Literature Index

Discussions of students' understandings of key course concepts typically investigate those understandings at one point in time. This paper reports results from a case study in which eighteen graduate students were interviewed throughout a fifteen-week introductory statistics course. Knowledge structures were assessed once every three weeks, and changes in these structures were discussed. One key finding was that students' understandings of certain course concepts change as the semester progresses, indicating that it may not be enough to assess these understandings at only one point in time. Two concepts in particular, mean and variance, are central to many ideas in the introductory course. Assessing how students "know" these concepts throughout the course may be beneficial to researchers and educators alike. Implications for both statistics education research and teaching introductory statistics are offered.

In response to the worldwide shortage of biostatisticians, Australia has established a national consortium of eight universities to develop and deliver a Masters program in biostatistics. This article describes our successful innovative multi-institutional training model, which may be of value to other countries. We first present the issues confronting the future of biostatistics in Australia, then relate our experience in establishing a new national consortium-based Masters program, and finally explore the extent to which our initiatives have addressed the current challenges of biostatistics workforce shortages.

Although numerous research studies have focused on issues related to the teaching of statistics, few studies have focused on the training of people who may become statistics teachers. The purpose of this study was to examine doctoral students' preparation in statistics in the field of education. A national survey was conducted of twenty-seven quantitative methods (QM) programs. One QM professor from each program was identified and asked to describe and evaluate the training of QM and non-QM doctoral students at his or her institution. The vast majority of professors indicated that most or all of the students in their QM programs received training in the "old standard" procedures--ANOVA, multiple regression, and traditional multivariate procedures, whereas fewer than half of the professors indicated that most or all of their QM students received training in more recent procedures such as bootstrapping and multilevel models. Professors were also asked to rate the skills of their QM students in areas such as mathematical statistics and computing on a scale from "Weak" to "Strong". Most professors gave high ratings to their QM students' skills with statistical packages, but gave much more mixed ratings of their QM students' training in mathematical statistics. Nearly half of the professors thought that most of their QM students could have benefited from one or two additional statistics courses. Results are discussed in terms of training future doctoral students.

Training institutional research (IR) professionals in the use of statistics is a complex and challenging task. It is complicated by the need to develop a functional model of institutional research that includes its various roles. In addition, the specific statistical and analytical tools used to perform necessary tasks must be better understood. This is important due to the need for IR professionals to teach others to use and interpret statistical results. IR professionals have tended to use basic tools and have limited statistical sophistication. The specific tools or statistical methodologies that are important in IR should differ based on the situation of the individual and the academic background of the audience and should not be limited by lack of training in statistics. The work done by Terenzini (1993) further indicates that IR professionals need to broaden their approach to research by operating within three types of intelligence.