Studying coincidences with network analysis and other multivariate tools

Modesto Escobar ()
Additional contact information
Modesto Escobar: Universidad de Salamanca

Stata Journal, 2015, vol. 15, issue 4, 1118-1156

Abstract: In this article, I introduce a new user-written command (coin) to study data structures. coin is founded on a combination of statistical and social network analyses. The purpose of this procedure is to ascertain the most frequent events in a given set of scenarios and to study the relationships between them. coin uses a variety of procedures, such as multidimensional scaling, principal components analysis, correspondence analysis, biplot representations, agglomeration techniques, and network analysis algorithms. Because of this, it works with dichotomous variables and can be applied to the exploratory analysis of questionnaires, the study of textual networks, the review of database contents, and the comparison of different statistical analyses of interdependence. Copyright 2015 by StataCorp LP.

Keywords: coin; coincidence analysis; multivariate analysis; social network analysis (search for similar items in EconPapers)
Date: 2015
Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj15-4/st0416/
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.stata-journal.com/article.html?article=st0416 link to article purchase

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tsj:stataj:v:15:y:2015:i:4:p:1118-1156

Ordering information: This journal article can be ordered from
http://www.stata-journal.com/subscription.html

Access Statistics for this article

Stata Journal is currently edited by Nicholas J. Cox and Stephen P. Jenkins

More articles in Stata Journal from StataCorp LLC
Bibliographic data for series maintained by Christopher F. Baum () and Lisa Gilmore ().