Sociologist. Quantitativist. University of Neuchâtel.
The social science at the University of Neuchâtel are looking for a lecturer in quantitative methods. Starting 1 July 2024.
You will teach an introductory course to 1st and 2nd year BA in the social sciences, prepare and support practical exercises, and have time for your own research. You’ll need a relevant PhD, a strong track record in quantitative methods, and teaching experience. Teaching is in French (non-negotiable).
Deadline : 31 March 2024
Advert (French)
The social science at the University of Neuchâtel are looking for a lecturer in quantitative methods. Starting 1 July 2024.
You will teach an introductory course to 1st and 2nd year BA in the social sciences, prepare and support practical exercises, and have time for your own research. You’ll need a relevant PhD, a strong track record in quantitative methods, and teaching experience. Teaching is in French (non-negotiable).
Deadline : 31 March 2024
Advert (French)
You should head over to PLOS to read this paper by Jonah Stunt et al. It’s the first qualitative study I’ve come across at PLOS, but it’s definitely worth a read to better understand why we’re still surrounded by p-values.
One thing I missed in the paper is a hint that we don’t have to engage in frequentists null-hypothesis significance testing. I realize that the authors are interested in the sociology of science here, but we have plenty of statements in the article how difficult it’d be to learn about alternative methods. It doesn’t have to be: We do have packages like rstanarm or software like JASP that do not leave much room for such excuses.
Stunt, Jonah, Leonie van Grootel, Lex Bouter, David Trafimow, Trynke Hoekstra, and Michiel de Boer. 2021. “Why We Habitually Engage in Null-Hypothesis Significance Testing: A Qualitative Study.” PLOS ONE 16(10):e0258330. doi: 10.1371/journal.pone.0258330.
It’s the time of the year when many of us do their share of grading. In my case, it’s quantitative projects, and every time I’m impressed how much the students learn. One thing that annoys me sometimes is to see how many of them (MA students) insist on interpreting R2 in absolute terms (rather than to compare similar models, for instance). That’s something they seem to learn in their BA course:
[in this simple model with three predictor variables], we only explain 3% of the variance; it’s a ‘bad’ model.
I paraphrased, of course. But I started to like low R2: They are a testament to the complexity of humans and their social world. They are a testament to the fact that we are not machines, we are in the world where quantitative analysis is about tendencies. Just imagine a world in which knowing your age and gender I could perfectly predict your political preferences… So there you have it: low R2 are great!
I have written about several free alternatives to SPSS, including PSPP, Jamovi, and JASP. Bob Munchen has reviewed a few more options: Deducer, RKWard, Rattle, and the good old R Commander (in the screenshot on the left). We also find a review of Blue Sky Statistics. Blue Sky Statistics is another option for those seeking SPSS “simplicity” with R power underneath.
Blue Sky Statistics is available for Windows, and is open source. They make money from paid support. I note that it comes with a polished interface and this data editor that reminds us of Excel. I was very happy to see that Blue Sky Statistics offers many options for data handling, like recoding, merging, computing variables, or subsetting — that’s much better than what say jamovi offers at the moment.
The dialogs are quite intuitive if you are familiar with SPSS, and they can also produce R code. This is a feature we know from the R Commander, and ostensibly the aim is to allow users to wean from the graphical interface and move to the console. Nice as the idea is, it is defeated by custom commands like BSkyOpenNewDataset() that we don’t normally use.
The models offered by Blue Sky Statistics are fine for many uses — for those not living on the cutting edge. A nice touch are the interactive tables in the output, where you can customize to some degree.
Exciting as Blue Sky Statistics and other GUI are at first sight, I’m gradually becoming less excited about GUI for R. Probably the biggest challenge is the “hey, this is all text!” shock when you first open R (or typically Rstudio these days). Once you realize that the biggest challenge is to make the right choices and then interpret your results, you become less hung up about the “right” software. Once you realize that you’ll have to remember either way — where to click, or what to type — copying and pasting code fragments becomes less daunting. If you restrict yourself to a few basic commands like lm(), plot(), and summary(), R isn’t that difficult. Sure, when you come across idiosyncrasies because different developers use different naming conventions, R can be hard. But then, there are also the moments where you realize that there are so many ready-made solutions (i.e. packages) available and that with R you really are in control of your analysis. And the day you learn about replication and knitr, there’s hardly a way back.
One reason I kept looking for GUI was my MA students. I’m excited to see more and more of them choosing Rstudio over SPSS (they are given the choice, we’re currently use both in parallel)… so I there might be simply no need for turning R into SPSS.
Didier Ruedin 