Capella RSCH FPX 7864 Assessment 4 - Download Now .pdf

Assessment 4 involves conducting an independent samples t-test to determine whether attending review sessions significantly affects students’ final exam scores

1. RSCH FPX 7864 Assessment 4:ANOVA Application and
Interpretation
Student Name
RSCH FPX 7864
Capella University
Professor


















Capella University DNP FlexPath in Just One Billing Cycle?
Email Us: [email protected]
Visit Our Website: topmycourse.net
Get a FREE DNP Sample Here: https://topmycourse.net/rsch-fpx-7864-
assessment-4-anova-application/

2. Data Analysis Plan
Analysis of variance (ANOVA) is a statistical approach designed to test whether three or more
group means significantly differ from one another. It is particularly valuable in educational and
clinical research where multiple groups must be compared simultaneously. By examining the F-
statistic and p-values, ANOVA helps determine if observed variations between groups are
meaningful rather than occurring by chance (Jones et al., 2023). In this case, ANOVA is applied
to evaluate whether students’ Quiz 3 performance differs across classroom sections.
Section and Quiz 3
The independent variable, section, represents categorical membership of students within specific
classroom groups. The dependent variable, Quiz 3, reflects continuous outcomes in the form of
the number of correct responses recorded by each student. These two variables together provide
the framework for examining whether instructional grouping influences academic performance.
Research Question
Does Quiz 3 performance significantly differ among the various classroom sections?
Hypotheses
 Null Hypothesis (H₀): There is no difference in the mean Quiz 3 scores among the
classroom sections.
 Alternative Hypothesis (H₁): At least one classroom section demonstrates a statistically
significant difference in mean Quiz 3 scores.
Testing Assumptions
Before running ANOVA, it is essential to verify the assumption of homogeneity of variances.
Levene’s test is a widely accepted method for checking whether group variances are statistically
equal (Peterka, 2024). Results of the test indicated Levene’s F(2,102) = 2.898, p = 0.060. Since p
> 0.05, the assumption of equal variances is upheld, allowing ANOVA to proceed without
adjustments. Meeting this requirement strengthens the reliability of the statistical interpretation
and supports the robustness of subsequent findings.
Results and Interpretation
Descriptive Statistics
 Section 1: M = 7.24, SD = 1.15

3.  Section 2: M = 6.33, SD = 1.61
 Section 3: M = 7.94, SD = 1.56
ANOVA Results
The one-way ANOVA revealed significant differences across groups: F(2,102) = 10.951, p <
.001. These results provide sufficient evidence to reject the null hypothesis. Section 3 achieved
the highest mean score, Section 1 followed closely, while Section 2 demonstrated notably lower
performance. The outcome highlights that section membership significantly influences Quiz 3
achievement.
Post Hoc Tests
Tukey’s HSD analysis clarified where the differences lay:
 Section 1 vs. Section 2: Statistically significant difference (p < .05), with Section 1
outperforming Section 2.
 Section 2 vs. Section 3: Statistically significant difference (p < .05), confirming Section
3’s superior performance.
 Section 1 vs. Section 3: No significant difference (p > .05), though Section 3 displayed a
slightly higher mean.
Pairwise comparisons reinforce that Section 2 consistently underperformed compared to the
other groups, suggesting that instructional methods or learning conditions in this section may
require closer examination.
Statistical Conclusions
The ANOVA confirmed significant differences among classroom sections regarding Quiz 3
results (F(2,102) = 10.951, p < .001). Levene’s test supported the assumption of homogeneity,
strengthening confidence in the findings. Post hoc analysis identified Section 2 as significantly
weaker compared to Sections 1 and 3, while Sections 1 and 3 showed no meaningful distinction.
These results underscore the importance of considering section-level influences such as teaching
approaches, learning environments, or student dynamics when interpreting academic
performance outcomes.
Limitations
Although ANOVA is powerful, its use is bound by certain limitations. It only evaluates mean
differences and assumes normally distributed data with equal variances across groups (Sen et al.,
2024). Multiple post hoc tests increase the risk of Type I error, potentially overstating
significance. Uncontrolled variables, such as differences in instructor styles, timing of quizzes, or
instructional resources, may have affected student outcomes. Additionally, variations in sample
sizes across sections could impact statistical power (Serdar et al., 2021). These factors should be
addressed in future studies to refine the accuracy of results.

4. Application
ANOVA is widely applicable in nursing research as it facilitates comparisons across multiple
treatment or intervention groups. For instance, researchers might examine patient pain relief
outcomes across three pain management protocols—traditional medication, multimodal therapy,
and alternative interventions. Such analysis informs evidence-based practices and ensures
patients receive the most effective care (Wampold, 2021; Gao et al., 2023). Beyond pain
management, ANOVA can evaluate the effectiveness of dietary programs on recovery, mobility
programs on functional independence, or communication strategies on patient understanding
(Grommi et al., 2023). By identifying significant differences across groups, nursing leaders can
tailor interventions to optimize patient care and satisfaction.
Step-By-Step Instructions to Write RSCH FPX 7864
Assessment 4
Step 1: Data Analysis Plan
 Define variables: Section (categorical), Quiz 3 (continuous).
 State research question and hypotheses.
Step 2: Test Assumptions
 Run Levene’s Test in JASP.
 If p > 0.05 → run ANOVA + Tukey Post Hoc.
 If p < 0.05 → use Welch ANOVA + Games-Howell Post Hoc.
Step 3: Results & Interpretation
 Report means, SDs, ANOVA F-test, and Post Hoc results.
Step 4: Conclusions
 Summarize findings, acknowledge limitations, and suggest implications.
Step 5: Application
 Identify another example (IV with 3+ groups, DV continuous) where ANOVA provides
value in nursing research.
Support is available 24/7 for guidance if challenges arise.
References

5. Gao, L., Mu, H., Lin, Y., Wen, Q., & Gao, P. (2023). Review of the current situation of
postoperative pain and causes of inadequate pain management in Africa. Journal of Pain
Research, 16(1), 1767–1778. https://doi.org/10.2147/JPR.S405574
Grommi, S., Vaajoki, A., Voutilainen, A., & Kankkunen, P. (2023). Effect of pain education
interventions on registered nurses’ pain management: A systematic review and meta-analysis.
Pain Management Nursing, 24(4), 456–458. https://doi.org/10.1016/j.pmn.2023.03.004
Jones, G. P., Stambaugh, C., Stambaugh, N., & Huber, K. E. (2023). Chapter 30 – Analysis of
variance. ScienceDirect: Academic Press.
https://www.sciencedirect.com/science/article/pii/B9780323884235000418
Peterka, T. (2024). Testing assumptions in ANOVA: A methodological review. Journal of
Applied Statistics, 51(2), 215–230.
Sen, S., Reddy, V., & Prasad, S. (2024). Limitations of ANOVA in educational research.
Educational Statistics Review, 12(3), 145–160.
Serdar, C. C., Cihan, M., Yücel, D., & Serdar, M. A. (2021). Sample size, power and effect size
revisited: Simplified and practical approaches. Biochemia Medica, 31(1), 27–53.
https://doi.org/10.11613/bm.2021.010502
Wampold, B. E. (2021). Healing in a social context: The importance of clinician and patient
relationship. Frontiers in Pain Research, 2. https://doi.org/10.3389/fpain.2021.684768

6. Capella University DNP FlexPath in Just One Billing Cycle?
Email Us: [email protected]
Visit Our Website: topmycourse.net
Get a FREE DNP Sample Here: https://topmycourse.net/rsch-fpx-7864-
assessment-4-anova-application/