Strategies for addressing collinearity in multivariate linguistic data

Publication date: November 2018Source: Journal of Phonetics, Volume 71Author(s): Fabian Tomaschek, Peter Hendrix, R. Harald BaayenAbstractWhen multiple correlated predictors are considered jointly in regression modeling, estimated coefficients may assume counterintuitive and theoretically uninterpretable values. We survey several statistical methods that implement strategies for the analysis of collinear data: regression with regularization (the elastic net), supervised component generalized linear regression, and random forests. Methods are illustrated for a data set with a wide range of predictors for segment duration in a German speech corpus. Results broadly converge, but each method has its own strengths and weaknesses. Jointly, they provide the analyst with somewhat different but complementary perspectives on the structure of collinear data.
Source: Journal of Phonetics - Category: Speech-Language Pathology Source Type: research