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The Semantic Priming Project was a large-scale effort to provide normed priming data of nearly 2000 concepts (Hutchison et al., 2013), and this data was combined with other lexical and relatedness variables in order to investigate how to predict the variability in priming effects. Word length, frequency, neighborhood/set sizes, and part of speech were used to predict priming effects, along with associative, semantic, and corpora-based relatedness measures. Across lexical decision and naming tasks, we found that priming was most commonly related to word frequency and neighborhood size at the lexical level, associative overlap and set size, semantic feature overlap, and a corpora-based pointwise mutual information measure. Predictive variables were mixed across stimulus onset asynchrony and type of prime-target relatedness portraying a medium effect size prediction, displaying the difficulty in capturing the variability in simple priming effects. Item versus subject level regression approaches will also be discussed.