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首页> 外文期刊>Frontiers in Psychology >Using Two-Step Cluster Analysis and Latent Class Cluster Analysis to Classify the Cognitive Heterogeneity of Cross-Diagnostic Psychiatric Inpatients
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Using Two-Step Cluster Analysis and Latent Class Cluster Analysis to Classify the Cognitive Heterogeneity of Cross-Diagnostic Psychiatric Inpatients

机译：使用两步聚类分析和潜在群体聚类分析来分类交叉诊断精神病院的认知异质性

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摘要

The heterogeneity of cognitive profiles among psychiatric patients has been reported to carry significant clinical information. However, how to best characterize such cognitive heterogeneity is still a matter of debate. Despite being well suited for clinical data, cluster analysis techniques, like the Two-Step and the Latent Class, received little to no attention in the literature. The present study aimed to test the validity of the cluster solutions obtained with Two-Step and Latent Class cluster analysis on the cognitive profile of a cross-diagnostic sample of 387 psychiatric inpatients. Two-Step and Latent Class cluster analysis produced similar and reliable solutions. The overall results reported that it is possible to group all psychiatric inpatients into Low and High Cognitive Profiles, with a higher degree of cognitive heterogeneity in schizophrenia and bipolar disorder patients than in depressive disorders and personality disorder patients.
机译：据报道，患有精神病患者的认知谱的相互作用载有重大的临床信息。 然而，如何最好地表征这种认知异质性仍然是一个辩论问题。 尽管适用于临床数据，聚类分析技术，如两步和潜在的阶级，但在文献中没有注意到没有注意。 本研究旨在测试用两步和潜在的群体聚类分析获得的群集解决方案的有效性，对387个精神病院病患者的交叉诊断样本的认知曲线。 两步和潜在的群集分析产生了类似可靠的解决方案。 总体结果报告称，可以将所有精神病院分泌到低和高认知的型材中，具有较高程度的精神分裂症和双相障碍患者的认知异质性，而不是抑郁症和人格障碍患者。

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