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Advanced Statistics: Generalized Additive Mixed Models (Workshop)

DateMonday, 3rd April 2017

veranstalter: Dr. Vincent Porretta
ansprechpartner: Luke Bradley
institution: HPSL
language: Englisch
location institution: Freiburg
date_raw: April 3rd (9:30-18:00)
date_sort: 03.04.2017, 00:00:00


Vincent Porretta of the University of Alberta will lead a day-long workshop entitled “Advanced Statistics: Generalized Additive Mixed Models” on Monday, April 3rd, between the hours of 9:30 and 18:00, in the Großer Sitzungssaal (Werthmannstraße 8). The event is intended to provide a hands-on introduction to the use of Generalized Additive Mixed Models in (psycho)linguistic research, building on existing knowledge of linear mixed-effects modelling.

Aside from the fact that attendants are expected to have some experience fitting linear mixed models in R, dont let the “advanced” in the title put you off! The day is sure to include useful insights theoretical and practical, for both early-stage graduate students and more experienced researchers—and this through the prism of a promising and powerful statistical modelling tool bound to continue growing in popularity throughout linguistics and psycholinguistics.

As usual with GRK workshops, we assume that participants would like to apply the material in their own research as speedily as possible. To facilitate this, Vincent has asked that everyone bring:

1. A dataset (preferably already in R-readable format). This can be any type of data that you happen to be working with (e.g., RTs, gaze data, EEG, accuracy, corpus data, etc). 

2. A short blurb (a couple of sentences) on the research question and basic design of the study. 

3. Brief description of the response (i.e., dependent) variable and how it was collected.

4. Brief description (or list) of variables of interest and control variables. Importantly, this will include how they have been operationalized in the study (i.e., continuous, categorical, etc). 

5. A short blurb on the current approach for the planned (or already executed) analysis.