Literature Index

Displaying 3271 - 3280 of 3326

  • When explanations compete: the role of explanatory coherence on judgments of likelihood

    Author(s):
    Sloman, S. A.
    Year:
    1994
    Abstract:
    The likelihood of a statement is often derived by generating an explanation for it and evaluating the plausibility of the explanation. The explanation discounting principle states that people tend to focus on a single explanation; alternative explanations compete with the effect of reducing one another's credibility. Two experiments tested the hypothesis that this principle applies to inductive inferences concerning the properties of everyday categories. In both experiments, subjects estimated the probability of a series of statements (conclusions) and the conditional probabilities of those conclusions given other related facts. For example, given that most lawyers make good sales people, what is the probability that most psychologists make good sales people? The result showed that when the fact and the conclusion had the same explanation the fact increased people's willingness to believe the conclusion, but when they had different explanations the fact decreased the conclusion's credibility. This decrease is attributed to explanation discounting; the explanation for the fact had the effect of reducing the plausibility of the explanation for the conclusion.

  • When Should Zero be Included on a Scale Showing Magnitude?

    Author(s):
    Marcin Kozak
    Year:
    2011
    Abstract:
    This article addresses an important problem of graphing quantitative data: should one include zero on the scale showing magnitude? Based on a real time series example, the problem is discussed and some recommendations are proposed
    Location:
    http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9639.2010.00456.x/abstract

  • When Statistical Literacy Really Matters: Understanding Published Information about the HIV/AIDS Epidemic in South Africa

    Author(s):
    Sally Hobden
    Year:
    2014
    Abstract:
    Information on the HIV/AIDS epidemic in Southern Africa is often interpreted through a veil of secrecy and shame and, I argue, with flawed understanding of basic statistics. This research determined the levels of statistical literacy evident in 316 future Mathematical Literacy teachers’ explanations of the median in the context of HIV/AIDS survival times. Drawing on the three-tiered statistical literacy hierarchy proposed by Watson (1998, 2006) and the SOLO taxonomy (Biggs & Collis, 1982), a categorization framework was constructed. About half the teachers were classified below the level of basic understanding of the median. Misunderstandings included confusion of the median survival time with the maximum survival time, and a failure to consider the spread of the data along with the centre.
    Location:
    http://iase-web.org/documents/SERJ/SERJ13(2)_Hobden.pdf

  • Where do Students Get Lost: The Concept Of Variation?

    Author(s):
    Lee, C., Zeleke, A. & Wachtel, H.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    Many college students have difficulties in understanding and making connections among the main concepts of statistics. Compounding the difficulties is the misconception of a variety of statistical concepts that students hold even before taking any statistics course. It is, thus, crucial to investigate how the understanding of statistical concepts is constructed and at which stage students start to lose making connections among various concepts. This article reports some findings from our study of investigating the path of learning statistical concepts, specifically on how students learn the concept of variation. We focus on investigating the missing connections about their understanding of variation. The framework of statistical thinking, PPDAC investigative cycle, is used as our guideline for analyzing our interview data.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/1/10_08_le.pdf

  • Which group is better?: The development of statistical reasoning in elementary school children

    Author(s):
    Gal, I., Rothschild, K., & Wagner, D. A.
    Year:
    1989
    Abstract:
    There are two main reasons for our interest in statistical reasoning in children. The first one is that research has shown that understanding of statistical principles, and their appropriate usage, are related to the quality of decisions, judgments and inferences people make. The second reason is that American children learn very little about statistics in school.

  • Which jar gives the better chance? Children's decision making strategies

    Author(s):
    Way, J.
    Editors:
    Biddulph, F. & Carr, K.
    Year:
    1997
    Abstract:
    In task-based interviews 48 Kindergarten to Year 6 children were asked to choose between two jars containing different mixes of read and yellow toy bears, with the aim of giving themselves a better chance at drawing out a read bear. The children applied a variety of strategies, ranging from idiosyncratic reasons to proportional reasoning. These strategies are examined in relation to the ratio pairs presented in each jar and are compared to other strategies reported in the literature.
    Location:
    See compilation of Research Papers from 1997 ID # 2852 (Garfield & Truran)

  • WHICH PROBABILITY DO WE HAVE TO MEET &hellip;?<br>A CASE STUDY ABOUT STATISTICAL AND CLASSICAL APPROACH TO PROBABILITY IN STUDENTS' BEHAVIOUR

    Author(s):
    Rossella Garuti, Aurelia Orlandoni and Roberto Ricci
    Year:
    2008
    Abstract:
    This case study report describes a training activity for in-service teachers working on a project related to statistical literacy. Collected data from the solutions to a problem given to the students are taken into account. The problem was chosen to give teachers the opportunity to reflect on their methods for teaching statistics and probability. Results about student understanding of statistical and classical probability and about the teacher methodologies to present these key concepts are described.
    Location:
    http://www.ugr.es/~icmi/iase_study/Files/Contents.htm

  • Who is afraid of statistics? Correlates of statistics anxiety among students of educational sciences

    Author(s):
    Birenbaum, M., &amp; Eylath, S.
    Year:
    1994
    Abstract:
    The purpose of the present short report is to investigate the correlates of statistics anxiety. The sample consisted of 151 first- and second-year-level female students in the department of educational sciences enrolled in statistics-related courses. The findings indicated that a priori anxiety of statistics was not reduced by acquaintance with the subject, nor was students' willingness to further study of statistics affected by this experience. Furthermore, grades in statistics were neither related to statistics anxiety nor to willingness to pursue further study of statistics. Inductive reasoning ability was significantly related to statistics anxiety but not to mathematics anxiety.

  • Who Is Teaching Introductory Statistics?

    Author(s):
    Frank P. Soler
    Year:
    2010
    Abstract:
    The growing popularity of the statistical sciences has brought about an unprecedented student demand for undergraduate statistics courses, especially courses of an introductory nature. The question of "Who Is Teaching Introductory Statistics?" is at the core of whether over the next 50 years the discipline of statistics would be desired or feared. This commentary addresses compelling issues currently facing the status of statistics education in this nation.
    Location:
    http://pubs.amstat.org/doi/pdf/10.1198/tast.2010.09183

  • Why aren't computers used more in our courses?

    Author(s):
    McKenzie, J. D. Jr.
    Year:
    1992
    Abstract:
    There are many reasons why computers should be used in our courses. Still they are underutilized in many statistics classes today. In this paper the author will address each of the possible reasons why this is so. He will conclude with some thoughts about the future of computers in our courses.

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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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