Abstract
There is no well-defined subfield of interpretive analysis applied to quantitative data, so it is hard to define its contours. Several classic works about quantification and social data took an interpretive approach, and some studies of statistical institutions, especially censuses, contribute importantly to the field, even though the authors didn’t define themselves as interpretivist. Unlike in some fields,
however, these works were not self-consciously written in conversation with each other. They did not engage in the kind of sectarian debates characteristic of development of new theories, nor were they chiefly concerned with cumulatively building a unified theory. In this chapter, I try to formulate an overarching interpretive theory of quantitative analysis, building on some classics and my own work.
A map of the field would highlight three major questions. I separate them analytically, but as will quickly become evident, these questions don’t willingly remain caged in separate categories. First, how does a number come to be? Quantitative methods are based on numerical data and numerical data are ultimately frequency counts-how much of something is there or how often does something happen? Arithmetic and statistics manipulate these counts, so before we can discuss quantitative analysis as narrative, we have to tell the backstory. Where do counts come from? Who does them? What are the mental processes by which people categorize and count?
What are the social processes that produce numerical data for public consumption? One body of work in this field, then, narrates the intellectual and social history of a number (or an indicator, a measure, an index, a data set). Second, what do numbers mean and how do they get their meaning? Quantitative data have their surface meaning, their face value, their taken-for-granted meaning to statisticians who produce them. Social scientists use the term “raw data” to describe the numbers and tallies that quantitative analysts manipulate in order to produce statistical results. But as I have argued (Stone 1988, 2012), quantitative analysts don’t present results “raw” either. They arrange the data in visual displays and they characterize their statistical results in words, telling readers or listeners what to make of the results. “We can see rapid growth.” “This finding is significant.” In short, producers of quantitative analyses give them meaning by narrating them. Interpretive scholars use techniques of literary analysis to show how numbers convey symbolic meanings and work as narrative. Third, how do numbers acquire their power? Scholars in many fields have long noted the ability of quantitative data to convince audiences of their truth. Quantitative data also have extraordinary power to persuade in policy and political debates. Numbers can say “This IS a problem” or “This solution IS effective (or is NOT).” It is almost a truism of the field to say that quantitative analyses gain their persuasive power from the more general cultural authority of science. But while the larger cultural frame of science surely does contribute to the authority of quantitative analyses, quantitative analysts self-consciously use narrative techniques to strengthen the credibility and authority of their numbers. After a brief history of the field, such as it is, I return to these three questions.