Department of Education Studies, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong.
Centre for Learning Sciences, Hong Kong Baptist University, Kowloon, Hong Kong.
Behav Res Methods. 2018 Oct;50(5):1763-1777. doi: 10.3758/s13428-017-0944-0.
Here we report on MELD-SCH (MEgastudy of Lexical Decision in Simplified CHinese), a dataset that contains the lexical decision data of 1,020 one-character, 10,022 two-character, 949 three-character, and 587 four-character simplified Chinese words obtained from 504 native Chinese users. It also includes a number of word-level and character-level variables. Analyses showed that the reliability of the dataset is satisfactory, as indicated by split-half correlations and comparisons with other datasets. Item-based regression showed that both word-level and character-level variables contributed significantly to the reaction times and error rates of lexical decision. Moreover, we discovered a U-shape relationship between word-length and reaction times, which has not been reported in Chinese before. MELD-SCH can facilitate research in Chinese word recognition by providing high quality normative data and information of different linguistic variables. It also encourages researchers to extend their empirical findings, which are mostly based on one-character and two-character words, to words of different lengths.
我们在此报告 MELD-SCH(简化中文词汇判断的大规模研究)数据集,该数据集包含了 504 位母语为中文的使用者对 1020 个单字词、10022 个双字词、949 个三字词和 587 个四字词进行词汇判断的数据,以及一些词级和字级变量。分析表明,数据集的可靠性较高,其分半相关系数和与其他数据集的比较结果均表明了这一点。基于项目的回归分析表明,词级和字级变量均显著影响词汇判断的反应时和错误率。此外,我们还发现了一个词长与反应时之间的 U 型关系,这在中文中以前尚未报道过。MELD-SCH 通过提供高质量的规范数据和不同语言变量的信息,能够促进中文词汇识别研究。它还鼓励研究人员将其主要基于单字词和双字词的实证发现扩展到不同长度的词。