Literature Index

The National Curriculum for Mathematics recently introduced to all state schools in any UK school curriculum. Help and support is constantly sought by both primary and secondary teachers now attempting to implement this new initiative. In my role as Project Officer: Data handling in the National Curriculum I am attempting to meet these needs. This initiative has given us the opportunity to review how statistics is taught in our schools and to develop new strategies to improve upon existing good practice. The heading given to the statistical content in the National Curriculum is Data Handling, an unfortunate title which implies a passive act. It is my belief that statistics is best taught through active statistical investigation, in real life, practical situations offering meaningful and enjoyable learning experiences. Examples of the materials I have produced were discussed and further references to other recent international curriculum developments made. Some of the implications of the"National Curriculum" on primary teaching, secondary teaching and teacher training (retraining) were also discussed.

The 1969-2000 Major League Baseball Attendance dataset contains Runs Scored, Runs Allowed, Wins, Losses, Number of Games Behind the Division Leader, and Home Game Attendance of each major league franchise for the 1969 through 2000 seasons. Also included for each franchise are its location, league affiliation (National or American), and division affiliation (East, Central, or West). These data have been used in a project-based modeling course to instruct students in basic data management, the use of exploratory data analysis to "clean" data, and construction of regression models. The dataset, which is both cross-sectional and time-series, is of a manageable size and easily understood. Furthermore, it provides a useful, interesting, and realistic classroom example for discussing many important statistical concepts.

Statistical thinking focuses on properties that belong not to individual data values but to the entire aggregate. We analyse students' statements from 3 different sources to explore possible building blocks of the idea of data as aggregate and speculate on how young students go about putting these ideas together. We identify 4 general perspectives that students use in working with data, which in addition to an aggregate perspective include regarding data as pointers, as case values, and as classifiers. Some students seem inclined to view data from one of these 3 alternative perspectives, which then influences the types of questions they ask, the representations they generate or prefer, the interpretations they give to notions such as the average, and the conclusions they draw from the data.

Examples are highly sought by both students and teachers. This is particularly true as many statistical instructors aim to engage their students and increase active participation. While simulated datasets are functional, they lack real perspective and the intricacies of actual data. In order to obtain real datasets, the principal investigator of a study must be willing to share the data. Understanding investigators’ opinions regarding data sharing would thus help elucidate the general lack of data sharing currently exhibited. Presented are the results of a survey designed to gather information regarding the proportion of researchers willing to share their data, conditions, formats, primary motivation, concerns and current availability of data for sharing. With 76% (56/74) responding favorably to the idea of sharing their published data, the creation of a new statistical educational resource was prompted. Thus, additionally described is a web-based dataset repository that can be used as a resource by both educators and students of statistics. This growing repository presents raw data from real medical studies and offers (a) a vignette summarizing the study, research question and study design; (b) a data dictionary with clear documentation of variables and codes; (c) a complete citation for the associated study publication; and (d) a variety of data formats compatible with the majority of statistical packages. The repository went online on 12/18/12 at the URL http://www.lerner.ccf.org/qhs/datasets/.