CAML—Maximum likelihood consensus analysis

Consensus analysis enables estimation of individual differences in competencies and response tendencies when answer keys to dichotomous forced-choice questions are unknown. CAML, a set of functions written in R, implements maximum likelihood estimation for the general Condorcet model that underlies consensus analysis. CAML avoids problems of alternative approaches that have often rendered consensus analysis impractical or unfeasible in the past. It provides (1) measures of model fit, (2) a measure of consensus, (3) point and interval estimates of competencies and response tendencies, and (4) an estimate of the unknown answer key. The present article describes the general Condorcet model, the CAML algorithms, and the handling of the software. In addition, the validity of CAML results is tested in a recognition memory study using selective experimental manipulations of the parameters. The results show that CAML works very well in practice and provides valid estimates of competencies, response tendencies, and answer keys.