JOURNAL OF STATISTICS
E D U C A T I O N
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Volume 6, Number 1 (March 1998) ISSN: 1069-1898
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CONTENTS
AUTHOR & TITLE FILENAME
Masthead, Contents, Abstracts, jse/v6n1/contents
Instructions for retrieving files
Deborah A. Curtis and Michael Harwell, jse/v6n1/curtis
"Training Doctoral Students in Educational
Statistics in the United States:
A National Survey"
Thomas E. Love, "A Project-Driven Second jse/v6n1/love
Course"
Deborah J. Rumsey, "A Cooperative Teaching jse/v6n1/rumsey
Approach to Introductory Statistics"
Thomas H. Short and Joseph G. Pigeon, jse/v6n1/short
"Protocols and Pilot Studies: Taking
Data Collection Projects Seriously"
Bradley A. Warner, David Pendergraft, and jse/v6n1/warner
Timothy Webb, "That Was Venn, This Is Now"
TEACHING BITS: A RESOURCE FOR TEACHERS jse/v6n1/resource
OF STATISTICS
DATASETS AND STORIES:
Robert Carver, "What Does It Take to jse/v6n1/datasets.carver
Heat a New Room? Estimating Utility
Demand in a Home"
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ABSTRACTS
Deborah A. Curtis and Michael Harwell, "Training Doctoral Students
in Educational Statistics in the United States: A National
Survey" (62K)
ABSTRACT: 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.
-- DAC
KEY WORDS: Doctoral student preparation; Statistics
education; Survey research.
Thomas E. Love, "A Project-Driven Second Course" (42K)
ABSTRACT: I trace the development of a new course in modern
data analysis involving a wide spectrum of statistical
techniques. Because the course is based entirely on case
studies, real-data settings, and student projects and is
computer-intensive, a series of challenges facing many
instructors are addressed. In a single semester, students
explore data using tools from EDA, multiple regression,
analysis of variance, time series analysis, and categorical
data analysis. The focus is on understanding and
forecasting in a variety of data settings, learning how to
summarize relationships and measure how well these
relationships fit data, and how to make meaningful
statistical inferences when the usual assumptions do not
hold. The course emphasizes what the statistical process is
all about: how to conduct studies, what the results mean,
and what can be inferred about the whole from pieces of
evidence. --TEL
KEY WORDS: Active learning; Data analysis; Data collection;
Problem-based learning.
Deborah J. Rumsey, "A Cooperative Teaching Approach to
Introductory Statistics" (77K)
ABSTRACT: Many of today's university undergraduate
curricula include two seemingly conflicting themes:
(1) increase the quality of teaching to include emphasis on
pedagogical elements, such as active learning, in the
undergraduate statistics classroom; and (2) cope with a
decrease in teaching resources. In this paper, a means by
which a department of mathematics or statistics can maintain
and increase its standards of teaching excellence in
introductory statistics while coping with ever-increasing
budgetary pressures is proposed. This process involves
promoting what we call cooperative teaching, applying the
concepts of cooperative learning to a group of instructors.
--DJR
KEY WORDS: Cooperative learning; Statistics education.
Thomas H. Short and Joseph G. Pigeon, "Protocols and Pilot
Studies: Taking Data Collection Projects Seriously" (25K)
ABSTRACT: Although there is consensus among statistics
educators that student data collection projects are of
substantial value, we feel that the planning and piloting
phases of data collection are often neglected. We ask our
students to write protocols or detailed plans for how the
data will be collected, and to plan and conduct pilot
studies before embarking on full scale data collections.
We present examples and results from situations including
college freshman introductory statistics courses, graduate
statistics courses, and teacher training workshops. --THS
KEY WORDS: Assessment; Clinical study; Planning; Rubric.
Bradley A. Warner, David Pendergraft, and Timothy Webb, "That Was
Venn, This Is Now" (10K)
ABSTRACT: Basic probability concepts are difficult for some
students to understand initially. Through the use of a Venn
diagram disguised as a pizza, we will discuss how to explain
introductory probability concepts. Students are able to
answer probability questions, including conditional
probability, by simply looking at a picture. This tool not
only enhances learning but retention as well. --BAW
KEY WORDS: Basic probability; Pizza; Venn diagram.
"Teaching Bits: A Resource for Teachers of Statistics" (37K)
ABSTRACT: This column features "bits" of information
sampled from a variety of sources that may be of interest
to teachers of statistics. Bob delMas abstracts
information from the literature on teaching and learning
statistics, while Bill Peterson summarizes articles from
the news and other media that may be used with students
to provoke discussions or serve as a basis for classroom
activities or student projects. --JG
Robert Carver, "What Does It Take to Heat a New Room? Estimating
Utility Demand in a Home" (30K)
ABSTRACT: In a residential home, energy consumption is
closely related to the outdoor temperature and size of the
house. In a home of a given size, fuel consumption varies
fairly predictably through the year. When homeowners add a
room, other things being equal, energy consumption should
increase. This dataset permits students to estimate the
energy demand, make forecasts for future months, and
investigate other relationships.
The dataset contains natural gas and electricity usage data
for a single-family residence in the Boston area from
September 1990 through May 1997, accompanied by monthly
climatological data. The dataset is useful for illustrating
the concepts and techniques of central tendency, dispersion,
time series analysis, correlation, simple and multiple
regression, and variable transformations. --RC
KEY WORDS: Forecasting; Measurement; Regression; Time
series; Variable transformation.
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Editor: E. Jacquelin Dietz
Editorial Assistant: Jeffrey N. Jonkman
Editors, Teaching Bits: A Resource for Teachers of Statistics:
Robert C. delMas
William P. Peterson
Editors, Datasets and Stories:
Robert W. Hayden
Robin H. Lock
Editorial Board:
Douglas M. Andrews Flavia Jolliffe
Karla V. Ballman Clifford Konold
Carol Joyce Blumberg Margaret Mackisack
Beth L. Chance Thomas L. Moore
William T. Coombs Jerry Moreno
Janice Derr William I. Notz
Iddo Gal Allan J. Rossman
Suzanne E. Graham Thomas H. Short
Katherine Halvorsen Jeffrey S. Simonoff
Gudmund R. Iversen Eric R. Sowey
Thomas Johnson David Sylwester
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