I am a crafting snob. There was a time in the not so distant past that I highly prized hand embroidery above all else. True, it took me several hours to complete even the smallest project, but I believed it was the struggle that added to the special-ness of the project, and anyone who would resort to using an embroidery machine was “cheating.” (That is, until I got an embroidery machine of my own, which I will discuss presently).
But what does all this have to do with Macroanalysis? While reading the beginning chapters of the book, I saw a parallel between my sewing by hand vs. machine and close reading vs. big data. I liked that Jockers made it clear there are places for both close reading and using big data for literary analysis. (I started to feel myself bristle at the idea of setting close reading aside for more computational methods).
Using a computer program to analyze works of literature, like using an embroidery machine to sew a design, isn’t superior/inferior—it is just different. Using an embroidery machine allows the sewer to create intricate designs quickly and with precision. Likewise, using the computer allows scholars to analyze a great deal of data quickly and helps them to see patterns they weren’t able to find before. For instance, Jockers gives examples throughout the book how the use of function words can help determine data about a certain text such as gender and nationality of the author. Close reading scholars are most likely to focus on content specific words and phrases, which means they might miss some of the important information function words contain.
Using macroanalysis also allows for less misinterpretation due to a lack of data. It is implausible, for example, for a nineteenth century British fiction expert to have read all of the approximately 6,000 novels that exist from that period. Jockers estimates it would take sixteen and a half years for the scholar to close read these novels, if the person maintains the pace of one book per day (19). Previous scholarship would consider close reading a subset of those novels and then making assumptions based on that data. Because not all of the data is being considered, there is a possibility the assumptions could be wrong, which Jockers makes apparent in his discussion of Fanning’s assumption that Irish American writers being inactive from 1900-1930 (38-9). After examining further data, Jockers was able to determine it was only Irish American male writers in the Eastern United States who weren’t writing during that period.
Once macroanalysis is able to give “big picture” type patterns, it is then that close reading can start to focus on the reasons behind those patterns. Because macroanalysis and close reading look at a set of data in different ways, using both creates a veritable one-two punch for scholars. Jockers writes, “The data through which our literary arguments are built will always require the careful and imaginative scrutiny of the scholar. There will always be a movement from facts to interpretation of facts” (30). Macroanalysis is good for determining the “what;” close reading excels at the “why.”