This book introduces a new methodology for the analysis of test
results. Free from ambiguous interpretations, the results truly
demonstrate an individual s progress. The methodology is ideal for
highlighting patterns derived from test scores used in evaluating
progress. Dr. Tatsuoka introduces readers to the Rule Space Method
(RSM), a technique that transforms unobservable knowledge and skill
variables into observable and measurable attributes. RSM converts
item response patterns into attribute mastery probabilities. RSM is
the only up-to-date methodology that can handle large scale
assessment for tests such as the SAT and PSAT. PSAT used the
results from this methodology to create cognitively diagnostic
scoring reports. In this capacity, RSM helps teachers understand
what scores mean by helping them ascertain an individual s
cognitive strengths and weaknesses. For example, two students may
have the exact same score, but for different reasons. One student
might excel at processing grammatically complex texts but miss the
main idea of the prose, while another excels at understanding the
global message. Such knowledge helps teachers customize a student s
education to his or her cognitive abilities. RSM is also used for
medical diagnoses, genetics research, and to help classify music
into various states of emotions for treating mental problems.
The book opens with an overview of cognitive assessment research
and nonparametric and parametric person-fit statistics. The
Q-matrix theory is then introduced followed by the Rule Space
method. Various properties of attribute mastery probabilities are
then introduced along with the reliability theory of attributes and
its connection to classical and item response theory. The book
concludes with a discussion of how the construct validity of a test
can be clarified with the Rule Space method.
Intended for researchers and graduate students in quantitative,
educational, and cognitive psychology, this book also appeals to
those in computer science, neuroscience, medicine, and mathematics.
The book is appropriate for advanced courses on cognometrics,
latent class structures, and advanced psychometrics as well as
statistical pattern recognition and classification courses taught
in statistics and/or math departments.
General
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