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.
BorgattiS. P., EverettM. G., and FreemanL. C.2002. UCINET for Windows: Software for Social Network Analysis.Harvard, MA: Analytic Technologies.http://www.analytictech.com/ucinet/.
2.
CortenR.2011. Visualization of social networks in Stata using multidimensional scaling. Stata Journal11: 52–63.
3.
CoxN. J., and KohlerU.2003. Speaking Stata: On structure and shape: The case of multiple responses. Stata Journal3: 81–99.
4.
De NooyW., MrvarA., and BatageljV.2011. Exploratory Social Network Analysis with Pajek.Revised and expanded second ed. New York: Cambridge University Press.
5.
EscobarM.2009. Redes semánticas en textos periodísticos: Propuestas técnicas para su representación. Empiria17: 13–39.
6.
EverittB.2003. The Cambridge Dictionary of Statistics.Cambridge: Cambridge University Press.
7.
GabrielK. R.1971. The biplot graphic display of matrices with application to principal component analysis. Biometrika58: 453–467.
8.
GalaskiewiczJ., and WassermanS.1989. Mimetic processes within an interorganizational field: An empirical test. Administrative Science Quarterly34: 454–479.
9.
GowerJ. C.1985. Measures of similarity, dissimilarity, and distance. In Vol. 5 of Encyclopedia of Statistical Sciences, ed. KotzS., JohnsonN. L., and ReadC. B., 397–405. New York: Wiley.
10.
GrundT. E.2015. nwcommands: Software tools for statistical modeling of network data in Stata.http://nwcommands.org.
11.
HabermanS. J.1973. The analysis of residuals in cross-classified tables. Biometrics29: 205–220.
HubálekZ.1982. Coefficients of association and similarity, based on binary (presence–absence) data: An evaluation. Biological Reviews57: 669–689.
14.
JannB.2005. Tabulation of multiple responses. Stata Journal5: 92–122.
15.
LeydesdorffL., and VaughanL.2006. Co-occurrence matrices and their applications in information science: Extending ACA to the web environment. Journal of the American Society for Information Science and Technology57: 1616–1628.
16.
MiuraH.2012. Stata graph library for network analysis. Stata Journal12: 94–129.
17.
PadgettJ. F., and AnsellC. K.1993. Robust action and the rise of the Medici, 1400–1434. American Journal of Sociology98: 1259–1319.
