# 2013 LSA Summer Institute class on ''Mixed effect regression''

The slides provided below can be used in teaching with proper acknowledgment of the source. If you need any of the source code, let me know. All slides were created with latex and knitr in RStudio, so that the code shown is also the code used to generate the content of the slides. The R scripts extracted from .Rnw files are provided along with the slides. **By downloading the slides you agree to inform me of any errors you find on them ;). Thank you.**

This http://lsa2013.lsa.umich.edu/2012/05/mixed-effect-models/ was held at the 2013 LSA Summer Institute.

## Class descriptions

With increasing use of quantitative behavioral data, statistical data analysis has rapidly become a crucial part of linguistic training. Linguistic data analysis is often particularly challenging because (i) the relevant data are often sparse, (ii) the data sets are often unbalanced with regard to the variables of interest, and (iii) data points are typically not sampled independently of each other, making it necessary to account for—possibly hierarchical—grouping structures (clusters) in the data. This course provides an introduction to several advanced data analyses techniques that help us to address these challenges. We will focus on the Generalized Linear Model (GLM) and Generalized Linear Mixed Model (GLMM) – what they are, how to fit them, what common ‘traps’ to be aware of, how to interpret them, and how to report and visualize results obtained from these models. GLMs and GLMMs are a powerful tool to understand complex data, including not only whether effects are significant but also what direction and shape they have. GLMs have been used in corpus and sociolinguistics since at least the 60s. GLMMs have recently been introduced to language research through corpus- and psycholinguistics. They are rapidly becoming a popular data analysis techniques in these and other fields (e.g. sociolinguistics).

In this course, I will assume a basic statistical background and a conceptual understanding of at least linear regression.

## Materials

### Lectures

Course overview PDF

Day 3: Beyond Linear Models PDF

Day 4: Tools for data analysis, exploration, and transformation: plyr and reshape2 PDF Script

Day 6: Common Issues and Solutions in GLM/GLMM modeling PDF

Day 6: Reporting Results PDF

### Auxiliary lectures

Auxiliary lecture 1: Coding Categorical Predictors & Auxiliary lecture 2: Interactions PDF

### Problem Set

### Assorted additional scripts

Additional scripts scripts zip