, 0.10, 0.15, and 0.20. Additionally, we computed test sizes for the mentioned configurations. To do this calculations, we have written a program in the R environment.