Strategies for addressing collinearity in multivariate linguistic data

Fabian Tomaschek*, Peter Hendrix, R. Harald Baayen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

111 Citations (Scopus)


When 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.

Original languageEnglish
Pages (from-to)249-267
Number of pages19
JournalJournal of Phonetics
Publication statusPublished - Nov 2018
Externally publishedYes


  • Collinearity
  • Concurvity
  • Elastic net
  • Random forests
  • Segment duration
  • Supervised component generalized linear regression


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