The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Ronald K. Hambleton - One of the best experts on this subject based on the ideXlab platform.
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Item Response Theory (IRT) Models for Dichotomous Data
Wiley StatsRef: Statistics Reference Online, 2014Co-Authors: Ronald K. Hambleton, Yue ZhaoAbstract:This entry provides an introduction to the topic of Item Response Theory. Shortcomings of classical test models are considered first. Second, current Item Response models for the analysis of dichotomously scored Item Response data are introduced. Estimation of model parameters, assessment of model fit, and available software, are described next. Finally, applications of Item Response Theory IRT models to test development, Item bias, equating, and computer-adaptive testing are briefly described. Keywords: Item Response Theory; latent trait Theory; Item characteristic functions; test characteristic functions; Item and test information functions; test development; identification of Item bias; test score equating; computer-adaptive testing
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Handbook of Modern Item Response Theory - Handbook of Modern Item Response Theory
1997Co-Authors: Wim J. Van Der Linden, Ronald K. HambletonAbstract:Item Response Theory has become an essential component in the toolkit of every researcher in the behavioral sciences. It provides a powerful means to study individual Responses to a variety of stimuli, and the methodology has been extended and developed to cover many different models of interaction. This volume presents a wide-ranging handbook to Item Response Theory - and its applications to educational and psychological testing. It will serve as both an introduction to the subject and also as a comprehensive reference volume for practitioners and researchers. It is organized into six major sections: the nominal categories model, models for Response time or multiple attempts on Items, models for multiple abilities or cognitive components, nonparametric models, models for nonmonotone Items, and models with special assumptions. Each chapter in the book has been written by an expert of that particular topic, and the chapters have been carefully edited to ensure that a uniform style of notation and presentation is used throughout. As a result, all researchers whose work uses Item Response Theory will find this an indispensable companion to their work and it will be the subject's reference volume for many years to come.
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handbook of modern Item Response Theory
Biometrics, 1997Co-Authors: Wim J. Van Der Linden, Ronald K. HambletonAbstract:Item Response Theory has become an essential component in the toolkit of every researcher in the behavioral sciences. It provides a powerful means to study individual Responses to a variety of stimuli, and the methodology has been extended and developed to cover many different models of interaction. This volume presents a wide-ranging handbook to Item Response Theory - and its applications to educational and psychological testing. It will serve as both an introduction to the subject and also as a comprehensive reference volume for practitioners and researchers. It is organized into six major sections: the nominal categories model, models for Response time or multiple attempts on Items, models for multiple abilities or cognitive components, nonparametric models, models for nonmonotone Items, and models with special assumptions. Each chapter in the book has been written by an expert of that particular topic, and the chapters have been carefully edited to ensure that a uniform style of notation and presentation is used throughout. As a result, all researchers whose work uses Item Response Theory will find this an indispensable companion to their work and it will be the subject's reference volume for many years to come.
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Item Response Theory Models and Testing Practices: Current International Status and Future Directions* , **
European Journal of Psychological Assessment, 1997Co-Authors: Ronald K. Hambleton, Sharon C. SlaterAbstract:Psychological testing has been undergoing major changes. One of the main changes is the transition from the use of classical to modern test models and methods in test development. The purposes of this paper are to describe the shortcomings of classical test models which are overcome with modern test Theory, i.e., Item Response Theory, to introduce the basic concepts of Item Response Theory, to describe several important international applications of Item Response Theory models, and finally, to describe some likely IRT directions in the next century.
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Fundamentals of Item Response Theory
1991Co-Authors: Ronald K. Hambleton, Hariharan Swaminathan, H. Jane RogersAbstract:Background Concepts, Models, and Features Ability and Item Parameter Estimation Assessment of Model-Data Fit The Ability Scale Item and Test Information and Efficiency Functions Test Construction Identification of Potentially Biased Test Items Test Score Equating Computerized Adaptive Testing Future Directions of Item Response Theory
Klaas Sijtsma - One of the best experts on this subject based on the ideXlab platform.
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Nonparametric Item Response Theory Models
Wiley StatsRef: Statistics Reference Online, 2014Co-Authors: Klaas SijtsmaAbstract:Nonparametric Item Response Theory serves two purposes. The first is to provide a framework for flexible, exploratory analysis of Item scores. Several procedures are available for investigating the dimensionality of test data. Estimated Item Response functions are checked for monotonicity and other order restrictions. Nonparametric Item Response models imply ordinal scales for persons and Items. Ordinal scales are sufficient for many practical applications in psychology, education, sociology, and so on. The second purpose is to provide a mathematical framework for studying the potentials and the limitations of Item Response Theory. Observable data properties implied by nonparametric Item Response Theory may also be valuable for fitting parametric models, and relationships between Item Response models may be clarified. Present research in nonparametric IRT involves preference data analysis and Bayesian methods for model fitting. Keywords: nonparametric Item Response Theory
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Item Response Theory past performance present developments and future expectations
Behaviormetrika, 2006Co-Authors: Klaas Sijtsma, Brian W JunkerAbstract:We give a historical introduction to Item Response Theory, which places the work of Thurstone, Lord, Guttman and Coombs in a present-day perspective. The general assumptions of modern Item Response Theory, local independence and monotonicity of Response functions, are discussed, followed by a general framework for estimating Item Response models. Six classes of well-known Item Response models and recent developments are discussed: (1) models for dichotomous Item scores; (2) models for polytomous Item scores; (3) nonparametric models; (4) unfolding models; (5) multidimensional models; and (6) models with restrictions on the parameters. Finally, it is noted that Item Response Theory has evolved from unidimensional scaling of Items and measurement of persons to data analysis tools for complicated research designs.
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Encyclopedia of Statistics in Behavioral Science - Nonparametric Item Response Theory Models
Encyclopedia of Statistics in Behavioral Science, 2005Co-Authors: Klaas SijtsmaAbstract:Nonparametric Item Response Theory serves two purposes. The first is to provide a framework for flexible, exploratory analysis of Item scores. Several procedures are available for investigating the dimensionality of test data. Estimated Item Response functions are checked for monotonicity and other order restrictions. Nonparametric Item Response models imply ordinal scales for persons and Items. Ordinal scales are sufficient for many practical applications in psychology, education, sociology, and so on. The second purpose is to provide a mathematical framework for studying the potentials and the limitations of Item Response Theory. Observable data properties implied by nonparametric Item Response Theory may also be valuable for fitting parametric models, and relationships between Item Response models may be clarified. Present research in nonparametric IRT involves preference data analysis and Bayesian methods for model fitting. Keywords: nonparametric Item Response Theory
Steven P. Reise - One of the best experts on this subject based on the ideXlab platform.
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handbook of Item Response Theory modeling applications to typical performance assessment
2014Co-Authors: Steven P. Reise, Dennis A RevickiAbstract:Part 1: Fundamental Issues in Item Response Theory 1. Introduction: Age-Old Problems and Modern Solutions S.P. Reise, D.A. Revicki 2. Evaluating the Impact of Multidimensionality on Unidimensional Item Response Theory Model Parameters S.P. Reise, K.F. Cook, T.M. Moore 3. Modern Approaches to Parameter Estimation in Item Response Theory L. Cai, D. Thissen 4. Estimating the Latent Density in Unidimensional IRT to Permit Nonnormality C.M. Woods 5. The Use of Nonparametric Item Response Theory to Explore Data Quality R.R. Meijer, J.N. Tendeiro, R.B. K. Wanders 6. Evaluating the Fit of IRT models A. Maydeu-Olivares 7. Assessing Person Fit in Typical-Response Measures P. J. Ferrando Part 2: Classic and Emerging IRT Modeling Approaches 8. Three (or Four) Factors, Four (or Three) Models M.C. Edwards, R. J. Wirth, C.R. Houts, A.J. Bodine 9. Using Hierarchical IRT Models to Create Unidimensional Measures from Multidimensional Data B.D. Stucky, M.O. Edelen 10. An Illustration of the Two-Tier Item Factor Analysis Model W.E. Bonifay 11. Using Projected Locally Dependent Unidimensional Models to Measure Multidimensional Response Data E.H. Ip, S. Chen 12. Multidimensional Explanatory Item Response Modeling P.D. Boeck, M. Wilson 13. Unipolar Item Response Models J.F. Lucke 14. Selecting Among Polytomous IRT Models R. Ostini, M. Finkelman, M. Nering Part 3: Using IRT Models in Applied Problems 15. Scoring and Estimating Score Precision Using Multidimensional IRT Models A. Brown, T.J. Croudace 16. Developing Item Banks for Patient-Reported Health Outcomes D.A. Revicki, W. Chen, C. Tucker 17. Using Item Response Theory to Evaluate Measurement Invariance in Health-Related Measures R.E. Millsap, H. Gunn, H.T. Everson, A. Zautra 18. Detecting Faulty Within-Item Category Functioning with the Nominal Response Model K.S. J. Preston, S.P. Reise 19. Multidimensional Test Linking J.P. Weeks 20. IRT for Growth and Change J.J. McArdle, K.T. Petway, E.S. Hishinuma 21. Summary: New IRT Problems and Future Directions S.P. Reise, D.A. Revicki
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Item Response Theory
The Encyclopedia of Clinical Psychology, 2014Co-Authors: Steven P. ReiseAbstract:Item Response Theory (IRT) methods are alternatives to classical test Theory (CTT) approaches to scale construction, analysis, and scoring. A differentiating feature of IRT modeling is the estimation of a mathematical function that relates individual differences on a continuous latent variable to the propensity to respond to a scale Item (e.g., respond correctly to a multiple-choice aptitude Item, or respond in category 3 on a five-point ordered rating scale). Commonly applied IRT models appropriate for dichotomous and polytomous Item Response data are described. Assumptions underlying these models and interpretation of indices derived from model parameters are presented. Finally, popular applications of IRT models, such as computerized adaptive testing, are summarized. Keywords: measurement in psychology; psychometric testing; methodology
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Item Response Theory and clinical measurement
Annual Review of Clinical Psychology, 2009Co-Authors: Steven P. Reise, Niels G WallerAbstract:In this review, we examine studies that use Item Response Theory (IRT) to explore the psychometric properties of clinical measures. Next, we consider how IRT has been used in clinical research for: scale linking, computerized adaptive testing, and differential Item functioning analysis. Finally, we consider the scale properties of IRT trait scores. We conclude that there are notable differences between cognitive and clinical measures that have relevance for IRT modeling. Future research should be directed toward a better understanding of the metric of the latent trait and the psychological processes that lead to individual differences in Item Response behaviors.
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Item Response Theory for psychologists
2000Co-Authors: Susan E. Embretson, Steven P. ReiseAbstract:Contents: Preface. Part I: Introduction. Introduction. Part II: Item Response Theory Principles: Some Contrasts and Comparisons. The New Rules of Measurement. Item Response Theory as Model-Based Measurement. Part III: The Fundamentals of Item Response Theory. Binary IRT Models. Polytomous IRT Models. The Trait Level Measurement Scale: Meaning, Interpretations, and Measurement-Scale Properties. Measuring Persons: Scoring Examinees With IRT Models. Calibrating Items: Estimation. Assessing the Fit of IRT Models. Part IV: Applications of IRT Models. IRT Applications: DIF, CAT, and Scale Analysis. IRT Applications in Cognitive and Developmental Assessment. Applications of IRT in Personality and Attitude Assessment. Computer Programs for Conducting IRT Parameter Estimation.
Tamás Antal - One of the best experts on this subject based on the ideXlab platform.
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On multidimensional Item Response Theory – a coordinate free approach
Electronic Journal of Statistics, 2007Co-Authors: Tamás AntalAbstract:A coordinate system free definition of complex structure multidimensional Item Response Theory (MIRT) for dichotomously scored Items is presented. The point of view taken emphasizes the possibilities and subtleties of understanding MIRT as a multidimensional extension of the ``classical'' unidimensional Item Response Theory models. The main theorem of the paper is that every monotonic MIRT model looks the same; they are all trivial extensions of univariate Item Response Theory.
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ON MULTIDIMENSIONAL Item Response Theory: A COORDINATE-FREE APPROACH
ETS Research Report Series, 2007Co-Authors: Tamás AntalAbstract:A coordinate-free definition of complex-structure multidimensional Item Response Theory (MIRT) for dichotomously scored Items is presented. The point of view taken emphasizes the possibilities and subtleties of understanding MIRT as a multidimensional extension of the classical unidimensional Item Response Theory models. The main theorem of the paper is that every monotonic MIRT model looks the same; they are all trivial extensions of univariate Item Response Theory.
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ADAPTIVE NUMERICAL INTEGRATION FOR Item Response Theory
ETS Research Report Series, 2007Co-Authors: Tamás Antal, Andreas OranjeAbstract:Well-known numerical integration methods are applied to Item Response Theory (IRT) with special emphasis on the estimation of the latent regression model of NAEP. An argument is made that the Gauss-Hermite rule enhanced with Cholesky decomposition and normal approximation of the Response likelihood is a fast, precise, and reliable alternative for the numerical integration in NAEP and in IRT in general.
Mark D. Reckase - One of the best experts on this subject based on the ideXlab platform.
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multidimensional Item Response Theory
Handbook of Statistics, 2009Co-Authors: Mark D. ReckaseAbstract:Multidimensional Item Response Theory is the first book to give thorough coverage to this emerging area of psychometrics. The book describes the commonly used multidimensional Item Response Theory (MIRT) models and the important methods needed for their practical application. These methods include ways to determine the number of dimensions required to adequately model data, procedures for estimating model parameters, ways to define the space for a MIRT model, and procedures for transforming calibrations from different samples to put them in the same space. A full chapter is devoted to methods for multidimensional computerized adaptive testing. The text is appropriate for an advanced course in psychometric Theory or as a reference work for those interested in applying MIRT methodology. A working knowledge of unidimensional Item Response Theory and matrix algebra is assumed. Knowledge of factor analysis is also helpful.
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Unidimensional Item Response Theory Models
Multidimensional Item Response Theory, 2009Co-Authors: Mark D. ReckaseAbstract:In Chap. 3, the point will be made that multidimensional Item Response Theory (MIRT) is an outgrowth of both factor analysis and unidimensional Item Response Theory (UIRT). Although this is clearly true, the way that MIRT analysis results are interpreted is much more akin to UIRT. This chapter provides a brief introduction to UIRT with a special emphasis on the components that will be generalized when MIRT models are presented in Chap. 4. This chapter is not a thorough description of UIRT models and their applications. Other texts such as Lord (1980), Hambleton and Swaminathan (1985), Hulin et al. (1983), Fischer and Molenaar (1995), and van der Linden and Hambleton (1997) should be consulted for a more thorough development of UIRT models.
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Multidimensional Item Response Theory Models
Multidimensional Item Response Theory, 2009Co-Authors: Mark D. ReckaseAbstract:As the previous chapters suggest, it is not difficult to conceive of test Items that require more than one hypothetical construct to determine the correct Response. However, when describing multidimensional Item Response Theory (MIRT) models, care should be taken to distinguish between dimensions as defined by MIRT models, which represent statistical abstractions of the observed data, and the hypothetical constructs that represent cognitive or affective dimensions of variation in a population of examinees. The earlier chapters present some of those distinctions. This chapter will elaborate on the distinctions between coordinates and constructs and the distinctions will be given additional treatment in Chaps. 6 and 7.
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Multidimensional Item Response Theory - Multidimensional Item Response Theory
Handbook of Statistics, 2006Co-Authors: Mark D. ReckaseAbstract:Multidimensional Item Response Theory is the first book to give thorough coverage to this emerging area of psychometrics. The book describes the commonly used multidimensional Item Response Theory (MIRT) models and the important methods needed for their practical application. These methods include ways to determine the number of dimensions required to adequately model data, procedures for estimating model parameters, ways to define the space for a MIRT model, and procedures for transforming calibrations from different samples to put them in the same space. A full chapter is devoted to methods for multidimensional computerized adaptive testing. The text is appropriate for an advanced course in psychometric Theory or as a reference work for those interested in applying MIRT methodology. A working knowledge of unidimensional Item Response Theory and matrix algebra is assumed. Knowledge of factor analysis is also helpful.
