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Methods for Identifying Biased Test Items
First Edition
- Gregory Camilli - Rutgers, USA
- Lorrie A. Shepard
Volume:
4
April 1994 | 181 pages | SAGE Publications, Inc
"This book is highly recommended for all involved in test
development
or who have any interest in the use of tests in education or in
other
fields. The necessary mathematics are presented clearly but
never
obscure the important messages in the book. It will certainly be
referred to constantly in my future work in this area."
--Educational Research
"The fundamental goal of the Measurement Methods for the Social
Sciences series is to make complex measurement concepts, topics,
and
methods available to readers with limited mathematical
background but
a strong desire to understand, as well as use, methods that are
on
the forefront of social science assessment. With this book on
item
bias detection methods, Gregory Camilli and Lorrie Shepard have
achieved this goal admirably."
--from the Foreword by Richard M. Jaeger
What can item bias methods do--and not do--when applied to real
test
data? Aimed at helping researchers understand how item bias
methods
work, this book provides practical advice and specific details
on the
most useful methods for particular testing situations. Beginning
with
a review of early bias methods and the fairness issues
associated
with the topic of test bias, the authors explain the logic of
each
method in terms of how differential item functioning (DIF) is
defined
by the method--and how well the method can be expected to work
in
various situations. In addition, chapters include a summary of
findings regarding the behavior of the various indexes in
empirical
studies, especially their reliability, correlation with known
bias
criteria, and correlations with other bias methods. The book
concludes with a set of principles for deciding when DIF should
be
interpreted as evidence of bias.
Introduction
Early Item Bias Indices Based on Classical Test Theory and Analysis of Variance
Item Response Theory as Applied to Differential Item Functioning
Contingency Table Approaches
Interpretations of Bias from DIF Statistics
Conclusions and Caveats