Coherence is a frequently-used term in the guidance for reviewers provided by TQR: The Qualitative Report. As used in this guidance, it refers primarily to alignment among elements. Many people pick a method first. Some pick a question, purpose or area of interest first. In the traditional approach, informed by the scientific method, a careful investigation of the existing body of knowledge reveals fruitful areas for further investigation. Sadly, in a world where the number of published research articles is increasing steadily (not to mention pre-prints, that I have very mixed, leaning toward negative, opinions on) the ability to develop solid knowledge of the state of any area of research is increasingly a challenge. I see a couple of frequent strategies used by authors or researchers in response to the overabundance of information. The first is to just not bother to read and to go ahead and do the research you want to do. Many authors figure they'll just back into a review of literature and are not particularly concerned about rationalizing the need for their study. For these authors, the outcome/product is driving their work. This may be because they need papers for professional positions, or tenure and promotion, or they are competitive about the number of projects/papers they complete, or they just enjoy the exploration process. The best thing these researchers can do sometimes is collaborate with a person who instead likes to plan rather than carry out research. And those people are for sure out there - I've known a few. This is also a skillset or orientation - more process than product- that people can develop.
And, going back to coherence - the question/purpose, the study direction or structure (when this is a separate aspect), the design or approach, and the method for gathering data (or the type of data if secondary analysis) all need to cohere. You might see where I'm going - going back after the fact to try to match up these things is so much harder than doing it from the start. It may be so difficult that it is impossible to compose a solid presentation or paper that describes the research as a whole (instead of just the results which are usually pretty easy to present, although perhaps not useful without context). Where I most often see research planned, then carried out without at least a basic review of existing research is in student work, that has come from an assignment, and in, perhaps surprisingly, funded research where sometimes trendy topics and novel methods impress reviewers enough that they overlook essential flaws in logic, novelty, and usefulness of research. Novelty, by the way, is not an ample rationale - lots of novel work doesn't ever need to be done or cannot ethically be done. It has to be useful too. The other strategy I see in response to inability to easily make sense of the body of existing research in a given area is to employ a data (or software) driven approach. This is mostly a secondary analysis thing but can be done with primary data as well. Where research is done without a solid question or purpose, the design and analysis cannot cohere, so all of the pieces may be treated separately - survey or interview information are gathered and people assume they will figure out later how to make sense of the data. This encourages fishing for something that looks important, like an association (often reinterpreted as a cause) or subgroup difference, or something else. It also has a tendency to divorce findings from whatever question or purpose thought process there was that inspired the work. In using a data-driven approach, researchers particularly like to find: a)what they already thought or b) something very different than what others have long thought. Findings that fall under b, by the way, can be super helpful, as long as these are derived from a credible and intentional project and not some convoluted analysis technique applied to data gotten from poor sampling methods, or illogical transformations, or over interpreted, or when not really justifiable methods were used to eliminate or create data points. I do not have a great solution for remaining intimate with the body of published scientific research in a given broad or narrow topic, that last year included, across subjects, a new high of 10,000 retracted articles (https://www.theguardian.com/science/2024/feb/03/the-situation-has-become-appalling-fake-scientific-papers-push-research-credibility-to-crisis-point) to the extent that one can gain expertise. I think increasingly, however, it is critical to assess every source, using a checklist if not your own expertise, before you consider citing it something that can inform your own work. I also suggest researchers consider beginning with an idea of a purpose and then try to build a rationale, rather than reading and trying to find a gap. This is not quite the same as backing into a rationale after the work is done, because I'm suggesting you begin with a conceptual, even somewhat abstract focus that is flexible and emergent. This actually is very consistent with the idea of Design Innovation (DI) and design thinking (https://designthinking.ideo.com/) and becomes more like DI when the impacted, or potentially impacted people contribute some of the knowledge being gathered. I suggest if you have good ideas or questions and strategically search published research to see whether these have been sufficiently explored or not, and cannot find evidence of this, you may have a solid researchable question. Then time can be taken to further fine tuen the question and align it with an appropriate structure, theoretical framework, method, and processing and analysis strategy, to ensure that at the end you can not only address the question of "But . . . what's the point?", and you might also show the coherence in your approach to figuring out what was the point. Picture taken by me, on 2/6/24 with Photo Booth 12.2 for Mac, no editing. Chalkboard by Super-SlateTM
1 Comment
3/6/2024 03:18:54 am
Software-defined vehicles represent a revolutionary approach to automotive design, where software controls critical vehicle functions traditionally managed by hardware. This innovative concept enables flexible customization, over-the-air updates, and adaptive features, enhancing vehicle performance, safety, and user experience. By leveraging advanced computing and connectivity, software-defined vehicles pave the way for autonomous driving and smart mobility solutions, signifying the convergence of automotive and technology industries.
Reply
Leave a Reply. |
AuthorI am Sheryl L. Chatfield, Ph.D, C.T.R.S. I am a member of the faculty in the College of Public Health at Kent State University. I also Co-coordinate the Graduate Certificate in Qualitative Research and I am a member of the Design Innovation Team at Kent State. Archives
February 2024
Categories
|