Grammatical Redundancy in Scales: Using the “ConGRe” Process to Create Better Measures
为量表开发者提供了一个名为ConGRe的流程，用于量化、识别和减少语法冗余，同时保持概念冗余，从而在不牺牲传统心理测量属性的前提下避免量表缺陷。
As theoretical models become more complex, there is more pressure to use less time-consuming methods generally, and shorter scales specifically. Although reliability is related to scale length, reliability cutoffs are easily met, even in very short scales, by writing or selecting items that are worded in nearly identical ways, that is, grammatical redundancy. However, grammatical redundancy increases reliability at the cost of domain sampling—a crucial early step in scale construction and one of the two pillars of content validity. Without it, a scale cannot capture the intended construct. The purpose of this paper is to provide scale developers (and shorteners) with a process for quantifying, identifying, and reducing grammatical redundancy without compromising conceptual redundancy, a process that we label ConGRe. Our process involves indices from the linguistics literature that can be used to guide decisions during item writing, that is, prior to data collection. We examine their relation to more traditional psychometric indicators and provide a set of benchmarks. Overall, we demonstrate that it is possible to reduce grammatical redundancy, thus avoiding scale deficiency, without sacrificing traditional psychometric properties.