Literature Index

Displaying 991 - 1000 of 3326
  • Author(s):
    Dayton, C. M.
    Editors:
    Vere-Jones, D., Carlyle, S., & Dawkins, B. P.
    Year:
    1991
    Abstract:
    In professional fields such as education, psychology, sociology, etc., applied statistics courses emphasise developing skills in planning quantitative research studies, properly analysing data, and correctly interpreting the results of analysis. In general, students lack the background which would be required to deal with mathematical derivations. Furthermore, it is doubtful that this background would materially benefit these students in the professional roles for which they are preparing. The vast majority of researchers in the behavioural sciences are able to conduct their data analyses using the sophisticated statistical packages that are readily available. Thus, it becomes critical that applied statistics courses realistically prepare them for their role as data analysts. Each academic year, our department enrolls about 1000 students in undergraduate and graduate applied statistics courses. Approximately 50% of these students are enrolled in an undergraduate elementary statistics course and the other 50% of the students are enrolled in a series of four graduate courses offered, primarily, for students in the College of Education.
  • Author(s):
    Garfield, J. B.
    Editors:
    Underhill, R. G.
    Year:
    1991
    Abstract:
    This paper describes the development of the Statistical Reasoning Assessment, an instrument designed to assess students' understanding of probability and statistics for the purpose of evaluating the effectiveness of new curricular programs and materials. A review of the literature related to assessment of statistical knowledge was used to determine the components and framework for this instrument.
  • Author(s):
    Giaimo, R., Bono, F. & Matranga , D.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    This paper follows two previous studies regarding an analysis of the efficacy and efficiency of the academic system. A bivariate, multilevel model has been proposed in order to measure the relative efficacy of each course by quantifying its contribution in obtaining a particular outcome, net of individual, environmental and course- specific factors. The concept of technical efficiency is also presented and two evaluating methodologies, which are based on a frontier function, are analysed. Both methods take into account differences in students' academic ability (which characterize the university system) and these are analysed on a geographical basis, thereby aiming at an investigation of differentials throughout all the regions in Italy. The results of this analysis will be presented during the conference.
  • Author(s):
    Hilton, S. C. & Christensen, H. B.
    Editors:
    Phillips, B.
    Year:
    2002
    Abstract:
    This paper presents the results from a large, randomized, controlled experiment conducted in the introductory statistics course at Brigham Young University. The purpose of the study was to assess the impact of multimedia lectures on student learning and attitudes. A randomized complete block design was implemented to evaluate the treatment that had two levels: multimedia versus overhead transparencies. Data was collected over four semesters on 5,603 students. Several student characteristics were measured and controlled for in the analyses. Our findings indicate that the multimedia lectures did not improve student learning or attitudes compared to the control group. However, our research also indicates that large, randomized, controlled experiments can be implemented in educational research. We advocate their use as the standard method of evaluation for educational innovations.
  • Author(s):
    Araújo, A. R., Ramos, E. M. L. S., Almeida, S. S., & Barbosa, R. C. C.
    Editors:
    Rossman, A., & Chance, B.
    Year:
    2006
    Abstract:
    This work has as objective to verify if the socioeconomic relation of the students of two schools of the metropolitan area of Belém, one of a public school and another of a private school, interferes in the habit of reading of the same ones. To obtain those results the techniques statistical Analysis of Correspondence and Faces of Chernoff were used to present those students' profile.
  • Author(s):
    Wasik, J. L.
    Year:
    1992
    Abstract:
    This report discusses the results of a project to evaluate the teaching of statistics.
    Location:
  • Author(s):
    Auðbjörg Björnsdóttir, Joan Garfield, and Michelle Everson
    Year:
    2015
    Abstract:
    This study explored the use of two different types of collaborative tests in an online introductory statistics course. A study was designed and carried out to investigate three research questions: (1) What is the difference in students’ learning between using consensus and non-consensus collaborative tests in the online environment?, (2) What is the effect of using consensus and non-consensus collaborative tests on students’ attitudes towards statistics?, and (3) How does using a required consensus vs. a non-consensus approach on collaborative tests affect group discussions? Qualitative and quantitative methods were used for data analysis. While no significant difference was found between groups using the two collaborative testing formats, there was a noticeable increase in students’ attitudes across both formats towards learning statistics. This supports prior research on the benefits of using collaborative tests in face-to-face courses.
  • Author(s):
    Aberson, C. L., Berger, D. E., Healy, M. R., Kyle, D. J., Romero, V. L.
    Year:
    2000
    Abstract:
    In this article, we present an evaluation of a Web-based, interactive tutorial used to present the sampling distribution of the mean. The tutorial allows students to draw samples and explore the shapes of sampling distributions for several sample sizes. To evaluate the effectiveness of the tutorial, 111 students enrolled in statistics or research methods courses used either the interactive tutorial or attended a lecture and a demonstration on the sampling distribution of the mean. Students in both groups improved from the pretest to posttest and no statistically significant differences between improvement scores were found between groups. Additionally, students rated the tutorial as easy to use and understand. In this study, we provided evidence that an Internet tutorial can be comparable in effectiveness to standard lecture or demonstration techniques.
  • Author(s):
    Webster, E.
    Year:
    1992
    Abstract:
    Examines the strengths and weaknesses of five selected statistical software packages with respect to how well the software enhances business statistics instructors's ability to teach traditionally difficult topics. Ranks the software on technical and pedagogical benefits. Results indicate (1) no advantage to using textbook-related software over generic software; and (2) teaching enhancement by appropriate software use. (MDH)
  • Author(s):
    Scott R. Evans, Rui Wang, Tzu-Min Yeh, Jeff Anderson, Rammy Haija, Paul<br>Madoc McBratney-Owen, Lynne Peeples, Subir Sinha, Vanessa Xanthakis,<br>Natasa Rajcic, and Jiameng Zhang
    Editors:
    Iddo Gal<br>Tom Short
    Year:
    2007
    Abstract:
    Biostatistics is not universally available in colleges/universities and is thus an attractive course to offer via distance education. However, evaluation of the impact of distance education on course enrollment and student success is lacking. We evaluated an "Introduction to Biostatistics" course at Harvard University that offered the distance option (Spring 2005).We assessed the effect on course enrollment and compared the grades of traditional students with non-traditional students, as well as with historical traditional students (Fall 2004). We further compared course evaluations from the inaugural semester with the distance option to evaluations from the prior semester. No evidence of dissimilarities was noted with respect to overall course grade averages or course evaluations.

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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