Using a binary metric for linguistic data analysis feedback should work perfectly. This means that your stakeholders need to answer 'yes' or 'no' for the question of whether they accept the data analysis. A 'no' is imperatively followed by another set of options related to the potential reasons of the data analysis rejection. The options are in relation with form, data analysis tools, data analysis results, and conclusions that relate to the results of the analysis. One may naturally add more details in relation to each of the options to highlight the exact reason for the rejection. One needs to be patient enough to discuss and defend one's analysis, as the stakeholder may be wrong. Explain linguistic theories for mutual understanding.

When I manage input from others on linguistics data, I start by having open discussions about project goals to make sure we’re all on the same page. I make it a point to translate their suggestions into clear, manageable tasks, and explain any complex concepts to support their understanding. Regular updates keep everyone connected to the project’s progress and help keep expectations aligned.

Use Critical Discourse Analysis (CDA) to interpret stakeholder language, uncovering biases and priorities. Apply Grice’s Maxims for clear, relevant, and concise communication with stakeholders. Conduct Thematic Analysis to identify patterns in feedback and prioritize linguistic insights effectively. Present findings with accessible language, visual aids, and balanced technical rigor for practical understanding. Respect sociolinguistic and cultural contexts, ensuring feedback is inclusive and contextually relevant. Integrate feedback iteratively, aligning project goals with stakeholder expectations. Communicate progress regularly to maintain trust and engagement, fostering collaboration for success.