Variables are valuable: Making a case for deductive modeling

Abstract

Following the quantitative turn in linguistics, the field appears to be in a methodological "wild west"state where much is possible and new frontiers are being explored, but there is relatively little guidance in terms of firm rules or conventions. In this article, we focus on the issue of variable selection in regression modeling. It is common to aim for a "minimal adequate model"and eliminate "non-significant"variables by statistical procedures. We advocate an alternative, "deductive modeling"approach that retains a "full"model of variables generated from our research questions and objectives. Comparing the statistical model to a camera, i.e., a tool to produce an image of reality, we contrast the deductive and predictive (minimal) modeling approaches on a dataset from a corpus study. While a minimal adequate model is more parsimonious, its selection procedure is blind to the research aim and may conceal relevant information. Deductive models, by contrast, are grounded in theory, have higher transparency (all relevant variables are reported) and potentially a greater accuracy of the reported effects. They are useful for answering research questions more directly, as they rely explicitly on prior knowledge and hypotheses, and allow for estimation and comparison across datasets.