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Balancing Resources in Stressful Contexts: Exploring Decision-Making in Low-Income Students

datacite.rightsrestricted
dc.contributor.advisorNiv, Yael
dc.contributor.authorSchiller, Kajal
dc.date.accessioned2025-08-05T17:28:41Z
dc.date.available2025-08-05T17:28:41Z
dc.date.issued2025-04-21
dc.description.abstractLearning to allocate our resources effectively is a critical part of understanding cognitive, emotional, and behavioral processes. College represents a unique and transitional period during which individuals develop the ability to make decisions and allocate resources independently, often for the first time, without parental influence. This balancing act can be particularly challenging and stressful for low-income students, who face numerous disadvantages, especially when juggling financial responsibilities and academic demands. While past studies have explored the relationship between stress, resource allocation, and income status, the directionality of these relationships remains unclear. To understand these relations, we adapted a task where participants had to balance gaining rewards with uncertainties to investigate whether low-income students will utilize their resources more in the present than conserving them for future needs. Our goal was not only to assess how low-income students manage resources when facing rewards and uncertainty, but how their resource use is affected under stressful conditions. We hypothesize that low-income college students will utilize their resources more than higher income students in environments of high uncertainty and stress. Keywords: Resource Allocation, Rewards, Income, Stress, Uncertainty
dc.identifier.urihttps://theses-dissertations.princeton.edu/handle/88435/dsp01np193d621
dc.language.isoen
dc.titleBalancing Resources in Stressful Contexts: Exploring Decision-Making in Low-Income Students
dc.typePrinceton University Senior Theses
dspace.entity.typePublication
dspace.workflow.startDateTime2025-04-21T20:51:39.993Z
pu.contributor.authorid920270329
pu.date.classyear2025
pu.departmentPsychology
pu.minorStatistics and Machine Learning

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