When individuals engage in divergent thinking, they vary on their Elaboration, or the degree to which they explain and embellish their responses. Although Elaboration has been considered relevant to creativity research for decades, its measurement has remained under-developed. Here, we leverage technical and methodological perspectives from the text-mining literature to posit four methods for quantifying elaboration: Unweighted Word Count, Stoplisted Inclusion, Part of Speech Inclusion, and Inverse Frequency Weighting. Although the Unweighted Word Count method is becoming typical in the field, more complex weighting methods appear to better fit the conceptualization of Elaboration. We explain the benefits of each of the included methods and demonstrate their application to responses from the Alternate Uses Task: showing that all four of these text-mining methods produced Elaboration scores with high levels of reliability, but the Stoplisted Inclusion method appeared to maximize the score validity both in terms of criteria correlations and power to discriminate among creative experts and non-experts. We offer an open-access module for creativity researchers to apply these methods to their own data via our laboratory website [https://openscoring.du.edu/].

中文翻译：

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测量精化程度的四种文本挖掘方法

当个体进行发散性思考时，他们的精化程度或解释和修饰反应的程度会有所不同。尽管几十年来，精细化一直被认为与创造力研究相关，但其衡量标准仍未得到充分发展。在这里，我们利用文本挖掘文献中的技术和方法论观点来提出四种量化阐述的方法：未加权的字数统计、停止列表包含、词性包含和逆频率加权。尽管未加权的字数统计方法在该领域逐渐成为典型，但更复杂的加权方法似乎更适合细化的概念化。我们解释了每个包含的方法的好处，并展示了它们对来自替代用途任务的响应的应用：表明所有这四种文本挖掘方法都产生了具有高度可靠性的细化分数，但停止列表包含方法似乎在标准相关性和区分创意专家和非专家的能力方面最大限度地提高了分数的有效性。我们为创造力研究人员提供了一个开放访问模块，可以通过我们的实验室网站 [https://openscoring.du.edu/] 将这些方法应用于他们自己的数据。