A Comparison of the Spatial Arrangement Method and the Total-Set Pairwise Rating Method for Obtaining Similarity Data in the Conceptual Domain

Bibliographic Data

ID19291347
AuthorsSteven Verheyen (0000-0002-6778-6744, Erasmus University Rotterdam, corresponding author), Anne White (0000-0002-4431-6707, KU Leuven), Gert Storms (0000-0002-4100-8725, KU Leuven)
Year2022
Volume57
Issue2-3
Pages356-384
Publication date2022-05-04
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueMultivariate Behavioral Research (JOURNAL)
Journal identifiersISSN: 0027-3171 • E-ISSN: 1532-7906
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00273171.2020.1857216
PMID33327792
OpenAlexW3112377065
LanguageEN
Citations received1
References cited66

We compare two methods for obtaining similarity data in the conceptual domain. In the Spatial Arrangement Method (SpAM), participants organize stimuli on a computer screen so that the distance between stimuli represents their perceived dissimilarity. In Total-Set Pairwise Rating Method (PRaM), participants rate the (dis)similarity of all pairs of stimuli on a Likert scale. In each of three studies, we had participants indicate the similarity of four sets of conceptual stimuli with either PRaM or SpAM. Studies 1 and 2 confirm two caveats that have been raised for SpAM. (i) While SpAM takes significantly less time to complete than PRaM, it yields less reliable data than PRaM does. (ii) Because of the spatial manner in which similarity is measured in SpAM, the method is biased against feature representations. Despite these differences, averaging SpAM and PRaM dissimilarity data across participants yields comparable aggregate data. Study 3 shows that by having participants only judge half of the pairs in PRaM, its duration can be significantly reduced, without affecting the dissimilarity distribution, but at the cost of a smaller reliability. Having participants arrange multiple subsets of the stimuli does not do away with the spatial bias of SpAM

Aggregate (composite) · Data mining · Data set · Feature (linguistics) · Image (mathematics) · Likert scale · Pairwise comparison · Pattern recognition (psychology) · Reliability (semiconductor) · Set (abstract data type) · Similarity (geometry) · Statistics · Advanced Text Analysis Techniques · Artificial Intelligence · Computer Science · Mathematics · Multi-Criteria Decision Making

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