RSCH FPX 7864 Assessment 3 t-Test Application and Interpretation
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Capella University
RSCH FPX 7864
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RSCH FPX 7864 Assessment 3 t-Test Application and Interpretation
The t-test is one way to statistically determine if there are significant differences between two groups’ means. Two variables of interest are analyzed: review-1 is a categorical variable with one indicating a session was attended (no = 1, yes = 2), and final is a continuous variable representing the final number of correct responses to the assessment questions. The variables in combination enable the study to generate evidence-based results and understand the impact participating in preparatory sessions has on overall student performance.
Data Analysis Plan
Research Question
Does there appear to be a significant difference in examination achievement between groups of students who attend review sessions and those who do not attend?
Null Hypothesis
No significant difference is found between the examination performance of review session takers and their performance in the same class when the sessions are not attended.
Alternative Hypothesis
Students who attended a review session have a significant difference in examination performance from those who did not attend a review session.
Testing Assumptions
Levene’s Test Assumption Check
Based on the results obtained from Levene’s test (F = 0.219, p = 0.641), the variance between each of the students attending review sessions was statistically comparable to that of the students who did not attend review sessions. Levene’s test is a well-documented and valid statistical test used to determine if the variance is equal between different groups, which is also known as the Six Sigma approach. The Six Sigma approach is an established and reliable statistical test to see if the variance is equal across different groups, known as Levene’s test. The resulting value of the p-value obtained (0.641) was far from its significance value of 0.05, indicating that there was no violation of the assumption of equal variances, and thus the standard independent samples t-test was used in the analysis of the data. There was not enough evidence to reject the null hypothesis of variance equality, as this result gave an F = 0.219, df1 = 1, df2 = 103, and p = 0.641. Results from the 105 participants showed no difference between the two groups in their mean performance, but large differences in scores were found for both of them (p < 0.05). Accordingly, the use of the standard independent sample t-test was well warranted as there was no need for other t-tests (e.g., Welch’s t-test) under the specific conditions.
Results and Interpretations
Descriptives
Independent Samples T-Test
An independent samples comparative study was used to explore whether or not the assessment results vary with the preparatory sessions attended. The participants in this study were divided into two groups: one group (55 students) had attended the preparatory course, and the other group (50 students) had not attended the preparatory course or the extra workshop. As per descriptive statistics, students who did not participate in the supplementary session (n = 50) had a mean score of M = 60.420 and SD = 8.680, whereas students who participated in the supplementary session (n = 55) had a mean score of M = 60.182 and SD = 7.930, with the mean scores being close. Variance equality was tested before the t-test (Levene’s test F = 0.219, df = 103, and p = 0.641) was performed, thus using the standard independent samples t-test.
There was no significant difference in the final examination scores between the two groups, as there was a t(103) = -0.147 and a p = 0.883. A p-value of 0.883 is significantly higher than the alpha level of 0.05, and the null hypothesis was not rejected in the case of academic performance, indicating that there was no significant difference in academic performance between the presence or absence of attendance at the different sessions. There was not much difference in the mean scores between the two groups (0.238 points, less than one per cent). The standard deviations of the scores in each group are also relatively similar (7.930 vs. 8.680), which again indicates similar distributions of scores. In general, the results indicate that there was no significant difference in the effect that the supplementary preparatory sessions had on students’ final assessment scores.
Statistical Conclusion
Statistical findings revealed minimal differences between participants (n = 55, M = 60.182, SD = 7.930) and non-participants (n = 50, M = 60.420, SD = 8.680) respectively. The standard independent samples t-test was used as the p-value of Levene’s test was > 0.05 (F = 0.219, p = 0.641), which proves that the variances were not different between the two groups. The mean score difference between the two groups was only 0.238 points, and was not significantly different from each other as indicated by the t-test result, t(103) = -0.147, p = 0.883. Hence, the null hypothesis was accepted, and thus, a difference in the performance of the exams was not found between those who attended the course and those who did not. The results suggest that the current format of review sessions (M = 60.182, SD = 7.930; M = 60.420, SD = 8.680) is possibly not effective enough to see a significant change in academic performance; therefore, the potential to change and/or redesign an instructional strategy/method or the format of review sessions or additional strategies/methods of teaching could be needed to help achieve a significant change in academic performance.
Limitations and Alternative Explanations
We describe some methodological weaknesses in our study in the statistical analysis. Due to the small sample size (n = 105), small differences for each group might not be detectable even if they are of value. In addition to being valid for comparing outcome measures between groups, an independent samples t-test also has the disadvantage that other factors that might impact the group or individual outcome measures (e.g., prior performance in school or previous study habits for each participant) might not be accounted for. However, because this is a binary outcome variable that indicates whether day campers have attended a review session or not, it does not reflect the total number of actual review sessions attended by all of the day campers. In addition, there could be selection bias depending on the level of motivation and/or class commitment of the students involved in the study who did or did not participate in review sessions. McCrudden et al. (2025) emphasise the need for proper methodology to be used in education research studies and clearly communicate the limitations of that study
Application
Analysis of variance will be used to analyze data statistically to assess how the number of nurses available affects nursing unit falls and how it influences falls. Independent variables: the number of nurses assigned to a unit (compared to an understaffed unit), dependent variables: the total number of falls in the unit during the specified time frame. The meaning of the relationship examined is important as falls are one of the most preventable adverse events within hospital settings and have serious implications for the patient’s health and wellbeing, with a greater likelihood of institutional responsibility and poorer recovery outcomes. Nurses who have too many patients may be unable to conduct timely safety evaluations, respond to the calls of patients quickly, and put into place individualized fall prevention plans; all are essential in ensuring good patient bedside care.
Evidence relating to adequate nurse staffing and improving patient safety outcomes remains in current research literature and suggests that the higher the level of nurse staffing, the fewer the number of preventable, adverse outcomes. So, not only is staffing a priority for the operation of nursing practices, but it is also the moral duty of a nursing practice to its patients and to the communities that they serve. The findings from the research will guide nurse managers/hospital administrators in developing policies around the appropriate staffing levels, appropriate requests for additional nursing staff to address the lack of staff for falls, and strategies for each unit on falls prevention. The overarching message from these observations was to be involved in promoting a culture for patient safety, reducing the risk of unintended harm experienced by patients, and increasing professional responsibility for the practice of excellence that nurses bring to the profession.
References
McCrudden, M. T., Bowman, M., & Sofia, G. (2025). Reporting of methodological rigor in empirical mixed methods research in educational psychology. Educational Psychology Review, 37(4), 111. https://doi.org/10.1007/s10648-025-10090-8
Six Sigma. (2024, December 10). Levene’s Test: Your guide to understanding, performing, and interpreting homogeneity of variance. 6sigma.us. https://www.6sigma.us/six-sigma-in-focus/levenes-test/
Udina, M. E. J., Adamuz, J., Samartino, M. G., Pérez, M. T., Martínez, E. J., Morello, C. B., Merchanskaya, O. P., Zabalegui, A., & Jiménez, M. M. L. (2025). Association between nurse staffing coverage and patient outcomes in a context of prepandemic structural understaffing: A patient‐unit‐level analysis. Journal of Nursing Management, 2025(1), e8003569. https://doi.org/10.1155/jonm/8003569
Yu, X., Li, M., Du, M., Wang, Y., Liu, Y., & Wang, H. (2024). Exploring factors that affect nurse staffing: A descriptive qualitative study from nurse managers’ perspective. BioMed Central Nursing, 23(1), 1–8. https://doi.org/10.1186/s12912-024-01766-7
FAQs
What is RSCH FPX 7864 Assessment 3?
RSCH FPX 7864 Assessment 3 requires students to apply and interpret t-test results, evaluate statistical significance, compare group means, and explain findings using evidence-based research methods.
What is the purpose of a t-test?
A t-test determines whether the means of two groups differ significantly.
What does a p-value less than 0.05 mean?
It indicates that the observed difference is statistically significant and unlikely due to chance.
What is the difference between an independent and paired t-test?
An independent t-test compares different groups, while a paired t-test compares the same participants across two conditions.