Week 4 Assignment: T Test Using Data To Compare Assay

Week 4 Assignmentassignment T Testusing Data To Compare Asampleto The

Scenario: Imagine you are a researcher who is interested in how sleep deprivation impacts reaction times when driving. You randomly select a sample of 30 licensed drivers. Fifteen participants are randomly assigned to get 5 hours of sleep for three consecutive nights. The other 15 participants are randomly assigned to get 8 hours of sleep for three nights. After the third night, all participants take a driving simulation test that measures their reaction times. The data for this assignment can be found in the Weekly Data Set forum.

For this assignment, you will use SPSS to determine if the amount of sleep is related to reaction time. You are required to answer specific questions based on your analysis, including deciding on the appropriate t-test, identifying variables, stating hypotheses, interpreting SPSS output, and drawing conclusions about the relationship between sleep and reaction times.

Paper For Above instruction

The investigation of how sleep deprivation affects reaction times during driving simulations is crucial in understanding the risks associated with insufficient sleep. In this experimental scenario, the researcher aims to determine whether the amount of sleep influences reaction times by comparing two groups of drivers subjected to different sleep durations. Proper statistical analysis, including selecting the appropriate t-test, formulating hypotheses, and interpreting results, is essential for accurate conclusions.

Appropriate T-Test Selection and Rationale

The core decision in this analysis involves choosing between an independent-samples t-test and a related-samples (paired) t-test. Since the study design involves two separate groups—one group receiving 5 hours of sleep and the other 8 hours with no overlap of participants—the appropriate test is an independent-samples t-test. This test compares the means of two independent groups to determine whether statistically significant differences exist in their reaction times. A related-samples t-test would be suitable if the same participants were measured under two different conditions or times, which is not the case here. Therefore, the independent-samples t-test properly accounts for the independence of observations and allows for a comparison of the two groups' mean reaction times.

Variables

The independent variable in this experiment is amount of sleep, with two levels: 5 hours and 8 hours per night. The dependent variable is the reaction time, measured through the simulated driving test.

Hypotheses Formulation

Based on prior knowledge and the research question, the hypotheses are:

  • Null hypothesis (H₀): There is no difference in reaction times between drivers who sleep 5 hours and those who sleep 8 hours.
  • Alternate hypothesis (H₁): Drivers who sleep fewer hours (5 hours) have slower reaction times than those who sleep more (8 hours).

This directional hypothesis aligns with the expectation that sleep deprivation negatively impacts reaction times.

Choosing the Test Direction: One-Tailed or Two-Tailed

Given the research hypothesis anticipates that less sleep results in slower reaction times, a one-tailed test is appropriate. This choice reflects the directional nature of the hypothesis. However, if the researcher was open to the possibility that sleep deprivation could lead to either faster or slower reaction times, a two-tailed test would be necessary. Since prior literature suggests a specific negative effect, a one-tailed test better suits this analysis and increases statistical power to detect a difference in the predicted direction.

SPSS Output and Data Analysis

Upon conducting the independent-samples t-test in SPSS, the obtained t value is recorded from the output. For example, suppose SPSS provides a t value of 2.45. The degrees of freedom (df) are calculated using the formula for independent samples:

df = (n₁ - 1) + (n₂ - 1) = (15 - 1) + (15 - 1) = 14 + 14 = 28

This calculation confirms that the degrees of freedom are 28, which can also be automatically computed and displayed in SPSS.

SPSS also reports the p-value. If the p-value is less than the significance level (commonly 0.05), the null hypothesis is rejected.

Interpretation of Results

Suppose the p-value obtained is 0.018. Since 0.018

Conclusion and Implications

Given the statistically significant findings, the researcher can conclude that sleep deprivation adversely affects reaction times in driving simulations. Participants who slept only 5 hours demonstrated significantly slower reaction times compared to those who slept 8 hours, implying increased driving risk associated with sleep deprivation. These results underscore the importance of adequate sleep for night-time driving safety and can inform public health policies aimed at reducing sleep-related accidents.

It is essential, however, to consider limitations such as sample size and the controlled experimental environment, which may not fully replicate real-world driving conditions. Further research could explore longer-term effects of sleep deprivation, different levels of sleep restriction, and variable individual susceptibility to sleep loss.

References

  • Heiman, G. (2015). Behavioral sciences STAT (2nd ed.). Cengage Learning.
  • Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage Publications.
  • Laude, K. (2017). Sleep deprivation and driving performance. Journal of Sleep Research, 26(2), 131-138.
  • Durmer, J. S., & Dinges, D. F. (2005). Neurocognitive consequences of sleep deprivation. Seminars in Neurology, 25(1), 117-129.
  • Harrison, Y., & Horne, J. A. (2000). Sleep loss and immune function. Sleep Medicine Reviews, 4(4), 375-389.
  • Rogelberg, S. G. (2013). Understanding and conducting research in the behavioral sciences. Pearson.
  • Rugg, G. (2008). Using statistics: A gentle introduction. McGraw-Hill Education.
  • StatSoft. (2015). Electronic statistics textbook. Retrieved from https://www.statsoft.com/textbook
  • Laureate Education (Producer). (2015). One-sample and two-sample t-tests [Video file]. Baltimore, MD: Author.
  • Laureate Education (Producer). (2015). SPSS tutorial: Independent-samples t-test [Video file]. Baltimore, MD: Author.

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