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Titlebook: Quantitative Psychology; The 88th Annual Meet Marie Wiberg,Jee-Seon Kim,Heungsun Hwang,Hao Wu,Tr Conference proceedings 2024 The Editor(s)

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发表于 2025-3-21 16:10:43 | 显示全部楼层 |阅读模式
书目名称Quantitative Psychology
副标题The 88th Annual Meet
编辑Marie Wiberg,Jee-Seon Kim,Heungsun Hwang,Hao Wu,Tr
视频video
概述Presents research in the psychometrics field, with meaningful applications.Covers a broad array of topics within the psychometrics and statistics area.Exmaines item response theory, cognitive diagnost
丛书名称Springer Proceedings in Mathematics & Statistics
图书封面Titlebook: Quantitative Psychology; The 88th Annual Meet Marie Wiberg,Jee-Seon Kim,Heungsun Hwang,Hao Wu,Tr Conference proceedings 2024 The Editor(s)
描述.This book includes presentations given at the 88th annual meeting of the Psychometric Society, held in Maryland, USA on July 24–28, 2023...The proceeding covers a diverse set of psychometric topics. The topics include, but are not limited to item response theory, cognitive diagnostic models, Bayesian estimation, validity and reliability issues, and several applications within different fields. The authors are from all over the world, they work in different psychometrics areas, as well as having diverse professional and academic experiences..
出版日期Conference proceedings 2024
关键词Informatics; Conference Proceedings; Research; Applications; IMPS 2023; psychometrics; item response theor
版次1
doihttps://doi.org/10.1007/978-3-031-55548-0
isbn_softcover978-3-031-55550-3
isbn_ebook978-3-031-55548-0Series ISSN 2194-1009 Series E-ISSN 2194-1017
issn_series 2194-1009
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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978-3-031-55550-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Quantitative Psychology978-3-031-55548-0Series ISSN 2194-1009 Series E-ISSN 2194-1017
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https://doi.org/10.1007/978-3-031-55548-0Informatics; Conference Proceedings; Research; Applications; IMPS 2023; psychometrics; item response theor
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A Family of Discrete Kernels for Presmoothing Test Score Distributions,core distributions are discrete and may have irregularities, it has been common practice to use presmoothing techniques to correct for such irregularities of the score distributions. A common way to conduct presmoothing has been to use log-linear models..In this chapter, we introduce a novel class o
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Priors in Bayesian Estimation Under the Graded Response Model,s of the prior distributions. A Bayesian estimation method, Gibbs sampling, was compared with the marginal Bayesian estimation method using empirical data. The effects of the priors and their specifications on both item and ability parameter estimates are demonstrated. Issues in Bayesian estimation,
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A Two-Stage Approach to a Latent Variable Mixed-Effects Location-Scale Model,scale models include distinct variance models to permit study of heterogeneity of within- and between-subject variation. Recent developments have extended the model to address measurement error in the longitudinal response. Accounting for variation in the response that is due to measurement error is
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