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Refinar búsquedaBayesian Evaluation of Informative Hypotheses / SpringerLink (Online service) ; Herbert Hoijtink ; Irene Klugkist ; Paul A. Boelen (2008)
Título : Bayesian Evaluation of Informative Hypotheses Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Herbert Hoijtink ; Irene Klugkist ; Paul A. Boelen Editorial: New York, NY : Springer New York Fecha de publicación: 2008 Colección: Statistics for Social and Behavioral Sciences, ISSN 2199-7357 Il.: online resource ISBN/ISSN/DL: 978-0-387-09612-4 Idioma : Inglés (eng) Palabras clave: Statistics for Social Science, Behavorial Education, Public Policy, and Law Clasificación: 51 Matemáticas Resumen: This book presents an alternative for traditional null hypothesis testing. It builds on the idea that researchers usually have more informative research-questions than the "nothing is going on" null hypothesis, or the "something is going on" alternative hypothesis. To be more precise, researchers often express their expectations in terms of expected orderings in parameters, for instance, in group means. This book introduces a novel approach, wherein theories or expectations of empirical researchers are translated into one or more so-called informative hypotheses, i.e., hypotheses imposing inequality constraints on (some of) the model parameters. As a consequence, informative hypotheses are much closer to the actual questions researchers have and therefore make optimal use of the data to provide more informative answers to these questions. A Bayesian approach is used for the evaluation of informative hypotheses and is introduced at a non-technical level in the context of analysis of variance models. Technical aspects of Bayesian evaluation of informative hypotheses are also considered and different approaches are presented by an international group of Bayesian statisticians. Furthermore, applications in a variety of statistical models including among others latent class analysis and multi-level modeling are presented, again at a non-technical level. Finally, the proposed method is evaluated from a psychological, statistical and philosophical point of view. This book contains numerous illustrations, all in the context of psychology. The proposed methodology, however, is equally relevant for research in other social sciences (e.g., sociology or educational sciences), as well as in other disciplines (e.g., medical or economical research). The editors are all affiliated at the faculty of Social Sciences at Utrecht University in the Netherlands. Herbert Hoijtink is a professor in applied Bayesian statistics at the Department of Methodology and Statistics. Irene Klugkist is assistant professor at the same department, and Paul A. Boelen is assistant professor at the Department of Clinical and Health Psychology Nota de contenido: An Introduction to Bayesian Evaluation of Informative Hypotheses -- An Introduction to Bayesian Evaluation of Informative Hypotheses -- Bayesian Evaluation of Informative Hypotheses -- Illustrative Psychological Data and Hypotheses for Bayesian Inequality Constrained Analysis of Variance -- Bayesian Estimation for Inequality Constrained Analysis of Variance -- Encompassing Prior Based Model Selection for Inequality Constrained Analysis of Variance -- An Evaluation of Bayesian Inequality Constrained Analysis of Variance -- A Further Study of Prior Distributions and the Bayes Factor -- Bayes Factors Based on Test Statistics Under Order Restrictions -- Objective Bayes Factors for Informative Hypotheses: “Completing” the Informative Hypothesis and “Splitting” the Bayes Factors -- The Bayes Factor Versus Other Model Selection Criteria for the Selection of Constrained Models -- Bayesian Versus Frequentist Inference -- Beyond Analysis of Variance -- Inequality Constrained Analysis of Covariance -- Inequality Constrained Latent Class Models -- Inequality Constrained Contingency Table Analysis -- Inequality Constrained Multilevel Models -- Evaluations -- A Psychologist’s View on Bayesian Evaluation of Informative Hypotheses -- A Statistician’s View on Bayesian Evaluation of Informative Hypotheses -- A Philosopher’s View on Bayesian Evaluation of Informative Hypotheses En línea: http://dx.doi.org/10.1007/978-0-387-09612-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34143 Bayesian Evaluation of Informative Hypotheses [documento electrónico] / SpringerLink (Online service) ; Herbert Hoijtink ; Irene Klugkist ; Paul A. Boelen . - New York, NY : Springer New York, 2008 . - : online resource. - (Statistics for Social and Behavioral Sciences, ISSN 2199-7357) .
ISBN : 978-0-387-09612-4
Idioma : Inglés (eng)
Palabras clave: Statistics for Social Science, Behavorial Education, Public Policy, and Law Clasificación: 51 Matemáticas Resumen: This book presents an alternative for traditional null hypothesis testing. It builds on the idea that researchers usually have more informative research-questions than the "nothing is going on" null hypothesis, or the "something is going on" alternative hypothesis. To be more precise, researchers often express their expectations in terms of expected orderings in parameters, for instance, in group means. This book introduces a novel approach, wherein theories or expectations of empirical researchers are translated into one or more so-called informative hypotheses, i.e., hypotheses imposing inequality constraints on (some of) the model parameters. As a consequence, informative hypotheses are much closer to the actual questions researchers have and therefore make optimal use of the data to provide more informative answers to these questions. A Bayesian approach is used for the evaluation of informative hypotheses and is introduced at a non-technical level in the context of analysis of variance models. Technical aspects of Bayesian evaluation of informative hypotheses are also considered and different approaches are presented by an international group of Bayesian statisticians. Furthermore, applications in a variety of statistical models including among others latent class analysis and multi-level modeling are presented, again at a non-technical level. Finally, the proposed method is evaluated from a psychological, statistical and philosophical point of view. This book contains numerous illustrations, all in the context of psychology. The proposed methodology, however, is equally relevant for research in other social sciences (e.g., sociology or educational sciences), as well as in other disciplines (e.g., medical or economical research). The editors are all affiliated at the faculty of Social Sciences at Utrecht University in the Netherlands. Herbert Hoijtink is a professor in applied Bayesian statistics at the Department of Methodology and Statistics. Irene Klugkist is assistant professor at the same department, and Paul A. Boelen is assistant professor at the Department of Clinical and Health Psychology Nota de contenido: An Introduction to Bayesian Evaluation of Informative Hypotheses -- An Introduction to Bayesian Evaluation of Informative Hypotheses -- Bayesian Evaluation of Informative Hypotheses -- Illustrative Psychological Data and Hypotheses for Bayesian Inequality Constrained Analysis of Variance -- Bayesian Estimation for Inequality Constrained Analysis of Variance -- Encompassing Prior Based Model Selection for Inequality Constrained Analysis of Variance -- An Evaluation of Bayesian Inequality Constrained Analysis of Variance -- A Further Study of Prior Distributions and the Bayes Factor -- Bayes Factors Based on Test Statistics Under Order Restrictions -- Objective Bayes Factors for Informative Hypotheses: “Completing” the Informative Hypothesis and “Splitting” the Bayes Factors -- The Bayes Factor Versus Other Model Selection Criteria for the Selection of Constrained Models -- Bayesian Versus Frequentist Inference -- Beyond Analysis of Variance -- Inequality Constrained Analysis of Covariance -- Inequality Constrained Latent Class Models -- Inequality Constrained Contingency Table Analysis -- Inequality Constrained Multilevel Models -- Evaluations -- A Psychologist’s View on Bayesian Evaluation of Informative Hypotheses -- A Statistician’s View on Bayesian Evaluation of Informative Hypotheses -- A Philosopher’s View on Bayesian Evaluation of Informative Hypotheses En línea: http://dx.doi.org/10.1007/978-0-387-09612-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34143 Ejemplares
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Título : Bayesian Item Response Modeling : Theory and Applications Tipo de documento: documento electrónico Autores: Jean-Paul Fox ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2010 Colección: Statistics for Social and Behavioral Sciences, ISSN 2199-7357 Número de páginas: XIV, 313 p Il.: online resource ISBN/ISSN/DL: 978-1-4419-0742-4 Idioma : Inglés (eng) Palabras clave: Social sciences Marketing Probabilities Assessment Statistics Psychometrics Sciences Methodology of the Probability Theory and Stochastic Processes for Science, Behavorial Education, Public Policy, Law Assessment, Testing Evaluation Clasificación: 51 Matemáticas Resumen: This book presents a thorough treatment and unified coverage of Bayesian item response modeling with applications in a variety of disciplines, including education, medicine, psychology, and sociology. Breakthroughs in computing technology have made the Bayesian approach particularly useful for many response modeling problems. Free from computational constraints, realistic and state-of-the-art latent variable response models are considered for complex assessment and survey data to solve real-world problems. The Bayesian framework described provides a unified approach for modeling and inference, dealing with (nondata) prior information and information across multiple data sources. The book discusses methods for analyzing item response data and the complex relationships commonly associated with human response behavior and features • Self-contained introduction to Bayesian item response modeling and a coverage of extending standard models to handle complex assessment data • A thorough overview of Bayesian estimation and testing methods for item response models, where MCMC methods are emphasized • Numerous examples that cover a wide range of application areas, including education, medicine, psychology, and sociology • Datasets and software (S+, R, and WinBUGS code) of the models and methods presented in the book are available on www.jean-paulfox.com Bayesian Item Response Modeling is an excellent book for research professionals, including applied statisticians, psychometricians, and social scientists who analyze item response data from a Bayesian perspective. It is a guide to the growing area of Bayesian response modeling for researchers and graduate students, and will also serve them as a good reference. Jean-Paul Fox is Associate Professor of Measurement and Data Analysis, University of Twente, The Netherlands. His main research activities are in several areas of Bayesian response modeling. Dr. Fox has published numerous articles in the areas of Bayesian item response analysis, statistical methods for analyzing multivariate categorical response data, and nonlinear mixed effects models Nota de contenido: to Bayesian Response Modeling -- Bayesian Hierarchical Response Modeling -- Basic Elements of Bayesian Statistics -- Estimation of Bayesian Item Response Models -- Assessment of Bayesian Item Response Models -- Multilevel Item Response Theory Models -- Random Item Effects Models -- Response Time Item Response Models -- Randomized Item Response Models En línea: http://dx.doi.org/10.1007/978-1-4419-0742-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33580 Bayesian Item Response Modeling : Theory and Applications [documento electrónico] / Jean-Paul Fox ; SpringerLink (Online service) . - New York, NY : Springer New York, 2010 . - XIV, 313 p : online resource. - (Statistics for Social and Behavioral Sciences, ISSN 2199-7357) .
ISBN : 978-1-4419-0742-4
Idioma : Inglés (eng)
Palabras clave: Social sciences Marketing Probabilities Assessment Statistics Psychometrics Sciences Methodology of the Probability Theory and Stochastic Processes for Science, Behavorial Education, Public Policy, Law Assessment, Testing Evaluation Clasificación: 51 Matemáticas Resumen: This book presents a thorough treatment and unified coverage of Bayesian item response modeling with applications in a variety of disciplines, including education, medicine, psychology, and sociology. Breakthroughs in computing technology have made the Bayesian approach particularly useful for many response modeling problems. Free from computational constraints, realistic and state-of-the-art latent variable response models are considered for complex assessment and survey data to solve real-world problems. The Bayesian framework described provides a unified approach for modeling and inference, dealing with (nondata) prior information and information across multiple data sources. The book discusses methods for analyzing item response data and the complex relationships commonly associated with human response behavior and features • Self-contained introduction to Bayesian item response modeling and a coverage of extending standard models to handle complex assessment data • A thorough overview of Bayesian estimation and testing methods for item response models, where MCMC methods are emphasized • Numerous examples that cover a wide range of application areas, including education, medicine, psychology, and sociology • Datasets and software (S+, R, and WinBUGS code) of the models and methods presented in the book are available on www.jean-paulfox.com Bayesian Item Response Modeling is an excellent book for research professionals, including applied statisticians, psychometricians, and social scientists who analyze item response data from a Bayesian perspective. It is a guide to the growing area of Bayesian response modeling for researchers and graduate students, and will also serve them as a good reference. Jean-Paul Fox is Associate Professor of Measurement and Data Analysis, University of Twente, The Netherlands. His main research activities are in several areas of Bayesian response modeling. Dr. Fox has published numerous articles in the areas of Bayesian item response analysis, statistical methods for analyzing multivariate categorical response data, and nonlinear mixed effects models Nota de contenido: to Bayesian Response Modeling -- Bayesian Hierarchical Response Modeling -- Basic Elements of Bayesian Statistics -- Estimation of Bayesian Item Response Models -- Assessment of Bayesian Item Response Models -- Multilevel Item Response Theory Models -- Random Item Effects Models -- Response Time Item Response Models -- Randomized Item Response Models En línea: http://dx.doi.org/10.1007/978-1-4419-0742-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33580 Ejemplares
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Título : Elements of Adaptive Testing Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Wim J. van der Linden ; Cees A. W. Glas Editorial: New York, NY : Springer New York Fecha de publicación: 2010 Colección: Statistics for Social and Behavioral Sciences, ISSN 2199-7357 Número de páginas: XIV, 438 p Il.: online resource ISBN/ISSN/DL: 978-0-387-85461-8 Idioma : Inglés (eng) Palabras clave: Statistics Assessment Psychometrics for Social Science, Behavorial Education, Public Policy, and Law Assessment, Testing Evaluation Clasificación: 51 Matemáticas Resumen: The arrival of the computer in educational and psychological testing has led to the current popularity of adaptive testing---a testing format in which the computer uses statistical information about the test items to automatically adapt their selection to a real-time update of the test taker’s ability estimate. This book covers such key features of adaptive testing as item selection and ability estimation, adaptive testing with multidimensional abilities, sequencing adaptive test batteries, multistage adaptive testing, item-pool design and maintenance, estimation of item and item-family parameters, item and person fit, as well as adaptive mastery and classification testing. It also shows how these features are used in the daily operations of several large-scale adaptive testing programs. Wim J. van der Linden is Chief Research Scientist at CTB/McGraw-Hill, Monterey, CA. His specialization is psychometric theory and methods, and he has been an active researcher of adaptive testing throughout his career. For Springer, he wrote Linear Models for Optimal Test Design (2005) and co-edited Handbook of Modern Item ResponseTheory (1997). He is a past president of the Psychometric Society and recipient of lifetime achievement awards from the National Council for Measurement in Education (NCME) and the Association of Test Publishers (ATP). Cees A. W. Glas is Professor of Social Science Research Methodology, University of Twente, the Netherlands. His specialization is psychometric theory and methods, with an emphasis on item response theory, adaptive testing, model fit analysis, and missing data. Professor Glas is a co-author of Educational Evaluation, Assessment, and Monitoring (Swets & Zetlinger, 2003). Currently, he is a member of the Editorial Board of Psychometrika and serves as a technical consultant to the OECD programs for international student assessment (PISA) and the assessment of adult competencies (PIAAC) Nota de contenido: ITEM SELECTION AND ABILITY ESTIMATION -- Item Selection and Ability Estimation in Adaptive Testing -- Constrained Adaptive Testing with Shadow Tests -- Principles of Multidimensional Adaptive Testing -- Multidimensional Adaptive Testing with Kullback#x2013;Leibler Information Item Selection -- Sequencing an Adaptive Test Battery -- APPLICATIONS IN LARGE-SCALE TESTING PROGRAMS -- Adaptive Tests for Measuring Anxiety and Depression -- MATHCAT: A Flexible Testing System in Mathematics Education for Adults -- Implementing the Graduate Management Admission Test Computerized Adaptive Test -- Designing and Implementing a Multistage Adaptive Test: The Uniform CPA Exam -- A Japanese Adaptive Test of English as a Foreign Language: Developmental and Operational Aspects -- ITEM POOL DEVELOPMENT AND MAINTENANCE -- Innovative Items for Computerized Testing -- Designing Item Pools for Adaptive Testing -- Assembling an Inventory of Multistage Adaptive Testing Systems -- ITEM CALIBRATION AND MODEL FIT -- Item Parameter Estimation and Item Fit Analysis -- Estimation of the Parameters in an Item-Cloning Model for Adaptive Testing -- Detecting Person Misfit in Adaptive Testing -- The Investigation of Differential Item Functioning in Adaptive Tests -- MULTISTAGE AND MASTERY TESTING -- Multistage Testing: Issues, Designs, and Research -- Three-Category Adaptive Classification Testing -- Testlet-Based Adaptive Mastery Testing -- Adaptive Mastery Testing Using a Multidimensional IRT Model En línea: http://dx.doi.org/10.1007/978-0-387-85461-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33513 Elements of Adaptive Testing [documento electrónico] / SpringerLink (Online service) ; Wim J. van der Linden ; Cees A. W. Glas . - New York, NY : Springer New York, 2010 . - XIV, 438 p : online resource. - (Statistics for Social and Behavioral Sciences, ISSN 2199-7357) .
ISBN : 978-0-387-85461-8
Idioma : Inglés (eng)
Palabras clave: Statistics Assessment Psychometrics for Social Science, Behavorial Education, Public Policy, and Law Assessment, Testing Evaluation Clasificación: 51 Matemáticas Resumen: The arrival of the computer in educational and psychological testing has led to the current popularity of adaptive testing---a testing format in which the computer uses statistical information about the test items to automatically adapt their selection to a real-time update of the test taker’s ability estimate. This book covers such key features of adaptive testing as item selection and ability estimation, adaptive testing with multidimensional abilities, sequencing adaptive test batteries, multistage adaptive testing, item-pool design and maintenance, estimation of item and item-family parameters, item and person fit, as well as adaptive mastery and classification testing. It also shows how these features are used in the daily operations of several large-scale adaptive testing programs. Wim J. van der Linden is Chief Research Scientist at CTB/McGraw-Hill, Monterey, CA. His specialization is psychometric theory and methods, and he has been an active researcher of adaptive testing throughout his career. For Springer, he wrote Linear Models for Optimal Test Design (2005) and co-edited Handbook of Modern Item ResponseTheory (1997). He is a past president of the Psychometric Society and recipient of lifetime achievement awards from the National Council for Measurement in Education (NCME) and the Association of Test Publishers (ATP). Cees A. W. Glas is Professor of Social Science Research Methodology, University of Twente, the Netherlands. His specialization is psychometric theory and methods, with an emphasis on item response theory, adaptive testing, model fit analysis, and missing data. Professor Glas is a co-author of Educational Evaluation, Assessment, and Monitoring (Swets & Zetlinger, 2003). Currently, he is a member of the Editorial Board of Psychometrika and serves as a technical consultant to the OECD programs for international student assessment (PISA) and the assessment of adult competencies (PIAAC) Nota de contenido: ITEM SELECTION AND ABILITY ESTIMATION -- Item Selection and Ability Estimation in Adaptive Testing -- Constrained Adaptive Testing with Shadow Tests -- Principles of Multidimensional Adaptive Testing -- Multidimensional Adaptive Testing with Kullback#x2013;Leibler Information Item Selection -- Sequencing an Adaptive Test Battery -- APPLICATIONS IN LARGE-SCALE TESTING PROGRAMS -- Adaptive Tests for Measuring Anxiety and Depression -- MATHCAT: A Flexible Testing System in Mathematics Education for Adults -- Implementing the Graduate Management Admission Test Computerized Adaptive Test -- Designing and Implementing a Multistage Adaptive Test: The Uniform CPA Exam -- A Japanese Adaptive Test of English as a Foreign Language: Developmental and Operational Aspects -- ITEM POOL DEVELOPMENT AND MAINTENANCE -- Innovative Items for Computerized Testing -- Designing Item Pools for Adaptive Testing -- Assembling an Inventory of Multistage Adaptive Testing Systems -- ITEM CALIBRATION AND MODEL FIT -- Item Parameter Estimation and Item Fit Analysis -- Estimation of the Parameters in an Item-Cloning Model for Adaptive Testing -- Detecting Person Misfit in Adaptive Testing -- The Investigation of Differential Item Functioning in Adaptive Tests -- MULTISTAGE AND MASTERY TESTING -- Multistage Testing: Issues, Designs, and Research -- Three-Category Adaptive Classification Testing -- Testlet-Based Adaptive Mastery Testing -- Adaptive Mastery Testing Using a Multidimensional IRT Model En línea: http://dx.doi.org/10.1007/978-0-387-85461-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33513 Ejemplares
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Título : Introduction to Variance Estimation Tipo de documento: documento electrónico Autores: Kirk M. Wolter ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2007 Colección: Statistics for Social and Behavioral Sciences, ISSN 2199-7357 Número de páginas: XIV, 450 p Il.: online resource ISBN/ISSN/DL: 978-0-387-35099-8 Idioma : Inglés (eng) Palabras clave: Statistics Statistical Theory and Methods Clasificación: 51 Matemáticas Resumen: We live in the information age. Statistical surveys are used every day to determine or evaluate public policy and to make important business decisions. Correct methods for computing the precision of the survey data and for making inferences to the target population are absolutely essential to sound decision making. Now in its second edition, Introduction to Variance Estimation has for more than twenty years provided the definitive account of the theory and methods for correct precision calculations and inference, including examples of modern, complex surveys in which the methods have been used successfully. The book provides instruction on the methods that are vital to data-driven decision making in business, government, and academe. It will appeal to survey statisticians and other scientists engaged in the planning and conduct of survey research, and to those analyzing survey data and charged with extracting compelling information from such data. It will appeal to graduate students and university faculty who are focused on the development of new theory and methods and on the evaluation of alternative methods. Software developers concerned with creating the computer tools necessary to enable sound decision-making will find it essential. Prerequisites include knowledge of the theory and methods of mathematical statistics and graduate coursework in survey statistics. Practical experience with real surveys is a plus and may be traded off against a portion of the requirement for graduate coursework. This second edition reflects shifts in the theory and practice of sample surveys that have occurred since the content of the first edition solidified in the early 1980’s. Additional replication type methods appeared during this period and have featured prominently journal publications. Reflecting these developments, the second edition now includes a new major chapter on the bootstrap method of variance estimation. This edition also includes extensive new material on Taylor series methods, especially as they apply to newer methods of analysis such as logistic regression or the generalized regression estimator. An introductory section on survey weighting has been added. Sections on Hadamard matrices and computer software have been substantially scaled back. Fresh material on these topics is now readily available on the Internet or from commercial sources. Kirk Wolter is a Senior Fellow at NORC, Director of the Center for Excellence in Survey Research, and Professor in the Department of Statistics, University of Chicago. He is a Fellow of the American Statistical Association and a Member of the International Statistical Institute. He is a past president of the International Association of Survey Statisticians and a past chair of the Survey Research Methods Section of the American Statistical Association. During the last 35 years, he has participated in the planning, execution, and analysis of large-scale complex surveys and has provided instruction in survey statistics both in America and around the world Nota de contenido: The Method of Random Groups -- Variance Estimation Based on Balanced Half-Samples -- The Jackknife Method -- The Bootstrap Method -- Taylor Series Methods -- Generalized Variance Functions -- Variance Estimation for Systematic Sampling -- Summary of Methods for Complex Surveys -- Hadamard Matrices -- Asymptotic Theory of Variance Estimators -- Transformations -- The Effect of Measurement Errors on Variance Estimation -- Computer Software for Variance Estimation -- The Effect of Imputation on Variance Estimation En línea: http://dx.doi.org/10.1007/978-0-387-35099-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34454 Introduction to Variance Estimation [documento electrónico] / Kirk M. Wolter ; SpringerLink (Online service) . - New York, NY : Springer New York, 2007 . - XIV, 450 p : online resource. - (Statistics for Social and Behavioral Sciences, ISSN 2199-7357) .
ISBN : 978-0-387-35099-8
Idioma : Inglés (eng)
Palabras clave: Statistics Statistical Theory and Methods Clasificación: 51 Matemáticas Resumen: We live in the information age. Statistical surveys are used every day to determine or evaluate public policy and to make important business decisions. Correct methods for computing the precision of the survey data and for making inferences to the target population are absolutely essential to sound decision making. Now in its second edition, Introduction to Variance Estimation has for more than twenty years provided the definitive account of the theory and methods for correct precision calculations and inference, including examples of modern, complex surveys in which the methods have been used successfully. The book provides instruction on the methods that are vital to data-driven decision making in business, government, and academe. It will appeal to survey statisticians and other scientists engaged in the planning and conduct of survey research, and to those analyzing survey data and charged with extracting compelling information from such data. It will appeal to graduate students and university faculty who are focused on the development of new theory and methods and on the evaluation of alternative methods. Software developers concerned with creating the computer tools necessary to enable sound decision-making will find it essential. Prerequisites include knowledge of the theory and methods of mathematical statistics and graduate coursework in survey statistics. Practical experience with real surveys is a plus and may be traded off against a portion of the requirement for graduate coursework. This second edition reflects shifts in the theory and practice of sample surveys that have occurred since the content of the first edition solidified in the early 1980’s. Additional replication type methods appeared during this period and have featured prominently journal publications. Reflecting these developments, the second edition now includes a new major chapter on the bootstrap method of variance estimation. This edition also includes extensive new material on Taylor series methods, especially as they apply to newer methods of analysis such as logistic regression or the generalized regression estimator. An introductory section on survey weighting has been added. Sections on Hadamard matrices and computer software have been substantially scaled back. Fresh material on these topics is now readily available on the Internet or from commercial sources. Kirk Wolter is a Senior Fellow at NORC, Director of the Center for Excellence in Survey Research, and Professor in the Department of Statistics, University of Chicago. He is a Fellow of the American Statistical Association and a Member of the International Statistical Institute. He is a past president of the International Association of Survey Statisticians and a past chair of the Survey Research Methods Section of the American Statistical Association. During the last 35 years, he has participated in the planning, execution, and analysis of large-scale complex surveys and has provided instruction in survey statistics both in America and around the world Nota de contenido: The Method of Random Groups -- Variance Estimation Based on Balanced Half-Samples -- The Jackknife Method -- The Bootstrap Method -- Taylor Series Methods -- Generalized Variance Functions -- Variance Estimation for Systematic Sampling -- Summary of Methods for Complex Surveys -- Hadamard Matrices -- Asymptotic Theory of Variance Estimators -- Transformations -- The Effect of Measurement Errors on Variance Estimation -- Computer Software for Variance Estimation -- The Effect of Imputation on Variance Estimation En línea: http://dx.doi.org/10.1007/978-0-387-35099-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34454 Ejemplares
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Título : Linear Models for Optimal Test Design Tipo de documento: documento electrónico Autores: Wim J. van der Linden ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2005 Colección: Statistics for Social and Behavioral Sciences, ISSN 2199-7357 Número de páginas: XXIV, 408 p. 44 illus Il.: online resource ISBN/ISSN/DL: 978-0-387-29054-6 Idioma : Inglés (eng) Palabras clave: Statistics Assessment Psychometrics for Social Science, Behavorial Education, Public Policy, and Law Assessment, Testing Evaluation Clasificación: 51 Matemáticas Resumen: This book begins with a reflection on the history of test design--the core activity of all educational and psychological testing. It then presents a standard language for modeling test design problems as instances of multi-objective constrained optimization. The main portion of the book discusses test design models for a large variety of problems from the daily practice of testing, and illustrates their use with the help of numerous empirical examples. The presentation includes models for the assembly of tests to an absolute or relative target for their information functions, classical test assembly, test equating problems, item matching, test splitting, simultaneous assembly of multiple tests, tests with item sets, multidimensional tests, and adaptive test assembly. Two separate chapters are devoted to the questions of how to design item banks for optimal support of programs with fixed and adaptive tests. Linear Models for Optimal Test Design, which does not require any specific mathematical background, has been written to be a helpful resource on the desk of any test specialist. Wim J. van der Linden is Professor of Measurement and Data Analysis, University of Twente, The Netherlands. His specialization is psychometric theory and methods, and he has been an active researcher of item response theory throughout his career. His current research is on test design, adaptive testing, test equating, and response-time modeling. Professor van der Linden is a past president of the Psychometric Society and a recipient of the NCME lifetime achievement award for his work on educational measurement Nota de contenido: Brief History of Test Theory and Design -- Formulating Test Specifications -- Modeling Test-Assembly Problems -- Solving Test-Assembly Problems -- Models for Assembling Single Tests -- Models for Assembling Multiple Tests -- Models for Assembling Tests with Item Sets -- Models for Assembling Tests Measuring Multiple Abilities -- Models for Adaptive Test Assembly -- Designing Item Pools for Programs with Fixed Tests -- Designing Item Pools for Programs with Adaptive Tests -- Epilogue En línea: http://dx.doi.org/10.1007/0-387-29054-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35162 Linear Models for Optimal Test Design [documento electrónico] / Wim J. van der Linden ; SpringerLink (Online service) . - New York, NY : Springer New York, 2005 . - XXIV, 408 p. 44 illus : online resource. - (Statistics for Social and Behavioral Sciences, ISSN 2199-7357) .
ISBN : 978-0-387-29054-6
Idioma : Inglés (eng)
Palabras clave: Statistics Assessment Psychometrics for Social Science, Behavorial Education, Public Policy, and Law Assessment, Testing Evaluation Clasificación: 51 Matemáticas Resumen: This book begins with a reflection on the history of test design--the core activity of all educational and psychological testing. It then presents a standard language for modeling test design problems as instances of multi-objective constrained optimization. The main portion of the book discusses test design models for a large variety of problems from the daily practice of testing, and illustrates their use with the help of numerous empirical examples. The presentation includes models for the assembly of tests to an absolute or relative target for their information functions, classical test assembly, test equating problems, item matching, test splitting, simultaneous assembly of multiple tests, tests with item sets, multidimensional tests, and adaptive test assembly. Two separate chapters are devoted to the questions of how to design item banks for optimal support of programs with fixed and adaptive tests. Linear Models for Optimal Test Design, which does not require any specific mathematical background, has been written to be a helpful resource on the desk of any test specialist. Wim J. van der Linden is Professor of Measurement and Data Analysis, University of Twente, The Netherlands. His specialization is psychometric theory and methods, and he has been an active researcher of item response theory throughout his career. His current research is on test design, adaptive testing, test equating, and response-time modeling. Professor van der Linden is a past president of the Psychometric Society and a recipient of the NCME lifetime achievement award for his work on educational measurement Nota de contenido: Brief History of Test Theory and Design -- Formulating Test Specifications -- Modeling Test-Assembly Problems -- Solving Test-Assembly Problems -- Models for Assembling Single Tests -- Models for Assembling Multiple Tests -- Models for Assembling Tests with Item Sets -- Models for Assembling Tests Measuring Multiple Abilities -- Models for Adaptive Test Assembly -- Designing Item Pools for Programs with Fixed Tests -- Designing Item Pools for Programs with Adaptive Tests -- Epilogue En línea: http://dx.doi.org/10.1007/0-387-29054-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35162 Ejemplares
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