Literature Index

Displaying 2991 - 3000 of 3326
  • Author(s):
    Biehler, R.
    Year:
    1995
    Abstract:
    This paper discusses the kinds of software tools that should be available to help support student learning in statistics.
  • Author(s):
    Gould, R., Kreuter, F., & Palmer, C.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    Although the Statistics Education community has advocated using real data to teach introductory statistics for quite some time, often these data sets are not recognizably real to statisticians since the students' limited experience with "real" statistical software and data management techniques precludes the use of truly messy data. But grappling with messy and complex data sets is important for teaching Statistical Thinking (broadly defined as "thinking like a statistician") and is appropriate for an introductory statistics course. We describe our experience collecting rich data sets and developing computer lab assignments using STATA to teach statistical thinking to first-year university students using these data sets. Collecting useable, real, data sets turns out to be fairly difficult for several reasons, and teaching data management and analysis without resorting to rote-based rules is quite challenging.
  • Author(s):
    Earley, M. A.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    Discussions of students' understandings of key course concepts typically investigate those understandings at one point in time. This paper reports results from a case study in which eighteen graduate students were interviewed throughout a fifteen-week introductory statistics course. Knowledge structures were assessed once every three weeks, and changes in these structures were discussed. One key finding was that students' understandings of certain course concepts change as the semester progresses, indicating that it may not be enough to assess these understandings at only one point in time. Two concepts in particular, mean and variance, are central to many ideas in the introductory course. Assessing how students "know" these concepts throughout the course may be beneficial to researchers and educators alike. Implications for both statistics education research and teaching introductory statistics are offered.
  • Author(s):
    Johnson, H. D.
    Year:
    2005
    Abstract:
    Although there has been a considerable amount of work evaluating the effects of different (non-traditional) instructional styles, inquiries into students?preferences of instructional style have been few. From 1998-2001, we surveyed introductory statistics students regarding various aspects of their class preferences, especially the teaching style they prefer. We analyzed the data for the purpose of seeing if there has been an increasing trend in preference towards non-traditional methods. Our results are inconclusive (p = 0.35) about the presence of such a trend. However, the overall proportion of students preferring non-traditional classes is higher than students preferring traditional classes (p < 0.001). We also used the survey data to investigate the possible attributes that relate to preference. Using Stepwise Logistic regression (with alpha = 0.10) we find that the students?ideal class-size, the number of years since they graduated from high school, the perceived learning styles of the students, and the attitudes of students towards the use of visual aids and hands-on activities are all significantly related to the teaching style preferences of students.
  • Author(s):
    Judy M Simpson, Philip Ryan, John B Carlin, Lyle Gurrin, and Ian Marschner
    Year:
    2009
    Abstract:
    In response to the worldwide shortage of biostatisticians, Australia has established a national consortium of eight universities to develop and deliver a Masters program in biostatistics. This article describes our successful innovative multi-institutional training model, which may be of value to other countries. We first present the issues confronting the future of biostatistics in Australia, then relate our experience in establishing a new national consortium-based Masters program, and finally explore the extent to which our initiatives have addressed the current challenges of biostatistics workforce shortages.
  • Author(s):
    Long, C. R.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    A two-year clinical research curriculum offered in a graduate program at a U.S. chiropractic college was implemented in Fall 2003 and enrolls three to six chiropractors per year. The curriculum includes ten credit hours of required courses in biostatistics. Introductory courses in biostatistical thinking and reasoning and data management are offered the first term, followed by basic statistical methods, statistical graphics, and advanced topics over the next three terms. Trainees typically have little previous exposure to statistics, so program objectives move from developing critical appraisal skills to writing strong data-related sections in grant applications. As graduates will likely pursue careers at chiropractic colleges with little or no research infrastructure, nor necessarily a research culture, it is paramount they develop a strong foundation in research methods and become proficient users of statistical tools to succeed.
  • Author(s):
    Curtis, D. A., & Harwell, M.
    Year:
    1998
    Abstract:
    Although numerous research studies have focused on issues related to the teaching of statistics, few studies have focused on the training of people who may become statistics teachers. The purpose of this study was to examine doctoral students' preparation in statistics in the field of education. A national survey was conducted of twenty-seven quantitative methods (QM) programs. One QM professor from each program was identified and asked to describe and evaluate the training of QM and non-QM doctoral students at his or her institution. The vast majority of professors indicated that most or all of the students in their QM programs received training in the "old standard" procedures--ANOVA, multiple regression, and traditional multivariate procedures, whereas fewer than half of the professors indicated that most or all of their QM students received training in more recent procedures such as bootstrapping and multilevel models. Professors were also asked to rate the skills of their QM students in areas such as mathematical statistics and computing on a scale from "Weak" to "Strong". Most professors gave high ratings to their QM students' skills with statistical packages, but gave much more mixed ratings of their QM students' training in mathematical statistics. Nearly half of the professors thought that most of their QM students could have benefited from one or two additional statistics courses. Results are discussed in terms of training future doctoral students.
  • Author(s):
    Batanero, C. & Godino, J. D.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    A main point to assure the future of statistics education research is the training of researchers through the Master's and Doctoral Programmes. Since in the majority of countries there are no specific departments of Statistics Education, this training is carried out from Mathematics Education, Statistics, Education, Psychology and other related departments, and even there starting a line of research in statistics education is not an easy task, due to the lack of trained supervisors, specific bibliography and funds. In this presentation I will describe the experience of starting the first Doctoral Programme in Mathematics Education at the University of Granada, and developing there a research group in statistics education. The contents of the Doctoral Programme will be analysed as a first step to establish what an ideal programme for training future researchers in statistics education would be.
  • Author(s):
    Mclaughlin, G. W. & Mclaughlin, J. S.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    Training institutional research (IR) professionals in the use of statistics is a complex and challenging task. It is complicated by the need to develop a functional model of institutional research that includes its various roles. In addition, the specific statistical and analytical tools used to perform necessary tasks must be better understood. This is important due to the need for IR professionals to teach others to use and interpret statistical results. IR professionals have tended to use basic tools and have limited statistical sophistication. The specific tools or statistical methodologies that are important in IR should differ based on the situation of the individual and the academic background of the audience and should not be limited by lack of training in statistics. The work done by Terenzini (1993) further indicates that IR professionals need to broaden their approach to research by operating within three types of intelligence.
  • Author(s):
    Coughlin, M. A.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    Professionals in the field of Institutional Research must use data analysis and statistical skills on a daily basis. Yet, professionals come to the field of Institutional Research with diverse backgrounds and differentiated knowledge of statistics. As a result, most professionals find themselves searching for review or refresher courses in data analysis and statistics. Thus, teaching a statistics course in six hours or fewer is the challenge. This paper will focus on the difficulties that are associated with teaching statistical content and skills in professional development settings to individuals with a wide range of statistical skills and abilities. The central tenet of the paper is that the art of teaching is what makes for effective training. Various pedagogical approaches designed to increase statistical understanding are explored and defined. Suggestions for sequencing and practical examples illustrating the use of statistics in Institutional Research will be given.

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The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education