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Constructing Personality Maps, Mapping Personality Constructs: Multidimensional Scaling Recovers the Big Five Factors from Internal and External Structure

机译:构造人格图,绘制人格图:多维尺度从内部和外部结构中恢复了五个主要因素

摘要

This report examines the structure of similarities underlying the lexicon of personality-trait description, when “similarity” is defined and measured in terms of (a) semantic judgment and (b) covariance in actual use. A lexicon of 60 trait adjectives was examined, using several procedures for collecting semantic judgments. Similarity data of both kinds were analyzed with multidimensional scaling (MDS) to provide a parsimonious representation of underlying structure. The convergence between semantic judgments and covariance within trait-attribution data was substantial; both kinds of data evinced the same structure when collected for subsets of adjectives. Canonical correlation was employed to find the number of dimensions shared across MDS solutions. Interpretation of the results was facilitated by individual-differences MDS, which can select an optimal set of underlying dimensions, and at the same time accommodate the differences between data sets that arise when data-collection procedures differ in the relative emphasis they place upon those dimensions. We interpret the small number and shared nature of the dimensions by arguing that the lexicon’s structure relates to trait perception rather than personality structure per se, even when probed with trait-attribution covariance.
机译:当“相似性”是根据(a)语义判断和(b)实际使用中的协方差来定义和衡量的时,本报告研究了基于个性特征描述词典的相似性结构。使用几种收集语义判断的程序检查了60个特质形容词的词典。使用多维缩放(MDS)对两种相似性数据进行了分析,以提供基本结构的简化表示。特质属性数据中语义判断和协方差之间的收敛是实质性的;当为形容词子集收集时,两种数据都表现出相同的结构。使用规范相关性来查找跨MDS解决方案共享的维数。通过个体差异MDS可以方便地解释结果,该MDS可以选择最佳的基础维度集,并同时容纳当数据收集程序对这些维度的相对重视程度不同时出现的数据集之间的差异。 。我们认为,词汇的结构与特质感知有关,而不是与人格结构本身有关,从而解释了维数的少量且共有的性质,即使用特质-属性协方差进行探讨也是如此。

著录项

  • 作者

    Bimler David; Kirkland John;

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  • 年度 2007
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