Ideal-Type Class Analysis: Bridging Theory and Data through Standardized and Comparable Measurement of Cultural Schemas

• In Event: Sociology of Culture Section Roundtables
  In Refereed Roundtable (90 minute): 9. Computational Sociology & Semantic Mapping

Abstract

Since Goldberg (2011) introduced relational class analysis (RCA) to identify individuals with shared understanding or cultural schemas, RCA and its methodological derivatives (e.g., Boutyline 2017; Hunzaker and Valentino 2019; Taylor and Stoltz 2020) have been extensively applied to investigate a wide array of cultural and cognitive phenomena. These include political ideology and beliefs, economic and market conceptions, scientific perspectives, gender and family culture, prison dynamics, and ethnic identity (Barbet 2020; Bátora and Baboš 2025; Han and Oh 2024; Karim 2024; Lindner et al. 2024; McDonnell et al. 2022; Young et al. 2023).

However, existing methods are constrained by four significant limitations: they are computationally demanding; classification criteria can be ambiguous or even arbitrary; the process is predominantly data-driven and often lacks theoretical transparency; and comparing results across disparate datasets remains difficult.

To address these limitations, I propose an extension to existing RCA-based methods to Ideal-Type class analysis (ITCA). This approach utilizes “ideal types”, which are derived from a synthesis of both empirical data-driven findings and theoretical constructs. Rather than forcing individuals into discrete, mutually exclusive categories, ITCA quantifies the degree of similarity between each respondent’s response pattern and these predefined ideal types. This refined method offers several distinct advantages: it is scalable for large-scale survey data, provides more interpretable and theoretically grounded classification criteria, and establishes a robust, standardized framework for comparative sociological research.

Author

• He WANG, The University of Hong Kong