Arias Victor B, Ponce Fernando P, Bruggeman Martin, Flores Noelia, Jenaro Cristina
Department of Personality, Assessment and Psychological Treatment, University of Salamanca, Salamanca, Spain.
School of Psychology, Pontificia Universidad Católica de Chile, Santiago, Chile.
PeerJ. 2020 Nov 17;8:e10209. doi: 10.7717/peerj.10209. eCollection 2020.
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.
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.
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.
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.
在最近的三项研究中，莫尔证明了无意义项目集可以具有出色的心理测量特性。我们的目的是找出对无意义项目的回答为何会形成明确的结构和高内部一致性。
我们设计了两项研究。在第一项研究中，610名参与者对八个项目做出了回答，其中核心术语（智力）被“gavagai”一词取代。在第二项研究中，548名参与者对七个内容完全虚构的项目做出了回答。我们询问参与者是否赋予了“gavagai”任何意义，并进行了分析，旨在找出对无意义项目的回答进行建模的最合适结构。
在第一项研究中，81.3%的样本赋予了“gavagai”意义，而18.7%的样本表示未对其进行解释。两组的因子结构彼此非常不同。在第二项研究中，因子模型拟合得几乎完美。然而，进一步分析表明，数据的结构不是连续的，而是分类的，有三个无序类别，非常类似于中点、不赞同和随机回答方式。
无意义量表上明显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答，或者（b）误报，因为因子模型的统计拟合对数据的实际结构并非来自共同因子的情况不敏感。总之，问题不在于因子分析，而在于研究人员能否阐述关于数据结构的实质性假设，采用与这些假设一致的分析程序，并理解因子分析中的良好拟合并没有单一的解释，也不是有效性或良好心理测量特性的充分证据。
