Data Quality

I have been thinking a bit this week about the issue of data quality; as our RAs are now wrapping up practice interviews and are starting the main interviews for our research. Research, much like graduate school in general, requires considerable attention to detail, organization, and the ability to sew together parts into a cohesive whole. A veritable feast for the Type-A's among us ;-) Once we get these research protocols down enough to start the main interviews, it seems like a good time to remind ourselves and think about data quality. It is truly not enough to get through such interviews/protocols - to just get them done. We need to be sure that while maintaining standards in research with human participants, we are mindful of how easily and even unintentionally, data can be distorted.

We need to be true to the measurement standards and be sure that our overall interactions with participants are professional. I can readily remember some really challenging interviews that I would rather one of my peers have taken as well as those interviews where my participant was remarkably kind and good spirited. For me, the trick was always to walk that very fine line between collecting quality data and yet still recognizing and enjoying the personal benefits of conducting research interviews.

AES