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An accessible textbook that explains Item Response Theory (IRT) to psychologists, contrasting its modern, model-based principles with Classical Test Theory (CTT) and detailing its models, methods, and applications in cognitive and personality assessment.
A profile of this book is on the way.
What it’s about
Item Response Theory (IRT) has revolutionized psychological testing, becoming the foundation for major assessments and offering powerful tools for research. However, its principles remain opaque to many psychologists accustomed to Classical Test Theory (CTT). This book demystifies IRT, providing a clear, intuitive guide for graduate students and professionals without requiring an advanced mathematical background. By contrasting the 'new rules of measurement' with the old, explaining core binary and polytomous models through graphs and familiar examples, and surveying practical applications from computerized adaptive testing to personality scale analysis, it equips readers to understand, critique, and apply modern psychometric methods to their own work, ultimately leading to more precise and scientifically rigorous measurement.
The through-line
- Who it’s for
- A psychologist, researcher, or graduate student who is knowledgeable about traditional methods of psychological testing (Classical Test Theory) and wants to understand and apply the more modern, powerful, and increasingly prevalent framework of Item Response Theory (IRT).
- The problem
- The principles of Classical Test Theory (CTT) are sample-dependent and have significant limitations (e.g., a single standard error for all scores, difficulty with test equating), and major standardized tests are now based on the unfamiliar principles of IRT. They feel confused and intimidated by the complex mathematics of IRT, leaving them feeling outdated and unable to leverage modern psychometric tools to develop better measures or conduct more rigorous research.
- The plan
- First, grasp the fundamental differences between CTT and IRT by learning the 'New Rules of Measurement'.
- Next, learn the core IRT models for both binary (pass/fail) and polytomous (rating scale) data.
- Then, understand the procedures for estimating person trait levels and item parameters, and for assessing how well a model fits the data.
- Finally, explore concrete applications of IRT to solve practical problems like test bias (DIF), create efficient tests (CAT), and conduct advanced scale analysis in cognitive, personality, and attitude assessment.
- The payoff
- The reader becomes a measurement-savvy psychologist, capable of developing more precise and valid instruments. · They can confidently interpret scores from the most advanced psychological and educational tests. · Their research becomes more rigorous and their conclusions more justifiable due to the use of superior measurement models.