Hierarchical Linear Model
Updated 2026-08-10
INTRODUCTION
English translation pending.
CORE DEFINITION
Also called a multilevel model, this approach handles data with a nested structure, such as students within classes within schools, where an individual outcome is shaped both by personal variables and by group-level variables. The mechanism is variance decomposition: total variation is split into within-group and between-group components, each modeled separately, so a group effect is not silently relabeled as individual ability and standard errors stay honest.
SCAFFOLDING EFFECT
Reduce cognitive load
- Respect the nesting: model the hierarchy instead of pooling everyone into one flat regression. - Split the variance: separate within-group from between-group variation. - Let effects vary: allow intercepts and slopes to differ across groups, then test cross-level interactions.
Anchor fast decisions
Ignoring the nesting pools people from different groups into one error term, which understates uncertainty and produces false significance. Decomposing the variance lets the model estimate how much of the outcome belongs to the group and how much to the person, so school effects are not misread as student talent.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Multilevel_modelverified
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