The aim of this study is to reanalyze the perceived stress test using machine learning to determine the perceived stress levels of 150 individuals and measure the impact of the test questions. The test consists of 14 questions, each scored on a scale of 0 to 4, resulting in a total score range of 0-56. Out of these questions, 7 are formulated in a negative context and scored accordingly, while the remaining 7 are formulated in a positive context and scored in reverse. The test is also designed to identify two sub-factors: perceived self-efficacy and stress/discomfort perception. The main objectives of this research are to demonstrate that test questions may not have equal importance using artificial intelligence techniques, reveal which questions exhibit variations in the society using machine learning, and ultimately demonstrate the existence of distinct patterns observed psychologically. This study provides a different perspective from the existing psychology literature by repeating the test through machine learning. Additionally, it questions the accuracy of the scale used to interpret the results of the perceived stress test and emphasizes the importance of considering differences in the prioritization of test questions. The findings of this study offer new insights into coping strategies and therapeutic approaches in dealing with stress. Source code: https://github.com/toygarr/ppl-r-stressed

翻译：本研究旨在利用机器学习技术重新分析感知压力测试，以评估150名个体的感知压力水平，并衡量测试问题的影响。该测试包含14个问题，每个问题按0至4分计分，总分范围为0-56分。其中，7个问题以负面情境表述并按正向计分，其余7个问题以积极情境表述并按反向计分。测试还设计用于识别两个子因子：感知自我效能感和压力/不适感知。本研究的主要目标包括：通过人工智能技术证明测试问题可能并非同等重要，利用机器学习揭示社会中哪些问题存在变异性，并最终证明心理层面可观察到的不同模式的存在。本研究通过机器学习重复测试，提供了与现有心理学文献不同的视角。此外，本研究对用于解释感知压力测试结果的量表准确性提出质疑，并强调了考虑测试问题优先级差异的重要性。研究结果为应对压力的策略和治疗方法提供了新的见解。源代码：https://github.com/toygarr/ppl-r-stressed