Results Student’s Ful Name Institutional Affiliation Course Full Title Instructor’s Full Name

Results

Student’s Ful Name

Institutional Affiliation

Course Full Title

Instructor’s Full Name

Date

Results

The results section includes diverse results of a multiple regression analysis. The analysis focuses on Hayes’ PROCESS macro based on an SPSS version (Hayes, 2018). The study focuses on 150 undergraduate students. The median Age for the students is 20.5 years. Further, SD = 1.2. The participants included 78 females and 72 males. The research constituted participants from diverse psychology courses from diverse universities. All participants provided informed consent (Volk et al., 2016). The university’s Institutional Review Board facilitated the approval of the study protocol.

One of the primary metrics evaluated was sleep quality, which was measured using the PSQI, a self-reported questionnaire where higher scores indicate poorer sleep quality (Volk et al., 2019). Another essential measure focused on stress levels. The measure focused on the Perceived Stress Scale. The PSS represents a self-reported questionnaire that assesses individuals who experience stress. Higher scores indicate more significant stress. The last measure focuses on academic performance. The measure uses self-reported cumulative GPA.

Data Analysis

The study includes multiple regression analysis with a bootstrapping procedure (5,000 resamples). The approach included Hayes’ PROCESS macro (Hayes, 2018) in SPSS version 28. The core independent variable included sleep quality (X), academic performance as the dependent variable (Y), and stress as the mediator (M). The included Age and gender comprised covariates in the model to control for potential confounding effects.

Results

Model Fit

The overall model was statistically significant, F(4, 145) = 12.47, p < .001, indicating that the combination of sleep quality, stress, and age/gender explained a significant portion of the variance in academic performance (R² = .26).

Direct and Indirect Effects

The impact of sleep quality on academic performance, as mediated by stress, was significant (β = -.12, 95% CI: -.20 to -.04). In contrast, the direct influence of sleep quality on academic performance was not significant (β = .08, p = .23). The above figures suggests that sleep quality negatively influences academic performance through its positive effect on stress levels. Individuals who reported poorer sleep quality also reported higher levels of stress. The approaches represented lower academic performance. Table 1 summarizes the regression coefficients and their significance levels.

Table 1. Regression Coefficients and Significance Levels

Variable

Path

β

SE

t

p-value

95% CI

Sleep Quality (X)

-> Stress (M)

.28

.05

5.32

<.001

[.18, .38]

Stress (M)

-> Academic Performance (Y)

-.15

.03

-4.87

<.001

[-.21, -.09]

Sleep Quality (X) -> Academic Performance (Y)

Direct Effect

.08

.07

1.14

.23

[-.06, .22]

Sleep Quality (X) -> Stress (M) -> Academic Performance (Y)

Indirect Effect

-.12

.04

-3.07

<.01

[-.20, -.04]

Age

-> Academic Performance (Y)

.15

.06

2.48

<.05

[.04, .26]

Gender

-> Academic Performance (Y)

-.02

.07

-.29

.78

[-.16, .12]

β = regression coefficient

SE = standard error

t = test statistic

CI = confidence interval.

Simple Slopes Analysis

A simple slopes analysis examined how sleep quality interacted with stress levels. The findings showed that the negative impact of poor sleep on academic performance was significantly stronger when stress levels were high (β = -.18, p < .01) compared to when stress levels were low (β = -.06, p = .12).The above suggests that the detrimental effect of poor sleep on academic performance remains notable when individuals experience high levels of stress.

Figure 1: Path model with regression coefficients

Figure 1 illustrates the path model for the study. Sleep quality (X) represents a notable outcome on stress (M) (β = .28, p < .001). Stress (M) hurts academic performance (Y) (β = -.15, p < .001). The emerging outcome of sleep quality on academic performance through stress indicates (β = -.12, 95% CI: -.20 to -.04). Age positively affects academic performance (β = .15, p < .05). Gender remains neutral (β = -.02, p = .78).

Covariates

Both Age and gender represented covariates in the model. Age is positively associated with academic performance (β = .15, p < .05). The approach suggested that older students report higher GPAs. Gender was unrelated to academic performance (β = -.02, p = .78).

References

Volk, F., Floyd, C. G., Bohannon, K. E., Cole, S. M., McNichol, K. M., Schott, E. A., Williams, Z. D. R. (2019) The Moderating Role of the tendency to blame others in the development of perceived addiction, shame, and depression in pornography users. Sexual Addiction & Compulsivity, 26(3-4), 239-261. https://doi.org/10.1080/10720162.2019.1670301

Volk, F., Thomas, J., Sosin, L., Jacob, V., & Moen, C. (2016). Religiosity, developmental context, and sexual shame in pornography users: A Serial mediation model. Sexual Addiction & Compulsivity, 23(2-3), 244–259. https://doi.org/10.1080/10720162.2016.1151391