Predictors of Grad School Publications

A new paper in PLoS ONE purports to report on the relationship between traditional graduate school selection factors and graduate school success.

Joshua D. Hall, Anna B. O’Connell, Jeanette G. Cook. Predictors of Student Productivity in Biomedical Graduate School Applications. 2017, PLoS ONE, Published: January 11, 2017
http://dx.doi.org/10.1371/journal.pone.0169121 [Publisher Link]

The setup:

The cohort studied comprised 280 graduate students who entered the BBSP at UNC from 2008-2010; 195 had graduated with a PhD at the time of this study (July 2016), 45 were still enrolled, and 40 graduated with a Master’s degree or withdrew. The cohort included all of the BBSP students who matriculated from 2008-2010.

The major outcome measure:

Publications by each student during graduate school were quantified with a custom Python script that queried Pubmed (http://www.ncbi.nlm.nih.gov/pubmed) using author searches for each student’s name paired with their research advisor’s name. The script returned XML attributes (https://www.nlm.nih.gov/bsd/licensee/elements_alphabetical.html) for all publications and generated the number of first-author publications and the total number of publications (including middle authorship) for each student/advisor pair.

For analysis they grouped the students into bins of 3+, 1-2 or 0 first author pubs with a ‘0+’ category for zero first-author pubs but at least one middle-author publication.

OMG! Nothing predicts graduate school performance (especially those evil, evil, biased – I mentioned evil, right? – standardized scores).

Yes, even people who score below the 50th percentile on quantitative or verbal GRE land first-author publications! (Apple-polishing GPA kids don’t seem to fare particularly well, either, plenty of first author publications earned by the 3.0-3.5 riff-raff.)

Oh bai the wai…prior research experience doesn’t predict anything either.

Guess what did predict first author publications? Recommendation scores. That’s right. The Good Old Boys/Girls Club of biased recommendations from undergraduate professors is predictive of the higher producing graduate students.

As the authors note in the Discussion, this analysis focused only on student characteristics. It could not account for the mentor lab, interaction of student characteristics with the mentor lab characteristics and the like.

I’ll let you Readers mull this one over for a bit but I was struck by one thing.

We may be talking at cross purposes when we discuss how application metrics are used to predict graduate student success because we do not have the same idea of success in mind.

This analysis suggests the primary measure of success of a graduate student is the degree to which they succeeded in being a good data-monkey who produces a lot of publishable stuff within the context of their given research laboratory. And by this measure, nothing is very predictive, going by the Hall et al analysis, except the recommendation letter of those who are trying to assess the whole package from their varied perspectives of “I know it when I see it*”.

Grad student publication number is, of course, related to who will go on to be a success as a creative independent scientist because of the very common belief that past performance predicts future performance. Those who exit grad school with zero pubs are facing an uphill battle to attain a faculty position. Those with 3+ first author pubs will generally be assumed to be more in the hunt as a potential future faculty member all along the postdoctoral arc.

Assuming all else equal.

This is another way we talk past each other about standardized scores, etc.

The choice of the PI who is trying to select a graduate student for their lab can assume “all else equal”. Approximately. Same lab, same basic environment. We don’t have this information from Hall et al. and I think it would be pretty difficult to do the study in a way that used same-lab as a covariate. Not impossible…you just are going to need a very large boat.

I think of it this way. Maybe there are some labs where everyone gets 3 or more first-author papers? Maybe there are some where it takes a very special individual indeed to get more than one in the course of graduate school? And without knowing if the student characteristics determine the host lab, we have to assume random (ish) assignment. Thus it could be the case that the better GREquant, for example, gives a slight advantage within lab but this is wiped out by the variability between-labs.

The choice of a selection committee for graduate programs can be less confident about all else being equal. They have to ask what sort of student can be successful across all of the lab environments in the program. Or successful in the majority of them. The Hall et al. data say that many types can be. But we are still asking a question of whether the training environment is such an overwhelming factor that almost nothing about the individual matters. This seems to be the message

If so, why are we bothering to select students at all? Why have them apply with any details other than the recommendation letters?

Maybe this is another place we are speaking at cross purposes. Some of us may still believe that the point of graduate school selection is to train the faculty (or insert any other specific career outcome if relevant) of tomorrow. Part of the goal, therefore, may be to select people on the basis of who we think would be best at that future role**, regardless of the variation in papers generated with first-author credit as a graduate student.

Is the Hall et al. paper based on a straw notion of “success”?

I think you’ve probably noticed, Dear Reader, that my opinion is that the career of grant-funded PI takes some personality characteristics that are not easily captured by the number of first-author pubs as a graduate student. Grit and resilience. Intrinsic motivation to git-er-done. Awareness of the structural, career-type aspects. At least a minimal amount of interpersonal skills.

What I am not often on about is the fact that I think that given approximately equal conditions, smarts matters. This is not saying that smarts is the only thing. If you are smart as all heck and you don’t have what it takes to be productive or to take a hit, you aren’t going to do well. It’s the flip side. If two people do have grit and resilience and motivation…the smarter person is going to have an easier time of it or achieve more for the same effort**. On average.

And this is a test that is not performed in the new paper. Figuring out how to compare outcomes within laboratory groups might be an advance on this question.

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*When I write recommendation letters for undergrads who have worked with me I do not have access to their standardized scores or grades. I have my subjective impressions of their smarts and industry from their work in my lab to go by. That’s it. Maybe other people formally review a transcript and scores before writing a letter? I doubt it but I guess that is possible.

**Regarding that future role, again it may be a question of what is most important for success. Within our own lab, we are assuming that differential opportunity to get publications is not a thing. So since this part of the environment is fixed, we should be thinking about what is going to lead to enhanced success down the road, given conceivable other environments. From the standpoint of a Program, the same? or do we just feel as though the best success in our Program is enough to ensure the best success in any subsequent environment? The way we look at this may be part of what keeps us talking past each other about what graduate selection is for.