Background In three recent studies, Maul demonstrated that sets of nonsense items can acquire excellent psychometric properties. Our aim was to find out why responses to nonsense items acquire a well-defined structure and high internal consistency. Method We designed two studies. In the first study, 610 participants responded to eight items where the central term (intelligence) was replaced by the term “gavagai”. In the second study, 548 participants responded to seven items whose content was totally invented. We asked the participants if they gave any meaning to “gavagai”, and conducted analyses aimed at uncovering the most suitable structure for modeling responses to meaningless items. Results In the first study, 81.3% of the sample gave “gavagai” meaning, while 18.7% showed they had given it no interpretation. The factorial structures of the two groups were very different from each other. In the second study, the factorial model fitted almost perfectly. However, further analysis revealed that the structure of the data was not continuous but categorical with three unordered classes very similar to midpoint, disacquiescent, and random response styles. Discussion Apparently good psychometric properties on meaningless scales may be due to (a) respondents actually giving an interpretation to the item and responding according to that interpretation, or (b) a false positive because the statistical fit of the factorial model is not sensitive to cases where the actual structure of the data does not come from a common factor. In conclusion, the problem is not in factor analysis, but in the ability of the researcher to elaborate substantive hypotheses about the structure of the data, to employ analytical procedures congruent with those hypotheses, and to understand that a good fit in factor analysis does not have a univocal interpretation and is not sufficient evidence of either validity nor good psychometric properties.

中文翻译：

---

有效且可靠的衡量标准：解开调查数据中的“加瓦盖效应”

背景 在最近的三项研究中，Maul 证明了一组无意义的项目可以获得出色的心理测量特性。我们的目的是找出为什么对无意义项目的反应会获得明确的结构和高度的内部一致性。方法 我们设计了两项研究。在第一项研究中，610 名参与者回答了八个项目，其中中心术语（智力）被术语“gavagai”取代。在第二项研究中，548 名参与者回答了 7 个内容完全是虚构的项目。我们询问参与者是否对“gavagai”赋予了任何意义，并进行了分析，旨在发现最适合对无意义项目的响应建模的结构。结果在第一项研究中，81.3% 的样本给出了“gavagai”的含义，而 18.7% 的样本表示他们没有给出任何解释。两组的因子结构彼此非常不同。在第二项研究中，因子模型几乎完美拟合。然而，进一步的分析表明，数据的结构不是连续的，而是分类的，三个无序的类与中点、不默许和随机响应风格非常相似。讨论 显然，在无意义量表上良好的心理测量特性可能是由于（a）受访者实际上对项目进行了解释并根据该解释做出回应，或（b）误报，因为阶乘模型的统计拟合对案例不敏感其中数据的实际结构并非来自共同因素。总之，问题不在因子分析中，