Multivariate Regression Analysis of Sleep Deprivation Through Academic Performance of Architecture Students.

This paper addresses a study on architecture students regarding the effect of sleep quality and various factors that affect their academic performance. The study was inspired by the lack of analysis done on architecture students regarding their sleep quality, which significantly affects their performance in school. The lack of sleep or poor sleep quality also affects the person's ability to function correctly. Quantitative methods, which utilized multivariate regression analysis to test the relationship between the two variables and numerous factors, were utilized in this study. Surveys were also distributed to gather data. The study had a sample size 221 from a population size of 2,128 recently enrolled architecture students. The Pearson Correlation Coefficient was utilized using IBM SPSS (Statistical Package for the Social Sciences) Statistics, a software package to determine the relationship between sleep deprivation and students' academic performance. The study measured the seven components of sleep quality using the Pittsburgh Sleep Quality Index (PSQI). The research findings show that all the respondents had poor sleep quality. The study on the relationship between sleep quality and academic performance of architecture students indicates a weak yet discernible correlation between their GPA and critical factors like sleep quality, number of units, schedule, and year level. The strength of these correlations indicates that there may be additional factors besides those examined in this study that significantly impact the students' academic performance. However, the links discovered are statistically significant. Two variables--year level and number of units--showed a substantial link with sleep quality when assessed for their correlation strength. However, sleep quality had a very weak correlation with academic performance. [ABSTRACT FROM AUTHOR]