Fundamentals of Item Response Theory
By using familiar concepts from classical measurement methods and basic statistics, this book introduces the basics of item response theory (IRT) and explains the application of IRT methods to problems in test construction, identification of potentially biased test items, test equating and computerized-adaptive testing. The book also includes a thorough discussion of alternative procedures for estimating IRT parameters and concludes with an exploration of new directions in IRT research and development.
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Concepts Models and Features
Ability and Item Parameter Estimation
Assessment of ModelData Fit
The Ability Scale
Item and Test Information and Efficiency Functions
Identification of Potentially Biased Test Items
Test Score Equating
Computerized Adaptive Testing
Future Directions of Item Response Theory
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ability estimates ability level ability parameters ability scale ability score ability values adaptive testing administered assessing assumption Bayesian chi-square chi-square statistic classical test theory common items common scale correct response criterion-referenced test cut-off score determine difficulty parameter difficulty values discrimination parameter eigenvalue equating examinee's ability example Exercises for Chapter Figure fits the data groups of examinees Hambleton hence ICCs IRT models item and ability item bank item characteristic curves item characteristic functions item difficulty item discrimination item indices item information functions item parameter estimates item response model item response theory likelihood function linear logistic model maximum likelihood estimates measurement minority group model fits model-data fit number of items obtained one-parameter model parameter model parameters are estimated Plot probability regression sample set of items standard deviation standard error standardized residuals Swaminathan test data test information function test items test scores three-parameter model transformation true score two-parameter model unidimensional variance-covariance matrix