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首页> 外文期刊>Psychiatry research >The taxonicity of schizotypy: Does the same taxonic class structure emerge from analyses of different attributes of schizotypy and from fundamentally different statistical methods?
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The taxonicity of schizotypy: Does the same taxonic class structure emerge from analyses of different attributes of schizotypy and from fundamentally different statistical methods?

机译:精神分裂症的分类学:是否通过分析精神分裂症的不同属性和根本不同的统计方法得出相同的分类学类别结构?

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摘要

Findings on the population distribution of schizotypy consistently point toward an underlying class structure. However, past research is methodologically homogeneous, chiefly involving analysis of attribute-specific indicators and coherent cut kinetic methods such as maximum covariance (MAXCOV) analysis. Two questions are examined. Are different or overlapping classes identified from different attributes of the schizophrenia phenotype? Do fundamentally different approaches to analysis yield consistent results? Participants (n = 1074) completed the Schizotypal Personality Questionnaire (SPQJ. Following item screening, MAXCOV analyses were conducted iteratively on attribute-specific item sets (cognitive-perceptual, interpersonal, and disorganized) and a general item set. Latent variable modeling (factor analysis, latent class analysis, and factor-mixture modeling) was used to examine the consistency of the MAXCOV results using items retained in the general set following MAXCOV analysis. Attribute-specific and general item sets gave taxonic MAXCOV curves and base rates of 8.4-10.4% and 3.6%, respectively. Classes were not independent. No latent variable model emerged as uniquely superior but five models distinguished a small high-scoring class populated by members of the MAXCOV general class. Different attributes distinguished overlapping yet nonredundant taxa, and a general schizotypy taxon identified with MAXCOV was also identified in latent variable modeling.
机译:关于精神分裂症的人口分布的发现始终指向潜在的阶级结构。但是,过去的研究在方法上是同质的,主要涉及属性特定指标的分析和连贯的切割动力学方法,例如最大协方差(MAXCOV)分析。研究了两个问题。是否从精神分裂症表型的不同属性中识别出不同或重叠的类别?根本不同的分析方法会产生一致的结果吗?参与者(n = 1074)完成了《精神分裂型人格问卷》(SPQJ)。项目筛选后,对属性特定的项目集(认知-感知,人际交往和杂乱无章)和常规项目集进行了迭代迭代MAXCOV分析。分析,潜在类别分析和因子混合模型)来检查MAXCOV分析后使用通用集中保留的项目检查MAXCOV结果的一致性,特定属性和通用项目集给出了分类的MAXCOV曲线和8.4-类别不是独立的,分别为10.4%和3.6%。没有潜在变量模型作为唯一的上乘者出现,但是有五个模型区分了由MAXCOV一般类别的成员组成的小型高分类别,不同的属性区分了重叠但非冗余的分类单元,以及在潜在变量建模中还确定了用MAXCOV识别的一般精神分裂症分类群。

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