University of Tartu, Jakobi 2-446, Tartu, Estonia.
University of Tartu, Jakobi 2-430, Tartu, Estonia.
Behav Res Methods. 2024 Aug;56(5):5178-5189. doi: 10.3758/s13428-023-02257-4. Epub 2023 Dec 21.
We present a collection of concreteness ratings for 35,979 words in Estonian. The data were collected via a web application from 2278 native Estonian speakers. Human ratings of concreteness have not been collected for Estonian beforehand. We compare our results to Aedmaa et al. (2018), who assigned concreteness ratings to 240,000 Estonian words by means of machine learning. We show that while these two datasets show reasonable correlation (R = 0.71), there are considerable differences in the distribution of the ratings, which we discuss in this paper. Furthermore, the results also raise questions about the importance of the type of scale used for collecting ratings. While most other datasets have been compiled based on questionnaires entailing five- or seven-point Likert scales, we used a continuous 0-10 scale. Comparing our rating distribution to those of other studies, we found that it is most similar to the distribution in Lahl et al. (Behavior Research Methods, 41(1), 13-19, 2009), who also used a 0-10 scale. Concreteness ratings for Estonian words are available at OSF .
我们呈现了一份爱沙尼亚语 35979 个单词的具体程度评级。这些数据是通过一个网络应用程序从 2278 位母语为爱沙尼亚语的人那里收集的。以前没有人为爱沙尼亚语收集过具体程度的评级。我们将我们的结果与 Aedmaa 等人(2018)进行了比较,他们通过机器学习为爱沙尼亚语的 240000 个单词分配了具体程度的评级。我们发现,虽然这两个数据集具有合理的相关性(R=0.71),但评级的分布存在相当大的差异,我们在本文中对此进行了讨论。此外,结果还提出了关于收集评级使用的量表类型的重要性的问题。虽然大多数其他数据集是基于包含五点或七点李克特量表的问卷编制的,但我们使用了连续的 0-10 量表。将我们的评级分布与其他研究进行比较后,我们发现它与 Lahl 等人(行为研究方法,41(1),13-19,2009)的分布最为相似,他们也使用了 0-10 量表。爱沙尼亚语单词的具体程度评级可在 OSF 上获得。