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Conflated Random Slopes in Multilevel Analysis
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DOI：10.1080/00273171.2026.2673276.png)
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Abstract
En 中文
Multilevel analysis is widely used for hierarchical data, enabling researchers to evaluate how individual- and cluster-level attributes relate to individual outcomes. A well-known concern involves conflated or smushed fixed slopes (Hoffman & Walters, Citation2022), which arise when a level-1 predictor that varies systematically across clusters is modeled with a single fixed effect, thereby blending its within- and between-cluster components.
Keywords:
Multilevel analysis
Conflated fixed slopes
Random slopes
Hierarchical data
Level-1 predictor
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