Literature Index

Displaying 321 - 330 of 3326

  • An investigation of statistical thinking in two different contexts: Detecting a signal in a noisy process and determining a typical value

    Author(s):
    Groth, R. E.
    Year:
    2005
    Abstract:
    The study describes students' patterns of thinking for statistical problems set in two different contexts. Fifteen students representing a wide range of experiences with high school mathematics participated in problem-solving clinical interview sessions. At one point during the interviews, each solved a problem that involved determining the typical value within a set of incomes. At another point, they solved a problem set in a signal-versus-noise context [Konold, C., & Pollatsek, A. (2002). Data analysis as the search for signals in noisy processes. Journal for Research in Mathematics Education, 33, 259-289]. Several patterns of thinking emerged in the responses to each task. In responding to the two tasks, some students attempted to incorporate formal measures, while others used informal estimating strategies. The different types of thinking employed in using formal measures and informal estimates are described. The types of thinking exhibited in the signal-versus-noise context are then compared against those in the typical value context. Students displayed varying amounts of attention to both data and context in formulating responses to both problems. Suggestions for teachers in regard to helping students attend to both data and context when analyzing statistical data are given.
    Location:
    http://www.sciencedirect.com/science?_ob=JournalURL&_cdi=6566&_auth=y&_acct=C000032378&_version=1&_urlVersion=0&_userid=616288&md5=917729900946a25332eb55cf11373df9

  • An investigation of the approaches to learning statistics questionnaire

    Author(s):
    Mecklin, C. J.
    Year:
    2001
    Abstract:
    Statistics is viewed as having no true connection with real-life activities. Even the typical components of an introductory statistics course (descriptive statistics, probability, and inferenctial statisticsP are seen as being unrelated to each other. Often descriptive statistics, being less mathematically sophisticated, is rushed through, then lase of probability and combinatorics are inroduced via formulas, and finally inferential statistics is presented. The link between inferential statistics and probability is often completely lost upon the student (Hawkins, Jolliffe, & Glickman, 1992). Most research in statisitcs education has been focused upon what the instructor can do to imrpove the cognitive side of instruction (Gal & Ginsberg, 1992); Fordon, 1999). RElatively less research has focused upon the statistics student (some examples are Gal & Ginsberg, 1994; Garfield, 1995; Gordon, 1995a, 1995b, 1999). In particular, little work has been done in exploring the approach to learning used by statistics students; one such work, that classified students as using either a deep or surface approach to learning, is by Gordon (1999).

  • An investigation of the knowledge of proportion and probability that students bring to university with them

    Author(s):
    Sharples, F., & Jolliffe, F. R.
    Editors:
    Vere-Jones, D., Carlyle, S., & Dawkins, B. P.
    Year:
    1991
    Abstract:
    A questionnaire was designed to study what first year university students already know about proportion and probability. Many questions were based on those previously used by others. The questionnaire was piloted at Brunel University, UK, in October 1989 with students newly enrolled on mathematics or statistics degree courses. An amended version was administered to some 60 students enrolled in a service course in statistics at the University of Waikato, NA, at the beginning of the 1990 academic year. The mathematical background of the NZ students varied from below the median in Form 5 (age 15-16) to slightly above the median in Form 7 (age 17-18). After answering each question the respondents were asked to give the reason for their reply. Analysis paid particular attention to the reasons given when questions were wrongly answered. The questionnaire used, together with tabulations of the students' answers and of the reasons given for those answers, are presented in a poster-paper.

  • An Investigation of the Large-Sample/Small-Sample Approach to the One-Sample Test for a Mean (Sigma Unknown)

    Author(s):
    Rhiel, G. S., & Chaffin, W. W.
    Year:
    1996
    Abstract:
    In this article we investigate the large-sample/small-sample approach to the one-sample test for a mean when the variance is unknown, using the probability of a Type I error as the criterion of interest. We show that in most cases using a t-test (t critical value) provides a more robust test than does using the z-test (standard normal critical value). The only case in which z has some advantage is when using a small sample from a parent population with extremely high kurtosis or with skewness in the direction of the rejection region tail. The implications for teaching the large-sample/small-sample approach in introductory statistics classes are discussed in light of these findings.
    Location:
    http://www.amstat.org/publications/jse/v4n3/rhiel.html

  • An investigation of the median-median method of linear regression.

    Author(s):
    Walters, E. J., Morrell, C. H., & Auer, R. E.
    Editors:
    Stephenson, W. R.
    Year:
    2006
    Abstract:
    Least squares regression is the most common method of fitting a straight line to a set of bivariate data. Another less known method that is available on Texas Instruments graphing calculators is median-median regression. This method is proposed as a simple method that may be used with middle and high school students to motivate the idea of fitting a straight line to data. The median-median line may also be viewed as a method that is not greatly affected by outliers (robust to outliers). Our paper briefly reviews the median-median regression method, considers various examples to compare the median-median line to the least squares line, and investigates the properties of the median-median line versus the least squares line using a simulation study.
    Location:
    http://www.amstat.org/publications/jse/v14n2/morrell.html

  • An item response analysis of statistics use in the workplace.

    Author(s):
    Harraway, J. A., & Andrade, D. F.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    Training in statistics at university should be informed at least in part by what graduates will have to do with acquired statistical knowledge after graduation. A sample of 977 employed graduates with PhD and Masters degrees in seven specialties with statistics pre-requisites at university identifies which of 46 statistics based techniques (the items) they use in their work. A two parameter item response model uses 32 of the 46 items to build a scale measuring the extent of statistics use in the workplace and creates a value for each graduate which is used to summarize differences between the use of statistics in the seven specialties. Implications for syllabus construction to better prepare graduates for the workplace are discussed.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/17/C435.pdf

  • AN ON-LINE PROFESSIONAL ENVIRONMENT TO IMPROVE THE TEACHING OF STATISTICS

    Author(s):
    Pilar Azc&aacute;rate, Ana Serrad&oacute;, Jose M. Carde&ntilde;oso, Maria Meletiou-Mavroteris<br>and Efi Paparistodemou
    Year:
    2008
    Abstract:
    We present the foundations of a professional development program supported by the European Union (COMENIUS Project 226573-CP-1-2005, developed from December 2005 to December 2008), whose objective is to propose professional development strategies that foster the integration of the teaching and learning of statistical reasoning in European schools. The intention of the program is to promote professional development through cross-cultural collaboration between teachers of different European countries. To this end, an online professional learning environment has been designed. We present the referents that allow us to interpret the teachers' reasoning and to understand how their intervention in the teaching and learning processes evolves.
    Location:
    http://www.ugr.es/~icmi/iase_study/Files/Contents.htm

  • An On-Line Workshop Using a Simple Capture-Recapture Experiment to Illustrate the Concepts of a Sampling Distribution

    Author(s):
    Schwarz, C. J., &amp; Sutherland, J.
    Year:
    1997
    Abstract:
    We describe a World Wide Web-accessible workshop designed for students in an introductory statistics course that uses a capture-recapture experiment to illustrate the concept of a sampling distribution. In addition to the usual "sampling bowl" experiment, the workshop contains a computer simulation program written in XLISP-STAT that will allow students to further investigate the properties of the estimator.
    Location:
    http://www.amstat.org/publications/jse/v5n1/schwarz.html

  • An open university service course

    Author(s):
    Bibby, J.
    Editors:
    Grey, D. R., Holmes, P., Barnett, V., &amp; Constable, G. M.
    Year:
    1983
    Abstract:
    This paper describes an Open University course entitled: MDST 242: Statistics in Society. This course will be available to Open University students for up to 10 years starting in February 1983. However, in addition to being anecdotal and specific, the paper also raises general issues and questions concerning the teaching of statistical methods to nonspecialists.

  • An Oral Contraceptive Drug Interaction Study.

    Author(s):
    Bradstreet, T. E., &amp; Panebianco, D. L.
    Year:
    2004
    Abstract:
    This article focuses on a two treatment, two period, two treatment sequence crossover drug interaction study of a new drug and a standard oral contraceptive therapy. Both normal theory and distribution-free statistical analyses are provided along with a notable amount of graphical insight into the dataset. For one of the variables, the decision on the presence or absence of a drug interaction is reversed depending on whether the normal theory or the distribution-free analysis is favored. The data also contain statistically significant period effects, statistically significant but clinically unimportant treatment effects, some modest degree of structural nonnormality; and modest to more extreme outliers. This and 28 other pedagogically useful datasets can be found at www.math.iup.edu/~tshort/Bradstreet.
    Location:
    http://www.amstat.org/publications/jse/v12n1/datasets.bradstreet.html

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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