Research

  • Statistics has gained recognition as an important component of many disciplines including medical and health sciences (MHS). Medical doctors and health related professionals need to understand the process of statistical investigations and be able to plan statistical inquiry in medical and health related decisions. In this respect, statistical knowledge and skills are considered as part of required competencies for medical doctors and health related professionals. Furthermore, planners and policy-makers in the MHS education institutions need to apply research-based information to maintain and improve quality of teaching-learning processes. Toward meeting the above objectives, a medical education research workshop was designed at Shaheed Beheshti University of Medical Sciences and Health Services in Iran. The workshop was developed and implemented to provide medical doctors with opportunities to get their hands on statistical investigations. In doing so, based on three types of research methods in medical education systems, statistical tools were presented through data-oriented approach. In this paper first the curricular structure of the workshop is briefly presented. Then, based on data from interviews with participants, the impact of the workshop on developing statistical knowledge and skills to solve medical education system problems is analyzed. The results indicate that workshop approach motivated the participants to develop a conceptual understanding of statistical ideas and their applications to investigate problems of teaching-learning systems.

  • About 800 students each year enroll in a subject Introduction to Biostatistics at the University of Otago. It is a compulsory subject for students applying to enter the health sciences professional courses. At school there are two subjects, mathematics with calculus and mathematics with statistics, with many students studying only one of these the year before university. There is debate about which one best prepares students for gaining the high marks in biostatistics necessary for entry to the competitive professional health sciences programmes. The school syllabus in mathematics with statistics is first compared with that in Introduction to Biostatistics. Results from the analysis of marks achieved in biostatistics are reported. The fitted regression models show prior knowledge of statistics from the school subject has no effect on performance in biostatistics, that there is no gender effect and that prior knowledge of calculus may be beneficial. Reasons for these results are discussed and proposals made to improve the presentation of statistics to students of the health and biological sciences.

  • This paper reviews the principal lessons for statistics education for business that can be drawn from the 17 annual U.S. Conferences held so far called Making Statistics More Effective in Schools and Business. The series of Conferences was begun in 1986 under the leadership of Professors Harry Roberts and George Tiao, both of the Graduate School of Business, The University of Chicago. "The mission of the annual Making Statistics More Effective In Schools and Business (MSMESB) conference is to improve the teaching and practice of Statistics in Schools of Business. We aim to encourage interaction between business faculty and others involved in teaching business statistics to business students, as well as interaction with professionals from industry and government, with publishers, and with software producers."

  • Pharmaceutical companies are constantly asked for information by government agencies, market research companies and often carry out their own investigations. However there has been no definitive independent source of information about field-based personnel in the pharmaceutical and healthcare industry. The authors report on the first ever survey of the UK medical sales field force and demonstrate the unique data interrogation tool developed to enable analysis of the data collected including the remuneration, values and perception of the sales force.

  • This paper describes two versions of a teaching experiment that traced the development of Basotho elementary students' thinking with regard to sample space and probability of an event. The instructional design phase of the teaching experiment was informed by a cognitive framework that describes and predicts Basotho elementary students' growth in probabilistic thinking (Polaki, Lefoka, & Jones, 2000). Twelve students (9-10 year olds) drawn from grades 4 and 5 of an elementary school took part in a six-week instructional program. Analysis of qualitative data revealed, amongst other things, a weak and often unstable part-part schema that was minimally effective in enabling the students to order probabilities in 1-dimensional situations, and a stronger and more stable part-part schema that made it possible for some students to experience greater success at listing complete sets of outcomes, and to order probabilities in 1- and 2-dimensional situations.

  • The idea of data as a mixture of signal and noise is perhaps the most fundamental concept in statistics. Research suggests, however, that current instruction is not helping students to develop this idea, and that though many students know, for example, how to compute means or medians, they do not know how to apply or interpret them. Part of the problem may be that the interpretations we often use to introduce data summaries, including viewing averages as typical scores or fair shares, provide a poor conceptual basis for using them to represent the entire group for purposes such as comparing one group to another. To explore the challenges of learning to think about data as signal and noise, the authors examine the "signal/noise" metaphor in the context of three different statistical processes: repeated measures, measuring individuals, and dichotomous events. On the basis of this analysis, several recommendations are made about research and instruction.

  • Although there is considerable research on the reasoning of college students, there is<br>relatively little on how younger students reason and learn about data. Because data<br>analysis has only recently become an integral part of the pre-college curriculum in<br>the United States, we have limited practical experience with what works and what<br>doesn't. Accordingly, we draw heavily in this chapter on what we, as researchers,<br>have learned from the episodes in the Working with Data Casebook, connecting our observations when we can to published findings. In our opinion, the reflections of<br>these teachers and their descriptions of students' thinking is one of the richest source<br>of information to date on children's reasoning about data and on how children's<br>thinking evolves during instruction.

  • Some questions that teachers who are ready to venture online for the first time or who are trying to rethink how they are currently using online resources in the classroom should ask are provided. These questions ask what the educational purpose of the activity is, where the activity fits into the curriculum, how using the Internet will enhance the activity, how students will use the online resources, what experience students have with data analysis and thoughtful discussion, and what will happen if the intended resources are not available.

  • In the third part of a series, advice for teachers on moving students engaged in Internet science projects beyond collecting and uploading data to analyzing data from many sites is presented. The advice deals with what is involved in a data-centered investigation and with leading such investigations by using reliable data, beginning with familiar contexts, using data with salient trends, and working with representations that students understand.

  • We interviewed 7th and 9th grade students to explore how they summarized and reasoned about data. The students were near the end of an eight-week collaborative research project in which they analyzed data they had collected on the types and frequencies of animals killed on town roads. During our interviews, students worked with data similar to those they had collected to answer questions we posed about conditions that might affect the number of animals struck by cars. To summarize their data, students tended to use a "modal clump," a range of data in the heart of a distribution of values. These clumps appear to allow students to express simultaneously what is average and how variable the data are. Modal clumps may provide useful beginning points for explorations of more formal statistical ideas of center.

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