使用 R 对大规模评估数据拟合多层次模型时使用可信值

Q1 Engineering

Visualization in Engineering Pub Date : 2024-03-07 DOI:10.1186/s40536-024-00192-0

Francis L. Huang

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引用次数: 0

摘要

在过去的十年中，大规模评估（LSA）在教育领域的应用日益增多，但使用 R 语言的多层次模型（MLM）对 LSA 进行分析却很有限。使用有限的一个原因可能是在 LSA 数据的多层次分析中纳入可信值和加权分析的复杂性。我们在 R 中提供了额外的函数，扩展了 WeMix（Bailey 等人，2023 年）软件包的功能，允许自动汇集可信值。此外，我们还提供了使用可信值进行模型比较的函数，以及将输出导出为不同格式（如 Word、html）的功能。

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Using plausible values when fitting multilevel models with large-scale assessment data using R

The use of large-scale assessments (LSAs) in education has grown in the past decade though analysis of LSAs using multilevel models (MLMs) using R has been limited. A reason for its limited use may be due to the complexity of incorporating both plausible values and weighted analyses in the multilevel analyses of LSA data. We provide additional functions in R that extend the functionality of the WeMix (Bailey et al., 2023) package to allow for the automatic pooling of plausible values. In addition, functions for model comparisons using plausible values and the ability to export output to different formats (e.g., Word, html) are also provided.

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来源期刊

Visualization in Engineering Engineering-Engineering (miscellaneous)

CiteScore

8.60

自引率

0.00%

发文量

期刊介绍： Visualization in Engineering publishes original research results regarding visualization paradigms, models, technologies, and applications that contribute significantly to the advancement of engineering in all branches, including medical, biological, civil, architectural, mechanical, manufacturing, industrial, aerospace, and meteorological engineering and beyond. The journal solicits research papers with particular emphasis on essential research problems, innovative solutions, and rigorous validations.