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Everything about Multilevel model

Type of statistical model

Multilevel models are statistical models of parameters that vary at more than one level. An example could be a model of student performance that contains measures for individual students as well as measures for classrooms within which the students are grouped. These models are also known as hierarchical linear models, linear mixed-effect models, mixed models, nested data models, random coefficient, random-effects models, random parameter models, or split-plot designs. These models can be seen as generalizations of linear models, although they can also extend to non-linear models. These models became much more popular after sufficient computing power and software became available. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Multilevel modelA 5-minute orientation
  2. Skim a recent paper: ML-GAT:A Multilevel Graph Attention Model for Stock PredictionInfluential work since 2022
  3. Pick up a classic book: Multilevel statistical modelsThe most-reprinted book on the subject
  4. Read the landmark paper: brms : An R Package for Bayesian Multilevel Models Using StanCited 9,137 times
  5. Go sideways: Nonlinear mixed-effects modelA closely related topic
  6. Go sideways: Vector generalized linear modelA closely related topic

📖 Definitions

No dictionary entry for this exact phrase.

🗂️ The subject at a glance

  1. Level 1 regression equation
  2. Level 2 regression equation
  3. Types of models
  4. Assumptions
  5. Statistical tests
  6. Statistical power
  7. Applications
  8. Alternative ways of analyzing hierarchical data
  9. Error terms
  10. Bayesian nonlinear mixed-effects model

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics