Theory, Data, Interpretations, and More Theory

Abstract

Even though the paper had been revised and resubmitted for a second round of reviews at JCA, at some level I felt like the authors were perversely begging the editorial referees to recommend rejection of the manuscript. The revision involved an extensive rewriting of some sections, citations were added, the data were subjected to additional new analysis, and the authors provided very detailed supplemental notes to explain the changes to the reviewers. Yet for the directive from the decision on the first version that they provide a stronger conceptual context for study, the authors only noted they considered it unnecessary. To my amazement, they refused to provide a theoretical basis for the research. The omission was explained with the simple assertion that are behaviorists and therefore not concerned with theory, as if that would excuse data collection for its own sake. At the most basic level, they misunderstood that psychologists invented behaviorism itself as a basis for theoretical explanations, prediction and testing. If you will forgive a minor lapse into the pedantic, the original term referenced a direction for research in a social science that would allow control and measurement of all relevant variables by ignoring human thought or cognition. Instead of speculating as to what might transpire in people's minds, only behavior responses would be measured in relation to test stimuli so researchers could conduct experiments that would provide stronger tests of theoretical predictions. In other words, behaviorism was a route to being more scientific in a manner similar to the so-called hard sciences of chemistry or physics. This narrow and more directly measurable focus, in turn, allowed for greater use of statistical analysis of experimental results. The goal was a greater use of methods for stronger theories, or so they hoped. This tendency for stimulus-response experiments and overall preference for some type of mathematical statement has today spread into other social sciences. To critics of these approaches, important research questions are ignored if they do not fit into confines of a quantitative study. The techniques for mathematical data manipulation have become increasingly sophisticated and complex, and governed by strict assumptions, as if the analytical method is an end unto itself. Making more sense might be the preeminent economist and mathematician of the late 19th century Alfred Marshall who is credited with stating a five-step directive for use of math in research: (1) use it as a shorthand language rather than as an engine of inquiry, (2) keep them until you are done, (3) translate into English; (4) illustrate with examples that are important in real life, and (5) bum the mathematics. In the 21st century, people have forgotten this. But my point here is not to condemn any research approach, be it quantitative or qualitative, but to place emphasis on the thought behind the research. Numbers suggest, constrain, and refute; they do not, by themselves, specify the content of (Gould 1981, 106). As the numbers and methods gain greater attention, some might forget or ignore the need for a conceptual foundation that must precede the analysis. The late naturalist Stephen Jay Gould repeatedly explained that often think, naively, that missing data are the primary impediments to intellectual progress--just find the right facts and all problems will dissipate. But barriers are often deeper and more abstract in thought. We must have access to the fight metaphor, not only the requisite information. Revolutionary thinkers are not, primarily, gatherers of facts, but weavers of new intellectual structures (Gould 1985, 151). As one of his many examples, he explained that the original theories of DNA and genetics needed the invention of a computer not to crunch existing data but to provide a conceptual metaphor for how biological binary signals can work. …