分层线性建模的原因:提醒

IF 2.2 4区教育学 Q1 Social Sciences

Journal of Experimental Education Pub Date : 1999-01-01 DOI:10.1080/00220979909598496

Jianjun Wang

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引用次数: 17

摘要

摘要从固定效应和随机效应两方面对层次线性模型(HLM)在多水平数据分析中的界定进行了研究。作者用地方和国家层面的例子来说明HLM和虚拟变量回归的正确应用。对于分层数据不需要HLM的情况提出了警告。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Reasons for Hierarchical Linear Modeling: A Reminder

Abstract Delimitations of hierarchical linear modeling (HLM) were examined in terms of fixed and random effects in multilevel data analyses. The author used examples at the local and national levels to illustrate proper applications of HLM and dummy variable regression. Cautions are raised regarding circumstances under which hierarchical data do not need HLM.

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来源期刊

Journal of Experimental Education Multiple-

CiteScore

6.70

自引率

0.00%

发文量

期刊介绍： The Journal of Experimental Education publishes theoretical, laboratory, and classroom research studies that use the range of quantitative and qualitative methodologies. Recent articles have explored the correlation between test preparation and performance, enhancing students" self-efficacy, the effects of peer collaboration among students, and arguments about statistical significance and effect size reporting. In recent issues, JXE has published examinations of statistical methodologies and editorial practices used in several educational research journals.