A Monte Carlo permutation test for co-occurrence data

Balázs Kovács ()

Quality & Quantity: International Journal of Methodology, 2014, vol. 48, issue 2, 955-960

Abstract: Researchers commonly use co-occurrence counts to assess the similarity of objects. This paper illustrates how traditional association measures can lead to misguided significance tests of co-occurrence in settings where the usual multinomial sampling assumptions do not hold. I propose a Monte Carlo permutation test that preserves the original distributions of the co-occurrence data. I illustrate the test on a dataset of organizational categorization, in which I investigate the relations between organizational categories (such as “Argentine restaurants” and “Steakhouses”). Copyright Springer Science+Business Media Dordrecht 2014

Keywords: Co-occurrence data; Association tests; Permutation tests; Non-parametric statistics (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:qualqt:v:48:y:2014:i:2:p:955-960

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DOI: 10.1007/s11135-012-9817-x

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