Overcoming scale usage heterogeneity: A Bayesian hierarchical approach

Questions that use a discrete ratings scale are commonplace in survey resea rch. Examples in marketing include customer satisfaction measurement and pu rchase intention. Survey research practitioners have long commented that re spondents vary in their usage of the scale: Common patterns include using o nly the middle of the scale or using the upper or lower end. These differen ces in scale usage can impart biases to correlation and regression analyses . To capture scale usage differences, we developed a new model with individ ual scale and location effects and a discrete outcome variable. We model th e joint distribution of all ratings scale responses rather than specific un ivariate conditional distributions as in the ordinal probit model. We apply our model to a customer satisfaction survey and show that the correlation inferences are much different once proper adjustments are made for the disc reteness of the data and scale usage. We also show that our adjusted or lat ent ratings scale is more closely related to actual purchase behavior.