EMPLOYING LATENT CLASS REGRESSION ANALYSIS TO EXAMINE LOGISTICS THEORY: AN APPLICATION OF TRUCK DRIVER RETENTION

Michael S. Garver, Zachary Williams, G. Stephen Taylor

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

Multiple regression analysis assumes that one model or theory is relevant for the entire population, yet research has shown that this assumption is often false and may severely limit valid theory development and testing. Latent class regression analysis overcomes this limitation and allows the researcher to identify and develop regression models that are relevant for different segments within the same population. Latent class regression analysis is introduced and is used to analyze truck drivers' intentions to stay with the same firm. This article demonstrates the advantages of testing logistics theory with latent class regression analysis and provides numerous applications for practitioners.

Original languageEnglish
Pages (from-to)233-257
Number of pages25
JournalJournal of Business Logistics
Volume29
Issue number2
DOIs
StatePublished - Sep 1 2008

Keywords

  • Latent class regression analysis
  • Logistics research methods
  • Multiple regression analysis
  • Truck driver retention

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