(Ph.D. University of Tennessee) is an associate professor of marketing at Central Michigan University. Dr. Garver stays active with the business community through speaking, consulting, and conducting best practice research. His research interests include using leading edge research methods to conduct marketing and logistics research. He has published articles in the Journal of Business Logistics, Supply Chain Management Review, Industrial Marketing Management, Marketing Research, Marketing Management, Business Horizons, Mid-American Journal of Business, Marketing News, and the Journal of Consumer Satisfaction, Dissatisfaction, and Complaining Behavior.
EMPLOYING LATENT CLASS REGRESSION ANALYSIS TO EXAMINE LOGISTICS THEORY: AN APPLICATION OF TRUCK DRIVER RETENTION
Article first published online: 10 MAY 2011
2008 Council of Supply Chain Management Professionals
Journal of Business Logistics
Volume 29, Issue 2, pages 233–257, Autumn 2008
How to Cite
Garver, M. S., Williams, Z. and Taylor, G. S. (2008), EMPLOYING LATENT CLASS REGRESSION ANALYSIS TO EXAMINE LOGISTICS THEORY: AN APPLICATION OF TRUCK DRIVER RETENTION. JOURNAL OF BUSINESS LOGISTICS, 29: 233–257. doi: 10.1002/j.2158-1592.2008.tb00094.x
- Issue published online: 10 MAY 2011
- Article first published online: 10 MAY 2011
- Latent class regression analysis;
- Logistics research methods;
- Multiple regression analysis;
- Truck driver retention
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.