Literature Index

Displaying 1281 - 1290 of 3326

  • Inference by Eye: Pictures of Confidence Intervals and Thinking About Levels of Confidence

    Author(s):
    Geoff Cumming
    Year:
    2007
    Abstract:
    A picture of a 95% confidence interval (CI) implicitly contains pictures of CIs of all other levels of confidence, and information about the p-value for testing a null hypothesis. This article discusses pictures, taken from interactive software, that suggest several ways to think about the level of confidence of a CI, p-values, and what conclusions can be drawn from inspecting a CI.
    Location:
    http://www.blackwell-synergy.com/toc/test/29/3

  • Inference under uncertainty via conditional probabilities

    Author(s):
    Falk, R.
    Editors:
    Morris, R.
    Year:
    1989
    Abstract:
    This analysis extends to the problem of the definition of the statistical experiment and the sample-space enumerating its outcomes.

  • Inferential reasoning and the influence of cognitive conflict

    Author(s):
    Watson, J. M.
    Year:
    2002
    Abstract:
    This study follows two earlier studies of school students' abilities to draw inferences when comparing two data sets presented in graphical form (Watson and Moritz, 1999; Watson, 2001). Using the same interview protocol with a new sample of 60 students, 20 from each of grades 3, 6 and 9, cognitive conflict was introduced in the form of video clips of reasoning expressed by students in the earlier studies. This methodology was intended to mimic the type of argumentation that might take place in the classroom but in a controlled setting where identical arguments could be presented to different students. Interviews were videotaped and analysed in a similar fashion to the earlier studies in order to document change associated with the presentation of cognitive conflict. Change was documented with respect to the levels of observed response for two parts of the protocol and for the use of displayed variation in the graphs. Implications of the methodology for future research and teaching are discussed.

  • Inferential Statistics: Understanding Expert Knowledge and its Implications for Statistics Education

    Author(s):
    Alacaci, C.
    Year:
    2004
    Abstract:
    This study investigated the knowledge base necessary for choosing appropriate statistical techniques in applied research. In this study, we compared knowledge used by six experts and six novices in two types of statistical tasks. The tasks were: 1) comparing research scenarios from the perspective of choosing a statistical technique, and 2) direct comparison of statistical techniques. The framework was based on expert knowledge in inferential statistics using the repertory grid technique for data collection. A qualitative analysis of data showed that of the three types of expert knowledge, research design knowledge comprised the biggest portion, with theoretical and procedural knowledge comprising relatively smaller parts. Little difference was observed between experts and novices in extensiveness of knowledge use, although experts' knowledge use was found to be more integrated than novices'. Finally, two implications were drawn regarding how to better teach selection skills in statistics education: (1) statistical techniques should be taught in relation to relevant research designs, and (2) conceptual connections between statistical techniques should be explicitly taught.
    Location:
    http://www.amstat.org/publications/jse/v12n2/alacaci.html

  • Influence Of Attitude Towards Statistics, English Language Ability And Mathematical Ability In The Subject Quantitative Techniques At The Vaal Triangle Technikon, South Africa

    Author(s):
    Nolan, V.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    This project was a quasi-experiment designed to investigate whether three factors influence student performance in Quantitative Techniques: (a) the attitude of students towards Quantitative Techniques as a service subject, (b) English language ability of students, and (c) Mathematical ability of students. The results show deficiencies in students' competencies with respect to both language and mathematical ability. The overall impression of the students is that their mathematical ability is the major problem.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/1/8a5_nola.pdf

  • Influence of consulting in the selection of topics when teaching statistics.

    Author(s):
    Seier, E.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    Statistical consulting not only provides real life examples to mention in class; it provides a reality check that influences the way we teach and the choice of topics to teach or emphasize. The focus of this paper is on topics that are frequently omitted or not emphasized but which we consider important as a direct result of consulting experiences. The first four belong to introductory courses and among them are data issues, the test of hypothesis about proportions for small samples using the binomial distribution and some topics on categorical data analysis. The last two topics, one on statistical models and the other in time series, belong to upper division courses.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/17/4D2_SEIE.pdf

  • Influence of Culture on Secondary School Students’ Understanding of Statistics: A Fijian Perspective

    Author(s):
    Sashi Sharma
    Year:
    2014
    Abstract:
    Although we use statistical notions daily in making decisions, research in statistics education has focused mostly on formal statistics. Further, everyday culture may influence informal ideas of statistics. Yet, there appears to be minimal literature that deals with the educational implications of the role of culture. This paper will discuss the interaction between statistical cognition and culture, reporting on the effects of culture on secondary students’ statistical ideas. It will draw on examples from my work and that of a few others who have studied cultural influences on statistical ideas to explain how statistics is tied to cultural practices. The paper will consider the issues arising out of the literature and offer suggestions for meeting the challenges.
    Location:
    http://iase-web.org/documents/SERJ/SERJ13(2)_Sharma.pdf

  • Influence Of Outcome Expectancy And Uncertainity On Statistics Anxiety And Achievement Among Psychology Undergraduates

    Author(s):
    Williamson, P. & Mattiske, J.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    Previous research has shown a consistent, albeit weak, negative correlation (r ( -0.20) between statistics anxiety and statistics achievement. Additionally, self-efficacy has been shown to be a consistent predictor of both anxiety and achievement. This study showed that if self-efficacy is assumed to reflect a distribution of confidence, then the relationship between statistics anxiety and statistics achievement can be explained by the differential impact of two features of the self-efficacy distribution. Although only outcome expectancies predict statistics achievement, statistics anxiety is predicted by the interaction between outcome expectancies and outcome uncertainty. It is suggested that these results are indicative of at least two sources (or cognitive interpretations) of statistics anxiety, namely lack of confidence about one's ability and uncertainty in one's performance. The results are discussed in terms of cognitive appraisals of threat and challenge.
    Location:
    http://www.stat.auckland.ac.nz/~iase/publications/1/10_65_wi.pdf

  • Informal conceptions of probability

    Author(s):
    Konold, C.
    Year:
    1989
    Abstract:
    A model of informal reasoning under conditions of uncertainty, the outcome approach, was developed to account for the non-normative responses of a subset of the 16 undergraduates who were interviewed. For individuals who reason according to the outcome approach, the goal in questions of uncertainty is to predict the outcome of an individual trial. Their predictions take the form of yes/no decisions of whether an outcome will occur on a particular trial. These predictions are then evaluated as having been either "right" or "wrong". Additionally, their predictions are often based on a deterministic model of the situation. In follow-up interviews using a different set of problems, responses of outcome-oriented subjects were predicted. In one problem, subjects' responses were at variance both with normative interpretations of probability and with the "representativeness heuristic". While the outcome approach is inconsistent with formal theories of probability, its components are logically consistent and reasonable in the context of everyday decision-making.

  • Informal covariation assessment: Data-based versus theory-based judgments

    Author(s):
    Jennings, D. L., Amabile, T. M., & Ross, L.
    Editors:
    Kahneman, D., Slovic, P., & Tversky, A.
    Year:
    1982
    Abstract:
    The flow of social experience frequently challenges us to recognize empirical covariations. Sometimes, these covariations are merely another test of our powers of observation and are of no immediate practical concern to us. At other times - for example, when those covariations involve early symptoms of problems and late manifestations, or behavioral strategies employed and outcomes obtained, or relatively overt characteristics of people or situations and relatively covert ones - such detection abilities may help to determine our success in adapting to the demands of everyday social life. More generally, covariation detection will play a large role in our continuing struggle as "intuitive scientists" (see Nisbett & Ross, 1980; Ross, 1977, 1978) to evaluate and update the hypotheses we hold about ourselves, our peers, and our society. An obvious question therefore presents itself: How proficient are we, as laypeople, at assessing the empirical covariations presented by experiential evidence.

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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