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| === Notes on the reading === In G&H07, the first example that leads to the motivation of multilevel models is a logit model, which we haven't yet talked about. Just ignore that detail and focus on the conceptual argument made in that section. Think of the logit model as predicting the likely outcome (here: treatment success vs. failure) given the predictors we put into the model, just like for linear regression. |
Session 3: Multilevel (a.k.a. Hierarchical, a.k.a. Mixed ) Linear Models
June 10 2008
Reading
G&H07 |
Sections 1.1-1.3 (pp. 1-3) |
Intro, examples, motivation |
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Chapter 11 (pp. 237-248) |
Multilevel structures |
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Chapter 12 (pp. 251-277) |
Multilevel linear models: the basics |
Baa08 |
Chapter 7 (pp. 263-282) |
Grouped data, functions, lmer |
Notes on the reading
In G&H07, the first example that leads to the motivation of multilevel models is a logit model, which we haven't yet talked about. Just ignore that detail and focus on the conceptual argument made in that section. Think of the logit model as predicting the likely outcome (here: treatment success vs. failure) given the predictors we put into the model, just like for linear regression.
Assignments
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