Ok, in my research methods class, we are hitting an overview of statistics in the closing weeks of the semester. As such, i would prefer to include some empirical examples to visualize the things we’re going to talk about that are fun / outside my typical wheelhouse. So, do you have any favorite (read: typical, atypical, surprising, bizarre, differentially distributed, etc.) examples of univariate distributions and/or bivariate associations that may “stick” in their memories when they see them presented visually? I have plenty of “standard” examples i could draw from, but they’re likely bored with the one’s i think of first by this point in the term. So, what are yours? It’s fine if you just have the numbers, i can convert them to visualizations, but if you have visual pointers, all the better.
An “ngram” is an n-word phrase. So, “human science” is a 2-gram. If you’ve been living under a rock, you may not have heard about the latest gift from google – having scanned most published books in a number of major languages, they recently provided us the data, and a tool for easy visualization, of the relative popularity of words and phrases over time. I thought I’d explore some terms of broad interest to sociologists with no particular idea about what I’d find. Please take a look and help me interpret them.
Below you’ll find the relative frequency with which the major social scientific disciplines (plus psychology) are mentioned in books. Let me explain the numbers on the Y-axis. “psychology” is the most common word. In 1950, it accounted for about 0.0034% of all words published. In other words, google takes all the books published in a given year, and counts how many occurrences there are for each word. Then it divides that number by the total number of words published. There are many methodological considerations… for example, each book only counts once, regardless of how many copies are sold.
So what do we see? Well, the rank order doesn’t really change over time. Psychology gets the most mentions, then economics, sociology, anthropology and finally political science. It’s tempting to interpret this as measuring the prominence of each discipline, but this isn’t a great test. For starters, authors aren’t generally referring to the academic discipline when they use the word “psychology,” but they are when they use the phrase “political science.” Sociology is probably between the two in terms of, “the share of word-mentions which actually refer to the academic discipline.”
I feel a bit more comfortable making inferences based on how each of these terms changes over time. For example. In in 1950, sociology received almost twice as many mentions as anthropology. The situation was similar in 1980. But in 1999, anthropology achieved parity with sociology, and they have been close to even in the decade since. This appears to be evidence that anthropology gained prominence, relative to sociology, in the last half of the twentieth century. Naturally, I don’t think we should put too much stock in this single measure of prominence. We might want to look at trends in the number of students, and people working in each discipline. We could count mentions in periodicals, citations to academic articles. We could look to see how each word is used, and how much their usage changes over time. Do these other measures corroborate, counter, or otherwise contextualize these trends?
I can’t give you easy access to all that data, but you can explore ngrams for yourself!
So readers, what do you see in this graph? Care to nominate and discuss plausible/potentially useful and/or plainly dangerous assumptions that help us interpret these ngrams or lead us astray?
Checking back on Code and Culture’s Network Analysis Software Poll (igraph and statnet are the top two)
Most quantitative social scientists, myself included, master particular statistical techniques, but have limited understanding of the breadth or history of statistical practice. Academic specialization is necessary, but sometimes we could learn a lot by taking a broader view. I found it interesting to learn a little more about some of the most influential statisticians and their contributions in this article by Daniel Wright: “Ten Statisticians and their Impacts for Psychologists.”
Though I enjoyed Wright’s piece, one thing I felt was missing was a connection to philosophy and sociology of science. What are the goals of empirical research in the social sciences? How have the methods these statisticians invented changed social science, and science more generally?
Wright, D. (2009). Ten Statisticians and Their Impacts for Psychologists Perspectives on Psychological Science, 4 (6), 587-597 DOI: 10.1111/j.1745-6924.2009.01167.x