Causal claims with qualitative data
Channel: Mikko Rönkkö
Date: 2019-10-11

The lecture elucidates the distinctive nature of causal claims in qualitative research, contrasting them with those in quantitative research. In quantitative research, three conditions are crucial for establishing causality: demonstrating an association between the cause (X) and the effect (Y), establishing the direction of influence, and eliminating rival explanations through strategies like randomized experiments and statistical controls. Quantitative research primarily relies on observing inputs and outputs, inferring that there must be a causal process between them, treating the causal process as a ‘black box’ not delved into.

Conversely, qualitative research places a substantial emphasis on delineating the causal process. It necessitates explicitly explaining and describing how X influences Y, focusing not just on the association but on the causal link between variables. The lecture stresses the significance of uncovering this ‘black box’ to provide a substantial and solid causal claim in qualitative studies. Ignoring this causal process and relying solely on associations in qualitative studies often leads to unconvincing claims and undermines the robustness of the research findings. The exploration of the causal process ultimately contributes to the development of new theories and enriches the contribution of the research to the field.

Link to the slides: https://osf.io/wa5jg