Development and Validation of a Multidimensional Measure of Role Ambiguity: Integrating NLP-Based Factor Analytic Techniques with Traditional Psychometrics

Although several measures of role ambiguity exist, many have been subject to sustained criticism. The most prominent example is the Rizzo, House, and Lirtzman (RHL) scale (Rizzo et al., 1972), which remains the most widely used measure of role ambiguity and role stress. Subsequent efforts, including Bowling et al. (2017), have improved aspects of earlier instruments, but existing scales continue to treat role ambiguity as a largely unidimensional construct. Consistent with these limitations, meta-analyses have reported mixed and inconsistent relationships between role ambiguity and its antecedents and outcomes. A primary source of these inconsistencies lies in how role ambiguity has been operationalized. Prior work has repeatedly noted that measurement deficiencies contribute to unstable and contradictory findings (Van Sell et al., 1981; Jackson & Schuler, 1985). Accordingly, the present study aims to develop and validate a multidimensional measure of role ambiguity that addresses these long-standing issues. In addition, we complement the closed-ended scale with a multidimensional open-ended measure analyzed using NLP-based confirmatory factor analysis, providing a novel and methodologically rigorous assessment of the construct.