范畴变量
心理学
验证性因素分析
潜在类模型
构造(python库)
潜变量模型
样品(材料)
潜变量
自恋
社会心理学
结构方程建模
统计
数学
计算机科学
化学
色谱法
程序设计语言
作者
Elizabeth N. Aslinger,Stephen B. Manuck,Paul A. Pilkonis,Leonard J. Simms,Aidan G.C. Wright
标识
DOI:10.31234/osf.io/tv7r9
摘要
We investigated the latent structure of narcissistic personality disorder (NPD) by comparing dimensional, hybrid, and categorical latent variable models, using confirmatory factor analysis (CFA), non-parametric (NP-FA) and semi-parametric factor analysis (SP-FA), and latent class analysis, respectively. We first explored these models in a clinical sample, and then pre-registered replication analyses in four additional datasets (with national, undergraduate, community, and mixed community/clinical samples) to test whether the best fitting model would generalize across different datasets with different sample compositions. A one-factor CFA outperformed categorical models in fit and reliability, suggesting the criteria do not serve to distinguish a “narcissist” class or subtypes; rather, a “narcissistic” dimension underlies the NPD construct. The CFA also outperformed hybrid models, indicating that people fall within the same continuous distribution, rather than composing homogenous groups of relative severity (NP-FA) or pulling apart into mixtures of discrete distributions (SP-FA) along that spectrum.
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