RSCH FPX 7864 Assessment 3 t-Test Application and Interpretation

RSCH FPX 7864 Assessment 3

t-Test Application and Interpretation

Student Name

Capella University

RSCH-FPX 7864 Quantitative Design and Analysis

Professor Name

Date

t-Test Application and Interpretation

A robust data analysis framework establishes the foundation for methodical research by determining specific goals, specifying variables, and identifying possible limitations before initiating data collection. A t-test is a statistical procedure that compares the means of two groups to determine differences significantly from each other (Hayes, 2024). The investigation compares achievement variations between students participating in preparatory meetings against non-participants. By analyzing mean test scores across the cohorts, investigators assess the “Review” session’s impact on academic results. The research examines the relationship between two key variables: “Review,” a categorical variable with binary values (1=no, 2=yes) denoting review session attendance, and “Final,” a continuous variable quantifying correct response counts on the summative assessment. The systematic investigation produces evidence-driven conclusions regarding how preparatory sessions influence student performance.

Data Analysis Plan

• Variable Definitions

Review Session Attendance 

To distinguish workshop attendees from non-attendees, the classification variable “Review Session” will be employed. The categorical parameter comprises two discrete designations: the value 1, representing “Affirmative” for those who took part in the preparation session, and the value 2, denoting “Negative” for students who elected not to participate. 

Final Exam Score 

The parameter is continuous and includes the uninterrupted measurement “AssessmentResult” establishes student achievement by quantifying correct responses on the terminal evaluation. Elevated scores reflect improved understanding and skill mastery.

Research Question

Does enrollment in review sessions impact students’ final examination results?

Null Hypothesis

There is no difference in exam scores between students who attended the review session and those who did not.

Alternative Hypothesis

There is a difference in exam scores between students who attended the review session and those who did not.

Testing Assumptions

Levene’s Test Assumption Check

Levene’s test results (F = 0.740, df1 = 1, df2 = 103, p = 0.392) confirm equal variances between review session attendees (n=55) and non-attendees (n=50). The p-value (0.392) exceeds the 0.05 threshold, justifying the use of the standard independent samples t-test. With F = 0.740, df1 = 1, df2 = 103, and p = 0.392, the analysis fails to reject the null hypothesis of equal variances. Since the p-value (0.375) substantially exceeds the conventional alpha threshold of 0.05, researchers can confidently assume homogeneity of variances across the two student groups using Levene’s test. The successful verification of the equal variances assumption validates proceeding with the standard independent samples t-test rather than requiring alternative approaches such as Welch’s correction (West, 2021). The test results, based on 105 total participants (df2+2), demonstrate that despite potential differences in mean scores, the spread or variability of scores remains statistically comparable between students who attended review sessions and those who did not.

Results and Interpretations

Descriptives

Independent Samples T-Test

To examine potential variations in evaluation outcomes related to engagement in readiness courses, a separate cohort comparative study could be employed. The research participants comprised learners categorized into two separate classifications. The preparatory class included 55 participating students (Group 1), while Group 2 consisted of 50 students who neither attended the preparatory class nor participated in the review seminar.

The descriptive statistics reveal that students who did not attend the review session (n = 50) achieved a marginally higher mean score (M = 62.160, SD= 7.993) compared to those who attended (n = 55, M = 61.545, SD= 7.356). Following verification of the homogeneity of variances assumption through Leven’s test (F = 0.740, df1 = 1, df2 = 103, p = 0.392), the standard Student’s t-test was appropriately implemented. The independent samples t-test yielded t(103) = -0.410, p = 0.682, indicating no statistically significant difference between the mean final exam scores of the two groups.

With a p-value of 0.682 substantially exceeding the conventional alpha threshold of 0.05, the analysis fails to reject the null hypothesis that posits equivalent academic performance between review session participants and non-participants. The difference in mean scores (0.615 points) is negligible both statistically and practically, representing less than 1% variation in performance. The similar standard deviations (7.356 vs. 7.993) further confirm comparable score distributions across both groups. The findings suggest that review session attendance did not significantly impact student achievement on the final assessment, contradicting the assumption that additional preparation sessions would enhance learning outcomes. Alternative instructional interventions or modifications to the existing review format may be necessary to meaningfully influence student performance.

Statistical Conclusion

The analysis examined whether review session attendance impacts final exam performance among undergraduate students. Descriptive statistics indicated minimal difference between attendees (n = 55, M = 61.545, SD= 7.356) and non-attendees (n = 50, M = 62.160, SD = 7.993). After confirming homogeneity of variances via Levene’s test results (F = 0.740, df1 = 1, df2 = 103, p = 0.392) confirm equal variances between review session attendees (n=55) and non-attendees (n=50).

This p-value (0.392) exceeds the 0.05 threshold, justifying use of the standard independent samples t-test. test (F = 0.740, df1 = 1, df2 = 103, p = 0.392), a standard independent samples t-test was conducted. Results revealed no statistically significant difference between groups: t(103) = -0.410, p = 0.682, with a negligible mean difference of 0.615 points. The null hypothesis of no difference in exam performance between attendees and non-attendees was therefore retained. The findings suggest that the current review session format may not effectively enhance student learning outcomes, prompting reconsideration of supplemental instructional strategies or review session content and delivery methods.

• Limitations and Alternative Explanations

The study’s statistical analysis presents several methodological limitations that potentially impact interpretation. The independent samples t-test, while appropriate for comparing means between two groups, cannot account for confounding variables such as students’ prior academic performance or study habits outside review sessions (Kent State University, 2025). Sample size considerations warrant attention, as the relatively modest participant pool (n = 105) may have insufficient statistical power to detect small but meaningful differences between groups.

Attendance at review sessions was treated as a binary variable, potentially obscuring the influence of engagement quality or duration during the preparatory meetings. Self-selection bias constitutes another significant concern, as students who chose to attend review sessions might systematically differ from non-attendees in motivation or academic need (Alarie & Lupien, 2021). Performance effects together with utility results from review sessions may have been influenced when those sessions occurred before the final examination. The research design needs to incorporate multiple variable analysis to manage possible confounding factors (Yan et al., 2020). Additional research should analyze how review sessions impact different student groups during educational journey.

Application

The independent samples t-test provides the nursing field with essential analytical capabilities to review educational interventions’ effects on clinical competence improvements. The study design would focus on a relevant application where simulation-based education gets compared to traditional clinical instruction using simulation as the independent variable while using clinical decision-making scores as the dependent variable. Medical settings now require nurses who demonstrate quick precise judgments while handling complex patient situations thus the comparison becomes essential.

The controlled nature of simulation-based education allows students to practice high-risk situations without patient harm while receiving real-time performance feedback (Elendu et al., 2024). Research conducted about simulation effectiveness has established that students using simulation techniques develop improved critical thinking and show greater confidence alongside better clinical judgment than typical classroom teaching methods (Saghafi et al., 2024). The evidence-based approach to educational methodology evaluation strengthens nursing practice.

Educational simulation provides instructors the tools to manage consistent educational activities which permit impartial learner performance outcome assessments between different student groups. The healthcare industry faces two major obstacles which simulation-based learning addresses through its approach like limited clinical placement availability and preceptor shortage problems (Khalil et al., 2023).

Educational curricula development based on evidence requires comparisons between traditional training students and simulation graduates to create optimal learning methods that enable healthcare-ready professionals (Rasesemola & Molabe, 2025). The findings from the analysis would lead to better nursing education methods which develop stronger clinical skills in prepared healthcare providers who deliver safe patient care of high quality. 

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