Force and Influence in Content Analysis: The Production of New Social Knowledge

We examine the two traditions of content analysis: the first in which one substitutes words of a text with categories, and the second in which one looks for clusters of words that may refer to a theme. In the first tradition, preexisting dictionary categories give meaning to the words; in the second, meaning comes after the fact. Preexisting dictionary categories (the substitution model) are calibrated instruments applied within experimental designs that leave no space for doubt; meanwhile, the ability of the correlational model to conjure up complex themes from fragments of a text yields no unique solution. These differences have bearings on the production of new social knowledge. We expound on the epistemological foundations of the two traditions of interpretation and draw from them decision rules upon which one may rely for choosing among appropriate content-analytic tactics. Two reasons make this essay timely and critical: (1) the increasing variety of new content-analyticsoftware for particular purposes and (2) the almost exclusive focusing on software and technology at the expense of adjusting the choice of the software to the nature of the text. Two studies, one in historiometry, the other in autobiography, illustrate the liabilities and benefits of the two models of content analysis.