Journal Article

  • Recent reforms in statistics education have initiated the need to prepare graduate<br><br>teaching assistants (TAs) for these changes. A focus group study explored the<br><br>experiences and perceptions of University of Nebraska-Lincoln TAs. The results<br><br>reinforced the idea that content, pedagogy, and technology are central aspects for<br><br>teaching an introductory statistics course. The TAs addressed the need for clear,<br><br>specific guidelines and examples, as well as collaboration between colleagues. The<br><br>TAs also sought opportunities to enrich their teaching skills and, ultimately, their<br><br>impact on students' learning. These findings support previous research on graduate<br><br>TAs and highlight the need for additional exploration of the role graduate statistics<br><br>TAs play in introductory statistics educatio

  • Statistics education in psychology often falls disappointingly short of its goals. The<br><br>increasing use of qualitative approaches in statistics education research has extended<br><br>and enriched our understanding of statistical cognition processes, and thus facilitated<br><br>improvements in statistical education and practices. Yet conceptual analysis, a<br><br>fundamental part of the scientific method and arguably the primary qualitative<br><br>method insofar as it is logically prior and equally applicable to all other empirical<br><br>research methods - quantitative, qualitative, and mixed - has been largely overlooked.<br><br>In this paper we present the case for this approach, and then report results from a<br><br>conceptual analysis of statistics education in psychology. The results highlight a<br><br>number of major problems that have received little attention in standard statistics<br><br>education research

  • Students learn to examine the distributional assumptions implicit in the usual t-tests and<br><br>associated confidence intervals, but are rarely shown what to do when those assumptions<br><br>are grossly violated. Three data sets are presented. Each data set involves a different<br><br>distributional anomaly and each illustrates the use of a different nonparametric test. The<br><br>problems illustrated are well-known, but the formulations of the nonparametric tests<br><br>given here are different from the large sample formulas usually presented. We restructure<br><br>the common rank-based tests to emphasize structural similarities between large sample<br><br>rank-based tests and their parametric analogs. By presenting large sample nonparametric<br><br>tests as slight extensions of their parametric counterparts, it is hoped that nonparametric<br><br>methods receive a wider audience.

  • This study compared levels of statistics anxiety and attitude toward statistics for graduate<br><br>students in on-campus and online statistics courses. The Survey of Attitudes Toward Statistics<br><br>and three subscales of the Statistics Anxiety Rating Scale were administered at the beginning and<br><br>end of graduate level educational statistic courses. Significant effects were observed for two<br><br>anxiety scales (Interpretation and Test and Class Anxiety) and two attitude scales (Affect and<br><br>Difficulty). Observed decreases in anxiety and increases in attitudes by online students offer<br><br>encouragement to faculty that materials and techniques can be used to reduce anxiety and<br><br>hopefully enhance learning within online statistics courses

  • Null distributions of permutation tests for two-sample, paired, and block designs are simulated<br><br>using the R statistical programming language. For each design and type of data, permutation<br><br>tests are compared with standard normal-theory and nonparametric tests. These examples (often<br><br>using real data) provide for classroom discussion use of metrics that are appropriate for the data.<br><br>Simple programs in R are provided and explained briefly. Suggestions are provided for use of<br><br>permutation tests and R in teaching statistics courses for upper-division and first year graduate<br><br>students

  • Confidence interval estimation is a fundamental technique in statistical inference.<br><br>Margin of error is used to delimit the error in estimation. Dispelling misinterpretations<br><br>that teachers and students give to these terms is important. In this note, we give examples<br><br>of the confusion that can arise in regard to confidence interval estimation and margin of<br><br>error

  • In the recent past, qualitative research methods have become more prevalent in the field of <br><br>statistics education. This paper offers thoughts on the process of framing a qualitative study by <br><br>means of an illustrative example. The decisions that influenced the framing of a study of preservice teachers  understanding of the concept of statistical sample are explained by describing <br><br>the goals, knowledge, and beliefs brought to the research project. Each framing decision is <br><br>portrayed as a function of these three overarching cognitions. It is suggested that mapping one s <br><br>goals, knowledge, and beliefs while framing and carrying out a qualitative study can be useful <br><br>for maintaining the quality of the stud

  • Two improvements in teaching linear regression are suggested. The first is to include the<br><br>population regression model at the beginning of the topic. The second is to use a geometric<br><br>approach: to interpret the regression estimate as an orthogonal projection and the estimation<br><br>error as the distance (which is minimized by the projection). Linear regression in finance is<br><br>described as an example of practical applications of the population regression model.<br><br>The paper also describes a geometric approach to teaching the topic of finding an optimal<br><br>portfolio in financial mathematics. The approach is to express the optimal portfolio through<br><br>an orthogonal projection in Euclidean space. This allows replacing the traditional solution of<br><br>the problem with a geometric solution, so the proof of the result is merely a reference to the<br><br>basic properties of orthogonal projection. This method improves the teaching of the topic by<br><br>avoiding tedious technical details of the traditional solution such as Lagrange multipliers and<br><br>partial derivatives. The described method is illustrated by two numerical examples

  • Courses for non-statistics majors (service courses) play an integral role in teaching statistics and pose some unique challenges. In these courses, students are often undermotivated on the one hand while on the other hand the syllabus frequently is overly crowded. In this manuscript we target the issues arising from the latter problem by making use of technology. The use of screen capture, a fast and easy way of recording lectures, is discussed through an example of an introductory statistics course for first year biology students at Lancaster University. Student feedback on the use of these recordings is discussed.

  • Language plays a crucial role in the classroom. The use of specialized language in a domain can cause a subject to seem more difficult to students than it actually is. When words that are part of everyday English are used differently in a domain, these words are said to have lexical ambiguity. Studies in other fields, such as mathematics and chemistry education suggest that in order to help students learn vocabulary instructors should exploit the lexical ambiguity of the words. The study presented here is a pilot study that is the first in a sequence of studies designed to understand the effects of and develop techniques for exploiting lexical ambiguities in the statistic classroom. In particular, this paper describes the meanings most commonly used by students entering an undergraduate statistics course of five statistical terms.

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