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Factorial Versus Typological Models: A Comparison of Methods for Personality Data

机译:阶乘与类型模型:人格数据方法的比较

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This article describes an exploration of the distinction between typological and factorial latent variables in the domain of personality theory. Traditionally, many personality variables have been considered to be factorial in nature, even though there are examples of typological constructs dating back to Hippocrates. Recently, some reconceptualizations of typological constructs have emerged due, in part, to the availability of more rigorous methodological tools for identification of types (or nominal latent traits). These tools include multidimensional item response theory (MIRT) and latent class analysis (LCA). Two studies, involving application of these methods, are discussed in this article. The first study uses data collected using a questionnaire based on the five-factor model (FFM) of personality. The second study is based on data collected to investigate the relationships between technology use and literacy skills. The findings of both studies indicate that, while a clear preference for a factorial or a typological model may exist in the literature, a choice between the two merely based on statistical criteria may not be as clear-cut. Moreover, typological and factorial models of individual difference may coexist in certain domains of individual differences research. The article closes with a series of recommendations for future research to better understand the nature of psychological variables.
机译:本文介绍了人格理论领域中类型和潜在隐变量之间区别的探索。传统上,即使存在一些可以追溯到希波克拉底的类型学构造的例子,许多人格变量在本质上也被认为是阶乘。最近,由于某些更严格的方法学工具可用于识别类型(或名义上的潜在性状),因此出现了对类型结构的一些重新概念化。这些工具包括多维项目响应理论(MIRT)和潜在类别分析(LCA)。本文讨论了涉及这些方法应用的两项研究。第一项研究使用基于五项性格模型(FFM)的问卷调查收集的数据。第二项研究基于收集的数据来调查技术使用与识字技能之间的关系。两项研究的结果表明,尽管文献中可能存在对因子模型或类型学模型的明确偏好,但仅基于统计标准在两者之间进行选择可能并不那么明确。而且,个体差异的类型学模型和阶乘模型可能在个体差异研究的某些领域中并存。本文以一系列建议作为结尾,以供将来研究以更好地理解心理变量的性质。

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