This article describes the content of a coures in multivariate analysis and the types of students who take this course.
This article describes the content of a coures in multivariate analysis and the types of students who take this course.
The purpose of the project that formed the basis of the work reported herein was to provide students a series of web-based statistical readings designed to illustrate common statistical concepts via "real-life" educational research situations.
The purpose of this study is to examine our efforts to help preservice teachers develop statistical knowledge and experience through integrating reasoning with data into existing K-6 curriculum via a professional field-based practicum.
Central in this study is the question why subjects who possess the necessary factual or propositional knowledge needed to solve a particular statistical problem often fail to find the solution to that problem. Ten undergraduate psychology students were trained so as to possess all the relevant knowledge needed to solve five multiple choice problems on descriptive regression analysis. They were asked to think aloud while attempting to solve the problems. Analysis of the think-aloud protocols showed that a failure to select the relevant information in the text, together with a failure to retrieve relevant propositional knowledge from memory and the inability to reason coherently combined to produce incorrect responses. Factual knowledge was less likely to be successfully retrieved when it was acquired only recently or when it concerned relationships of a highly abstract nature.
In this research forum we present results from a research project concerning students' understanding of statitical association and its evolution after teaching experiments using computers. This research has been carried out at the Universities of Granada and Jaen over the years 1991-98. We have identified different incorrect preconceptions and strategies to assess statistical association and performed two different teaching experiments designed to overcome these difficulties and to identify the critical points, which arise in attempting to do this.
This paper defines statistical reasoning, provides a model of statistical reasoning, and discusses the assessment of statistical reasoning.
This paper examines the goals of an introductory statistics course, statistical literacy, statistical competence, data awareness, data production, and communication. It also discusses some of the misconceptions that statistics education research is trying to dispel.
The recent statistics education reform movement has advocated the adoption of many supplements to the introductory statistics course. These include hands-on activities, extensive use of technology, student projects, reflective writing, oral presentations, collaborative learning, and case studies. Combined with a full curriculum of topics for a variety of majors, this appears to be a daunting wish list. This paper offers some suggestions, based on experience at a small university, as to how to integrate many of these techniques, allowing them to build on and complement each other. Benefits and tradeoffs of implementing these techniques will be discussed, including issues of time commitment from the perspective of both students and instructors.
Many students do not understand what representational problems a particular notation solves, thus limiting their ability to use the notation, as well as their understanding of the problem situation it applies to. Forty-six undergraduates completed a lesson designed to help them understand variance and its notation. Students in the invention group were asked to create a procedure for calculating the variance of contrasting distributions of numbers; students in the procedural group were presented with a procedure for calculating variance and asked to practice it on the numbers. Results indicate that invention students learned to reflect on the quantitative properties of distributions, and to evaluate statistical procedures in terms of their ability to differentiate those properties. Students in the procedural condition tended to evaluate a procedure simply in terms of whether or not it was like the "correct" procedure. We plan to extend this instructional method to facilitate classroom conversations and as a platform for a complementary intelligent instructional system.
This paper describes three courses that could be taught by statisticians in departments of mathematics. These courses have three features in common: (1) they are serious about data and contemporary applications of statistics, (2) they are mathematical enough to count towards a major in mathematics, and (3) they are accessible to sophomore math majors or first-year students with advanced standing.