Improving Rating Scale Measures by Detecting and Correcting Bias Components in Some Response Styles
研究评分量表中的“是”倾向和标准差是否反映真实态度或造成偏差，提出用态度-行为模型预测误差分离信息与偏差成分，发现标准差含偏差但“是”倾向不含，纠正偏差可提高调查准确性。
The author examines whether the response styles of yeasaying and standard deviation in rating scale responses convey information on respondents’ attitudes or create bias that distorts attitude information and marketing research. A method is proposed to identify attitude information components and bias components in response styles, using prediction errors in attitude-behavior models. Analysis of data from a large-scale consumer survey supports the presence of both attitude information and bias components in standard deviation, and an attitude information but not a bias component in yeasaying. This finding suggests that correcting rating scale data by removing the bias but not the attitude information in standard deviation can increase the accuracy of survey research. Examples are given of how bias in standard deviation, and the scoring correction, affect segmentation research.