Sleep Quality and Perceived Stress in Students
Primary Investigator (PI) Name
Kevin Gittner
Department
CCSE – Data Science and Analytics
Abstract
Sleep and stress have a big effect on how healthy young adults are. This small project uses the Student Stress Monitoring dataset (Kaggle: StressLevelDataset.csv) to talk about sleep quality and perceived stress and to make a plan on how to look at their relationship in a group of students. I recorded data import and cleaning choices in SPSS (correct naming, types, measurement levels, missing values, and value labels), make variables that are easy to analyze, and do univariate exploratory data analysis with frequency tables and checks of the distribution. Stress level and sleep quality are the working variables. The uploaded SPSS run showed that the quality of sleep ranged from 0 to 5 (1=Strongly disagree - 5=Strongly agree) while stress level ranged from 1 to 2 (0=low and 2=high.) Univariate EDA reveals a nearly equal distribution across stress categories and a wide range of quality of sleep scores (0–5), with 3.1% coded as 0. The planned inferential analysis for the complete paper is a chi-square test of correlation between quality of sleep and their level of stress, utilizing stacked bar visualization and effect size measurement (Cramer’s V). I conclude with limitations (survey self-report, cross-section, coding constraints) and provide a reference template for the incorporation of peer-reviewed sources in the KSU Library. This paper takes into consideration and expands on the teacher's previous comments about variable choice, recoding logic, labels, and needed descriptives.
AI was used to clean up the paragraph and summarize my project into 300 words or less.
Disciplines
Medicine and Health Sciences | Social and Behavioral Sciences
Sleep Quality and Perceived Stress in Students
Sleep and stress have a big effect on how healthy young adults are. This small project uses the Student Stress Monitoring dataset (Kaggle: StressLevelDataset.csv) to talk about sleep quality and perceived stress and to make a plan on how to look at their relationship in a group of students. I recorded data import and cleaning choices in SPSS (correct naming, types, measurement levels, missing values, and value labels), make variables that are easy to analyze, and do univariate exploratory data analysis with frequency tables and checks of the distribution. Stress level and sleep quality are the working variables. The uploaded SPSS run showed that the quality of sleep ranged from 0 to 5 (1=Strongly disagree - 5=Strongly agree) while stress level ranged from 1 to 2 (0=low and 2=high.) Univariate EDA reveals a nearly equal distribution across stress categories and a wide range of quality of sleep scores (0–5), with 3.1% coded as 0. The planned inferential analysis for the complete paper is a chi-square test of correlation between quality of sleep and their level of stress, utilizing stacked bar visualization and effect size measurement (Cramer’s V). I conclude with limitations (survey self-report, cross-section, coding constraints) and provide a reference template for the incorporation of peer-reviewed sources in the KSU Library. This paper takes into consideration and expands on the teacher's previous comments about variable choice, recoding logic, labels, and needed descriptives.
AI was used to clean up the paragraph and summarize my project into 300 words or less.