Affective Modelling of Eustress and Distress using Psychological Scales

Hani Kadouf, Abdul Wahab Abdul Rahman, Norhaslinda Kamaruddin, Jamilah Hanum Abdul Khaiyom
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Abstract

This article is a case study that illustrates how a linear regression model can be implemented in eustress and distress analysis based on the correlation between emotion and stress and uses it to develop prediction equations of stress. This study proposes the use of five questionnaires; Perceived Stress Scale 10, Academic Eustress scale, Academic Distress scale, Bosse’s Distress Eustress scale and Adolescent Distress Eustress scale to determine perceived stress, eustress or distress. A sixth questionnaire, the Self-Assessment Manikin was used to determine emotional state in terms of valence and arousal, which are represented on 2-dimensional axis, where the x axis represents valence, and the y axis represents arousal. An analysis of the relationship between the results of the stress questionnaires and results of SAM based valence and arousal is carried out. Significant correlations are then used to derive regression equations used to predict eustress, distress or perceived stress. The findings showed that neither valence nor arousal was correlated with perceived stress, hence no regression equation was derived for it. However, valence and/or arousal were correlated with the remaining five questionnaires. Finally, this article analyzes the predictions comparing actual vs predicted values. Error analysis showed that the ADES questionnaire had the lowest average error, making it the most suitable in predicting eustress and distress from emotion.
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使用心理量表对压力和苦恼进行情感建模
本文以案例研究的形式,说明了如何根据情绪与压力之间的相关性,将线性回归模型应用于优思和苦恼分析,并利用该模型建立压力预测方程。本研究建议使用五份问卷:感知压力量表 10、学业优越感量表、学业压力量表、博斯压力优越感量表和青少年压力优越感量表来确定感知压力、优越感或压力困扰。第六份问卷,即 "自我评估模拟人",用于确定情绪状态的 "情感 "和 "唤醒",情感和唤醒在二维轴上表示,其中 x 轴代表情感,y 轴代表唤醒。对压力问卷调查结果与基于情绪和唤醒水平的 SAM 结果之间的关系进行了分析。然后,利用显著的相关性推导出回归方程,用于预测舒畅、痛苦或感知压力。研究结果表明,情绪和唤醒都与感知到的压力无关,因此没有得出相关的回归方程。然而,情绪和/或唤醒与其余五份问卷都有相关性。最后,本文对预测值进行了分析,比较了实际值与预测值。误差分析表明,ADES 问卷的平均误差最小,因此最适合预测来自情绪的舒畅感和痛苦感。
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