Since the development of the first intelligence test in the early
20th century, educational and psychological tests have become
important measurement techniques to quantify human behavior.
Focusing on this ubiquitous yet fruitful area of research,
Statistical Test Theory for the Behavioral Sciences provides both a
broad overview and a critical survey of assorted testing theories
and models used in psychology, education, and other behavioral
science fields.
Following a logical progression from basic concepts to more
advanced topics, the book first explains classical test theory,
covering true score, measurement error, and reliability. It then
presents generalizability theory, which provides a framework to
deal with various aspects of test scores. In addition, the authors
discuss the concept of validity in testing, offering a strategy for
evidence-based validity. In the two chapters devoted to item
response theory (IRT), the book explores item response models, such
as the Rasch model, and applications, including computerized
adaptive testing (CAT). The last chapter looks at some methods used
to equate tests.
Equipped with the essential material found in this book,
advanced undergraduate and graduate students in the behavioral
sciences as well as researchers involved in measurement and testing
will gain valuable insight into the research methodologies and
statistical data analyses of behavioral testing.
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