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Titlebook: New Developments in Psychometrics; Proceedings of the I H. Yanai,A. Okada,J. J. Meulman Conference proceedings 2003 Springer Japan 2003 Fac

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书目名称New Developments in Psychometrics
副标题Proceedings of the I
编辑H. Yanai,A. Okada,J. J. Meulman
视频video
概述Contains many papers on various methods of psychometrics such as structural equation model and Item response theory recently developed which enables readers to handle complicated data so as to clarify
图书封面Titlebook: New Developments in Psychometrics; Proceedings of the I H. Yanai,A. Okada,J. J. Meulman Conference proceedings 2003 Springer Japan 2003 Fac
描述.At the International Meeting of the Psychometric Society in Osaka, Japan, more than 300 participants from 19 countries gathered to discuss recent developments in the theory and application of psychometrics. This volume of proceedings includes papers on methods of psychometrics such as the structural equation model and item response theory. The book is in eight major sections: keynote speeches and invited lectures; structural equation modeling and factor analysis; IRT and adaptive testing; multivariate statistical methods; scaling; classification methods; and independent and principal component analysis. The 80 papers collected here provide a valuable source of information for all who are concerned with psychometrics, mathematical and statistical applications, and data analysis in psychological and behavioral sciences..
出版日期Conference proceedings 2003
关键词Factor analysis; classification; data analysis; principal component analysis; structural equation modeli
版次1
doihttps://doi.org/10.1007/978-4-431-66996-8
isbn_softcover978-4-431-66998-2
isbn_ebook978-4-431-66996-8
copyrightSpringer Japan 2003
The information of publication is updating

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On Analysis of Nonlinear Structural Equation Modelsated multiple integrals involved, the E-step is completed by a Metropolis-Hastings algorithm. The M-step can be completed efficiently by simple conditional maximization. Convergence is monitored by bridging sample and standard errors estimates are obtained via Louis’s formula. The methodology is illustrated with a real example.
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Estimating the Statistical Power in Small Samples by Empirical Distributionssumption may lead to wrong results. We will use resampling methods, like the parametric bootstrap, to investigate the empirical distribution of certain statistics. On the basis of this empirical distribution we can investigate the power of some tests, even in cases with small samples. An example will be given.
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Identifying Influential Observations for Loadings in Factor Analysise difference between two different factors, instead of the change of one factor before and after an observation is omitted. The switching problem is studied by investigating the factor loadings pattern, variances explained and factor scores.
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The Respondent-Generated Intervals Approach to Sample Surveys: From Theory to Experimentar?” Also, “What is the largest value the true value is likely to be, and what is the smallest value the true value is likely to be?” Bayesian and other estimators are proposed. We describe several empirical studies that have used, and are using the RGI protocol.
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Vectors and Matrices in Psychometrics with Special Emphasis on Generalized Inverse and Projection Maps on multiple, canonical and partial canonical correlations among some sets of residual variables are discussed. Our emphasis given in this paper is that uses of vectors and matrices allow one to understand the complex problems in psychometrics quite easily without making use of complicated algebra.
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