RSCH 7864 Assessment 3: t-Test Application and Interpretation

Data Analysis Plan

RSCH 7864 Assessment 3 This represents a plan focusing on the independent samples t-test, with two variables: review and final, to determine whether attendance at a review session affects scores in the final exam. To check the validity of the t-test, we had to assess the assumption of homogeneous variances, which we carried out by doing Levene’s assessment for variance equality. It would determine if there is a constant variance in scores between the last exam of two groups: attendees and non-attendees of the review session.RSCH 7864 Assessment 3: t-Test Application and Interpretation The outcomes for Levene’s test suggest that the homogeneity presumption held (F = 0.219, p = 0.641). Accordingly, the independent samples t-test standard was computed without needing a Welch adjustment.

There are two variables: final and review. It would be viewed as a continuous variable for the final as it forms scores for all correct answers provided during the final test. Reviewing, on the other hand, presents if the student had attended the review (1= no;2=yes), with being a two-group categorical variable.

The research question for this analysis is: Does a notable difference exist in final exam scores between students who attended the review session and those who did not?

The null hypothesis, H₀: The final exam scores do not show a significant difference between the two groups.

The alternative hypothesis, H₁: There is a notable difference in the final exam scores between the two groups.

The t-test results showed no significant difference in final exam scores related to review session attendance, t = -0.147, df = 103, p = 0.883.

Testing Assumptions

Assumption Checks

Test of Equality of Variances (Levene’s)
	F	df₁	df₂	p
final	0.219	1	103	0.641

The following table gives Levene’s test for equality of variances with F = 0.219 and a p-value of 0.641, which is greater than the significance level of 0.05. The null hypothesis of Levene’s test cannot be dismissed and the assumption of equal variances is satisfied. This finding, therefore, states that the difference in final exam scores is not based on the variable of whether they attended the review session or not. The success of this means that the differences in final examination scores are controlled by the variables regardless of whether students participated in a review session; thus, an independent samples standard t-test might be used.

Results & Interpretations

Decriptives

Group Descriptives
	Group	N	Mean	SD	SE	Coefficient of variation
final	Attended review session	55	60.182	7.930	1.069		0.132
	Did not attend a review session	50	60.420	8.680	1.228		0.144

Independent Samples T-Test
	t	df	p
final	-0.147	103	0.883

Note. Student’s t-test.

Based on the group’s descriptive statistics, 55 students attended the review session with an average final exam score of 60.182 and a standard deviation of 7.930. Students who did not participate in the review session recorded a mean score of 60.420 with a standard deviation of 8.680 for 50 students. Since Levene’s test confirmed that the assumption of equal variances is satisfied was met (F = 0.219, p = 0.641), the standard independent samples t-test was used. The t-test had a t-value of -0.147 and a p-value of 0.883, which is above the alpha threshold of 0.05. Because there are 103 degrees of freedom, based on the formula 55 + 50 – 2, it implies that there is no notable variation in final exam scores between the two groups. Thus the null hypothesis cannot be rejected, and attendance at the review session does not significantly influence the performance on final exams.

Statistical Conclusions

Indeed, the analysis verified that the condition of homogeneity has been met by the groups for the independent samples t-test whose p-value happens to be greater than 0.883 when compared to its alpha threshold 0.05 means that there exist no significant scores as far as their final exams performance is concerned about the students going to the review session and others who did not. Since in both groups, average scores are just about the same, the hypothesis of the research cannot be denied. However, the analysis has the limitation of relying only on the final exam scores at the time of participation without considering other variables that may be influencing, such as study habits or prior educational history. Additionally, the sample size and variability in scores may limit the generalizability of the findings. Future research including additional variables would be recommended to understand the full scope.

Applications

The independent samples t-test may be used to determine variables in nursing research that influence patient outcomes. It might be used, for instance, to compare two types of interventions designed to reduce the readmission of patients in the hospital, including whether one form of education was video-based and the other was nurse-led counseling. In this scenario, the type of intervention would serve as the independent variable, while the readmission rate of patients would be the dependent variable. One group may reflect a 10% readmission rate after video-based education, but the other could reflect a 5% readmission rate from nurse-led counseling. It will be important to understand these types of differences when identifying the best interventions that make a positive difference in helping patients and cutting healthcare costs, ultimately.