There are a few solutions out there for collaborative writing, and currently I like SciFlow best. The thing about collaborative writing platforms is that while there are many options out there, we’ll have to consider the least technical of the co-authors. Yes, we could use LaTeX (or perhaps better: Markdown because most journals want Word documents during submission) on GitHub, but in the social sciences this is often no realistic because many shy away from anything that doesn’t quite look like a word processor.
I guess a widely approach consists of a Word document that is either e-mailed around, or these days shared on Dropbox. It’s not too bad as long as one of the authors knows how to combine different versions of the same document, tracked changes are accepted from time to time, and someone is willing to clean up the messed-up formatting in the end.
In terms of collaboration, an online platform can be better: there is only one version — the latest one –, and all authors can write on the document at once. SciFlow offers a basic service for just this, and the “basic” part makes it just so suitable: the least technical of the co-authors is likely to handle it well. It offers all the necessary bits without distracting from the most important bit: writing.
It handles basic formatting, footnotes, references, figures, and equations. We are forced to use styles rather than direct formatting — something we should be doing in Word, too, but the least technical of the co-authors typically doesn’t do. Citations are built in (though not quite as nicely as in Authorea, where we can import references from the web, too!), and there are many templates to format the document and export it to PDF or Word documents as needed.
Pre-registration plans (PAP) rightly become more common (they are still not common enough yet, I think), but here’s a reason to write up a PAP that I have never seen mentioned before: Pre-registration plans can be immensely useful for yourself!
So, you have come up with a clever analysis, and writing the PAP has helped sharpen your mind what exactly you are looking for. You then collect your data, finish off another project, and … what was it exactly I was going to do with these data? Did I need to recode the predictor variable? etc.? Yes it happens, and a pre-analysis plan would be an ideal reminder to get back into the project: PAP can be like a good lab journal or good documentation of the data and analysis we do — a reminder to our future selves.
Richard Swedberg urges us to theorize more to make social sciences more interesting. His recent article in BJS summarizes Swedberg’s 2014 book in a short and accessible manner. While we’re more used to seeing the article first followed by a longer book, I’m happy to see this article as Swedberg’s message deserves to be heard. Contrary to what I chose as the title of this post, Swedberg actually doesn’t call for more theory as such, but for more of the right kind of theory. Good theory isn’t abstract and empirically irrelevant (i.e. much of what passes as ‘theory’ today). Interestingly Swedberg focuses on observation and creativity, and not formal modelling as it is done in economics (which he regards as mechanistic).
Swedberg, Richard. 2016. ‘Before Theory Comes Theorizing or How to Make Social Science More Interesting’. The British Journal of Sociology, February. doi:10.1111/1468-4446.12184.
Swedberg, Richard. 2014. The Art of Social Theory. Princeton: Princeton University Press.
My colleagues are sometimes surprised to learn that I teach statistics using SPSS and R/Rstudio in parallel. (Part of this is due to a misconception that R is hard to learn, ignoring that there are more difficult problems like proper model specifications and interpretation of results.) In my opinion, there are many benefits in doing so; here’s an unordered (and incomplete) list:
– introduction to a statistics package that remains available after they leave university and have access to the SPSS site licence (between jobs, moving to another university, out of academia)
– exposure to a different paradigm, making the shift to other software like Stata or SAS appear less threatening
– understanding that it doesn’t matter what package we use for basic statistics (we could even do it by hand)
– that line on the CV
– overcoming limitations in SPSS (ever tried to plot an interaction effect the way we want them?)
– ensuring that those who want to progress to more advanced (contemporary) methods actually can (being “future ready”)
– encourage a mindset that we are in control of the analyses, not the software package
At the same time, I acknowledge that many students have been exposed to SPSS before and feel more at ease when they can see the menu bar. (And the day the university gets rid of that site licence, PSPP will do nicely to work in parallel with R/Rstudio).
Just last week I wrote about two papers that examined the validity of QCA. They were by no means the first ones to do so, but that doesn’t make these papers any less important.
Now, QCA isn’t exactly static, even though it remains focused on its founding father. Fuzzyset QCA (fsQCA) is often used these days as it promises to overcome some of the shortcomings of QCA. Unfortunately, even if you buy into the concept and epistemology, the empirics still don’t add up.
Krogslund, Chris, Donghyun Danny Choi, and Mathias Poertner. 2014. “Fuzzy Sets on Shaky Ground: Parameter Sensitivity and Confirmation Bias in fsQCA.” Political Analysis, November, mpu016. doi:10.1093/pan/mpu016.
Krogslund and colleagues used simulations to check how robust fsQCA is. The approach is quite intriguing. Rather than using data generated in the computer as is often done in such situations, they have used three existing studies. After replicating these studies, they modified tiny bits. With a robust method, such tiny changes will not have a substantive impact on the results. With fsQCA, however, the results often changed radically: it is a very sensitive method.