Variation between syndicated research or industry studies and your own research can frequently happen. The dissonance is usually not a matter of right or wrong but more an acknowledgement of methodological differences. The external survey may be perfectly accurate, but its findings may appear in a media source that does not fully explain the context of the study. Timing, question placement, sample construction, method of questionnaire administration, attribute randomization methods and many other factors can drive significant differences in survey findings. Meticulously document the differences so you will be able to explain variation when asked. Your job should not be to dispute others’ research outcomes, do not fall into this trap.

When facing conflicting data, I verify my methods and datasets, ensuring they are accurate and contextually relevant. I check for time dependencies or sampling differences that might cause discrepancies. Engaging with industry experts helps gain alternative perspectives. Additionally, I consider how my research objectives might differ from those in industry reports, as this can shift the focus of findings. Clear communication is key to presenting the discrepancy transparently. For example, if industry reports show employees typically work 40 hours per week, but my research finds an average of 35 hours, I would check for time dependencies like seasonal work patterns or part-time employees.

When primary research contradicts industry reports, I typically 1. Validate the data quality-flipped scales, inattentive respondents 2. Run cross-tabs by sample or recruitment source to see if there is one or two that are driving the difference. 4. reach out to industry experts or research scientists to see if they have any thoughts on what could be driving differences 5. Conduct qualitative follow-up interviews with respondents that have answered in a way that is contradicting industry reports. There may be bleeding edge changes that haven’t bubbled up to the industry reports. 6. If none of these show a reason, clear and transparent communication to the key stakeholders so they understand exactly where they are with the data.