Literature Index

The publication of research findings that are not statistically significant presents a novel probelm in interpretation of research results. The contribution of nonsignificant results depends in part on whether the statistical test was powerful enough to detect an effect of "meaningful" size. The primary responsibility rests with the authors of articles reporting nonsignificant results to demonstrate the worth of the results by discussion the power of the tests. If they do not assume this responsibility, then consumers of research should be prepared to conduct their own power analyses to aid interpretation of the research results. This ariicle demonstrates the use of power analysis for the interpretation of nonsignificant findings. The power of many common statistical tests can be determined without difficult computation using Cohen's (1977) or Stevens's (1980) tables.

Technology has become an inseparable part of modern statistical practice (Gould, 2010), and, to a large extent, modern statistics courses. The literature on technology in statistics education has focused heavily on the role of technology for improving students’ understanding. However, limited research has examined the development of technological skills for “doing” statistics, e.g. using statistical packages. This paper proposes a distinction between these two roles of technology and how both benefit student learning. The paper then applies Kanfer and Ackerman’s (1989) integrative model of skill acquisition to explain the variability in students’ technological skill development. The ability to use statistical packages, arguably the most pervasive example of statistics technology, is used as an example to illustrate this model. The implications of the model are then discussed in the context of teaching technological skills in statistics courses. Future directions and challenges related to this area of are discussed

This paper presents five principles of learning, derived from cognitive theory and supported by empirical results in cognitive psychology. To bridge the gap between theory and practice, each of these principles is transformed into a practical guideline and exemplified in a real teaching context. It is argued that this approach of putting cognitive theory into practice can offer several benefits to statistics education: a means for explaining and understanding why reform efforts work, a set of guidelines that can help instructors make well-informed design decisions when implementing these reforms, and a framework for generating new and effective instructional innovations.

The purpose of my presentation is to review trends in assessment in quantitative courses and illustrate several options and appropaches to assessment for advanced courses at the graduate level, particularly multivariate analysis.