Transcript The Journal of Statistics Education

The Journal of Statistics Education
An International Journal on the
Teaching and Learning of Statistics
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JSE Mission

To disseminate knowledge for the
improvement of statistics education at all
levels. It is distributed electronically and
publishes articles that enhance the exchange
of diversity of interesting and useful
information among educators, practitioners,
and researchers around the world.
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Intended Audience and Peer Review
The intended audience includes anyone who
teaches statistics, as well as those interested
in research on statistical and probabilistic
reasoning.
 All submissions are rigorously refereed
using a double-blind peer review process.

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Potential Topics
Curricular reform in statistics
 The use of cooperative learning and projects
 Innovative methods of instruction
 Assessment
 Research on students’ understanding of
probability and statistics
 Research on the teaching of statistics,
attitudes and beliefs about statistics

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Potential Topics (Continued)
Creative and tested ideas for teaching
probability and statistics topics
 The use of computers and other media in
teaching statistics
 Statistical literacy
 Distance education
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Potential Topics (Continued)
New ways of looking at statistical topics
that teachers would find useful.
 Articles that provide a scholarly overview
of the literature on a particular topic are also
of interest.
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Recently Published Articles
G. Rex Bryce (2005), “Developing
Tomorrow’s Statisticians,” 13(1).
 Barbara Ward (2004), “The Best of Both
Worlds: A Hybrid Statistics Course,” 12(3).
 Ulf Olsson (2005), “Confidence Intervals for
the Mean of a Log-Normal Distribution,”
13(1).
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Recently Published Articles
Timothy S. Vaughan and Kelly E. Berry
(2005), “Using Monte Carlo Techniques to
Demonstrate the Meaning and Implications
of Multicollinearity,” 13(1).
 Mary Richardson, John Gabrosek, Diann
Reischman, and Phyllis Curtis (2004),
“Morse Code, Scrabble and the Alphabet,”
12(3).
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Data Sets and Stories
Robert W. Hayden (2005), “A Data Set that
is 44% Outliers,” 13(1).
 David E. Kalist (2004), “Data from the TV
Show Friend or Foe?,” 12(3).
 Neil Binnie (2004), “Using EDA, ANOVA
and Regression to Optimise some
Microbiology Data,” 12(2).
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What Not to Submit.
Articles that deal entirely with a theoretical
result, its derivation and proof.
 Articles on educational statistics that do not
deal with teaching of statistics.
 Articles that describe analysis of data in a
particular field if the focus of the article is
on the analysis of the data for consumption
by researchers in other disciplines.
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Common Mistakes to Avoid
Multiple spelling and grammatical errors.
 Incomplete and/or obvious mistakes in the
references.
 A narrow or biased review of the literature.
 Inappropriate data analysis.
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Raising the Bar.
Activities should model good statistical
practice.
 Studies should be well planned and well
executed.
 Good assessment of student outcomes.
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