In this session we will simulate participants’ item-level response data, where the observed response is binary (0 = incorrect, 1 = correct). We will look at this in the context of a multilevel design, where the dataset includes multiple observations of the same participants and the same items. To do this, we will use a logistic multilevel model to describe the response generating process. Building from a simple model of the response generating process, we will look at including single and multiple predictors which change the log odds of observing a correct response (i.e. effects). Random intercept and effect variances, and the covariance between these, will also be explored. We will also look at how we could estimate the statistical power to detect an effect of interest according to our defined model, for a given experimental design. The freely available programs R and R Studio will be used to code and run the simulations. All code will be available for the simulations in this session.
