Inferring Causal Complexity

February 7, 2010

Michael Baumgartner Inferring Causal Complexity Sociological Methods & Research 2009 38: 71-101.

In The Comparative Method, Ragin (1987) outlined a procedure of Boolean causal reasoning operating on pure coincidence data that has since become widely known as qualitative comparative analysis (QCA) among social scientists. QCA—including its recent forms as presented in Ragin (2000, 2008) Read the rest of this entry »


Christopher Winship Introduction to the Special Section on Replication and Data Access Sociological Methods & Research 2007 36: 151-152.

February 7, 2010

Introduction to the Special Section on Replication and Data Analysis.


Richard A. Berk, An Introduction to Ensemble Methods for Data Analysis, Sociological Methods & Research 2006 34: 263-295.

February 7, 2010
This article provides an introduction to ensemble statistical procedures as a special case of algorithmic methods. The discussion begins with classification and regression trees (CART) as adidactic device to introduce many of the key issues. Following the material on CART is a consideration of cross-validation, bagging, random forests, and boosting. Major points are illustrated with analyses of real data.

Key Words: CART • data analysis • algorithmic methods • ensemble methods


Michael Smithson, Fuzzy Set Inclusion: Linking Fuzzy Set Methods With Mainstream Techniques, Sociological Methods & Research 2005 33: 431-461.

February 7, 2010

Abstract: The concept of set inclusion has remained insufficiently developed in the fuzzy set literature to be of much use to social scientists. However, a fully fledged concept of fuzzy set inclusion, along with appropriate statistical methods for evaluating it, could be very useful in the social sciences. This article combines fuzzy set and statistical methods, in the form of a cumulative distribution-based approach to evaluating fuzzy set inclusion without making strong assumptions about measurement levels. It establishes criteria for distinguishing an “inclusion relation” from independence plus skew as well as from other kinds of relationships. A measure of inclusion is developed that is sensitive to the degree to which individual cases violate a strict inclusion rule. A technique for modeling localized inclusion relations in contingency tables and scatter plots is also presented. Finally, the connections between the fuzzy set approach to set inclusion and mainstream statistical techniques are briefly adumbrated.

Key Words: fuzzy set theory • data analysis • set inclusion • cumulative distribution function