A Monte Carlo Comparison of Two Item Response Theory Based Item Bias Detection Methods PDF Download
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Author: Peter Uher
Publisher:
ISBN:
Category : Educational tests and measurements
Languages : en
Pages : 74
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Book Description
Author: Peter Uher
Publisher:
ISBN:
Category : Educational tests and measurements
Languages : en
Pages : 74
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Book Description
Author: C. K. Parsons
Publisher:
ISBN:
Category :
Languages : en
Pages : 51
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Book Description
Persistent differences between racial groups on standardized aptitude test scores have suggested the potential for unfair discrimination against members of different racial and ethnic subpopulations. Because many occupational and educational opportunities are affected by mental test scores, the issue of test bias has consequences for many people in our society. Of the many statistical techniques proposed for detecting biased items there appears to be a preference for techniques based on a latent trait or item response theory (IRT) because sample estimates of population item parameters are invariant. This advantage occurs because, when the IRT model is valid, item parameters are invariant with respect to subpopulation ability distributions. This study concerns the effects of test multidimensionality on recommended item bias statistics. Simulation data samples (N=1,000 each) on a 50 item test were generated using a factor model described and used by Drasgow and Parsons. Subpopulation differences on common factors led to item bias that was identified to some extent by both chi-square and IRT bias indices. The signed indices were especially effective in distinguishing biased items from unbiased items. However, the use of either the signed chi-square or signed IRT index in multidimensional data clearly requires a priori knowledge of which subpopulation is at a disadvantage. This unexpected finding suggests further study of the properties of signed indices as well as a reevaluation of previous simulation research that has appeared to support their validity.
Author: Roger E. Millsap
Publisher: Routledge
ISBN: 1136761128
Category : Psychology
Languages : en
Pages : 359
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Book Description
This book reviews the statistical procedures used to detect measurement bias. Measurement bias is examined from a general latent variable perspective so as to accommodate different forms of testing in a variety of contexts including cognitive or clinical variables, attitudes, personality dimensions, or emotional states. Measurement models that underlie psychometric practice are described, including their strengths and limitations. Practical strategies and examples for dealing with bias detection are provided throughout. The book begins with an introduction to the general topic, followed by a review of the measurement models used in psychometric theory. Emphasis is placed on latent variable models, with introductions to classical test theory, factor analysis, and item response theory, and the controversies associated with each, being provided. Measurement invariance and bias in the context of multiple populations is defined in chapter 3 followed by chapter 4 that describes the common factor model for continuous measures in multiple populations and its use in the investigation of factorial invariance. Identification problems in confirmatory factor analysis are examined along with estimation and fit evaluation and an example using WAIS-R data. The factor analysis model for discrete measures in multiple populations with an emphasis on the specification, identification, estimation, and fit evaluation issues is addressed in the next chapter. An MMPI item data example is provided. Chapter 6 reviews both dichotomous and polytomous item response scales emphasizing estimation methods and model fit evaluation. The use of models in item response theory in evaluating invariance across multiple populations is then described, including an example that uses data from a large-scale achievement test. Chapter 8 examines item bias evaluation methods that use observed scores to match individuals and provides an example that applies item response theory to data introduced earlier in the book. The book concludes with the implications of measurement bias for the use of tests in prediction in educational or employment settings. A valuable supplement for advanced courses on psychometrics, testing, measurement, assessment, latent variable modeling, and/or quantitative methods taught in departments of psychology and education, researchers faced with considering bias in measurement will also value this book.
Author: Woo-yeol Lee
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 48
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Book Description
Author: Linda Baumunk Chard
Publisher:
ISBN:
Category : Educational tests and measurements
Languages : en
Pages : 394
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Book Description
Author: Tae-Je Seong
Publisher:
ISBN:
Category :
Languages : en
Pages : 204
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Book Description
Author: M.D. Reckase
Publisher: Springer Science & Business Media
ISBN: 0387899766
Category : Social Science
Languages : en
Pages : 355
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Book Description
First thorough treatment of multidimensional item response theory Description of methods is supported by numerous practical examples Describes procedures for multidimensional computerized adaptive testing
Author: Steven P. Reise
Publisher: Routledge
ISBN: 1317565703
Category : Psychology
Languages : en
Pages : 484
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Book Description
Item response theory (IRT) has moved beyond the confines of educational measurement into assessment domains such as personality, psychopathology, and patient-reported outcomes. Classic and emerging IRT methods and applications that are revolutionizing psychological measurement, particularly for health assessments used to demonstrate treatment effectiveness, are reviewed in this new volume. World renowned contributors present the latest research and methodologies about these models along with their applications and related challenges. Examples using real data, some from NIH-PROMIS, show how to apply these models in actual research situations. Chapters review fundamental issues of IRT, modern estimation methods, testing assumptions, evaluating fit, item banking, scoring in multidimensional models, and advanced IRT methods. New multidimensional models are provided along with suggestions for deciding among the family of IRT models available. Each chapter provides an introduction, describes state-of-the art research methods, demonstrates an application, and provides a summary. The book addresses the most critical IRT conceptual and statistical issues confronting researchers and advanced students in psychology, education, and medicine today. Although the chapters highlight health outcomes data the issues addressed are relevant to any content domain. The book addresses: IRT models applied to non-educational data especially patient reported outcomes Differences between cognitive and non-cognitive constructs and the challenges these bring to modeling. The application of multidimensional IRT models designed to capture typical performance data. Cutting-edge methods for deriving a single latent dimension from multidimensional data A new model designed for the measurement of constructs that are defined on one end of a continuum such as substance abuse Scoring individuals under different multidimensional IRT models and item banking for patient-reported health outcomes How to evaluate measurement invariance, diagnose problems with response categories, and assess growth and change. Part 1 reviews fundamental topics such as assumption testing, parameter estimation, and the assessment of model and person fit. New, emerging, and classic IRT models including modeling multidimensional data and the use of new IRT models in typical performance measurement contexts are examined in Part 2. Part 3 reviews the major applications of IRT models such as scoring, item banking for patient-reported health outcomes, evaluating measurement invariance, linking scales to a common metric, and measuring growth and change. The book concludes with a look at future IRT applications in health outcomes measurement. The book summarizes the latest advances and critiques foundational topics such a multidimensionality, assessment of fit, handling non-normality, as well as applied topics such as differential item functioning and multidimensional linking. Intended for researchers, advanced students, and practitioners in psychology, education, and medicine interested in applying IRT methods, this book also serves as a text in advanced graduate courses on IRT or measurement. Familiarity with factor analysis, latent variables, IRT, and basic measurement theory is assumed.
Author: Sohan Modgil
Publisher: Routledge
ISBN: 1135389217
Category : Education
Languages : en
Pages : 578
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Book Description
First Published in 1987. During the last thirty years, Arthur Jensen’s brilliant contribution to knowledge has been well-known world-wide. From its early transmission, his work has not been without its critics. Naturally, criticisms persist, although his work continues to be frequently acknowledged with great admiration in the channels of psychology. With such prolific work, it would seem justified to consider the discrepancies, the omissions, together with the various interpretations which have been and are currently being highlighted. No theory or practice in modern psychology has been the object of more stringent attack than mental testing, and among the most severe criticisms is that of cultural bias.
Author: R. J. de Ayala
Publisher: Guilford Publications
ISBN: 1462547753
Category : Business & Economics
Languages : en
Pages : 674
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Book Description
Introduction to measurement -- The one-parameter model -- Joint maximum likelihood parameter estimation -- Marginal maximum likelihood parameter estimation -- The two-parameter model -- The three-parameter model -- Rasch models for ordered polytomous data -- Non-Rasch models for ordered polytomous data -- Models for nominal polytomous data -- Models for multidimensional data -- Linking and equating -- Differential item functioning -- Multilevel IRT models.
