Guidance on how to validate computational text-based measures of social science constructs is fragmented. Whereas scholars are generally acknowledging the importance of validating their text-based measures, they often lack common terminology and a unified framework to do so. This paper introduces a new validation framework called ValiTex, designed to assist scholars to measure social science constructs based on textual data. The framework draws on a long-established tradition within psychometrics while extending the framework for the purpose of computational text analysis. ValiTex consists of two components, a conceptual model, and a dynamic checklist. Whereas the conceptual model provides a general structure along distinct phases on how to approach validation, the dynamic checklist defines specific validation steps and provides guidance on which steps might be considered recommendable (i.e., providing relevant and necessary validation evidence) or optional (i.e., useful for providing additional supporting validation evidence. The utility of the framework is demonstrated by applying it to a use case of detecting sexism from social media data.

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