Abstract
Diagnostic theories are fundamental to Information Systems practice and are represented in trees. One way of creating diagnostic trees is by employing independent experts to construct such trees and compare them. However, good measures of similarity to compare diagnostic trees have not been identified. This paper presents an analysis of the suitability of various measures of association to determine the similarity of two diagnostic trees using bootstrap simulations. We find that three measures of association, Goodman and Kruskal's Lambda, Cohen's Kappa, and Goodman and Kruskal's Gamma (J Am Stat Assoc 49(268):732-764, 1954) each behave differently depending on what is inconsistent between the two trees thus providing both measures for assessing alignment between two trees developed by independent experts as well as identifying the causes of the differences.
Recommended Citation
Sabbaghan, S., Chua, C. E., & Gardner, L. A. (2020). Statistical Measurement of Trees' Similarity. Quality and Quantity, 54, pp. 781-806. Springer.
The definitive version is available at https://doi.org/10.1007/s11135-019-00957-8
Department(s)
Business and Information Technology
Keywords and Phrases
Diagnostic theory; Threshold building; Tree
International Standard Serial Number (ISSN)
0033-5177; 1573-7845
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2020 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jun 2020
