Validity of the Structural Properties of Text-Based Causal Maps: An Empirical Assessment

Abstract

Recently, text-based causal maps (TBCMs) have generated enthusiasm as a methodological tool because they provide a way of accessing large, untapped sources of data generated by organizations. Although TBCMs have been used extensively in organizational behavior and strategic management research, studies assessing the psychometric properties of TBCM measures are virtually nonexistent. With the intention of facilitating large-sample substantive research using TBCMs, the authors examine the construct validity of two most frequently employed structural properties of TBCMs: complexity and centrality. In assessing construct validity, they examine the internal consistency, dimensionality, and predictive validity of the structural properties. The results suggest that complexity is not a general cognitive attribute. Rather, it is indicative of domain knowledge. On the other hand, centrality, which reflects the degree of hierarchy characterizing the TBCM, is related to cognitive ability and may reflect general information processing. Moreover, complexity and centrality, but not cognitive ability, predicted student performance.