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.

中文翻译：

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量表中的语法冗余：使用 “ConGRe” 过程创建更好的度量


随着理论模型变得越来越复杂，通常使用耗时更少的方法，特别是更短的尺度的压力更大。尽管可靠性与量表长度有关，但即使在非常短的量表中，也很容易通过编写或选择措辞方式几乎相同的项目（即语法冗余）来满足可靠性临界值。然而，语法冗余以牺牲域采样为代价提高了可靠性——这是规模构建的关键早期步骤，也是内容有效性的两大支柱之一。没有它，秤就无法捕获预期的结构。本文的目的是为音阶开发人员（和缩短者）提供一个在不影响概念冗余的情况下量化、识别和减少语法冗余的过程，我们将这个过程称为 ConGRe。我们的流程涉及语言学文献中的索引，这些索引可用于在项目编写期间（即数据收集之前）指导决策。我们研究了它们与更传统的心理测量指标的关系，并提供了一组基准。总的来说，我们证明了可以减少语法冗余，从而避免音阶缺陷，而不会牺牲传统的心理测量特性。