Demographic data inclusion tenets in applied data practice: the utilization of the QuantCrit lens to capture demographic fluidness in context

Applied data analysts who recognize the power of numbers express concern for erasing smaller demographic groups with their statistical analyses. Data analysts ought to strive for representative data categories when numbers impact participants’ lives, yet underrepresented groupmembers are often lumped into nonrepresentative categories or excluded from analysis. We conducted interviews with 31 applied data analysts to thematically investigate how they prioritized group inclusion. We learned analysts employed mostly a QuantCrit approach toward social group inclusion by (a) disaggregating demographic social groups when appropriate, (b) educating leadership, (c) acknowledging demographic missingness and error, (d) providing response-fluid survey options, (e) engaging in reflexivity, and (f) codesigning demographic measures. They also employed qualitative methods to present marginalized groups’ stories and counterstories. Contributions are five-fold: First, the results provide evidence that qualitative data representing smaller demographic groups were perceived as more effective in attracting funding and influencing change than numerical data. Second, inclusion conceptual boundaries are explored by studying professionals who confront normative quantitative data practices. Third, applied professionals treated demographic measurement structure with flexibility to create culturally and locally relevant demographic measures. Fourth, we share the research utility of QuantCrit tenets. Last, and most importantly, reflexivity is required at every data stage.