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Reliability and construct validity of the Automated Neuropsychological Assessment Metrics (ANAM) mood scale

机译:自动化神经心理评估指标(ANAM)情绪量表的可靠性和建构效度

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摘要

The reliability and construct validity of the Automated Neuropsychological Assessment Metrics (ANAM) mood scale (AMS) were examined using concurrent, well-validated measures of mood and confirmatory factor analysis (CFA) with a sample of 210 volunteer college students. The AMS was given in computerized format with multiple adjectives using a visual analog Likert scale yielding seven dimensions of mood including vigor, restlessness, depression, anger, fatigue, anxiety, and happiness. All seven mood dimensions of the AMS demonstrated excellent test–retest reliability and internal consistency. Also, the AMS anxiety dimension correlated strongly with the Spielberger's State Anxiety Inventory (r=0.67) and the AMS depression dimension correlated strongly with the Beck Depression Inventory-II (r=0.71). CFA revealed that the AMS 7-factor mood model fit the data well and significantly better than an alternative, theoretically plausible model. When concurrent measures of mood were incorporated in the CFA model, the AMS demonstrated both convergent and discriminant validity. The AMS 7-factor model explained 55.12% of the total variance in the items. It was concluded that the AMS provides a brief yet reasonably complete and valid assessment of mood.
机译:自动化神经心理评估指标(ANAM)情绪量表(AMS)的信度和结构效度,是通过对210名志愿者大学生进行的并发,有效验证的情绪和确认性因子分析(CFA)进行检查的。 AMS以计算机化格式给出,带有多个形容词,使用视觉类似的李克特量表,产生七个方面的情绪,包括活力,躁动不安,沮丧,愤怒,疲劳,焦虑和幸福。 AMS的所有七个情绪维度都表现出出色的重测信度和内部一致性。同样,AMS焦虑量表与斯皮尔伯格状态焦虑量表(r = 0.67)密切相关,而AMS抑郁量表与贝克抑郁量表-II(r = 0.71)密切相关。 CFA透露,AMS的7因素情绪模型非常适合数据,并且比替代的理论上合理的模型要好得多。当同时测量情绪时将其纳入CFA模型中时,AMS证明了收敛性和判别性均有效。 AMS 7因子模型解释了项目中总差异的55.12%。结论是,AMS提供了一个简短而合理,完整而有效的情绪评估。

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