Abstract
Without internal validity, experimental data are uninterpretable. With intensive designs, most methods presented to quantify a design's internal validity have been subject to criticism. A probabilistic model of intensive designs is presented that demonstrates the high degree of internal validity of these designs without relying on adaptations from traditional inferential statistics. Where the experimenter is able to conform to the restrictions of the model, the equations provide an estimation of internal validity for either reversal or multiple-baseline designs. More importantly, the model provides mathematical bases for some of the common recommendations and design considerations in intensive research (such as the desirability of within-subject replications and of four or more multiple baselines).