The aim of this study is to determine the perceived stress levels of 150 individuals and analyze the responses given to adapted questions in Turkish using machine learning. 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：本研究旨在通过机器学习方法，确定150名个体的感知压力水平，并分析他们对土耳其语改编问题的回答。该测试包含14个问题，每个问题按0至4分评分，总分范围为0至56分。其中7个问题以负面情境进行表述并按其原始分值计分，其余7个问题以正面情境表述并采用反向计分。该测试还设计用于识别两个子因素：感知自我效能感与压力/不适感知。本研究的主要目标是：利用人工智能技术证明测试问题可能具有不相等的重要性；通过机器学习揭示哪些问题在人群中表现出差异；最终证明心理学中观察到的不同模式的存在性。本研究通过机器学习重复测试，为现有心理学文献提供了不同视角。此外，本研究质疑了用于解释感知压力测试结果的量表的准确性，并强调了考虑测试问题优先级差异的重要性。研究结果为压力应对策略和治疗方案提供了新见解。源代码：https://github.com/toygarr/ppl-r-stressed