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Using edit distance to analyze card sorts
Author(s) -
Deibel Katherine,
Anderson Richard,
Anderson Ruth
Publication year - 2005
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2005.00304.x
Subject(s) - computer science , terminology , sort , partition (number theory) , card sorting , metric (unit) , information retrieval , similarity (geometry) , data mining , software , measure (data warehouse) , data science , natural language processing , artificial intelligence , philosophy , linguistics , operations management , mathematics , management , combinatorics , programming language , economics , image (mathematics) , task (project management)
Card sorts are a knowledge elicitation technique in which participants are given a collection of items and are asked to partition them into groups based on their own criteria. Information about the participant's knowledge structure is inferred from the groups formed and the names used to describe the groups through various methods ranging from simple quantitative statistical measures (e.g. co‐occurrence frequencies) to complex qualitative methods (e.g. content analysis on the group names). This paper introduces a new technique for analyzing card sort data that uses quantitative measures to discover rich qualitative results. This method is based upon a distance metric between sorts that allows one to measure the similarity of groupings and then look for clusters of closely related sorts across individuals. By using software for computing these clusters, it is possible to identify common concepts across individuals, despite the use of different terminology.