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Quality & Quantity
Published in: Quality & Quantity 2/2014

01-03-2014

A Monte Carlo permutation test for co-occurrence data

Author: Balázs Kovács

Published in: Quality & Quantity | Issue 2/2014

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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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Footnotes
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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Metadata
Title
A Monte Carlo permutation test for co-occurrence data
Author
Balázs Kovács
Publication date
01-03-2014
Publisher
Springer Netherlands
Published in
Quality & Quantity / Issue 2/2014
Print ISSN: 0033-5177
Electronic ISSN: 1573-7845
DOI
https://doi.org/10.1007/s11135-012-9817-x

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