Author Archives: Emil

Personality correlates of breadth vs. depth of research scholarship

Cross-post from my blog.

An interesting study has been published:

This is relevant to the study of polymathy, which of course involves making broader contributions to academic areas. The authors’ own abstract is actually not very good, so here is mine: They sent a personality questionnaire to two random sample of scientists (diabetes researchers). This field was chosen because it is large and old, thus providing researchers with lots of researchers to analyze. They out a couple of thousand of these questionnaires and received received 748 and 478 useful answers. They then hired some other company to provide researcher information regarding the researchers. To measure depth vs. breadth, they used the keywords associated with the articles. More different keywords, means more breadth.
They used this information as well as other measures and their personality measures in four regression models:

polymath_tableS3 polymath_tableS2 polymath_table2 polymath_table1

The difference between the sets of regression models is the use of total publications vs. centrality as a control. These variables also correlate .52, so it not surprisingly made little difference.

They also report the full correlation matrix:


Of note in the results: Their measures of depth and breadth correlated strongly (.59), so this makes things more difficult. Preferably, one would want a single dimension to measure these along, not two highly positively correlated dimensions. The authors claimed to do this, but didn’t:

The two dependent variables, depth and breadth, were correlated positively (r = 0.59), and therefore we analyzed them separately (in each case, controlling for the other) rather than using the same predictive model. Discriminant validity is sup- ported by roughly 65% of variance unshared. At the same time, sharing 35% variance renders the statistical tests somewhat conservative, making the many significant and distinguishing relationships particularly noteworthy.

Openness (5 factor model) correlated positively with both depth and breadth, perhaps just because these are themselves correlated. Thus it seems preferable to control for the other depth/breadth measure when modeling. In any case, O seems to be related to creative output in these data. Conscientiousness had negligible betas, perhaps because they control for centrality/total publications thru which the effect of C is likely to be mediated. They apparently did not use the other scales of the FFM inventory, or at least give the impression they didn’t. Maybe they did and didn’t report because near-zero results (publication bias).

Their four other personality variables correlated in the expected directions. Exploration and learning goal orientation with breadth and performance goal orientation and competitiveness with depth.

Since the correlation matrix is published, one can do path and factor analysis on the data, but cannot run more regression models without case-level data. Perhaps the authors will supply it (they generally won’t).

The reporting on results in the main article is lacking. They report test-statistics without sample sizes and proper (d or r, or RR or something) effect sizes, a big no-no:

Study 1. In a simple test of scientists’ appraisals of deep, specialized studies vs. broader studies that span multiple domains, we created brief hypothetical descriptions of two studies (Fig. 1; see details in Supporting Information). Counterbalancing the sequence of the descriptions in a sample separate from our primary (Study 2) sample, we found that these scientists considered the broader study to be riskier (means = 4.61 vs. 3.15; t = 12.94, P < 0.001), a less significant opportunity (5.17 vs. 5.83; t = 6.13, P < 0.001), and of lower potential importance (5.35 vs. 5.72; t = 3.47, P < 0.001). They reported being less likely to pursue the broader project (on a 100% probability scale, 59.9 vs. 73.5; t = 14.45, P < 0.001). Forced to choose, 64% chose the deep project and 33% (t = 30.12, P < 0.001) chose the broad project (3% were missing). These results support the assumptions underlying our Study 2 predictions, that the perceived risk/return trade-off generally favors choosing depth over breadth.

Since they don’t mean the SDs, one cannot calculate r or d from their data I think. Unless one can get it from the t-values (not sure). One can of course calculate odds ratios using their mean values, but I’m not sure this would be a meaningful statistic (not a ratio scale, maybe not even an interval scale).

Their model fitting comparison is pretty bad, since they only tried their preferred model vs. an implausible straw man model:

Study 2. We conducted confirmatory factor analysis to assess the adequacy of the measurement component of the proposed model and to evaluate the model relative to alternative models (21). A six-factor model, in which items measuring our six self-reported dispositional variables loaded on separate correlated factors, had a significant χ 2 test [χ 2 (175) = 615.09, P < 0.001], and exhibited good fit [comparative fit index (CFI) = 0.90, root mean square error of approximation (RMSEA) = 0.07]. Moreover, the six-factor model’s standardized loadings were strong and significant, ranging from 0.50 to 0.93 (all P < 0.01). We compared the hypothesized measurement model to a one-factor model (22) in which all of the items loaded on a common factor [χ 2 (202) = 1315.5, P < 0.001, CFI = 0.72, RMSEA = 0.17] and found that the hypothesized six-factor model fit the data better than the one-factor model [χ 2 (27) = 700.41, P < 0.001].

Not quite sure how this was done. Too little information given. Did they use item-level modeling or? It sort of sounds like it. Since the data isn’t given, one cannot confirm this, or do other item-level modeling. For instance, if I were to analyze it, I would probably have the items of their competitiveness and performance scales load on a common latent factor (r=.39), as well as the items from the exploration and learning scales on their latent factor, maybe try with openness too (r’s .23, .30, .17).

Of other notes in their correlations: Openness is correlated with being in academia vs. non-academia (r=.22), so there is some selection going on not just with general intelligence there.


Polymathy and Reddit

Interdisciplinarity is growing. The internet has made it easy for people with diverse interests to research and work on many projects unrelated to their job or primary study. Reddit, the popular link-site / discussion board, is no exception. Reddit is a great tool for people with diverse interests because it makes it easier to follow developments in many different fields without belonging to the relevant social circles.

I’m familiar with at least two subreddits: /r/interdisciplinary and /r/polymath. The first is the largest and perhaps has the best content. Better, we have control over it, so we can attempt to link it up with the polymath project if there is some occasion to do so.


Scientific genius is associated with abilities in the fine arts

A study finds scientific genius (measured in various ways) is associated with abilities in the fine arts. The abstract of the study is:

Various investigators have proposed that “scientific geniuses” are polymaths. To test this hypothesis, auto­ biographies, biographies, and obituary notices of Nobel Prize winners in the sciences, members of the Royal Society, and the U.S. National Academy of Sciences were read and adult arts and crafts avocations tabulated. Data were compared with a 1936 avocation survey of Sigma Xi members and a 1982 survey of arts avocations among the U.S. public. Nobel laureates were significantly more likely to engage in arts and crafts avocations than Royal Society and National Academy of Sciences members, who were in turn significantly more likely than Sigma Xi members and the U.S. public. Scientists and their biographers often commented on the utility of their avocations as stimuli for their science. The utility of arts and crafts training for scientists may have important public policy and educational implications in light of the marginalization of these subjects in most curricula.

Full citation: Root-Bernstein, Robert, et al. “Arts foster scientific success: Avocations of Nobel, National Academy, Royal Society, and Sigma Xi members.” Journal of the Psychology of Science and Technology 1 (2008): 51-63. Non-gated download link.

This should have the interest of followers of this blog. Here’s some of the data:


As can be seen, Nobel winners were much, much more likely to have artistic interests than members of the general public. By all means, read the paper yourself. It is only 13 pages. The authors have spent some time collecting anecdotes from various scientific geniuses that illustrate their love for the arts and science.


Polymaths, freedom of information, and copyright – why we need copyright reform to more effectively increase the number of polymaths

Emil Kirkegaard, board member of Pirate Party Denmark


Polymaths are people with a deep knowledge of multiple academic fields, and often various other interests as well, especially artistic, but sometimes even things like tropical exploring. Here I will focus on acquiring deep knowledge about academic fields, and why copyright reform is necessary to increase the number of polymaths in the world.

Learning method
What is the fastest way to learn about some field of study? There are a few methods of learning, 1) listening to speeches/lectures/podcasts and the like, 2) reading, 3) figuring out things oneself. The last method will not work well for any established academic field. It takes too long to work out all the things other people have already worked out, if indeed it can be done at all. Many experiments are not possible to do oneself. But it can work out well for a very recent field, or some field of study that isn’t in development at all, or some field where it is very easy to work it things oneself (gather and analyze data). Using data mining from the internet is a very easy way to find out many things without having to spend money. However, usually it is faster to find someone else who has already done it. But surely programming ability is a very valuable skill to have for polymaths.

For most fields, however, this leaves either listening in some form, or reading. I have recently discussed these at greater length, so I will just summarize my findings here. Reading is by far the best choice. Not only can one read faster than one can listen, the written language is also of greater complexity, which allows for more information acquired per word, hence per time. Listening to live lectures is probably the most common way of learning by listening. It is the standard at universities. Usually these lectures last too long for one to concentrate throughout them, and if one misses something, it is not possible to go back and get it repeated. It is also not possible to skip ahead if one has already learned whatever it is the that speaker is talking about. Listening to recorded (= non-live) speech is better in both of these ways, but it is still much slower than reading. Khan Academy is probably the best way to learn things like math and physics by listening to recorded, short-length lectures. It also has built-in tests with instant feedback, and a helpful community. See also the book Salman Khan recently wrote about it.

If one seriously wants to be a polymath, one will need to learn at speeds much, much faster than the speeds that people usually learn at, even very clever people (≥2 sd above the mean). This means lots, and lots of self-study, self-directed learning, mostly in the form of reading, but not limited to reading. There are probably some things that are faster and easier to learn by having them explained in speech. Having a knowledgeable tutor surely helps in helping one make a good choice of what to read. When I started studying philosophy, I spent hundreds of hours on internet discussions forums, and from them, I acquired quite a few friends who were knowledgeable about philosophy. They helped me choose good books/texts to read to increase the speed of my learning.

Finally, there is one more way of listening that I didn’t mention, it is the one-to-one tutor-based learning. It is very fast compared to regular classroom learning, usually resulting in a 2 standard deviation improvement. But this method is unavailable for almost everybody, and so not worth discussing. Individual tutoring can be written or verbal or some mix, so it doesn’t fall under precisely one category of those mentioned before.

How to start learning about a new field
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