ObjectivePast applications of the lexical approach to type‐noun personality structures have yielded different results compared with those generated for adjectival personality structures, since then new methods have arisen for identifying robust higher‐dimensionality structure in data. This research aims to identify an optimal taxonomy of English language type‐nouns.MethodCurrent study reanalyzed 372 type‐nouns from a previous study emphasizing robustness across methodological variations (original vs. ipsatized data, oblique vs. orthogonal rotations, convergence between male and female target ratings) to determine a replicable but more comprehensive model of personality type‐noun structure.ResultsA 13‐factor original‐data oblimin‐rotated solution was determined to be the most robust model, except for a one‐factor model that was far less comprehensive and informative; an original‐data 32‐factor oblimin‐rotated solution was also fairly robust. Although each of the Big Five adjectival markers indicated a large correlation with one or more type‐noun factors; nearly half of the 13 type‐noun factors lacked such large correlations with the Big Five.ConclusionsA high‐dimensionality approach thus indicated that type‐nouns capture substantial content beyond the Big Five. A comparison with the character‐types described by an ancient philosopher (Theophrastus) signified that some granular type‐noun dimensions may have stability across multiple millennia.

中文翻译：

---

英语人格类型名词的高维结构


目的过去，词汇方法在类型名词人格结构上的应用与形容词人格结构所产生的结果不同，从那时起，出现了用于识别数据中稳健的高维结构的新方法。本研究旨在确定英语语言类型名词的最佳分类法。方法当前的研究重新分析了先前研究中的 372 个类型名词，强调方法论变化的稳健性（原始数据与原数据、倾斜与正交旋转、男性和女性目标之间的趋同）评级）来确定一个可复制但更全面的人格类型-名词结构模型。结果 13 因素原始数据obmin-rotated 解决方案被确定为最稳健的模型，除了单因素模型远不那么全面和信息丰富；原始数据 32 因子 Oblimin 旋转解决方案也相当稳健。尽管大五形容词标记中的每一个都表明与一个或多个类型名词因素有很大的相关性； 13 个类型名词因素中近一半与大五缺乏如此大的相关性。结论因此，高维方法表明类型名词捕获了大五之外的实质性内容。与古代哲学家（Theophrastus）描述的字符类型的比较表明，某些粒状类型名词维度可能在数千年中具有稳定性。