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.
What people are saying - Write a review
We haven't found any reviews in the usual places.
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
Other editions - View all
ability estimates ability parameters administered analysis answering applications approach appropriate assessing assumed assumption chapter chosen classical common items compared comparison computed considered constructed correlation corresponding cut-off defined depend described determine developed difficulty discrimination distribution Educational equating error examinee's example expected Figure follows given groups of examinees Hambleton hence ICCs important independence indices information function interest invariance IRT models item and ability item bank item characteristic item difficulty item parameter estimates item response model item response theory known likelihood function linear logistic Lord maximum likelihood mean measurement methods minority observed obtained one-parameter model performance placed Plot possible practice probability problem procedure programs range relationship respectively sample scale score selection shows simulated standard standard error standardized residuals statistic success Table test data test items test scores three-parameter model transformation true score two-parameter model values