We contend that clusters of cases co-constitute statistical interactions among variables. Interactions among variables imply clusters of cases within which statistical effects differ. Regression coefficients may be productively viewed as sums across clusters of cases, and in this sense regression coefficients may be said to be “composed” of clusters of cases. We explicate a four-step procedure that discovers interaction effects based on clusters of cases in the data matrix, hence aiding in inductive model specification. We illustrate with two examples. One is a reanalysis of data from a published study of the effect of social welfare policy extensiveness on poverty rates across 15 countries. The second uses General Social Survey data to predict four different dimensions of ego-network homophily. We find support for our contention that clusters of the rows of a data matrix may be exploited to discover statistical interactions among variables that improve model fit.
BelsleyDavid A.KuhEdwinWelschRoy E.. 2004. Regression DiagnosticsHoboken, NJ: Wiley.
3.
BreigerRonald L.1974. “The Duality of Persons and Groups.”Social Forces53:181–90.
4.
BreigerRonald L.2009. “On the Duality of Cases and Variables: Correspondence Analysis (CA) and Qualitative Comparative Analysis (QCA).” Pp. 243–59 in The Sage Handbook of Case-Based Methods, edited by BryneD.RaginC.. London, UK: Sage.
5.
BreigerRonaldAckermanGaryAsalVictorMelamedDavidMilwardBrintonRethemeyerKarlSchoonEric. 2011. “Application of a Profile Similarity Methodology for Identifying Terrorist Groups that Use or Pursue CBRN Weapons.” Pp. 26-33 in Social Computing, Behavioral-Cultural Modeling and Prediction, edited by SalernoJ.YangS.NauD.ChaiS.. New York, NY: Springer.
6.
BreigerRonald L.MelamedDavidSchoonEric. 2010. “Report on a Profile Similarity Methodology for turning Terrorist Attributes into Network Connections.”Working Paper 2010 (August). Department of Sociology, University of Arizona, AZ.
7.
ByrneDavidRaginCharles. 2009. The Sage Handbook of Comparative Politics. London, UK: Sage.
8.
DavisJames AllanSmithTom W.. General Social Surveys, 1972-2006. [machine- Readable data file]. Principal Investigator, James A. Davis; Director and Co-Principal Investigator, Tom W. Smith; Co-Principal Investigator, Peter V. Marsden, NORC ed. Chicago: National Opinion Research Center, producer, 2005; Storrs, CT: The Roper Center for Public Opinion Research, University of Connecticut, distributor. 1 data file (51,020 logical records) and 1 codebook (2,552 pp.)
9.
EverittBrian S.HothornTorsten. 2006. A Handbook of Statistical Analyses Using R. Boca Raton, FL: Taylor and Francis.
10.
GrofmanBernardSchneiderCarsten Q.. 2009. “An Introduction to Crisp Set QCA, with a Comparison to Binary Logistic Regression.”Political Research Quarterly62:662–72.
MarsdenPeter V.1987. “Core Discussion Networks of Americans.”American Sociological Review52:122–31.
13.
MartinJohn Levi. 2006. “Jointness and Duality in Algebraic Approaches to Dichotomous Data.”Sociological Methods & Research35:159–92.
14.
McPhersonMillerSmith-LovinLynnCookJames. 2001. “Birds of a Feather: Homophily in Social Networks.”Annual Review of Sociology27:415–44.
15.
MelamedDavidSchoonEricBreigerRonald L.AsalVictorKarl RethemeyerR.. 2012. “Using Organizational Similarity to Identify Statistical Interactions for Improving Situational Awareness of CBRN Activities.” Pp. 61–68 in Social Computing, Behavioral-Cultural Modeling and Prediction, edited by YangS. J.GreenbergA.EndsleyM.. Berlin, Germany: Springer-Verlag.
16.
MooreGwen. 1990. “Structural Determinants of Men’s and Women’s Personal Networks.”American Sociological Review55:726–35.
17.
NeterJohnKutnerMichaelNachtsheimChristopherWassermanWilliam. 1996Applied Linear Statistical Models. New York, NY: McGraw Hill.
18.
RaginCharles. 2008. Redesigning Social Inquiry. Chicago, IL: University of Chicago Press.
19.
RihouxBenoitRaginCharles. 2008. Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques. Thousand Oaks, CA: Sage.
20.
SchaeferDavid. 2010. “A Configurational Appraoch to Homophily Using Lattice Visualization.”Connections30:21–40.
21.
SkillicornDavid B.2006. “Social Network Analysis via Matrix Decomposition.” Pp. 367–91 in Emergent Information Technologies and Enabling Policies for Counter-Terrorism, edited by PoppR.YenJ.. Hoboken, NJ: Wiley.
22.
Van de GeerJohn P. 1971. Introduction to Multivariate Analysis for the Social Sciences. San Francisco, CA: W. H. Freeman.
WimmerAndreasLewisKevin. 2010. “Beyond and Below Racial Homophily: ERG Models of a Friendship Network Documented on Facebook.”American Journal of Sociology116:583–642.
