EMPLOYING LATENT CLASS REGRESSION ANALYSIS TO EXAMINE LOGISTICS THEORY: AN APPLICATION OF TRUCK DRIVER RETENTION
针对传统回归假设单一模型适用于全体人群的局限，本文引入潜在类别回归分析，识别同一群体内不同细分市场的回归模型，并以卡车司机留任意愿为例展示其在物流理论检验中的优势。
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.