Corkins Clarissa M, Harrist Amanda W, Washburn Isaac J, Hubbs-Tait Laura, Topham Glade L, Swindle Taren
Human Development and Family Science, Oklahoma State University, Stillwater, OK 74078, USA.
Department of Applied Human Sciences, Kansas State University, Manhattan, KS 66506, USA.
Early Child Res Q. 2025;70:178-186. doi: 10.1016/j.ecresq.2024.09.007. Epub 2024 Oct 17.
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.
本文强调了在幼儿教育研究中同时考察个体、课堂和学校层面变量的重要性。虽然在校本研究中，众所周知分层线性模型（HLM）可用于解释学生在课堂或学校内的聚类情况，但鲜为人知的是，HLM可以使用随机效应来研究更高层次的因素（例如，因学校而异的效应）如何调节较低层次因素之间的关联。即使没有收集更高层次的数据，这种可能的调节作用也可以被检测到。尽管HLM有这种重要用途，但对于幼儿教育研究人员来说，尚无一份清晰的资源来解释如何检验这类效应。本文通过呈现三个使用幼儿教育实证数据的分析示例，展示了HLM的这种用途。首先，我们回顾学校层面效应的文献和HLM概念，为在教育研究中检验跨层次调节效应提供理论依据；接下来，我们简要回顾一下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；然后我们描述将要分析的数据集；最后，我们逐步引导读者进行分析，这些分析表明了教师信念对学生社会成果的固定效应的存在与否，以及如果在分析中排除学校层面的调节（即随机效应）检验可能得出的错误结论。本文为检验教师和学生如何因学校差异而相互影响的重要性提供了证据，展示了如何做到这一点，并强调了在教育模型中考察聚类的随机效应的必要性，以确保在预测学生成果时考虑到完整的背景情况。
