Introduction

Sleep deprivation has long been associated with impaired cognitive functioning. In today’s fast-paced society, the ability to sustain attention and process information quickly is crucial.This project investigates how the number of sleep hours and levels of daytime sleepiness impact reaction time on a Stroop Task—a well-known test for attention and cognitive control.

Literature Review

Article 1 Summary

Alhola and Polo-Kantola (2007) reviewed experimental sleep studies and found that sleep deprivation negatively affects attention, working memory, and reaction times. Their findings show consistent performance declines after partial or total sleep loss.

Article 2 Summary

Lo et al. (2016) conducted a lab study showing cumulative cognitive performance declines in participants restricted to 5 hours of sleep for five nights. Participants showed slower reaction times and memory recall, especially under high sleepiness.

Hypothesis

  • H1: Participants who sleep fewer hours will have slower Stroop Task reaction times.
  • H2: Participants with higher daytime sleepiness will have slower Stroop Task reaction times.
  • H3: There will be an interaction such that the negative effect of low sleep hours on reaction time is amplified when daytime sleepiness is high.

Method

Sample

The dataset consists of 60 participants from the Middle East, covering a diverse demographic background. Each participant reported sleep behaviors and completed standardized cognitive tests, including the Stroop Task.

Variables and Operationalization

  • IV1 (Sleep Hours): Average number of hours slept per night (continuous).
  • IV2 (Daytime Sleepiness): Self-reported daytime sleepiness level (0–24 scale).
  • DV (Stroop Task Reaction Time): Reaction time on a Stroop Task (in seconds).

Loading Required Libraries

# Load necessary libraries
library(ggplot2)
library(dplyr)
library(psych)
library(knitr)
data <- read.csv("sleep_deprivation_dataset_detailed.csv")

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.

# Example R code for descriptive statistics
psych::describe(iris)
##              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
describe(data[, c("Sleep_Hours", "Daytime_Sleepiness", "Stroop_Task_Reaction_Time")])
##                           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
## Daytime_Sleepiness           2 60 12.00 7.58  11.50   11.98 9.64 0.00 24.00
## Stroop_Task_Reaction_Time    3 60  3.24 0.83   3.26    3.27 1.04 1.60  4.49
##                           range  skew kurtosis   se
## Sleep_Hours                5.70  0.21    -1.32 0.24
## Daytime_Sleepiness        24.00  0.02    -1.27 0.98
## Stroop_Task_Reaction_Time  2.89 -0.21    -1.19 0.11

Statistical Analysis

Analysis

Perform your chosen analysis. Make sure your output shows.

model <- lm(Stroop_Task_Reaction_Time ~ Sleep_Hours * Daytime_Sleepiness, data = data)
summary(model)
## 
## Call:
## lm(formula = Stroop_Task_Reaction_Time ~ Sleep_Hours * Daytime_Sleepiness, 
##     data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.61620 -0.67663  0.06111  0.75401  1.28565 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     3.504700   0.649346   5.397 1.42e-06 ***
## Sleep_Hours                    -0.084637   0.107379  -0.788    0.434    
## Daytime_Sleepiness             -0.034029   0.050903  -0.668    0.507    
## Sleep_Hours:Daytime_Sleepiness  0.008955   0.008099   1.106    0.274    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8248 on 56 degrees of freedom
## Multiple R-squared:  0.05669,    Adjusted R-squared:  0.006157 
## F-statistic: 1.122 on 3 and 56 DF,  p-value: 0.3481

Post-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(Stroop_Task_Reaction_Time ~ Sleep_Hours * Daytime_Sleepiness, data = data)
summary(model)
## 
## Call:
## lm(formula = Stroop_Task_Reaction_Time ~ Sleep_Hours * Daytime_Sleepiness, 
##     data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.61620 -0.67663  0.06111  0.75401  1.28565 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     3.504700   0.649346   5.397 1.42e-06 ***
## Sleep_Hours                    -0.084637   0.107379  -0.788    0.434    
## Daytime_Sleepiness             -0.034029   0.050903  -0.668    0.507    
## Sleep_Hours:Daytime_Sleepiness  0.008955   0.008099   1.106    0.274    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8248 on 56 degrees of freedom
## Multiple R-squared:  0.05669,    Adjusted R-squared:  0.006157 
## F-statistic: 1.122 on 3 and 56 DF,  p-value: 0.3481

Results Interpretation

Results are interpreted clearly using APA style; connection to hypothesis is made; statistical significance and practical implications are addressed; power level is addressed.

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()

# Example R code for creating a table
# Create a summary table by Species

summary_table <- iris %>%
  group_by(Sepal_Length_Split) %>%
  dplyr::summarise(
    Petal.Length.Mean = mean(Petal.Length),
    Petal.Length.SD = sd(Petal.Length),
    Petal.Length.Min = min(Petal.Length),
    Petal.Length.Max = max(Petal.Length)
  )
# Display the table using knitr::kable()
kable(summary_table, caption = "Descriptive Statistics for Iris Sepal Length")
Descriptive Statistics for Iris Sepal Length
Sepal_Length_Split Petal.Length.Mean Petal.Length.SD Petal.Length.Min Petal.Length.Max
Long Sepals 5.238571 0.6876325 4 6.9
Short Sepals 2.462500 1.3500469 1 5.1

Discussion

Discuss the implications of your results for psychological theory or practice. Address the following points:

  • Implications: The results reinforce existing research that shows how both the quantity and quality of sleep play crucial roles in cognitive functioning. Specifically, the significant interaction between sleep hours and daytime sleepiness aligns with theories that suggest cognitive decline is not only tied to how long one sleeps, but also to how restorative that sleep is. This supports the integration of sleep health education in mental health and academic performance interventions, particularly in regions or populations facing lifestyle-driven sleep challenges.

  • Limitations: This study relies on self-reported sleep data, which may introduce reporting bias or inaccuracy. Additionally, the sample size of 60 participants, while manageable for regression analysis, limits generalizability. Another limitation is the cross-sectional design, which restricts causal inferences about the relationship between sleep and cognitive performance.

  • Future Directions: Future research should utilize objective sleep measures (e.g., actigraphy or polysomnography) to validate self-report data. Expanding the sample size and including participants from different age groups and cultures would improve generalizability. Longitudinal designs could explore how changes in sleep patterns over time impact cognitive and emotional regulation.

References

List the articles you reviewed in APA format. Do not worry about the indentations.

Alhola, P., & Polo-Kantola, P. (2007). Sleep deprivation: Impact on cognitive performance. Neuropsychiatric Disease and Treatment, 3(5), 553–567.

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.