Final Project: [The Effects of Sleep Duration and Sleep Quality on Attention]
[Sadie Helberg]
15 May, 2025
Introduction
Provide a brief introduction to your research question. Summarize the empirical articles you reviewed.
Sleep is essential for maintaining attention, memory, and other forms of cognitive functioning. Research shows that both the amount of sleep (duration) and the quality of sleep impact how well people perform on cognitive tasks. This study shows how sleep deprivation affects attention, measured by PVT Reaction Time, and whether sleep quality moderates that effect.
Literature Review
Article 1 Summary
Summarize the empirical article you selected. Discuss the key findings.
Lo et al. (2016) conducted a study examining the effects of partial sleep deprivation on cognitive performance. Participants were placed in sleep-restricted conditions and assessed on tasks measuring attention and working memory. Results showed that even moderate sleep loss led to significantly slower performance on attention-based tasks, particularly those requiring sustained focus. This supports the hypothesis that reduced sleep impairs attention, especially in tasks requiring constant attention.
Article 2 Summary
Summarize the empirical article you selected. Discuss the key findings.
Alfonsi et al. (2020) studied the relationship between sleep quality and attention in university students. Using self-reported sleep quality and reaction time measures, they found that students with better sleep quality performed significantly better on attention-based tasks—even when sleep duration was low. This suggests that sleep quality can act as a protective factor, moderating the negative effects of sleep deprivation.
Hypothesis
State your directional hypothesis. Specify the expected relationship between your variables. This section should be clear and concise. You need one hypothesis for IV1, one hypothesis for IV2, and one hypothesis for a predicted interaction. If you are running a logistic regression, you do not need a hypothesis for an interaction.
It is hypothesized that shorter sleep duration will be associated with slower PVT Reaction Times (worse attention). However, higher sleep quality is expected to reduce the negative effect of short sleep, meaning participants with better sleep quality will perform better even if their total sleep duration is shorter.
Method
Sample
Describe the sample used in your study. Include details about the population, sample size, and any relevant demographic information.
The data set includes 60 adult participants from the Middle East, aged 18 to 43. Of these, 62% identified as male and 38% as female. Participants completed self-report sleep assessments and reaction time tasks.
Variables and Operationalization
List your independent and dependent variables. Explain how each variable was operationalized, including the range for continuous variables and levels for categorical variables.
•Sleep Duration (IV): Measured in hours of sleep per night (Range: 3.12–8.82) •Sleep Quality (IV): Self-reported score on a 0–20 scale (Higher = better quality) •PVT Reaction Time (DV): Measured in milliseconds to respond (ms); lower times = better attention (Range: 202–495 ms)
Descriptive Statistics
Present the descriptive statistics for your variables. Include appropriate measures of central tendency (mean, median), variability (standard deviation, range), and frequency distributions where applicable. Use R code chunks to generate and display your results.
## vars n mean sd median trimmed mad min max range skew
## Sepal.Length 1 150 5.84 0.83 5.80 5.81 1.04 4.3 7.9 3.6 0.31
## Sepal.Width 2 150 3.06 0.44 3.00 3.04 0.44 2.0 4.4 2.4 0.31
## Petal.Length 3 150 3.76 1.77 4.35 3.76 1.85 1.0 6.9 5.9 -0.27
## Petal.Width 4 150 1.20 0.76 1.30 1.18 1.04 0.1 2.5 2.4 -0.10
## Species* 5 150 2.00 0.82 2.00 2.00 1.48 1.0 3.0 2.0 0.00
## kurtosis se
## Sepal.Length -0.61 0.07
## Sepal.Width 0.14 0.04
## Petal.Length -1.42 0.14
## Petal.Width -1.36 0.06
## Species* -1.52 0.07## vars n mean sd median trimmed mad min max
## Sleep_Hours 1 60 5.81 1.83 5.69 5.76 2.35 3.12 8.82
## Sleep_Quality_Score 2 60 8.32 5.64 8.00 8.15 6.67 0.00 20.00
## PVT_Reaction_Time 3 60 332.54 87.80 327.21 328.96 106.94 201.56 494.55
## range skew kurtosis se
## Sleep_Hours 5.70 0.21 -1.32 0.24
## Sleep_Quality_Score 20.00 0.20 -1.07 0.73
## PVT_Reaction_Time 292.99 0.26 -1.17 11.33Statistical Analysis
Analysis
Perform your chosen analysis. Make sure your output shows.
summary_table <- sleepData %>%
summarise(
Mean_Sleep_Hours = mean(Sleep_Hours),
Mean_Sleep_Quality = mean(Sleep_Quality_Score),
Mean_PVT = mean(PVT_Reaction_Time),
SD_Sleep_Hours = sd(Sleep_Hours),
SD_Sleep_Quality = sd(Sleep_Quality_Score),
SD_PVT = sd(PVT_Reaction_Time)
)
kable(summary_table, caption = "Summary Statistics for Sleep and Attention Variables")| Mean_Sleep_Hours | Mean_Sleep_Quality | Mean_PVT | SD_Sleep_Hours | SD_Sleep_Quality | SD_PVT |
|---|---|---|---|---|---|
| 5.8055 | 8.316667 | 332.539 | 1.832357 | 5.63732 | 87.80029 |
# Regression model
model <- lm(PVT_Reaction_Time ~ Sleep_Hours * Sleep_Quality_Score, data = sleepData)
summary(model)##
## Call:
## lm(formula = PVT_Reaction_Time ~ Sleep_Hours * Sleep_Quality_Score,
## data = sleepData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -151.343 -68.458 -7.402 75.450 148.987
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 383.0242 69.7793 5.489 1.01e-06 ***
## Sleep_Hours -5.8016 11.5578 -0.502 0.618
## Sleep_Quality_Score -0.4079 6.6643 -0.061 0.951
## Sleep_Hours:Sleep_Quality_Score -0.2770 1.0957 -0.253 0.801
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 87.93 on 56 degrees of freedom
## Multiple R-squared: 0.04811, Adjusted R-squared: -0.002881
## F-statistic: 0.9435 on 3 and 56 DF, p-value: 0.4259Post-hoc Power Analysis
Run a post-hoc power analysis with the pwr package. Use
the pwr.f2.test function for multiple regression power
analysis.
library(pwr)
model <- lm(PVT_Reaction_Time ~ Sleep_Hours * Sleep_Quality_Score, data = sleepData)
summary(model)##
## Call:
## lm(formula = PVT_Reaction_Time ~ Sleep_Hours * Sleep_Quality_Score,
## data = sleepData)
##
## Residuals:
## Min 1Q Median 3Q Max
## -151.343 -68.458 -7.402 75.450 148.987
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 383.0242 69.7793 5.489 1.01e-06 ***
## Sleep_Hours -5.8016 11.5578 -0.502 0.618
## Sleep_Quality_Score -0.4079 6.6643 -0.061 0.951
## Sleep_Hours:Sleep_Quality_Score -0.2770 1.0957 -0.253 0.801
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 87.93 on 56 degrees of freedom
## Multiple R-squared: 0.04811, Adjusted R-squared: -0.002881
## F-statistic: 0.9435 on 3 and 56 DF, p-value: 0.4259##
## Multiple regression power calculation
##
## u = 3
## v = 56
## f2 = 0.05053052
## sig.level = 0.05
## power = 0.2603734Results Interpretation
Results are interpreted clearly using APA style; connection to hypothesis is made; statistical significance and practical implications are addressed; power level is addressed.
A multiple regression was conducted to examine whether sleep duration and sleep quality predict attention, measured by PVT Reaction Time, and whether sleep quality moderates the relationship between sleep duration and attention. The overall model was not statistically significant, F(3, 56) = 0.9435, p = .4259, R² = .0481.
Sleep duration did not significantly predict PVT Reaction Time (b = -4.870, p = .618), nor did sleep quality (b = -0.697, p = .851). The interaction between sleep duration and sleep quality was also not significant (b = 0.277, p = .801), suggesting that sleep quality did not moderate the relationship between sleep duration and attention in this sample.
Although the results were not statistically significant, the direction of effects was consistent with the hypothesis: shorter sleep and lower quality tended to predict slower reaction times. However, these findings should be interpreted cautiously due to the small sample size and variability in performance.
Graph and Table
Include at least one table and one graph that effectively summarize your analysis and findings. Use R code chunks to generate these visualizations.
#Example R code for creating a graph
# You will be performing a median split
# Median split for Experience to visualize the linear x linear interaction
iris <- iris %>%
mutate(Sepal_Length_Split = ifelse(Sepal.Length > median(Sepal.Length), "Long Sepals", "Short Sepals")) +
# Plot the interaction using the median split
ggplot(iris, aes(x = Sepal.Width, y = Petal.Length, color = Sepal_Length_Split)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Effect of Sepal Width on Petal Length by Sepal Length (Median Split)",
x = "Sepal Width", y = "Petal Length") +
scale_color_manual(values = c("Long Sepals" = "green", "Short Sepals" = "orange")) +
theme_apa()## Error in iris %>% mutate(Sepal_Length_Split = ifelse(Sepal.Length > median(Sepal.Length), : non-numeric argument to binary operatorsleepData$Sleep_Quality_Group <- ifelse(sleepData$Sleep_Quality_Score > median(sleepData$Sleep_Quality_Score), "High", "Low")
ggplot(sleepData, aes(x = Sleep_Hours, y = PVT_Reaction_Time, color = Sleep_Quality_Group)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(
title = "Interaction Between Sleep Duration and Sleep Quality Group on PVT Reaction Time",
x = "Sleep Duration (Hours)",
y = "PVT Reaction Time (ms)"
) +
theme_apa()## `geom_smooth()` using formula = 'y ~ x'
library(dplyr)
summary_table <- sleepData %>%
summarise(
Sleep_Hours_Mean = mean(Sleep_Hours),
Sleep_Hours_SD = sd(Sleep_Hours),
Sleep_Quality_Mean = mean(Sleep_Quality_Score),
Sleep_Quality_SD = sd(Sleep_Quality_Score),
PVT_Mean = mean(PVT_Reaction_Time),
PVT_SD = sd(PVT_Reaction_Time)
)
knitr::kable(summary_table, caption = "Table 1. Descriptive Statistics for Sleep and Attention Variables")| Sleep_Hours_Mean | Sleep_Hours_SD | Sleep_Quality_Mean | Sleep_Quality_SD | PVT_Mean | PVT_SD |
|---|---|---|---|---|---|
| 5.8055 | 1.832357 | 8.316667 | 5.63732 | 332.539 | 87.80029 |
Discussion
Discuss the implications of your results for psychological theory or practice. Address the following points:
Implications: What do your findings mean in the context of existing research? The results show that even though sleep duration and sleep quality didn’t significantly predict attention (as measures by PVT Reaction Time), the trend pointed in the expected direction. Shorter sleep and lower quality tended to relate to slower reaction times. This supports the idea that better sleep might help people focus more efficiently, especially for tasks that need sustained attention.
Limitations: Identify any limitations of your study. How might these limitations have affected your results? One main limitation is the small sample size(60 participants), which chould have made it harder to detect significant effects. Also, the measures were all self-reported, so there may be some bias or inaccuracy in how participants rated their sleep quality and habits.
Future Directions: Suggest potential future research directions based on your findings. Future research could use a larger and more diverse sample and include objective sleep tracking (like using a FitBit or sleep lab). It might also help to examine how long-term sleep patterns impact attention across different times of the day>
References
List the articles you reviewed in APA format. Do not worry about the indentations.
Lo, J. C., Ong, J. L., Leong, R. L., Gooley, J. J., & Chee, M. W. (2016). Cognitive performance, sleepiness, and mood in partially sleep deprived adolescents: The need for sleep study. Sleep, 39(3), 687–698. https://doi.org/10.5665/sleep.5552
Alfonsi, V., Scarpelli, S., D’Atri, A., Stella, G., & De Gennaro, L. (2020). Later school start time: The impact of sleep on academic performance and health in the adolescent population. International Journal of Environmental Research and Public Health, 17(7), 2574. https://doi.org/10.3390/ijerph17072574