Chapter 4: Principles of statistical analyses: old and new tools Franziska Kretzschmar1,2 & Phillip M. Alday3 1 CRC 1252 Prominence in Language, University of Cologne, Cologne, Germany 2 Leibniz-Institute for the German Language, Mannheim, Germany 3 Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands running head: principles of statistical analyses Corresponding author: Phillip M. Alday
[email protected] To appear in: Grimaldi, M., Y. Shtyrov, & E. Brattico, (eds.), Language Electrified. Techniques, Methods, Applications, and Future Perspectives in the Neurophysiological Investigation of Language. Springer. 1 Abstract The present chapter provides an overview of old and new statistical tools to analyze electro- physiological data on language processing. We will first introduce the very basic tenets of ex- perimental designs and their intimate links to statistical design. Based on this, we introduce the analysis of variance (ANOVA) approach which has been the classical statistical tool to analyze event-related potentials (ERPs) in language research. After discussing the merits and disadvantages of the approach, we focus on introducing mixed-effects regression models as a viable alternative to traditional ANOVA analyses. We close with an overview of future direc- tions that mixed-effects modeling opens up for language researchers using ERPs or other elec- trophysiological measures. Key words: Experimental design, analysis of variance, mixed-effects models, ERPs 2 1. Introduction Electrophysiological research on language mainly uses experiments in highly controlled laboratory settings, with the goal of finding systematic relationships between some linguistic manipulation (the independent variable) and participants’ brain activity (the dependent vari- able). Using inferential statistics, we can infer how the systematic relationship between inde- pendent and dependent variables in the sample data (with known characteristics) will general- ize to the population level (with unknown characteristics).