Improving the validity of theory testing in logistics research using correlated components regression

Michael S. Garver, Zachary Williams

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The purpose of this logistics research methods article is to empirically test and introduce correlated components regression (CCR) as a new statistical technique that will improve the accuracy and validity in testing logistics theoretical models and hypothesised relationships. The purpose of the current study is to use CCR analysis as technique to address multicollinearity. Customer satisfaction data with parcel carriers is analysed with using CCR and multiple regression. To determine the best regression model of these two approaches, cross-validation R2 values are used. In addition, comparisons are made to examine the standardised beta coefficients from both methods and to assess the possible impact from high levels of multicollinearity. Findings of the analysis suggest that CCR has a significantly higher cross-validation R2 value and thus is determined the best model of these two approaches.

Original languageEnglish
Pages (from-to)363-377
Number of pages15
JournalInternational Journal of Logistics Research and Applications
Volume21
Issue number4
DOIs
StatePublished - Jul 4 2018

Keywords

  • Research methods
  • correlated components regression
  • multicollinearity
  • multiple regression

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