Quantitative History
Let’s be honest. When History majors hear the words “quantifying” and “history” together, it results in a groan–many of us immediately think of numbers and data that can be extremely tedious and boring to look at. I confess, I was one of these people: the thought of looking at large numbers and analyzing them sounded as exciting as this (click it, it’s riveting). However, once I learned the powerful potential of quantitative historical analysis, the importance of this practice dawned on me.
I gravitated to The Revival of Quantification: Reflections on Old New Histories article by Ruggles because the topic of the history of historical studies is always fascinating to me. The notion that the new first wave of historians that were reformists believed there was no historical objectivity yet engaging in statistical analysis seemed at first glance, contradictory to me. Seeing that both the New Economic historians and New Politicians historians were objectivists, it seemed puzzling why the first new historians would be relativists. Statistics initially seemed to be in the domain of objectivity, however, eventually I understood that if one examines the sample size, the people sampled, and the different methods of analysis that have their flaws, it becomes clear that the case for historical relativity seems more cogent.
Those that objected to “Quanto-History” such as Himmelfarb, disagreed on the basis that it was “bottom-up”, which is something I wanted to read more in the Ruggles article. I was curious as to why a historical approach that was “bottom-up” would be a negative thing, so I did more research on my own. I read Gertrude Himmelfarb’s The New History and the Old book, which describes old history as one that focused on constitutions, war, regimes, and revolutions. The new histories that rely on empirical evidence focuses on the common man and often alienates political history which is the realm of old history. The danger of alienating political history is that it allows the historian to impose their sense of reality onto the past while ignoring the politics that while are controlled by the ruling class, still affect everyone.
Himmelfarb also saliently brings up that quantitative history lacks in the field of morality, as one could use the statistical data to prove that slaves reveled in great economic conditions. Himmelfarb writes:
The critique of the economic analysis of slaves is not in its analysis, but rather the entire approach itself. As Himmelfarb states: “no amount of statistical ingenuity will suffice.” While Ruggles brings up the problems with quantohistory and the slavery example, I wish Ruggles expounded a bit more on Himmelfarb’s arguments; Himmelfarb’s case is presented in a manner that seems almost deficient in the article.
HGIS
Having previous experience working with QGIS in Dr. Hoy’s Mapping History class, I can summarize my experience in one sentence: magical when it works right, headache-inducing when it doesn’t work. The unfortunate problem is that for a student that possesses only entry-level knowledge of mapping software and a laptop that is barely more powerful than the newest phone, QGIS can lead to a lot of frustration.
My experience during the computer lab was definitely not ideal. In the middle of the lab, the software froze and did not respond to any input. When I opened the snipping tool on windows to take pictures of software, that also did not work as well. I had the absolute joy of working with a computer that had both software that refused to respond. Finally, the computer presented a black screen and that was the end of my lab.
However, despite my not-so-positive experience with mapping software, when it is utilized correctly, it can produce results such as Cunfer’s Scaling the Dust Bowl, an extremely effective and convincing historical analysis that would have not been possible without quantitative history and HGIS. The way it presents information in a systematic manner that is extremely thorough would be extremely difficult to show using other tools and methods of anaylsis. Quantitative history and HGIS are both things that are not perfect and have many flaws, but if utilized correctly, it can be extremely valuable.
