艾森克两大人格因素及其与慢性特发性疼痛障碍患者抑郁的关系:临床验证分析。

ISRN Psychiatry Pub Date : 2012-09-04 Print Date: 2012-01-01 DOI:10.5402/2012/140458
Per Bech, Marianne Lunde, Stine Bjerrum Møller
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引用次数: 13

摘要

的目标。艾森克的两大人格因素(神经质和外向性)的临床计量学方面最初是由主成分分析确定的,但项目反应理论模型分析不足。随后分析了它们与抑郁和焦虑状态的关系。方法。慢性特发性疼痛障碍患者被纳入研究。采用非参数项目反应模型(Mokken)与alpha系数比较,对神经质量表中的焦虑和抑郁子量表以及外向量表中的外向和内向子量表进行验证。在测量抑郁和焦虑状态时,使用忧郁症量表和汉密尔顿焦虑量表。结果。我们在艾森克神经质因子中确定了焦虑和抑郁的可接受分量表,在艾森克外向因子中确定了外向与内向的分量表。在“你的情绪经常起伏吗?”这一项上,我们发现在这一项上得分为正的患者与抑郁和焦虑有统计学上显著的关联。结论。在艾森克神经质因子中,区分焦虑和抑郁分量表是很重要的。外倾性艾森克因子的临床分析确定了有效的子量表。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Eysenck's Two Big Personality Factors and Their Relationship to Depression in Patients with Chronic Idiopathic Pain Disorder: A Clinimetric Validation Analysis.

Aim. The clinimetric aspects of Eysenck's two big personality factors (neuroticism and extraversion) were originally identified by principal component analysis but have been insufficiently analysed with item response theory models. Their relationship to states of melancholia and anxiety was subsequently analysed. Method. Patients with chronic idiopathic pain disorder were included in the study. The nonparametric item response model (Mokken) was compared to the coefficient alpha to validate the anxiety and depression subscales within the neuroticism scale and the extraversion and introversion subscales within the extraversion scale. When measuring states of depression and anxiety, the Melancholia Scale and the Hamilton Anxiety Scale were used. Results. We identified acceptable subscales of anxiety and depression in the Eysenck factor of neuroticism and extraversion versus introversion subscales within the Eysenck factor of extraversion. Focusing on the item of "Does your mood often go up and down?" we showed a statistically significant association with melancholia and anxiety for patients with a positive score on this item. Conclusion. Within the Eysenck factor of neuroticism it is important to differentiate between the anxiety and depression subscales. The clinimetric analysis of the Eysenck factor of extraversion identified valid subscales.

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