The process of collecting scientific research at any stage is laden with bias. As a researcher, I have bias in my collection, in my analysis, and in my discussion. The trick is to find ways to collect and analyze data that is objective. I find that increasing different ways to look at the data the more chance I have of the inevitable findings coming forward, rather than finding what I would like the data to say. I hope that this redundancy will serve to help capture a tighter story in the end. And it will be told as a story in the end. I have decided to tell my research, not just write about it in a dissertation and in academic papers. I want to reach beyond the typical audience. Otherwise, to me, I have not done justice to the dozens of people who gave their time and mind to me in the form of interviews since August 2012. Or as Dr. Larry Davis, my advisor at the University of New Haven a decade ago would ask, "Who cares?". The work must have a point. But the content of the work must have gone through rigorous testing to get to that answer that makes the point.
In the case of qualitative data collection, social scientists have struggled for years to be taken as seriously in their findings as quantitative data scientists - usually termed natural scientists - are, but I would argue that qualitative data is still not respected in it's own merit. Unfairly sometimes terminology discloses the prejudice. Natural scientists are described as "hard" scientists while social scientists are termed "soft" scientists. This hard and soft is related to the perception of the data results or the rigor with which social scientists test their hypothesis vs. what natural scientists do. I have thought long about how to navigate this issue. I think the answer is that neither, on their own, is valuable enough. I am not intending to quantify my qualitative data - for those of you who are not into academic jargon - this means putting numbers and numbered values against people's opinions or personally held values. This is ridiculous. Apparently so is statistical analysis of frequency in my work. Why does it matter if the word "fish" comes up 90% of the time and "food" comes up only 40% of the time. It doesn't on its own. The point of qualitative data is to give you the background and context of why the word fish would come up more often than the word food.
The ways in which I am currently analyzing my work are limited because the data is complex. There is overlap not only in a linear sense - this thing reaches out of the screen. I want to make a 3D model of the complex analysis I have built. There is no software easily at my disposal that can do this. Most of the software out there on spatial models requires intimate knowledge of programming. Even a program as widely used as GIS - geographic information systems - requires training - it is not very user friendly without a course, or a few courses to learn about all the tools.
Increasingly the scientific community embraces the idea of interdisciplinary work - look at the USA's National Science Foundation moneys in the last decade. They have morphed to insist upon interdisciplinary teams - data with multi-dimensions. This is especially important in issues of global change - climate, water, biodiversity, populations, and things of this nature. But it is a challenge. The research I have done trying to capture social as well as economic, political as well as environmental, the scope is vast - and the amount of time to thoroughly cover each of these areas in relation to change and risk or stabilization due to development of a dam could be dissertations each in themselves.
All of this is to state that to take on interdisciplinary work is scientifically responsible, but one hell of a task. Making sure that the qualitative data is analyzed rigorously takes several approaches and lots of time to allow for redundancy. Complex systems are not easily graphed, mapped, or described in words. I hope in the future there will be more efficient ways to describe interdisciplinary, complex systems revealed through diverse data collection.