On a whim, I asked my Twitterverse (which includes a fair number of computer scientists) what they think about the following question:
Continue reading “Anecdotal Evidence on the Method Wars”
When peer-reviewing somebody else’s work submitted for publication, what should you do if you find that the authors have a different belief than you about what can be known?
Most people believe that scientists first perform basic (“fundamental”) research and then perform applied research. Basic research delivers the fundamental insights that then get detailed and refined as they hit reality in applied research. Along with this comes the request that basic research funding should be provided by the country (because few companies would ever pay for it) before industry kicks in and supports applied research. Nothing could be further from the situation in my engineering process research.
Continue reading “Inverted Research Funding”
When planning a publication strategy for a dissertation, invariably the question comes up where to submit your papers. Ph.D. students naturally are biased towards conferences, because if a paper gets accepted to a conference they get to travel to a (usually) nice place. I nip this bias in the bud right away: For a journal paper, every Ph.D. student gets a conference to attend for free. This lets us focus then on the economic value of a journal vs. a conference paper and how to best reap the benefits of hard research work. Here, I’m a contrarian (to most colleagues): I’m in favor of journals. It is also the economically smart choice for a Ph.D. student.
Continue reading “What’s better: Submit to ICSE or TSE? (Conference or Journal?)”
Research should be presented with appropriate choice of words to the world. So it bugs me if researchers, maybe unknowingly, overreach and call the evaluation of a theory a validation thereof. I don’t think you can ever fully validate a theory, you can only validate individual hypotheses.
The following figure shows how I think key terms should be used.
Continue reading “Evaluation of Theories vs. Validation of Hypotheses”
From my excursion into qualitative research land (the aforementioned Berliner Methodentreffen) I took away some rather confusing impressions about the variety of what people consider science. I’m well aware of different philosophies of science (from positivism to radical constructivism) and their impact on research methodology (from controlled experiments to action research, ethnographies, etc.) I did not expect, however, for people to be so divided about fundamental assumptions about what constitutes good science.
One of the initial surprises for me was to learn that it is acceptable for a dissertation to apply only one method and for that method to only deliver descriptive results (and thereby not really make a contribution to theory). In computer science, it is difficult to publish solely theory development research (let alone purely descriptive results) without any theory validation attempt, even if only selective. The limits of what can be done in 3-5 Ph.D. student years are clear, but this shouldn’t lead anyone to lower expectations.
Continue reading “We May not Know What We are Doing…”
A researcher-friend recently complained to me that her research paper had been rejected, because the reviewers considered it “boring”. I recommended that she complain to the editor-in-chief of the journal, because in my book, “boring” is no acceptable reason to reject a paper. (“Trivial” may be, but that is a different issue.)
The reasoning behind rejecting a paper, because it is “boring”, is as follows: Research should be novel and provide new and perhaps even unintuitive insights. Results that are not surprising (in the mind of the reviewer, at least) are not novel and therefore not worthy of publication.
Continue reading “Why “Boring” is no Reason for Rejection”
Traditional science has a clear idea of how research is to progress, rationally speaking: First you build a theory, for example, by observation, expert interviews, and the like, and then you generate hypotheses to test the theory. Over time, some theories will stand the test of time and will be considered valid.
Sadly, most software engineering research today, even the one published in top journals and conferences, often skips the theory building process and jumps straight to hypothesis testing. Vicky the Viking, from the accordingly named TV series of way back comes to my mind: Out of the blue, some genius idea pops into the researcher’s mind. This idea forms the hypothesis to be tested in research.
Continue reading “How the Lack of Theory Building in Software Engineering Research is Hurting Us”
Researchers often use the term “qualitative research” to mean research without substantial empirical data, and use “quantitative research” to mean research with substantial empirical data. That doesn’t make sense to me, as most “qualitative researchers” will quickly point out, because qualitative research utilizes as much data in a structured way as it can. Everything else would not be research.
Continue reading “On the Misuse of the Terms Qualitative and Quantitative Research”
Some of my colleagues like to talk about how research that involves programming is “hard”, while research that involves human subjects is “soft”. Similarly, some colleagues like to call exploratory (qualitative) research “soft” and confirmatory (quantitative) research “hard”. Soft and hard are often used as synonyms for easy and difficult, and this is plain wrong.
Pretty much any research worth its salt is difficult in some way, and working with human subjects makes it even more difficult. I find research methods like qualitative surveys, involving interview analyses, for example, much harder than the statistical analysis of some data or some algorithm design. The reason is the lack of immediate feedback.
While you can (and should) put quality assurance measures in place for your interview analyses, ranging from basic member checking to complex forms of triangulation, it might take a long time until you learn whether what you did was any good. So you have to focus hard in your analysis without knowing whether you are on the right track. It doesn’t get more difficult than this.