Item response theory. First edition

種類:

電子ブック

責任表示:

R. Darrell Bock, Robert D. Gibbons, University of Chicago

出版情報:

Hoboken, NJ : John Wiley & Sons, Inc., 2021

著者名:

• Bock, R. Darrell
• Gibbons, Robert D.

ISBN:

9781119716716 [1119716713]    
9781119716679 [1119716675]    
9781119716723 [1119716721]    

注記:

Includes bibliographical references and index.
"To date, much of the application of IRT has been in the field of educational measurement, where for example, IRT has been used extensively by the Educational Testing Service for the development of scholastic aptitude tests. IRT has played a major role in all major college and graduate school admission tests (SAT, ACT, GRE, GMAT, MCAT, ...). Unlike traditional tests based on classical test theory that summarizes the test result by a simple counting operation of number of correct responses, IRT provides model-based measurements in which the difficulty of the items, discrimination of high and low levels of the underlying latent variable(s) and the corresponding ability of the respondents can be estimated. In IRT scoring of tests, a certain number of items can be arbitrarily added, deleted, or replaced without losing comparability of scores on the scale. Only the precision of measurement at some points on the scale is affected. This property of scaled measurement, as opposed to counts of events, is the most sali
Description based on online resource; title from digital title page (viewed on November 11, 2021).
136 3.2.2.8 Illustration 136 3.2.2.9 Rating ScaleModels 136 3.2.3 RankingModel 139 4 Item Parameter Estimation -- Binary Data 141 4.1 Estimation of Item Parameters Assuming Known Attribute Values of the Respondents 142 4.1.1 Estimation 143 4.1.1.1 The 1-parameterModel 143 4.1.1.2 The 2-parameterModel 144 4.1.1.3 The 3-parameterModel 145 4.2 Estimation of Item Parameters Assuming Unknown Attribute Values of the Respondents 146 4.2.1 Joint Maximum Likelihood Estimation (JML) 147 4.2.1.1 The 1-parameter Logistic Model 147 4.2.1.2 Logit-linearAnalysis 148 4.2.1.3 Proportional Marginal Adjustments 153 4.2.2 Marginal Maximum Likelihood Estimation (MML) 158 4.2.2.1 The 2-parameterModel 162 5 Item Parameter Estimation -- Polytomous Data 177 5.1 General Results 177 5.2 The Normal OgiveModel 182 5.3 The NominalCategoriesModel 183 5.4 The Graded
8.2 Computerized Adaptive Testing -- An Overview 244 8.3 Item Selection 245 8.3.1 UnidimensionalComputerized Adaptive Testing (UCAT) 246 8.3.1.1 Fisher Information in IRT Model 246 8.3.1.2 Maximizing Fisher Information (MFI) and Its Limitations 248 8.3.1.3 Modifications toMFI 249 8.3.2 MultidimensionalComputerized Adaptive Testing (MCAT) 251 8.3.2.1 Two Conceptualizations of the Information Function in Multidimensional Space 252 8.3.2.2 SelectionMethods inMCAT 253 8.3.3 Bifactor IRT 256 8.4 Terminating an Adaptive Test 257 8.5 AdditionalConsiderations 258 8.6 An Example fromMental HealthMeasurement 260 8.6.1 The CAT-Mental Health 261 8.6.2 Discussion 264 9 Differential Item Functioning 267 9.1 Introduction 267 9.2 Types of DIF 268 9.3 TheMantel-Haenszel Procedure 270 9.4 Lord'sWald Test 271 9.5 LagrangeMultiplier Test 272 9.6
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