Arnulf Jan Ketil, Olsson Ulf Henning, Nimon Kim
BI Norwegian Business School, Oslo, Norway.
Department of Human Resource Development, University of Texas at Tyler, Tyler, TX, United States.
Front Psychol. 2024 Mar 21;15:1308098. doi: 10.3389/fpsyg.2024.1308098. eCollection 2024.
This is a review of a range of empirical studies that use digital text algorithms to predict and model response patterns from humans to Likert-scale items, using texts only as inputs. The studies show that statistics used in construct validation is predictable on sample and individual levels, that this happens across languages and cultures, and that the relationship between variables are often semantic instead of empirical. That is, the relationships among variables are given a priori and evidently computable as such. We explain this by replacing the idea of "nomological networks" with "semantic networks" to designate computable relationships between abstract concepts. Understanding constructs as nodes in semantic networks makes it clear why psychological research has produced constant average explained variance at 42% since 1956. Together, these findings shed new light on the formidable capability of human minds to operate with fast and intersubjectively similar semantic processing. Our review identifies a categorical error present in much psychological research, measuring representations instead of the purportedly represented. We discuss how this has grave consequences for the empirical truth in research using traditional psychometric methods.
这是一篇对一系列实证研究的综述，这些研究使用数字文本算法，仅将文本作为输入，来预测和建模人类对李克特量表项目的反应模式。研究表明，在构念效度中使用的统计数据在样本和个体层面上是可预测的，这种情况在不同语言和文化中都会发生，而且变量之间的关系通常是语义上的而非实证的。也就是说，变量之间的关系是先验给定的，并且显然可以如此计算。我们通过用“语义网络”取代“法理网络”的概念来解释这一点，以指定抽象概念之间的可计算关系。将构念理解为语义网络中的节点，就清楚地说明了为什么自1956年以来，心理学研究的平均解释方差一直保持在42%。这些发现共同为人类思维以快速且主体间相似的语义处理方式运作的强大能力提供了新的见解。我们的综述指出了许多心理学研究中存在的一个范畴错误，即测量的是表征而非据称被表征的事物。我们讨论了这对使用传统心理测量方法的研究中的实证真理会产生怎样严重的后果。
