In graded paired comparisons (GPCs), two items are compared using a multipoint rating scale. GPCs are expected to reduce faking compared with Likert-type scales and to produce more reliable, less ipsative trait scores than traditional binary forced-choice formats. To investigate the statistical properties of GPCs, we simulated 960 conditions in which we varied six independent factors and additionally implemented conditions with algorithmically optimized item combinations. Using Thurstonian IRT models, good reliabilities and low ipsativity of trait score estimates were achieved for questionnaires with 50% unequally keyed item pairs or equally keyed item pairs with an optimized combination of loadings. However, in conditions with 20% unequally keyed item pairs and equally keyed conditions without optimization, reliabilities were lower with evidence of ipsativity. Overall, more response categories led to higher reliabilities and nearly fully normative trait scores. In an empirical example, we demonstrate the identified mechanisms under both honest and faking conditions and study the effects of social desirability matching on reliability. In sum, our studies inform about the psychometric properties of GPCs under different conditions and make specific recommendations for improving these properties.

中文翻译：

---

在高风险时测量性格：分级配对比较是比传统强制选择方法更可靠的替代方案吗？


在分级配对比较 （GPC） 中，使用多点评定量表比较两个项目。与李克特式量表相比，GPC 有望减少伪造，并产生比传统的二元强制选择格式更可靠、更不易影响的特征评分。为了研究 GPC 的统计特性，我们模拟了 960 种条件，在这些条件下，我们改变了 6 个独立因素，并使用算法优化的项目组合额外实现了条件。使用 Thurstonian IRT 模型，对于具有 50% 不等键控项目对或具有优化加载组合的等键项目对的问卷，实现了良好的可靠性和特征分数估计的低 ipsatiity。然而，在具有 20% 不相等键控项目对和没有优化的相等键控条件下，可靠性较低，有 ipsati 的证据。总体而言，更多的响应类别导致更高的可靠性和几乎完全符合规范的特征分数。在一个实证例子中，我们展示了在诚实和虚假条件下确定的机制，并研究了社会期望匹配对可靠性的影响。总之，我们的研究介绍了 GPC 在不同条件下的心理测量特性，并为改善这些特性提出了具体建议。