Quantitative methods in linguistics: The danger ahead

Peter Hagoort has written a nice piece on his take on the future of linguistics:

http://www.mpi.nl/departments/neurobiology-of-language/news/linguistics-quo-vadis-an-outsider-perspective

He's very gentle on linguists in this piece. One of his suggestions is to do proper experimental research instead of relying on intuition. Indeed, the field of linguistics is already moving in that direction. I want to point out a potentially dangerous consequence of the move towards quantitative methods in linguistics.

My expectation is that with the arrival of more and more quantitative work in linguistics, we are going to see (actually, we are already there) a different kind of degradation in the quality of work done. This degradation will be different from the kind linguistics has already experienced thanks to the tyranny of intuition in theory-building.

Here are some things that I have personally seen linguists do (and psycholinguists do this too, even though they should know better!):

1. Run an experiment until you hit significance. ("Is the result non-significant? Just run more subjects; it's going in the right direction.")
2. Alternatively, if you are looking to prove the null hypothesis, stop early or just run a low power study, where the probability of finding an effect is nice and low.
3. Run dozens (in ERP, even more than dozens) of tests and declare significance at 0.05.
4. Vary the region of interest post-hoc to get significance.
5. Never check model assumptions.
6. Never replicate results.
7. Don't release data and code with your publication.
8. Remove data as needed to get below the 0.05 threshold.
9. Only look for evidence in favor of your theory; never publish against your own theoretical position.
10. Argue from null results that you actually found that there is no effect.
11. Reverse-engineer your predictions post-hoc after the results show something unexpected.

I could go on. The central problem is that doing experiments requires a strong grounding in statistical theory. But linguists (and psycholinguists) are pretty cavalier about acquiring the relevant background: have button, will click. No linguist would think of running his sentences through some software to print out his formal analyses; you need to have expert knowledge to do linguistics. But the same linguist will happily gather rating data and run some scripts or press some buttons to get an illusion of quantitative rigor. I wonder why people think that statistical analysis is exempt from the deep background so necessary for doing linguistics.  Many people tell me that they don't have the time to study statistics. But the statistics is the science. If you're not willing to put in the time, don't use statistics!

I suppose I should be giving specific examples here; but that would just insult a bunch of people and would distract us from the main point, which is that the move to doing quantitative work in linguistics has a good chance of backfiring and leading to a false sense of security that we've found something "real" about language.

I can offer one real example of a person I don't mind insulting: myself. I have made many, possibly all, of the mistakes I list above. I started out with formal syntax and semantics, and transitioned to doing experiments in 2000. Everything I knew about statistical analysis I learnt from a four-week course I did at Ohio State.  I discovered R by googling for alternatives to SPSS and Excel, which had by then given me RSI. I had the opportunity to go over to the Statistics department to take courses there, but I missed that chance because I didn't understand how deep my ignorance was.  The only reason I didn't make a complete fool of myself in my PhD was that I had the good sense to go to the Statistical Consulting section of OSU's Stats department, where they introduced me to linear mixed models ("why are you fitting repeated measures ANOVAs? Use nlme.").  It was after I did a one-year course in Sheffield's Statistics department that I finally started to see what I had missed (I reviewed this course here).
 
For linguistics, becoming a quantitative discipline is not going to give us the payoff that people expect, unless we systematically work at making a formal statistical education a core part of the curriculum. Currently, what's happening is that we have advanced fast in using experimental methods, but have made little progress in developing a solid understanding of statistical inference.

Obviously, not everyone who uses experimental methods in linguistics falls into this category. But the problems are serious, both in linguistics (and psycholinguistics), and it's better to recognize this now rather than let thousands of badly done experiments and analyses lead us down some other garden-path.

A weird and unintended consequence of Barr et al's Keep It Maximal paper

Barr et al's well-intentioned paper is starting to lead to some seriously weird behavior in psycholinguistics! As a reviewer, I'm seeing submissions where people take the following approach:

1. Try to fit a "maximal" linear mixed model.  If you get a convergence failure (this happens a lot since we routinely run low power studies!), move to step 2.

[Aside:
By the way, the word maximal is ambiguous here, because you can have a "maximal" model with no correlation parameters estimated, or have one with correlations estimated. For a 2x2 design, the difference would look like:

correlations estimated: (1+factor1+factor2+interaction|subject) etc.

no correlations estimated: (factor1+factor2+interaction || subject) etc.

Both options can be considered maximal.]

2. Fit a repeated measures ANOVA. This means that you average over items to get F1 scores in the by-subject ANOVA. But this is cheating and amounts to p-value hacking. This effectively changes the between items variance to 0 because we aggregated over items for each subject in each condition. That is the whole reason why linear mixed models are so important; we can take both between item and between subject variance into account simultaneously. People mistakenly think that the linear mixed model and rmANOVA are exactly identical. If your experiment design calls for crossed varying intercepts and varying slopes (and it always does in psycholinguistics), an rmANOVA is not identical to the LMM, for the reason I give above. In the old days we used to compute minF.  In 2014, I mean, 2015, it makes no sense to do that if you have a tool like lmer.

As always, I'm happy to get comments on this.