Statistical Know-how Research Articles

Development, implementation, and evaluation of a curriculum for medical students on conflicts of interest and communicating risk.

Background: Insufficient risk competence of physicians, conflicts of interests from interactions with pharmaceutical companies, and the often distorted presentation of benefits and risks of therapies compromise the advising of patients by physicians in the framework of shared decision-making. An important cause of this is that teaching on this subject is mostly lacking, or fragmented when it does take place [1], [2], [3], [4]. Even though the German National Competence-Based Catalog of Learning Goals in Medicine defines learning goals on the topics of conflicts of interest and communication of risk, there are no classes that integrate both topics. Our goal was to develop a model curriculum to teach conflicts of interest and communication of risk that would integrate statistical know-how, communicational competency on the presentation of benefits and risks, and the meaning and management of conflicts of interest.Project Description: The development of the curriculum took place according to the six-step cycle of Kern et al [5]. An integrated curriculum was conceptualized, piloted, and adapted with the support of experts for the topics of shared decision-making, conflicts of interest, and communication of risk. The final version of the curriculum was implemented at the medical schools of Mainz and Heidelberg and evaluated by the students.Results: The final curriculum consists of 19 lesson units. The contents are the fundamentals of statistics, theory of risk communication, practical exercises on communication of risk, and the fundamentals of the mechanisms of effect of conflicts of interest, recognition of distortions in data, and introductions to professional management of conflicts of interest. The course was implemented three times at two different medical schools with a total of 32 students, and it was positively rated by most of the 27 participating students who evaluated it on the 1-6 German school grading scale (mean: 1.4; SD: 0.49; range: 1-2).Discussion: The curriculum we developed fills a gap in the current medical education. The innovative concept, which sensibly connects the transmission of theory and practice, was positively received by the students. The next steps are an evaluation of the curriculum by means of a two-center randomized study and the implementation at German and international medical schools. The process should be accompanied by continuous evaluation and further improvement.

Statistics in Pursuit of Social Justice

Abstract Data skills are in demand. E-commerce, advertising and artificial intelligence companies are willing to pay substantial sums for statistical knowhow. But statisticians can do more than make their mark in business. They can have real impact on people's lives, write Harrison Schramm, Tracey Pérez Koehlmoos and Scott Nestler

How A Smartphone Network Detects Earthquakes in Real Time

Abstract With the aid of thousands of accelerometers and some statistical know-how, Francesco Finazzi has built a crowd-sourced early warning system for major quakes

How Do Fans Feel? A Case Analysis of Measuring and Modeling Attitudes Using Partial Least Squares Structural Equation Modeling

Virtual fan communities (VFC) have become very popular among fans of sports teams. A VFC provides an online place for fans to meet and discuss the team, consume media, and develop friendships. Students will learn, in this case study, how to use partial least squares structural equation modeling (PLS-SEM) to assess fan attitudes toward the VFC and sponsors of the firm. Students will also learn how sport organizations can benefit from leadership with statistical know-how. The case is fictional, but it is based on an actual research study conducted in conjunction with a prominent virtual fan community in which ownership had an interest in fans’ attitudes toward their service.

Undergraduate Students’ Statistical Literacy: A Survey Study

Statistical literacy has been recognized as essential knowledge that all citizens need to possess in today's information-driven society. Undergraduate students require statistical knowhow and skills to apply in their studies and everyday's life. The aim of this study was to assess the level of statistical literacy among undergraduate students in Thailand. The two element model of statistical literacy by Gal (2004), knowledge component (comprised of five cognitive elements: literacy skills, statistical knowledge, mathematical knowledge, context knowledge, and critical questions) and a dispositional component (comprised of two elements: critical stance, and beliefs and attitudes), was used. A survey was administered to 103 undergraduate students of Faculty of Education, Chulalongkorn University. The results revealed that the undergraduate students had moderate level of overall statistical literacy, knowledge component and a dispositional component. In the knowledge component, the students had high level of literacy skills and mathematical knowledge, moderate level of statistical knowledge and critical questions, and low level of context knowledge. Additionally, in the dispositional component, the students had moderate level of critical stance, and beliefs and attitudes. The practical implications for educators to improve students’ statistical literacy were discussed.

Building the evidence base for ecological impact assessment and mitigation

Building the evidence base for ecological impact assessment and mitigation

The State of Surveying Cell Phone Numbers in the United States: 2007 and Beyond

By the late 1970s, household telephone coverage grew to exceed 90 percent in the United States, and by the mid-1980s telephone surveying of the general public had become commonplace. Nevertheless, 20 years later, the ability of researchers to reach representative samples of the U.S. public via landline (wired) telephone surveys and gather reliable data is being seriously challenged for many reasons, especially those related to cell phones and the growth of the “cell phone only” population. However, at present there exists no widely accepted set of Cell Phone Surveying “best practices” for U.S. survey researchers to follow. Despite what some appear to believe, surveying persons reached on cell phone numbers in the United States currently is a very complex undertaking if one wants to do it “right,” i.e. to do it legally, ethically, and in ways that optimally allocate one's finite resources to gather the highest quality data, and to analyze and interpret those data accurately. This final “wrap-up” article in the special issue provides a review of the empirical articles in the issue with a focus on their practical implications for the decisions that researchers need to make regarding sampling, coverage, nonresponse, measurement, and weighting in surveys that include interviews with persons reached on cell phones. The article also highlights the practical implications of a number of legal, ethical, and other issues that relate to surveys in the United States that include cell phone numbers. Surveying the U.S. cell phone population is possible, if at a higher cost than surveying their landline counterparts, and if with less precision than currently can be done surveying the landline population. The next five years should see a considerable growth in the methodological and statistical know-how that the survey community uses to plan, implement, and interpret cell phone surveys. There is a great deal that still must be learned.

Statistical know-how

Rare indeed is the statistics book that can steer a course between being esoteric and intimidating (and therefore practically useless) and being trivially obvious (and therefore practically useless). But Statistical evidence in medical trials: what do the data really tell us? by Stephen D Simon achieves that elusive goal, imparting a wealth of useful information and sage advice in an easily approachable and non-threatening way.

Physics for fairer voting

In October 2004 representatives of the European Union's 25 member states signed a treaty to establish a European Constitution. Since then the treaty has endured a bumpy ride, with citizens in France and the Netherlands voting against ratification last year. One of the sticking points has been the way votes are distributed between member states in the Council of Ministers, the main decision-making body of the European Union (EU). Physicists and mathematicians are now applying their statistical know-how to propose a solution to this problem.

Mathematical economics in the Soviet Union

The time has come for a summary, no matter how perfunctory. The trouble with rapid surveys of this kind is that the inevitably arbitrary selection process introduces a strong bias. It is quite likely that we have erred in two directions: (1) by including material that has meanwhile been superseded by theoretically weightier results; and (2) by indulging in a mildly avuncular bit of “adumbrationism,” a bit of belated and not quite communicable enthusiasm for the tempo of improvement in Soviet theoretical economics that may look uncalled for and even awkward in the years to come. For the sake of balance, let us say that we do not think Soviet mathematical economics has as yet reached or surpassed Western economics in its plenitude of the topics dealt with and its depth and subtlety of the very best achievements. But they are catching up fast. It should also be remarked that in many East European countries (Hungary, Poland, Czechoslovakia, Rumania) excellent work is being done in mathematical economics and operations research, as well as in econometrics. As far as econometrics (in its narrower sense) is concerned, the lag is far greater. In one respect it is bound to remain great for the simple reason that the Russians are not “competing” with us in this domain. Given their order of priorities, there is simply no motivation to channel much energy into our type of econometric endeavor. Unrelated to this observation, it must, however, be recorded that the quality of the statistical know-how in what can loosely be described as Soviet econometric work barely deserves a passing grade. By and large, what we have seen in this respect is at the stage of Western work in the 1930's. We are at a loss to explain this phenomenon, for at least three reasons would make one conjecture that the opposite was true: (1) the very high level of Soviet work in probability theory—which is, after all, the basis of statistical methodology; (2) the enormous amount of statistical data collected over the years (but—and that is the point—perhaps not of the “right kind”); (3) the many opportunities to eschew politically sensitive issues by the device of “letting the facts speak for themselves”—facts brought to light, of course, by statistics. We said at the beginning that a certain convergence of Western and Soviet economic thought and economic techniques seemed more than likely to us. At this juncture we hope that the reader will see why we think so. Such a convergence, incidentally, has literally nothing to do with the likelihood of convergent developments in economic institutions, a likelihood that has received a (from our point of view) very pessimistic appraisal by students of reality on both sides.