Cumulative Progression Eye-movement Data: Traditional measures of reading difficulty require the researcher to divide the sentence into discrete, categorical “regions of interest” over which to calculate reading-time dependent measures. This technique has been immensely fruitful, but introduces at least two concerns: (1) there is considerable variability in how researchers may define regions of interest (i.e. researcher degrees of freedom); (2) effects which may be diffusely spread across several regions of text are unlikely to be uncovered by this technique. Cumulative progression analyses address these concerns by providing a continuous measure of reading difficulty (as indexed by rate of progression) after some critical inflection point. However, while this measure has the potential to be highly informative, there is not yet an agreed upon analysis technique, nor is its relation to more traditional measures well understood. Working with Dr. Klinton Bicknell, I have been engaged in redressing these gaps by replicating several well known effects from sentence processing for a direct comparison of their profile in traditional measures and cumulative progression data. In addition, we propose a non-parametric means of analyzing this continuous measure: the cluster-mass permutation test previously developed for continuous ERP data (Maris & Oostenveld 2007).