Classifying Arts and Culture Organizations for Effective Comparative Research

Overlapping and competing taxonomical systems are persistent barriers to comparative research in the cultural sector. Cross-discipline studies typically navigate this challenge by relying on a single taxonomical datapoint. However, data quality issues and the inconsistent application of each system amplify the validity risks inherent in this practice. In this study, we demonstrate a systematic-yet-flexible protocol that groups organizations by reconciling taxonomical discrepancies and incorporating mission sensitivity. Drawing from a dataset of 2,044 cultural-sector organizations provided by SMU DataArts, we use this technique to combine NTEE data with arts-focused National Information Systems Project (NISP) codes and map 11 aggregated organizational categories. We validate this process through an audit against National Center for Charitable Statistics data. By focusing on firm-level coding accuracy and datapoint triangulation, this protocol addresses a common challenge for arts-sector studies. We also discuss this technique’s transferability to other nonprofit sectors and its potential for facilitating cross-sector research.