RSCH FPX 7864 Assessment 3 t-Test Application and Interpretation

Name

Capella University

RSCH 7864: t-Test Application and Interpretation

Prof Name

October, 2024

Data Analysis Plan

One obvious place to start when developing an acceptable data analysis plan would be in clear definition of the variables for the study with the specification of whether the variables are categorical or continuous. In case one is performing an independent samples t-test, the basis of such analysis is to examine the mean values of both samples. The independent variable for this analysis is whether the students attended a review session-that is, a categorical variable coded as 1 = no and 2 = yes-and the dependent variable is the final exam score-that is, continuous, measured in terms of the guiding research question for this study is: How will students’ final performance on the quiz in terms of correct answers be affected by participation in a review session?

To address this question, we first establish our hypotheses. The null hypothesis (H₀) states that the final grades of students present at the review session are not substantially varied from those of students who missed the session. Alternative hypothesis H1: Final scores do exist significantly different about attendance to the review session or not.

The second phase of the strategy is concerned with testing the assumptions of the t-test. Major assumptions, such as homogeneity of variances, need to be checked usually by Levene’s test. In case where equal variance assumptions hold then the “Student” version of the t-test will be used otherwise, the Welch version will be adopted. This analysis would also include descriptive statistics – calculate mean and standard deviation for each group which would provide some background to the results. Finally, we will translate the results of the t-test into an interpretation of the postulated hypotheses. Thus, we would determine whether to accept or refute the null hypothesis.

Testing Assumptions

Verification of Assumptions
Levene’s Test for Equality of Variances

The findings from Levene’s Test for Equality of Variances measure whether the variances among the groups are equal.

The findings from Levene’s Test assessing the equality of variances determine whether an assumption of uniformity of variances is met, which must be a precursor to the standard independent samples t-test. In this example, the calculated F-value is 1.820 with degrees of freedom 1 and 98 and has a p-value = 0.180. Because the p-value exceeds the typical significance threshold of 0.05, we accept the null hypothesis that the two groups have statistically equal variances. Thus, the homogeneity of variances assumption can be used, and we can apply the standard “Student” t-test instead of the alternative “Welch” t-test. This has the consequence of guaranteeing the comparison of groups to be valid under the condition of equal variances of final exam scores.

Results & Interpretation

Descriptives

Group Descriptives

Independent Samples T-Test

The Group Descriptives table summarises the performance of two groups in terms of attendance at review sessions. For the students attending the review sessions, with a sample size of N = 52, the final exam average was 78.50. Having a standard deviation of 10.23, it would seem to be moderate spread of scores around the mean. The standard error of the mean for this group was 1.42, which was an estimation of the mean’s accuracy. The other group was made up of individuals who did not attend the review sessions; a mean score of 72.30 was less of a value with a standard deviation of 9.85 which suggests little more variability in the scores of these individuals as against the review attendees. The standard error for this group is 1.34, which also displays the reliability of the mean score estimate for the same group.

The Independent Samples T-Test result illustrates further the group differences. Here, Levene’s Test for Equality of Variances assumes that equal variances have been met since the t-value is 2.521 with a pdf of 104 and p = 0.013. This p-value is smaller than the established significance level of 0.05, and hence it nullifies the null hypothesis stating that there would be a variation in final exam scores among the two groups. The mean variation between the two groups stands at 6.20 with the standard error of the difference at 2.46, which means there is a statistically significant margin of victory for students who attended review sessions versus those who did not participate. Analysis of final exams has revealed that, in general, review sessions have a positive impact on the final exams.

Statistical Conclusions

The statistical inferences taken from the Independent Samples T-Test infer that a significant difference exists between the two groups regarding the final exams, meaning that participating in review sessions offers a positive academic effect. However, it is also important to recognize that the analysis intrinsically has certain limitations by itself. Besides the main and moderating factors, other factors will explain prior academic performance, motivation levels, and study habits, among others, which may influence exam scores and not be appropriately controlled in the analysis. The sample size is satisfactory but cannot be said to represent the student population about generalisability. More generally, self-reported attendance introduces biases, as students who decide to attend may be systematically different from students not attending. Alternative explanations for differences observed could be the quality of instruction during review sessions or other factors affecting student performance, such as test anxiety or health issues. Though the results are very informative, there is a need to consider these limitations and further research be conducted to see if these results can be confirmed and if so, what are the other variables that add to the success of students at school. 

Application

The independent samples T-test is a tool for extracting the factors behind student performance based on several parameters across various school systems, especially the usefulness of review sessions during final exams. This analysis statistically evaluates the difference in achievement between groups of students so that teachers and administrators can then make decisions on curriculum design and instructional strategies that yield better outputs. For example, if research conducted across studies consistently shows that students who attend review sessions substantially outperform their classmates, then institutions may standardize or expand these reviews more. This could, in turn, improve academic support and may yield better aggregate performance and retention.

However, the implications are not just within the academy; knowing which methods of instruction work better can inform the preparation and professional development of teachers. Using the emphasis on conducting targeted review sessions, teachers can thus modify their teaching to become more interactive and reviewing, which is, consequently, an encouragement towards a fun learning experience. In addition, the study creates a platform for further studies that would find other variables determining student success, like teaching approach variations, class size differences, or student demographics. Finally, the statistical findings can contribute to the educational community by providing insights into a better understanding of educational practices to achieve better academic success and improvement in the learning environment for all students.

RSCH FPX 7864 Assessment 3 Conclusion

Therefore, in general conclusion, based on the Independent Samples T-Test analysis, there is reasonable evidence showing that attending review sessions positively correlates to better scores of students during final exams  (Hassan, 2023). The proof is statistically significant since there exists a t-value of 2.521, with a corresponding p-value of 0.013, meaning that extra academic support through review sessions has an effect of influencing the positive side concerning students’ performance. In this regard, it could be crucial to appreciate the study’s limitations concerning possible confounding variables and sample representativeness among others that will impinge on the generalisability of results. Even so, practice implications at this end are evident; access to review sessions, which can be resource-intensive, is an important leverage strategy in winning educational outcomes. This analysis not only brings together the importance of targeted academic support but also provides avenues for further exploration into effective instructional practices that can contribute to student success. Ultimately, this deepens insights into the educational landscape and underlines the importance of continuously assessing and adapting teaching methods to better manage the diversity in learners.

RSCH FPX 7864 Assessment 3 References

Hassan, E. M. G. (2023). Addressing academic challenges: A quasi-experimental study on the effect of remedial exam strategy for nursing students with low academic performance. Belitung Nursing Journal, 9(4), 369–376. https://www.belitungraya.org/BRP/index.php/bnj/article/view/2699