This book describes the Item Response Theory (IRT) methodology as developed by Darrell Bock and his students over the past 25 years and implemented in their computer programs BILOG-MG, MULTILOG, PARSCALE, and TESTFACT. It is especially useful for users of these computer programs in the educational assessment field. IRT is now also applied in the healthcare field in combination with computer adaptive testing (CAT).
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Inside This Book
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First Sentence:
The only type of data that the IRT programs currently can handle is fixed format with one or more lines per record (case) and one-character response codes. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
variable format statement, bifactor analysis, classical item statistics, main test items, score command, original response data, full information item factor analysis, attempting item, bifactor solution, item information indices, normal response function, estimated latent distributions, format statement describing, item information curves, science assessment test, bifactor model, fractional quadrature, response codes field, weights for these distributions, generalized partial credit model, item parameter estimation, standardized difficulty, syntax wizard, test information curves, uncorrelated group factors Key Phrases - Capitalized Phrases (CAPs): (learn more)
Input Parameters, Monte Carlo, Default None, Default Supplied, New Analysis, Cancel Help, Scoring Prior Latent Distribution, Assign Item, Item Keys, Back Next, Calibration Options, Data Options, Item Parameter Prior Constraints, Item Parameter Starting Values, Fixed Theta, Text Parameters, Score Options, Ability Figure, Calibration Prior Latent Distribution, Enter Values, External Ability, Item Parameter Estimation Type of Data, Line Parameters, Default All, Fixed-Column Records New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
The only type of data that the IRT programs currently can handle is fixed format with one or more lines per record (case) and one-character response codes. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
variable format statement, bifactor analysis, classical item statistics, main test items, score command, original response data, full information item factor analysis, attempting item, bifactor solution, item information indices, normal response function, estimated latent distributions, format statement describing, item information curves, science assessment test, bifactor model, fractional quadrature, response codes field, weights for these distributions, generalized partial credit model, item parameter estimation, standardized difficulty, syntax wizard, test information curves, uncorrelated group factors Key Phrases - Capitalized Phrases (CAPs): (learn more)
Input Parameters, Monte Carlo, Default None, Default Supplied, New Analysis, Cancel Help, Scoring Prior Latent Distribution, Assign Item, Item Keys, Back Next, Calibration Options, Data Options, Item Parameter Prior Constraints, Item Parameter Starting Values, Fixed Theta, Text Parameters, Score Options, Ability Figure, Calibration Prior Latent Distribution, Enter Values, External Ability, Item Parameter Estimation Type of Data, Line Parameters, Default All, Fixed-Column Records New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 12
books:
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- Handbook of Modern Item Response Theory by Wim J. van der Linden in Back Matter (1), Back Matter (2), and Back Matter (3)
- Educational Measurement (American Council on Education/Oryx Series on Higher Education) by Robert L. Linn in Back Matter (1), and Back Matter (2)
- Test Scoring by David Thissen on page 91, and page 209
- Educational Psychology: Briefer Course (1914) (Thoemmes Press - Classics in Psychology) (Vol 50) by Edward L. Thorndike in Back Matter (1), and Back Matter (2)
- Differential Item Functioning by Paul W. Holland on page 770, and Back Matter
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