Literature Index

Displaying 801 - 810 of 3326

  • Desired and Feared - What Do We Do Now and Over the Next 50 Years?

    Author(s):
    Xiao-Li Meng
    Year:
    2009
    Abstract:
    An intense debate about Harvard University's General Education Curriculum demonstrates that statistics, as a discipline, is now both desired and feared. With this new status comes a set of enormous challenges. We no longer simply enjoy the privilege of playing in or cleaning up everyone's backyard. We are now being invited into everyone's study or living room, and trusted with the task of being their offspring's first quantitative nanny. Are we up to such a nerve-wracking task, given the insignificant size of our profession relative to the sheer number of our hosts and their progeny? Echoing Brown and Kass's "What Is Statistics?" (2009), this article further suggests ways to prepare our profession to meet the ever-increasing demand, in terms of both quantity and quality. Discussed are (1) the need to supplement our graduate curricula with a professional development curriculum (PDC); (2) the need to develop more subject oriented statistics (SOS) courses and happy courses at the undergraduate level; (3) the need to have the most qualified statisticians - in terms of both teaching and research credentials - to teach introductory statistical courses, especially those for other disciplines; (4) the need to deepen our foundation while expanding our horizon in both teaching and research; and (5) the need to greatly increase the general awareness and avoidance of unprincipled data analysis methods, through our practice and teaching, as a way to combat "incentive bias," a main culprit of false discoveries in science, misleading information in media, and misguided policies in society.
    Location:
    http://pubs.amstat.org/doi/abs/10.1198/tast.2009.09045?prevSearch=authorsfield%253A%2528Meng%252C%2BXiao%255C-Li%2529&searchHistoryKey=

  • Detection of errors in statistical problem solving

    Author(s):
    Allwood, C. M., & Montgomery, H.
    Year:
    1982
    Abstract:
    Ten subjects were asked to think aloud while solving two statistical problems. Ten subjects were instructed after each substep of his/her problem solving, to check in various ways the solution of the previous substep. The subjects detected 25 out of a total of 56 errors when they solved the problems. About half of the detected errors were computational errors. Nine errors were eliminated in response to the checking instructions. The think aloud data indicated that subjects' most common way of detecting their own errors was by noting that computations resulted in extreme values. Subjects also detected errors by (a) "spontaneous discovery"; (b) discontent with other aspects of a solution than the numerical value of the answer; (c) repeating a solution. The last mentioned type of error detection only occurred when subjects responded to the checking instructions. Finally it was found that subjects had a strong tendency to respond to the checking instructions either in a routinized or in a non-elaborated way. It was discussed how the formulation of checking instructions can be improved in order to avoid this effect.

  • Determining the educational potential of computer based strategies for developing and understanding of sampling distributions

    Author(s):
    Jones, P., & Lipson, K.
    Editors:
    Atweh, B., Kanes, C., Carss, M., Booker, G.
    Year:
    1993
    Abstract:
    An analysis of the steps involved in forming the idea of an empirical sampling distribution and the nature of the methods and/or images used in most computer based strategies to teach this idea suggest that this way of using the computer adds little insight to the usual text based explanations that they are designed to complement. This analysis suggests reasons why a more recent approach which uses the computer to model and dynamically display the processes that underlie the idea is more likely to be successful. (orig.)

  • Developent of the concept of statistical variation: An exploratory study

    Author(s):
    Torok, R., & Watson, J.
    Year:
    2000
    Abstract:
    An appreciation of variation is central to statistical thinking. In this study, four students from each of grades 4,6,8 and 10 were interviewed individually on aspects of variation present in three settings. The first setting was an isolated random sampling situation, whereas the other two settings were real worlds sampling situations. Four levels of responding were identified and described in relation to developing concepts of variation. Implications for teaching and future research on variation are considered.

  • Developing a Biostatistical Collaboration Course in a Health Science Research Methodology Program

    Author(s):
    Lehana Thabane, Stephen D Walter, Steven Hanna, Charles H Goldsmith, and Eleanor Pullenayegum
    Year:
    2008
    Abstract:
    Effective statistical collaboration in a multidisciplinary health research environment requires skills not taught in the usual statistics courses. Graduates often learn such collaborative skills through trial and error. In this paper, we discuss the development of a biostatistical collaboration course aimed at graduate students in a Health Research Methodology PhD program with Specialization in Biostatistics. The objectives of the course are to promote enthusiasm and commitment to excellence in statistical collaboration in clinical research; to enhance communication of statistical issues to non-statistician collaborators; to build statistical self-sufficiency and develop skill in applied statistics; and to enhance a culture of collaboration among statisticians and non-statistician researchers. The course uses a combination of lectures and tutorials led by faculty members, videotaped consulting practice sessions, and internship with mentoring of each student by an experienced biostatistician.
    Location:
    http://www.amstat.org/publications/jse/v16n2/thabane.html

  • Developing a computer interaction to enhance students understanding in statistical inference.

    Author(s):
    Lipson, K., Francis, G., & Kokonis, S.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    Prior investigation of student experiences with a computer interaction indicated that the simulation was only partly successful in facilitating developmental learning of statistical inference. The simulation was re examined in the light of subsequent multimedia design research and cognitive theory. A new simulation was developed with less extraneous information and reduced on screen text. In addition the new simulation incorporated audio narration and a higher degree of student control in progressing through signalled stages of development.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/17/6E2_LIPS.pdf

  • Developing a model to describe the use of context knowledge in data explorations.

    Author(s):
    Mooney, E., Langrall, C., & Nisbet, S.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    This study aimed to examine the role knowledge of context plays in supporting or interfering with middle school students' statistical thinking. A model of context knowledge use was developed based on a model of context support developed by Beck, McKeown and McCaslin (1983) to describe students' use of context knowledge. The results of the study showed that students' use of context knowledge fell into three categories.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/17/2A4_MOON.pdf

  • Developing a Questionnaire to Assess the Probability Content Knowledge of Prospective Primary School Teachers

    Author(s):
    Emilse Gómez-Torres, Carmen Batanero, Carmen Díaz, and José Miguel Contreras
    Year:
    2016
    Abstract:
    In this paper we describe the development of a questionnaire designed to assess the probability content knowledge of prospective primary school teachers. Three components of mathematical knowledge for teaching and three different meanings of probability (classical, frequentist and subjective) are considered. The questionnaire content is based on curricular guidelines and primary school textbooks in Spain. The items were selected and adapted, after expert judgment, from previous research. The responses of 157 prospective primary school teachers were used to analyze the psychometric properties of the questionnaire and to provide information about various aspects of participants’ probability content knowledge.
    Location:
    http://iase-web.org/documents/SERJ/SERJ15(2)_GomezTorres.pdf

  • Developing and presenting conceptual maps of course content.

    Author(s):
    Carr, R.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    Teaching online involves providing an environment that is interactive and engaging. A large part of this is providing suitable learning resources. In this talk we will demonstrate an efficient method for producing conceptual maps of the actual course content, showing the structure of the subject for students in a visual way. The structures that result allows for learning resources to be linked in as required. The maps are developed using PowerPoint but they can be deployed in a web-friendly format or on CD-ROM.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/17/7B4_CARR.pdf

  • Developing concepts of sampling

    Author(s):
    Watson, J. M., & Moritz, J. B.
    Year:
    2000
    Abstract:
    A key element in developing ideas associated with statistical inference involves developing concepts of sampling. The objective of this research was to understand the characteristics of students' constructions of the concept of sample. Sixty-two students in Grades 3, 6, 9 were interviewed using open-ended questions related to sampling; written responses to a questionnaire were also analyzed. Responses were characterized in relation to the content, structure, and objectives of statistical literacy. Six categories of construction were identified and described in relation to the sophistication of developing concepts of sampling. These categories illustrate helpful and unhelpful foundations for an appropriate understanding of representativeness and hence will help curriculum developers and teachers plan interventions.

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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

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