We consider a situation where r raters select subsets from a set of n items by marking them by ‘0’ or ‘1’, as in classification problems, approval voting and in general subset voting. The number r of raters is small in comparison to the number n of items. We intend to classify the raters, to understand their behavior and to go beyond the possibilities of classical statistical methods such as Fleiss’ kappa, cluster analysis or latent class analysis. We use a non-parametric set-theoretic approach, which is natural for the given dichotomous setting. We recommend the determination of a set-theoretic mean, the Vorob’ev expectation, to play a role similar to the classical mean of a sample. In particular, we use distances of the raters’ subsets from the mean as characteristics of the individual raters. Furthermore, we introduce a new measure of conformity of a given rater with all others, characterizing the extent to which the rater deviates from the whole group of raters. We demonstrate the use of these methods in a case study, where the raters are forest managers and the items are trees in a forest thinning experiment. Our aim is to contribute to an understanding of the psychological processes involved, when forest managers mark trees for forest operations.