Department of Psychology, University of Pittsburgh.
Department of Psychiatry, University of Pittsburgh School of Medicine.
J Abnorm Psychol. 2018 Jul;127(5):496-502. doi: 10.1037/abn0000363.
We investigated the latent structure of narcissistic personality disorder by comparing dimensional, hybrid, and categorical latent variable models, using confirmatory factor analysis (CFA), nonparametric and semiparametric factor analysis, and latent class analysis, respectively. We first explored these models in a clinical sample and then preregistered replication analyses in 4 additional data sets (with national, undergraduate, community, and mixed community/clinical samples) to test whether the best-fitting model would generalize across different data sets with different sample compositions. A 1-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 narcissistic personality disorder construct. The CFA also outperformed hybrid models, indicating that people fall within the same continuous distribution, rather than composing homogenous groups of relative severity (nonparametric factor analysis) or pulling apart into mixtures of discrete distributions (semiparametric factor analysis) along that spectrum. (PsycINFO Database Record
我们通过比较维度、混合和分类潜在变量模型，分别使用验证性因子分析（CFA）、非参数和半参数因子分析以及潜在类别分析，研究了自恋型人格障碍的潜在结构。我们首先在临床样本中探索了这些模型，然后在另外 4 个数据集（具有全国性、本科生、社区和混合社区/临床样本）中预先注册了复制分析，以测试最佳拟合模型是否可以推广到具有不同样本组成的不同数据集。一个 1 因素 CFA 在拟合度和可靠性方面优于分类模型，这表明这些标准不能用于区分自恋者类别或亚型；相反，自恋维度是自恋型人格障碍结构的基础。CFA 也优于混合模型，这表明人们处于相同的连续分布中，而不是沿着该谱系组成同质的严重程度组（非参数因子分析）或离散分布的混合物（半参数因子分析）。（PsycINFO 数据库记录）
