kittens wearing mittens 77: The Interactions Between Screen Time, Sleep, and GPA

Pitzer College

The Interactions Between Screen Time, Sleep, and GPA:

The Effect on Students

Rhyus Goldman, Mila King, Diane Orozco

Psychological Statistics PSYC 091

Professor Steffanie Guillermo

December 8, 2023

Introduction

In this study, we set out to explore the relationships between screen time, sleep patterns, academic performance, and university affiliation among 5C students. Our research hypotheses were driven by the surrounding impact of screen time on academic outcomes and the potential interaction between screen time, sleep duration, and institutional differences. Specifically, we hypothesized that students with a higher average screen time per week would exhibit lower GPAs (directional, negative), positing a potential negative influence of longer hours spent on screen engagement on academic achievement. Additionally, we expected that students with lower screen time would report sleeping more hours per week (directional, positive), reflecting a potential trade-off between screen engagement and sleep duration. Furthermore, we explored the hypothesis that Pitzer students would report longer sleep durations compared to non-Pitzer students (directional, positive), considering potential variations in lifestyle and academic demands associated with different institutional affiliations.

Method

Participants in this study comprised of 84 individuals who were surveyed anonymously via a Qualtrics online form. The total group value for each hypothesis ranged depending on if participants left certain responses blank or not. The age range of the participants was 18 to 21 years, and the sample was predominantly composed of individuals identifying as white. Independent variables assessed in the study encompassed average screen time usage per week, categorized as less than 5 hours, 5-10 hours, and more than 10 hours, as well as Pitzer student status (yes/no). Dependent variables included GPA, measured through an open-ended response format, and hours slept per week, also measured through an open-ended response format. Additionally, participants provided information on age (open-ended response), gender identity (categorized as Woman, Man, Gender nonbinary, Other), and race/ethnicity (open-ended response).

Results

Hypothesis 1

The omnibus test with ɑ = 0.05 from a one way analysis of variance was not significant, F(2, 60) = .767 p = .469, η2 = .025. There was no difference between GPAs of students who averaged less than five hours of screen time per week (M = 3.82, SD = .12) than students who spent around five to ten hours per week on screens (M = 3.75, SD = .17), p = .957. Similarly, there was also not a noticeable difference between the GPAs of students who averaged five to ten hours of screen time to week (M = 3.75, SD = .17) to those that spent more than ten hours per week on screens (M = 3.62, SD = .57), p = .630. Lastly, there was not a large difference between students who spent less than 5 hours per week on screens versus those who spent more than ten hours per week on screens, p = .583. These results are shown in Table 1.

Hypothesis 2

The omnibus test with ɑ = 0.05 from a one way analysis of variance was not significant, F(2, 64) = 1.062, p = .352, η2 = .032. There was no difference between screen less than five hours time and students who sleep less than five to ten hours (M = 54.00, SD = 6.34) p = .993 with students who averaged more than five hours to ten hours of sleep (M = 53.55, SD = 5.35) p = .567. Students who had more than ten hours of sleep and less than five hours of screen time per week = had slightly higher screen levels than students who get more than ten hours of sleep (M = 50.51, SD = 9.40) p = .451. These results are shown in Table 2.

Hypothesis 3

Using ɑ = 0.05 for an independent samples t-test, there were no differences in how much sleep a student got per week based on if they were a Pitzer student (M = 52.08, SD = 8.00) or a non-Pitzer student (M = 48.22, SD = 10.66), t(65) = 1.23, p = .203, 95% CI [-2.14, 9.85], r2 = .023. These results are shown in Table 3.

Discussion

We conclude that none of our findings were significant due to the small sample size and self-reported measures of screen time and sleep per week. The potential for reporting bias introduces a level of uncertainty, as participants may not have been entirely truthful in their responses, leading to skewed results. Additionally, inadvertent over or under-reporting of hours spent on each activity further complicates the accuracy of the gathered data. If we were to redo this experiment in the future, we suggest conducting controlled experiments that would allow for a more precise examination of the impact of screen time on academic performance and sleep patterns. By directly measuring screen time and sleep duration, rather than relying on self-reports, researchers can mitigate issues associated with participant honesty and recall accuracy. Furthermore, a larger and more diverse participant pool of applicants would increase the external validity of the results, providing a clearer understanding of the potential relationships between screen time, sleep, and academic outcomes among 5C students. Overall, implementing experimental designs and increasing sample sizes will contribute to more reliable results of the associations between screen time, sleep, and academic performance in the context of 5C students.

Table 1

Means and Standard Deviations for Survey Results