Trust is a foundational concept of contemporary sociological theory. Still, empirical research on trust relies on a relatively small set of measures. These are increasingly debated, potentially undermining large swathes of empirical evidence. Drawing on a combination of open-ended probing data, supervised machine learning, and a U.S. representative quota sample, our study compares the validity of standard measures of generalized social trust with more recent, situation-specific measures of trust. We find that survey measures that refer to “strangers” in their question wording best reflect the concept of generalized trust, also known as trust in unknown others. While situation-specific measures should have the desirable property of further reducing variation in associations, that is, producing more similar frames of reference across respondents, they also seem to increase associations with known others, which is undesirable. In addition, we explore to what extent trust survey questions may evoke negative associations. We find that there is indeed variation across measures, which calls for more research.

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信任调查措施的有效性如何？来自开放式探测数据和监督机器学习的新见解

信任是当代社会学理论的基本概念。尽管如此，关于信任的实证研究仍然依赖于一组相对较小的衡量标准。这些问题引起了越来越多的争论，可能会破坏大量的经验证据。我们的研究结合了开放式探索数据、监督机器学习和美国代表性配额样本，将广义社会信任的标准衡量标准与最新的针对具体情况的信任衡量标准的有效性进行了比较。我们发现，在问题措辞中提到“陌生人”的调查措施最能反映普遍信任的概念，也称为对未知他人的信任。虽然针对具体情况的措施应该具有进一步减少关联差异的理想特性，即在受访者之间产生更相似的参考框架，但它们似乎也增加了与已知其他人的关联，这是不可取的。此外，我们还探讨了信任调查问题在多大程度上可能引起负面关联。我们发现不同的衡量标准确实存在差异，这需要更多的研究。