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

被引：8

作者：

Huang, Francis L. ^{[1
,2
]}

机构：

[1] Univ Missouri, 16 Hill Hall, Columbia, MO 65211 USA

[2] Univ Missouri, Missouri Prevent Sci Inst, 16 Hill Hall, Columbia, MO 65211 USA

来源：

LARGE-SCALE ASSESSMENTS IN EDUCATION | 2024年 / 12卷 / 01期

关键词：

R; Multilevel models; Plausible values; Weights; MULTIPLE IMPUTATION; SECONDARY ANALYSIS; SAMPLING WEIGHTS; MISSING-DATA;

D O I：

10.1186/s40536-024-00192-0

中图分类号：

G40 [教育学];

学科分类号：

040101 ; 120403 ;

摘要：

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.

引用

页数：15

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