Variables are valuable

Making a Case for Deductive Modeling

Bibliographic Data

ID7913239
AuthorsDavid Tizón-Couto (0000-0003-0788-7954, Universidade de Vigo, corresponding author), David Lorenz (0000-0002-7451-099X, Institut für Anglistik/Amerikanistik, Universität Rostock , Rostock , Germany)
Year2021
Volume59
Issue5
Pages1279-1309
Publication date2021-09-27
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueLinguistics (JOURNAL)
Journal identifiersISSN: 0024-3949 • E-ISSN: 1613-396X
PublisherWalter de Gruyter GmbH (PUBLISHER • DE)
DOI10.1515/ling-2019-0050
OpenAlexW3197452240
LanguageEN
Citations received10
References cited64

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

Computational linguistics · Contrast (vision) · Econometrics · Field (mathematics) · Focus (optics) · Machine learning · Model selection · Natural language processing · Selection (genetic algorithm) · Statistical model · Transparency (behavior) · Variable (mathematics) · Artificial Intelligence · Categorization, perception, and language · Computational and Text Analysis Methods · Computer Science · Mathematics · Natural Language Processing Techniques