Graduate students in the health sciences who hope to become independent researchers
must be able to write up their results at a standard suitable for submission to peer-reviewed
journals. Bayesian analyses are still rare in the medical literature, and students are often
unclear on what should be included in a manuscript. Whilst there are published guidelines on
reporting of Bayesian analyses, students should also be encouraged to think about why some items
need to be reported whereas others do not. We describe a classroom activity in which students
develop their own reporting guideline. The guideline that the students produce is not intended to
replace existing guidelines, rather we have found that the process of developing the guideline is
helpful in encouraging students to think through the "why?" as well as the "what?" of reporting.
Key Words: Active learning; Writing; Graduate education; Biostatistics;
Clinicians have characteristics - high scientific maturity, low tolerance for symbol manipulation and
programming, limited time outside of class - that limit the effectiveness of traditional methods for
teaching multi-predictor modeling. We describe an active-learning-based approach that shows particular
promise for accommodating these characteristics.
Key Words: Active learning; Statistics education; Graduate education; Deconstructing
the disciplines; Multi-predictor modeling; Non-statisticians.
This paper reports on an instrument designed to assess the practices and beliefs of instructors of
introductory statistics courses across the disciplines. Funded by a grant from the National Science
Foundation, this project developed, piloted, and gathered validity evidence for the Statistics Teaching
Inventory (STI). The instrument consists of 50 items in six parts and is administered online. The
development of the instrument and the gathering and analysis of validity evidence are described. Plans
and suggestions for use of the STI are offered.
Key Words: Statistics education research; Teaching practice; Teacher beliefs.
From Research to Practice
Team-based learning (TBL) is a pedagogical strategy that uses groups of students working
together in teams to learn course material. The main learning objective in TBL is to provide
students the opportunity to practice course concepts during class-time. A key feature is
multiple-choice quizzes that students take individually and then re-take as a team. TBL was
originally conceived by Larry Michaelsen (University of Central Missouri) for his business
classes and has proven to be especially effective in training medical students. In this paper,
we describe an adaptation of TBL for an undergraduate statistical literacy course.
Key Words: Team-based learning; Pedagogical strategy; Group work;
Interviews with Statistics Educators
Dick Scheaffer is Professor Emeritus of Statistics at the University of Florida.
He is a Fellow of the American Statistical Association and a recipient of ASA's Founders Award.
He served as President of ASA in 2001 and received the USCOTS Lifetime Achievement Award in 2011.
The following interview took place via email on December 1, 2011 - February 14, 2012.
We located 18 articles that have been published from November 2011 through January 2012 that pertained
to statistics education. In this column, we highlight a few of these articles that represent a variety of
different journals that include statistics education in their focus. We also provide information about the
journal and a link to their website so that abstracts of additional articles may be accessed and viewed.
As always, we want to update you on the latest CAUSEweb (www.causeweb.org) and MERLOT (www.merlot.org) news.
A lot is going on as we head into a summer full of great conferences and professional development opportunities!
Data Sets and Stories
A pizza chain in Australia made a number of claims about the size of its pizzas relative to those from another
pizza chain. Interestingly, the pizza chain made publically available the data upon which those claims were made.
The claims of the pizza company can be assessed using these data. Instructors can use the data to guide students
to form research questions and hypotheses; to produce numerous graphical, numerical and tabular summaries; and for
conducting some simple analyses such as one- and two-sample t-tests. Notes are made on how the data can be used to
demonstrate the importance of initial data analysis, and the importance of understanding the source of the data and
the research design. In addition, suggestions are made for how students can use these results in a way that taps
students' creative potential.
Key Words: Initial data analysis; t-tests; Boxplots; Study design; Real data.