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A Monte Carlo permutation test for co-occurrence data

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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”).

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Notes

  1. Obviously, the number of permutation needed depends on the sample size. Given that there are about 5,000 organizations with two or more categories, and given that most organizations are in two or three categories, 10,000 permutations are likely enough to arrive at random category associations.

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Authors and Affiliations

  1. Institute of Management, University of Lugano, Via Buffi 13, Lugano, 6900, Switzerland

    Balázs Kovács

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  1. Balázs Kovács
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Correspondence to Balázs Kovács.

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Kovács, B. A Monte Carlo permutation test for co-occurrence data. Qual Quant 48, 955–960 (2014). https://doi.org/10.1007/s11135-012-9817-x

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  • DOI: https://doi.org/10.1007/s11135-012-9817-x

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