Alternative Estimation Methods for Conjoint Analysis: A Monté Carlo Study
通过蒙特卡洛模拟比较了ANOVA、LINMAP、LOGIT和MONANOVA四种联合分析估计方法，发现ANOVA在补偿性模型中表现最佳，而LINMAP在具有主导属性的模型中预测效度最高。
Conjoint analysis has been applied in a large number of commercial projects as well as in many noncommercial studies. Often MONANOVA, a nonmetric technique, is applied to a preference rank order obtained for a set of hypothetical objects. The authors report simulation results obtained for four alternative estimation procedures, ANOVA, LINMAP, LOGIT, and MONANOVA. The results suggest, within the limitations of the simulation study, that ANOVA may be the preferred procedure for compensatory models, whereas LINMAP is most likely to provide the best predictive validity for models with a dominant attribute.