James Pennebaker offers to reduce tenure-track & adjunct jobs even more with SMOCs. His data are pretty hard to argue with. #aps15nyc



I think I speak for all those who spent the last week grading papers when I ask, “Thann what?!” #aps15nyc




Q: Are you worried that this software will be abused like SPSS often is ?
A: There’s no defense against incompetence.

Intro Stats Videos on YouTube

I spend a lot of my time (no, I mean a lot of my time) preparing my stats class this semester, because I reconceived it a bit, based on feedback from last semester. As part of that, I’ve been revising and rewriting lectures, and re-recording videos.

And this is to say, just in case anyone finds them useful, that I’m posting the videos I make for my class in this playlist on my YouTube channel. I am absolutely not going to win any awards for production values or performance thrills (despite the feats of animation I occasionally achieve with PowerPoint), but perhaps the way I explain things could be useful to students who have had things explained a different way–I am a big believer in the principle of explaining tricky concepts as many ways as possible.

America’s STEM obsession is not only narrow, it is dangerous

In this excellent article, Fareed Zakaria argues that our recent practice of valuing STEM education at the expense of a more broad-based liberal education will screw America over, in the long (and perhaps not-so-long) run. For one thing, it is becoming obvious that critical thinking, creativity, “people skills,” etc. are what make economies flexible and successful (a point that emerging powerhouses like China are starting to emphasize)–and America has been a flexible, vibrant economy for a century, despite the fact that our students’ math and technical skill test averages have never been particularly good, compared to other nations’. Another point is that STEM skills are exactly those skills next on the chopping block for computerization. Computers will soon be able to write their own code, for instance, but they will not (yet) be able to write their own rich, engaging narratives. So we are pushing students increasingly toward those fields whose skills are most likely to be obsolete in a generation or two, and systematically strangling the university programs that teach the skills most likely to help our current and future graduates survive the economic and career upheavals that will only increase in frequency and intensity from here on out.


There are two increasingly cynical possibilities for why the American government and people are so willing to ignore the (potentially obvious) points Zakaria made: First, nobody currently calling the shots for higher education funding has any incentive to think about other people or about the long run, because they are blinded by their own short-term financial motivations and/or their own thinking, crippled by a lack of the benefits of a liberal education.  Second (and this one is much more cynical; I wish I could find the source, where it was said far better than this): The last thing people currently in power in this government want is a populace capable of imagining alternative systems. I’m pretty sure, however, that even the most Machiavellian government administrator is perfectly OK with a population full of highly skilled technicians who have never taken a humanities or social science course.

Reproducible Research – a big Gotcha

This post by Jeff Leek nicely sums up one of my anxieties about the reproducible research movement. A snippet:

for high-profile and important problems, people  largely use reproducibility to:
1. Impose regulatory hurdles in the short term while people transition to reproducibility…
2. Humiliate people who aren’t good coders or who make mistakes in their code…

I am 100% in support of reproducible research, but I’ve been worried about this; I’m not a coder, so I worry my code will be criticizable (or, worse, mockable). What I suppose everyone is worried about is that we all have warts and scars on our data, so to speak, and we have ways we’ve dealt with these. I suspect that, if the full truth were known, most researchers would have several decisions per published analysis that don’t fit the (largely false) idealized prototype for how a research study should go. I also suspect that, in most cases, we have dealt with these issues in reasonable ways that are very similar to how others have done so. However, publishing all of our data, procedures, and analyses will leave these open to criticism based on a failure to meet that perfect, idealized method. If everyone’s flaws and responses were known, then we could start an important conversation about how to deal with the inevitable glitches in research projects; but if only a few people do it–who, almost by definition, will probably be the most conscientious researchers, as evidenced by their concern for reproducible research–then those people will become targets for absolutist sniping, personal humiliation, and professional ridicule.

Correlation and Causation: Getting to know each other

Thanks to Graham Toal for pointing this out!

As has been noted frequently by others, correlation absolutely does (usually) imply causation (just not necessarily the simplistic X → Y model that immediately forms in our head after reading “X is correlated with Y”). The problem has always been that correlation itself is never enough to know where this causation came from. There are too many possibilities, such as:

  • X → Y
  • Y → X
  • X + Z → Y
  • X + Z – (H + L) / J + (K + S)TU → Y (I mean it can be really complicated; you get the picture…)

Apparently, however, in relatively simple two-variable systems, causality can be identified accurately about 80% of the time, from purely observational data. A recent paper, from a team of German, Swiss, and Dutch researchers, reports the findings. Using a variety of known and simulated cause-effect situations, and utilizing only the observational aspects of the data (not using any experimentally-manipulated situations, for example), the researchers report a very high success rate at figuring out whether X caused Y or vice-versa, by analyzing asymmetries in the “noise” (or error variance) associated with X and Y. The process is called the “additive noise method.” Because, as it turns out, noise in the causal variable can influence the noise in the effect, but not the other way around.

I suppose it’s still possible that this is bad science or bad reporting or something, but to my not-very-astute eye it looks legit. If so, I’m sure it will be developed quickly for other applications. If it is even moderately effective with the very noisy variables many of us in the behavioral sciences deal with, I think it will get itself integrated into SEM methodology and its many descendants and cousins. This would be a huge step forward, as SEM models are currently criticizable for almost certainly mis-specifying cause and effect a lot. This would reduce that a lot. And would probably reduce the number of SEM analyses appearing in behavioral sciences journals, once it became extremely difficult for one’s model to fit both the covariance structure of the data and the patterns of error variance suggesting which variables were later in the causal chain and which earlier.

I am excited 🙂

Fredonia students vs. UTPA students

I’ve been teaching here at Fredonia for only a few weeks, but it certainly seems to me that the students are incredibly similar, as a whole, and in their diversity of personality, educational preparation, etc., to students I got to know in my nine years in Texas. This is hugely reassuring–I came to love the RGV student population, and had started to worry that perhaps other students would be so alien to me, after nearly a decade in Texas, that I could not work with them. I’m glad to have that fear resolved 🙂

Crimes I don’t even remember

I’ve recently learned that I can’t get a driver’s license in NY (though I’m required to within 30 days) because of an unpaid speeding ticket. In Charlotte, North Carolina. In late summer, 2006. While I was apparently driving a Mercedes.

  1. I wasn’t anywhere near Charlotte, North Carolina in 2006.
  2. I don’t have a Mercedes.
  3. I’ve never driven a Mercedes.
  4. Honestly, I don’t even think I’ve touched a Mercedes since approximately 1986 (when I washed a guy’s car). I mean, even in a parking lot, you avoid the rich cars because you figure that if you even brush against them they’ll probably damage your hearing with some hair-trigger alarm system, right?

So, yeah. I gotta get that taken care of.

Taking off… to the Great White North

Two of the three readers of this blog might find it informative (the other one already knows) that I will be leaving my beloved UTPA. I’ve accepted a job at a university in Western NY (more about that, I’m sure, later). It is very similar to UTPA in the kind of job it represents for me, the kind of teaching and research I’ll be doing, and even the background of many of the students I’ll be working with; in other words, it seems like it will be at least as satisfying to me as the last nine years at UTPA have been–as soon as I stop missing people from UTPA, that is. Because of the many similarities, the main factors pushing me toward NY were work opportunities for Alex (my wife) and closeness to her family, who are also, of course, our daughter’s grandparents/aunts/uncles/honorary family. We will be a mere 2 to 5 hours (<< 36!) from her hometown, her parents, her sister’s family, and many of her friends. We’ll be one hour (plus border delays) from her eternally-beloved Canada (technically, as I’m a dual citizen, it’s also my beloved Canada, but she probably beloves it more than I do, growing up there and all).

In the vein of assuaging guilt at abandoning students and colleagues I’ll say that this was absolutely not an easy decision to make. It will not represent any kind of clear financial or career gain for me (it’s a lateral career move), it will cost us a lot of time, hassle, and money to relocate, it will delay aspects of my research for a little while and send it in a slightly different direction, and I will miss important milestones for some students I care about.  On the other hand, the department up there seems to have all the positive characteristics I’ve come to love about the UTPA Psychology Department, and I have no doubt that I will become as involved with students and colleagues there as I have been here. I’m sure the unique characteristics I will miss from the RGV will be replaced by unique characteristics of Western New York. I’m both sad and hopeful. I’m excited for the change, despite the difficulty.

Anyway, change is hard. We all know this. I’ll be around (except the last couple of weeks of June) until August 1, more or less.