Abstract
Most studies addressing lexical processing make use of factorial designs. For many re-searchers in this field of inquiry, a real experiment is a factorial experiment. Methods such as regression and factor analysis would not allow for hypothesis testing and would not contribute substantially to the advancement of scientific knowledge. Their use would be restricted to exploratory studies at best. This paper is an apology coming to the defense of regression designs for experiments including lexical distributional variables as predictors. In studies of the mental lexicon, we often are dealing with two kinds of predictors, to which I will refer as treatments and covariates. Stimulus-onset asynchrony (soa) is an example of a treatment. If we want to study the effect of a long versus a short soa, it makes sense to choose sensible values, say 200 ms versus 50 ms, and to run experiments with these two settings. If the researcher knows that the effect of soa is linear, and that it can be administered independently of the intrinsic properties of the items, then the optimal design testing for an effect of soa is factorial. One would loose power by using a regression design testing for an effect at a sequence of SOA intervals, say 50, 60, 70,..., 200 ms. This advantage of sampling at the extremes is well-known (see, e.g., Crawley, 2002, p. 67): the