This paper highlights the importance of examining individual, classroom, and school-level variables simultaneously in early childhood education research. While it is well known that Hierarchical Linear Modeling (HLM) in school-based studies can be used to account for the clustering of students within classrooms or schools, less known is that HLM can use random effects to investigate how higher-level factors (e.g., effects that vary by school) moderate associations between lower-level factors. This possible moderation can be detected even if higher-level data are not collected. Despite this important use of HLM, a clear resource explaining how to test this type of effect is not available for early childhood researchers. This paper demonstrates this use of HLM by presenting three analytic examples using empirical early childhood education data. First, we review school-level effects literature and HLM concepts to provide the rationale for testing cross-level moderation effects in education research; next we do a short review of literature on the variables that will be used in our three examples (viz., teacher beliefs and student socioemotional behavior); next we describe the dataset that will be analyzed; and finally we guide the reader step-by-step through analyses that show the presence and absence of fixed effects of teacher beliefs on student social outcomes and the erroneous conclusions that can occur if school-level moderation (i.e., random effects) tests are excluded from analyses. This paper provides evidence for the importance of testing for how teachers and students impact each other as a function of school differences, shows how this can be accomplished, and highlights the need to examine random effects of clustering in educational models to ensure the full context is accounted for when predicting student outcomes.

中文翻译：

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背景很重要：研究分层模型中的随机效应对幼儿教育研究人员的重要性


本文强调了在幼儿教育研究中同时检查个人、课堂和学校层面变量的重要性。虽然众所周知，基于学校的研究中分层线性建模 （HLM） 可用于解释教室或学校内学生的聚集，但鲜为人知的是，HLM 可以使用随机效应来研究高级因素（例如，因学校而异的效应）如何调节较低级别因素之间的关联。即使未收集更高级别的数据，也可以检测到这种可能的审核。尽管 HLM 有如此重要的用途，但幼儿研究人员无法获得解释如何测试此类效果的明确资源。本文通过使用实证幼儿教育数据提供三个分析示例来演示 HLM 的这种使用。首先，我们回顾了学校层面的效果文献和 HLM 概念，为在教育研究中测试跨层面的调节效应提供了基本原理;接下来，我们对将在我们的三个示例中使用的变量（即教师信念和学生社会情感行为）的文献进行简短回顾;接下来，我们描述将要分析的数据集;最后，我们引导读者逐步完成分析，这些分析显示了教师信念对学生社会结果的固定影响的存在和不存在，以及如果从分析中排除学校层面的节制（即随机效应）测试，可能会出现的错误结论。 本文提供了证据，证明了测试教师和学生如何根据学校差异相互影响的重要性，展示了如何做到这一点，并强调了检查教育模型中聚类的随机效应的必要性，以确保在预测学生成绩时考虑到完整的背景。