HLP Lab Mini Course on Regression Methods
November 24 & 25, Copenhagen, Denmark
Goal of this workshop
To provide sufficient background and a platform for a discussion of common issues in linear and logistic regression fitting, including applications of multilevel models.
Some of the specific questions that you have sent me:
- Data distributions and transformations to increase normality
- How robust are LME's in the face of normality violations (short answer: relatively robust; there are worse violations)
- Evaluation of multiple logistic regression model
- also: "Precision measures" -- I assume this refers to tests of the quality of fit of a model?
- decision criteria to include or exclude variables. Why researchers sometimes keep insignificant predictors in the model?
- When to use mixed-effects models
- Conceptual background
November 17 |
Prepare |
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November 24 |
Linear and logistic regression |
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November 25 |
multilevel models |
Readings
In the sessions, I refer to the following readings:
Obligatory:
Analyzing Linguistic Data: A Practical Introduction to Statistics using R by Harald Baayen (2008). hardback ($97) paperback ($35) Complete electronic draft. Baa08.
Recommended:
Data Analysis Using Regression and Multilevel/Hierarchical Models by Gelman & Hill (2007). Online resources. G&H07.
Also useful:
Introductory Statistics with R by Peter Dalgaard (2004). Online resources. Electronic copy through U of R libraries. Dal04.
Categorical Data Analysis by Alan Agresti (2002). Online resources. Agr02.
R packages
Please make sure you have the following R packages installed before you attend the workshop. Please also note that I will assume R version 2.7.1, and it's easier if you have that same version (or at least that recent a version).