In an open card sorting study of 356 facial photographs, each of 25 participants created an unconstrained number of piles. We consider all 63,190 possible pairs of photos: if both photos are in the same pile for a participant, we consider them as rated similar; otherwise we consider them as rated dissimilar. Each pair of photos is an attribute in an information system where the participants are the objects. We consider whether the attribute values permit accurate classification of the objects according to binary decision classes, without loss of generality. We propose a discernibility coefficient to measure the support of an attribute for classification according to a given decision class pair. We hypothesize that decision class pairs with the support of many attributes are more representative of the data than those with the support of few attributes. We present some computational experiments and discuss opportunities for future work. © 2013 Springer-Verlag.
CITATION STYLE
Hepting, D. H., & Almestadi, E. H. (2013). Discernibility in the analysis of binary card sort data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8170 LNAI, pp. 380–387). https://doi.org/10.1007/978-3-642-41218-9_41
Mendeley helps you to discover research relevant for your work.