On Beginnings: Part 20

This essay (serialized here across 24 separate posts) uses words and numbers to discuss the uses of words and numbers — particularly examining evaluations of university degrees that employ statistical data to substantiate competing claims. Statistical analyses are crudely introduced as the mode du jour of popular logic, but any ratiocinative technique could likely be inserted in this re-fillable space and applied to create and defend categories of meaning with or without quantitative support. Questions posed across the series include: Is the data informing or affirming what we believe? What are the implications of granting this approach broader authority? The author, Melanie Williams, graduated from UA in 2006, with a B.A. in Anthropology and Religious Studies.

 

All I set out to examine is the highly connected, highly contingent, and insular type of dialogue that informs conventional knowledge – whether it’s science, pop culture, or technological trend – whose re-presentation as “truth” is doing some behind-the-curtain work. And to ask, then, if we should be mindful of the questions we pose, of the methods we use to frame the data we select, and the ends toward which we strive in analyzing that data?  In the case of these many articles and studies rating the worth of university education vis-à-vis any other course of life, is the question really fair?  Should we expect any choice we make to have some degree of intrinsic, immutable value?  Maybe “facts” have a more precarious existence than we like to believe, since they require continued corroboration to retain their reverent status as “fact,” and since countless bits of information so categorized we later brusquely tossed under the bus. The models we use to view certain issues in certain ways aren’t really stable – they’re formed and re-formed in the arenas of their respective discursive participants. Methodologies change constantly in light of new circumstances and cross-examinations. This may have been Baseball-Bill James’s point:  We assemble meaningful collections of data to respond to a question, which in turn will likely generate another question. This is the basic form of the peer-review process academics and researchers use to exchange the ideas that occupy them and affect the rest of us, if at all, with an aura of natural progression. But peer-review-style dialogue doesn’t reduce all submissions down to the “truth;” it’s just a mechanisms of conservation – an organized way to sustain the debates over the meanings of signs within shared paradigms.  My diversion into the issues I take with the Big Data trend stems from these distinctions, since large aggregates of data seem to be used to authorize certain assertions that – due to the fact that math is even employed – lends a degree of inscrutability that keeps people from challenging the associated narratives.  And why shouldn’t we challenge them?  In this regard, I love statistical analyses, not because they provide definitive or non-definitive answers, but because they often raise questions about answers we thought we had in the bag.  To return to the popular discourse on the real, imagined, spiritual or economic merits of a college education:  there likely is no inherent value to a degree whose usefulness you will not be able to predict. There is no correct path to a career you probably won’t plan.  It may not be helpful to assume people will have any definitive opinion of the usefulness of their degree – or any type of training, for that matter.  Many of us pursue whatever will occupy us for the moment, but we may very well change our minds in 6 months, 2 years, or, if you’re like me, I can’t remember what we were talking about.

Part 21 coming tomorrow morning…

On Beginnings: Part 12

This essay (serialized here across 24 separate posts) uses words and numbers to discuss the uses of words and numbers — particularly examining evaluations of university degrees that employ statistical data to substantiate competing claims. Statistical analyses are crudely introduced as the mode du jour of popular logic, but any ratiocinative technique could likely be inserted in this re-fillable space and applied to create and defend categories of meaning with or without quantitative support. Questions posed across the series include: Is the data informing or affirming what we believe? What are the implications of granting this approach broader authority? The author, Melanie Williams, graduated from UA in 2006, with a B.A. in Anthropology and Religious Studies.

 

Nate Silver’s methodology, to the extent he describes it, seems to be stitching together a few different types of analyses.  His aims and limits are fairly well defined:  Forecast the results of elections in various states in which a nomination process whittles the initial field of candidates down to a handful.  For each open seat, one candidate will triumph.  Silver then sets about combining political polling data, from which he derives the probabilities of his forecasts, the way you would bet on Natter Phineas to win at 6:1.  We could stop there, and chalk his success up to the inviolable laws of math.  Or we could ask a few more questions.  After all, elections don’t offer a clean analogy to sporting events, or poker, or red cars vs. blue cars.  How often are elections contests between equals?  Or contests, for that matter, at all?  I voted last week.  Of the near-dozen positions on the ballot, two were contested.  A closer examination of the 2008 senatorial election results suggests there were a great many seats in the U.S. Senate in which the incumbent was not challenged aggressively, or at all.  I don’t mean to diminish Nate Silver’s achievements.  I just want to point out that many candidates have ostensible advantages in our elections, making any survey of individual odds less clear cut than it might appear at the outset.  Nate Silver’s data analyses may work, then, because he doesn’t solely rely on them.  There are other sources of data, after all, for deriving an estimate of a candidate’s ephemeral popularity.  When polling data does not inspire an adequate level of confidence, or when the results yield no statistically significant edge to any party, you might turn to the effects of factors that may or may not be reflected in the polling data:  the exigencies of re-districting lines, political alliances and legacies, successful state and federal social or economic programs, popular scandals, infamous gaffes, (it’s so hard to choose), or any other histrionic hoo-ha of our political stage that invites the intrepid to nestle up to the glow of a wider world’s bad reality tv.

What happened this year?

Part 13 coming tomorrow morning…

On Beginnings: Part 11

This essay (serialized here across 24 separate posts) uses words and numbers to discuss the uses of words and numbers — particularly examining evaluations of university degrees that employ statistical data to substantiate competing claims. Statistical analyses are crudely introduced as the mode du jour of popular logic, but any ratiocinative technique could likely be inserted in this re-fillable space and applied to create and defend categories of meaning with or without quantitative support. Questions posed across the series include: Is the data informing or affirming what we believe? What are the implications of granting this approach broader authority? The author, Melanie Williams, graduated from UA in 2006, with a B.A. in Anthropology and Religious Studies.

 

Nate Silver’s B.A. in Economics landed him his own crappy job, which led him to cultivate an interest in baseball statistics that preceded his fortuitous entrée into political forecasting.  Silver’s early influences included the pioneers of sabermetrics, so I dug up an edition of the New Baseball Historical Abstract of liberal arts major Bill James.  Maybe Bill can speak to his own motivations for collecting the baseball statistics he helps innovate:

“Baseball statistics are simplifications of much more complex realities.  It may be unnecessary to say this because, of course, all human understanding is based on simplifications of more complex realities….  Baseball statistics are interesting not because they answer questions for us, but because they may be used to study issues.  The value of baseball statistics in identifying the greatest players is not that they answer all of the questions involved, but that they provide definitive answers to some of the questions involved, which enables us to focus on others.” Continue reading

A Response to “Responsible Research Practices,” Part 9: Broader Public

lecturehallThis is an installment in an ongoing series on the American Academy of Religion’s recently released draft statement on research responsibilities.
An index of the
complete series (updated as each
article is posted) can be found here.

Much like the earlier post on doing human subjects research, we find a truism enshrined in the draft document’s eighth bullet point (at least in the opening clause; I include the ninth also since it too is related):

publicunderstandingI’m not sure if there are many scholars out there who decline to provide an account of what they’re up to — it would not be difficult to understand conference presentations, publications, and even the teaching that we do to be doing just that. So I’m unsure why this needs to be included as one of the thirteen obligations the AAR’s committee sees fit to put into their document. Even paying attention to the threefold grouping into which they divide this reporting — our research questions, methods, and findings — isn’t innovative and therefore doesn’t help to clarify why this item was included; for this reads as if it was offering instructions to a lower level undergraduate students on how to write a research paper.

In fact, given that this is pretty much what we, as scholars, all already do, without being told to, it’s somewhat surprising that we also weren’t advised to have a thesis when we write a paper. Continue reading