Skip to main content
eScholarship
Open Access Publications from the University of California

UC Berkeley Library

Berkeley Research Impact Initiative (BRII) bannerUC Berkeley

Improving the Measurement of Shared Cultural Schemas with Correlational Class Analysis: Theory and Method

  • Author(s): Boutyline, Andrei
  • et al.

Published Web Location

https://www.sociologicalscience.com/articles-v4-15-353/
No data is associated with this publication.
Abstract

Measurement of shared cultural schemas is a central methodological challenge for the sociology of culture. Relational Class Analysis (RCA) is a recently developed technique for identifying such schemas in survey data. However, existing work lacks a clear definition of such schemas, which leaves RCA’s accuracy largely unknown. Here, I build on the theoretical intuitions behind RCA to arrive at this definition. I demonstrate that shared schemas should result in linear dependencies between survey rows—the relationship usually measured with Pearson’s correlation. I thus modify RCA into a “Correlational Class Analysis” (CCA). When I compare the methods using a broad set of simulations, results show that CCA is reliably more accurate at detecting shared schemas than RCA, even in scenarios that substantially violate CCA’s assumptions. I find no evidence of theoretical settings where RCA is more accurate. I then revisit a previous RCA analysis of the 1993 General Social Survey musical tastes module. Whereas RCA partitioned these data into three schematic classes, CCA partitions them into four. I compare these results with a multiple-groups analysis in structural equation modeling and find that CCA’s partition yields greatly improved model fit over RCA. I conclude with a parsimonious framework for future work.

Item not freely available? Link broken?
Report a problem accessing this item