## Fundamentals of Item Response TheoryBy 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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### Contents

Concepts Models and Features | 7 |

Ability and Item Parameter Estimation | 32 |

Assessment of ModelData Fit | 53 |

The Ability Scale | 77 |

Item and Test Information and Efficiency Functions | 91 |

Test Construction | 99 |

Identification of Potentially Biased Test Items | 109 |

Test Score Equating | 123 |

Computerized Adaptive Testing | 145 |

Future Directions of Item Response Theory | 153 |

### Other editions - View all

Fundamentals of Item Response Theory Ronald K. Hambleton,Hariharan Swaminathan,H. Jane Rogers Limited preview - 1991 |

### Common terms and phrases

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 described determine developed difficulty discrimination distribution Educational equal 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 Lord maximum likelihood mean measurement methods minority observed obtained one-parameter model performance placed Plot possible 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 test X three-parameter model transformation true score two-parameter model values