Linacre John M
University of Chicago, P. O. Box 811322, IL 60681-1322, USA.
J Appl Meas. 2002;3(1):85-106.
Rating scales are employed as a means of extracting more information out of an item than would be obtained from a mere "yes/no", "right/wrong" or other dichotomy. But does this additional information increase measurement accuracy and precision? Eight guidelines are suggested to aid the analyst in optimizing the manner in which rating scales categories cooperate in order to improve the utility of the resultant measures. Though these guidelines are presented within the context of Rasch analysis, they reflect aspects of rating scale functioning which impact all methods of analysis. The guidelines feature rating-scale-based data such as category frequency, ordering, rating-to-measure inferential coherence, and the quality of the scale from measurement and statistical perspectives. The manner in which the guidelines prompt recategorization or reconceptualization of the rating scale is indicated. Utilization of the guidelines is illustrated through their application to two published data sets.
评分量表被用作一种从一个项目中提取比仅仅通过“是/否”、“对/错”或其他二分法所能获得的更多信息的手段。但是，这些额外的信息是否会提高测量的准确性和精确性呢？本文提出了八条指导原则，以帮助分析人员优化评分量表类别之间的协同方式，从而提高所得测量结果的效用。尽管这些指导原则是在拉施分析的背景下提出的，但它们反映了评分量表功能中影响所有分析方法的各个方面。这些指导原则的特点是基于评分量表的数据，如类别频率、排序、评分与测量的推断一致性，以及从测量和统计角度看量表的质量。文中指出了这些指导原则促使对评分量表进行重新分类或重新概念化的方式。通过将这些指导原则应用于两个已发表的数据集来说明其使用方法。
