# Methods

## Longitudinal data analysis and multilevel modelling

Much of the data used in the social sciences consist of a number of observations from some larger group. Often time is an important dimension. This workshop will introduce methods for running multilevel models and for analyzing time series and panel data. Multilevel methods will be introduced from a Bayesian perspective. However, students will be exposed to both Bayesian and frequentist approaches.

Advanced computer or mathematical skills are not necessary, although basic knowledge of regression is strongly recommended. Knowledge of R would be useful. You can learn R by watching the recording of the R workshop I gave in December 2013. Here's the __link__.

If possible, bring a laptop to the workshop. If you want to follow all the examples, you should install R, RStudio, JAGS, and the R packages listed below. You can also download the R code file, containing code and explanations of everything we cover, and the datasets we will use. You can get the slides below.

You can download R __here__ and RStudio __here__.

For JAGS, go to __this page__

You also need to install R2jags, coda, R2WinBUGS, lattice, and rjags, in that order. You should do so in R by typing the following in R:

install.packages("R2jags", dependencies = TRUE)

install.packages(“coda", dependencies = TRUE)

install.packages("R2WinBUGS", dependencies = TRUE)

install.packages(“lattice", dependencies = TRUE)

install.packages(“rjags", dependencies = TRUE)

__R code__

__Dataset 1__

__Dataset 2__

__Dataset 3__

__Slides__