Literature Index

Displaying 1401 - 1410 of 3326
  • Author(s):
    Falk, R.
    Year:
    1989
    Abstract:
    Previous research indicated that subjects are not very surprised when reading coincidence stories, apparently because they regard the coincidence as one of many events that could have happened. This was true with respect to coincidences written by somebody else. However, there were indications that subjects found their own coincidences more surprising than those of others. The present study examines that egocentric bias and explores it further .
  • Author(s):
    Crocker, J.
    Year:
    1981
    Abstract:
    Judgments about relationships or covariation between events are central to several areas of research and theory in social psychology. In this article the normative, or statistically correct, model for making covariation judgments is outlined in detail. Six steps of the normative model, from deciding what data are relevant to the judgment to using the judgment as a basis for predictions and decisions, are specified. Potential sources of error in social perceivers' covariation judgments are identified at each step, and research on social perceivers' ability to follow each step in the normative model is reviewed. It is concluded that statistically naive individuals have a tenuous grasp of the concept of covariation, and circumstances under which covariation judgments tend to be accurate or inaccurate are considered. Finally, implications for research on attribution theory, implicit personality theory, stereotyping, and perceived control are discussed.
  • Author(s):
    Tversky, A., & Kahnman, D.
    Year:
    1974
    Abstract:
    This article described three heuristics that are employed in making judgments under uncertainty: (i) representativness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgments and decisions in situations of uncertainty.
  • Author(s):
    Rachlin, H.
    Year:
    1989
    Abstract:
    The author seeks to bridge the longstanding gap between behavioral and cognitive perspectives on choice. Acknowledging the existence and the relevance of internal judgments and decisions in the external choices we make, he shows not only how these cognitive processes can be understood in behavioral terms but also how cognitive and behavioral views can be reconciled.
  • Author(s):
    Tversky, A., & Kahneman, D.
    Editors:
    Kahneman, D., Slovic, P., & Tversky, A.
    Year:
    1982
    Abstract:
    The first part of this chapter is concerned with the nature of the representativeness relation and and also with the conditions on which the concept of representativeness is usefully invoked to explain intuitive predictions and judgments of probability. In the second part of the chapter we illustrate the contrast between the logic of representativeness and the logic of probability in judgments of the likelihood.
  • Author(s):
    Estepa, A., & Batanero. C.
    Year:
    1994
    Abstract:
    In this paper an experimental study of students' strategies in solving a judgment of association in scatter plots is presented. The classification of these strategies from a mathematical point of view allows us to determine concepts and theorems in action and to identify students' conceptions concerning statistical association in scatter plots. Finally, correspondence analysis is used to show the effect of task variables of the items on students' strategies.
  • Author(s):
    Ben-Zvi, D., & Arcavi, A.
    Year:
    2001
    Abstract:
    The purpose of this paper is to describe and analyze the first steps of a pair of 7th-grade students working through an especially designed curriculum on Exploratory Data Analysis (EDA) in a technological environment. The verbal abilities of these students allowed us to follow, at a very fine level of detail, the ways in which they begin to make sense of data, data representations, and the "culture" of data handling and analysis. We describe in detail the process of learning skills, procedures and concepts, as well as the process of adopting and exercising the habits and points of view which are common among experts. We concentrate on the issue of the development of a global view of data and its representations on the basis of students' previous knowledge and different kinds of local observations. In the light of the analysis, we propose a description of what it may mean to learn EDA, and draw educational and curricular implications.
  • Author(s):
    Ben-Zvi, D., & Arcavi, A.
    Year:
    2001
    Abstract:
    The purpose of this paper is to describe and analyze the first steps of a pair of 7th grade students working through an especially designed curriculum on Exploratory Data Analysis (EDA)ina technological environment. Theverbal abilitiesof these students allowed us to follow, at a very fine level of detail, the ways in which they begin to make sense of data, data representations, and the ‘culture’ of data handling and analysis. We describe in detail the process of learning skills, procedures and concepts, as well as the process of adopting and exercising the habits and points of view that are common among experts. We concentrate on the issue of the development of a global view of data and their representations on the basis of students’ previous knowledge and different kinds of local observations. In the light of the analysis, we propose a description of what it may mean to learn EDA, and draw educational and curricular implications.
  • Author(s):
    Vermeire, L. & Carbonez, A.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    The paper reflects on the growing demand for statistics training, in permanent education in general, and in just-in-time training with direct applicability in particular. It reports on work in short-course and in in-company training in statistics. Special attention is given to two cases, on the one hand a self-study course for a government department, on the other hand a system of highly interactive applets for visualization of statistical concepts related to the linear model.
  • Author(s):
    Monnie McGee, Lynne Stokes & Pavel Nadolsky
    Year:
    2016
    Abstract:
    Much has been made of the flipped classroom as an approach to teaching, and its effect on student learning. The volume of material showing that the flipped classroom technique helps students better learn and better retain material is increasing at a rapid pace. Coupled with this technique is active learning in the classroom. There are many ways of “flipping the classroom.” The particular realization of the flipped classroom that we discuss in this article is based on a method called “Just-in-Time Teaching (JiTT).” However, JiTT, in particular, and the flipped classroom, in general, is not just about watching videos before class, or doing activities during class time. JiTT includes assigning short, web-based questions to students based on previously viewed material. Typically, Internet-based questions are constructed to elicit common misunderstandings from the students, so that the instructor can correct such misunderstandings immediately in the next class period, hence the name, “Just-in-Time Teaching.” The addition of these pre-class questions is what separates JiTT from a general flipped classroom model. Even as the research on the superiority of JiTT as a learner-centered pedagogical method mounts, aids for the instructor have not, at least not as quickly. This article is focused on the instructor—with aids to help the instructor begin using the JiTT flipped classroom model in statistics 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