Improving Measure Quality by Alternating Least Squares Optimal Scaling
提出PRINCIPALS方法，通过交替最小二乘最优尺度重新编码测量项目的原始类别，提升营销研究中量表的信度、收敛效度和判别效度，并用两个态度研究实例验证其效果。
PRINCIPALS analysis (principal components analysis by alternating least squares optimal scaling) provides an approach for improving the reliability and convergent and discriminant validity of measures used in marketing research. Given a set of items designed to measure a theoretical construct or conceptual dimension, PRINCIPALS rescales the original response categories of each item to interval-level measurement and maximizes their unidimensional communality. PRINCIPALS is complementary to the traditional approaches for improving the measurement quality of scales used in marketing. The authors present the transformation in the general form, then illustrate it in two attitude research examples. The stability of the results is also examined. In both examples the reliability and convergent and discriminant validity of measures based on the tripartite attitude model are substantially improved after PRINCIPALS rescaling.