Context Matters: The Importance of Investigating Random Effects in Hierarchical Models for Early Childhood Education Researchers.

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.
Competing Interests: Declarations of interest: none.