Bayesian Aiken’s V: Credibility Intervals and Probabilistic Rules for Content Validity

Panels assembled to judge content validity seldom exceed seven members. The statistical apparatus available for these ratings, however, developed largely without small samples in mind. This paper introduces a Bayesian formulation of the Aiken V coefficient that rests on Dirichlet-Multinomial assumptions. One expert rating an item produces one categorical datum, nothing else. The Beta-Binomial alternative, examined below for comparison, converts each rating into multiple pseudo-observations by treating scale increments as if they were independent binary trials. Simulation experiments across 500 replications suggest that Dirichlet credible intervals track nominal coverage more closely when panels number fewer than seven, though at the cost of wider intervals. Conditions favoring each approach are laid out, R code is supplied, and reporting conventions foregrounding uncertainty in validity decisions are proposed.