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.

If your research contradicts reports, it does not mean that it is wrong. It can create your name .. If you are sure that you have found certain insights and there is evidence for this, you can safely leave such points. If the contradictions are related to information without confirmed sources - mark it separately, indicating that this is your opinion - this way you will form your name as researcher. And whether this name will be good or not will depend on the results of your recommendations and the results of those projects that used your analysis.

Document the differences and analyze potential reasons for the contradictions. Consider external factors such as market changes, technological advancements, or regional variations. If your research indicates a growing trend in a particular market that industry reports do not reflect, investigate whether recent changes or innovations might have influenced your findings. Industry reports may have a lag in capturing these changes.

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.

When my primary research contradicts industry reports, I go full detective mode—double-checking my data, making sure I didn’t miss a glaring typo. Next, I talk to experts for a sanity check because, hey, two heads (or 10) are better than one. Finally, I present the findings like a plot twist, explaining the possible reasons for the difference and giving everyone a chance to process. Have you ever had your data challenge the status quo? Tell me your secrets!

Results of research are very much dependent on certain variables such as sample size, the type of population you are testing, the methodology, timing, etc. It is important to keep the frame of reference with respect to these variables in mind while comparing with industry trends. I would study both researches carefully to identify differences which could possibly result in deviation in results while also ensuring that there is no error in methodology or execution of my primary research .

When primary research contradicts industry reports, I first verify the accuracy of my data and methodologies to rule out errors. Engaging with industry experts and peers provides additional perspectives and potential explanations. I then develop a transparent communication plan to present findings, highlight discrepancies, and offer reasoned interpretations, ensuring stakeholders understand the context and implications.

Reflect on the examples of methodology, sample size and bias noting disparities that pertain to both datasets. One should corroborate the analysed outcomes using other sources or secondary data. Always it is better to explain differences to the stakeholders, describe how the studies were conducted, and what exactly was the reason for achieving the discrepancies. For instance, during a project, our primary data was incongruous to that of an industry report we used. We then reworded our findings to show the trends as not constant, but rather with variations which we ascertained using third party tools. It helps develop credibility for insights while adapting them for decision-making.