RSCH FPX 7864 Assessment 3 ANOVA Application and Interpretation
RSCH FPX 7864 Assessment 3 ANOVA Application and Interpretation
Name
Capella university
RSCH-FPX 7864 Quantitative Design and Analysis
Prof. Name
Date
Data Analysis Plan
This study aims to provide a thorough review that includes all possible outcomes by examining a data sample from the “grades. jasp” file. We compare the final exam scores of students who participated in the review session with those of students who did not. The main objective is to determine if the difference is significant (Tomasevic et al., 2020). Keywords in the study are “Review” and “Final.” Students’ participation in review sessions is indicated by the “Review” variable, which can take on values of 1 or 2. A continuous variable representing the sum of all right answers is the “Final” score.
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Research Question |
Is going to a review session going to help students do better on final exams? |
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Null Hypothesis (H0) |
On average, students’ final exam scores were the same for those who attended and those who did not attend review sessions. |
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Alternative Hypothesis (H1) |
Whether or not students attended review sessions has a substantial impact on their average final exam performance. |
Identification of Variables
Independent Variable
The review session attendance independent variable shows whether or not a student was present at a review session (Vale et al., 2020). Students who took part in the review session are on one level, and those who did not are on the other.
Dependent Variable
This variable, called the final score, represents the results of final exams. The total number of questions that were answered correctly is represented by this continuous variable. The final findings are used to compare students’ grades from both the review session attendees and the non-participants.
During the evaluation, these variables were determined to be crucial (Vale et al., 2020). Review session attendance is the independent variable being compared or modified between groups, whereas the dependent variable is the outcome being measured, which is the outcome of the final test.
Testing Assumptions
The assumptions include crucial statistics such as the group variances, F-values, and p-values. If Levene’s test yields a non-significant result (p >.05), then the assumption of variance homogeneity is met, and additional statistical research can be deemed acceptable. If the results of the test are statistically significant (p <.05), suggesting a violation of this assumption, further procedures such as Welch’s t-test will have to be used. These findings show that you understand how Levene’s test affects statistical validity (Saliya, 2022). Make sure you pay attention to how adhering to the homogeneity of variance assumption impacts the dependability of inferential inferences drawn from subsequent tests, like t-tests.
Valid and trustworthy statistical analyses, such as the t-test, need testing of hypotheses. The homogeneity of the variances, as confirmed by Levene’s test, is a crucial assumption of this approach (Liu & Wang, 2020). On this assumption, we anticipate that the two sets of students, those who attended review sessions and those who did not, will have similar variances. If there is a significant difference in the group variances, the t-test could produce false results. To rule out the possibility of random chance as the only explanation for any disparities in means, researchers need to confirm that the variances are comparable across groups.
Results & Interpretation
We compared the final test scores of the two groups of students by calculating their averages and standard deviations. The first set of fifty students who skipped the study sessions had an average final score of 61.545 and a standard deviation of 7.356. The average score of the second group, which included 55 students who took part in the study sessions, was 62.160, with a standard deviation of 7.993.
Statistical analysis revealed no statistically significant difference between the two groups’ mean final scores (t-value = -0.41 and p-value = 0.68). On average, students who took part in the review sessions outperformed their non-participating peers by a small margin (M = 62.2, SD = 7.993) on the final exam (Kuldoshev et al., 2023). This indicates that the review sessions had a moderate but inconclusive effect on the mean final scores.
Statistical Conclusions
A formal statement of findings from the t-test results requires a summary of the results from comparing the two groups’ final test scores. At the end of the day, when comparing the two groups’ test results using t-tests, there was no discernible difference in the two groups’ mean final scores.
The t-test, when conducted with a two-tailed test, yielded a t-value of -0.41 and a p-value more than 0.05 (p = 0.68). The results show that the groups that had review sessions and those that did not did not differ significantly in terms of their mean final scores (Liu & Wang, 2020). Review session participants had a marginally better mean final score (M = 62.2, SD = 7.993) than non-participants (M = 61.545, SD = 7.356). However, the p-value was higher than the usual significance level, so the difference could not be considered statistically significant.
Although the two groups’ mean final scores were somewhat different, the results indicate that this difference was not statistically significant. The results of the t-test show that there was no statistically significant improvement in final exam scores after participating in the review sessions (Liu & Wang, 2020). The effect size shows a moderate degree of dissimilarity between the means, which supports the conclusions. Even if the differences did not achieve statistical significance, their practical impact is highlighted by this.
Limitations
It is critical to note the specific limits of the study, even though the analysis incorporates discussions on practical consequences and alternative hypotheses. The sample size is a significant restriction that affects the analysis’s statistical power and the findings’ generalizability (Tomasevic et al., 2020). The 105 students split evenly between the two groups may need to be bigger to discover subtle but significant differences reliably. Further, the demographics, academic backgrounds, and other pertinent aspects of the sample may affect the study’s external validity, reducing the generalizability of the results to different student populations or educational settings.
Inadequate controls for potential confounding variables raise the possibility that the study needs to be revised. The study did not account for students’ background knowledge, intrinsic desire, level of participation in the course outside of review sessions, or differences in the quality of instruction that occurred during review sessions, all of which could affect students’ final test scores. The study’s internal validity and the ability to attribute observed variations in test results to review session attendance alone are called into question because these potential confounding factors were not controlled for (Wysocki et al., 2022). It is critical to recognize these limitations and offer a balanced evaluation of the results in order to direct future research towards a better understanding of how review sessions affect student learning outcomes.
Application
The independent samples t-test is a potent instrument for studying a variety of biostatistical phenomena, and it can be quite applicable to this specialized field of study. This statistical method can enhance neurological illnesses, clinical decision-making, and patient care.
Patients suffering from neurodegenerative illnesses, such as Alzheimer’s, may benefit from comparing two treatment approaches using the independent samples t-test. Novel pharmaceutical therapies and an established cognitive rehabilitation program would make up the “Neurological Treatment Approach” independent variable (Mathur et al., 2023). The “Cognitive Improvement Score,” a metric for measuring progress in cognitive ability as assessed by standardized exams, would serve as the dependent variable in this case.
The cognitive abilities and quality of life of individuals are impacted by neurodegenerative disorders, making the study of this application vital to the field of neurology (Kumar et al., 2023). To find out whether a therapy approach yields higher cognitive improvements, an in-depth study employing the independent samples t-test is necessary. More effective and efficient therapy with better patient health outcomes may come from clinicians using this data to personalize treatment plans. Researchers and clinicians could enhance existing treatment procedures and develop novel approaches to neurological care by examining the nuances of therapeutic impact.
References
Kuldoshev, R., Nigmatova, M., Rajabova, I., & Raxmonova, G. (2023). Mathematical, statistical analysis of attainment levels of primary left-handed students based on Pearson’s conformity criteria. E3S Web of Conferences, 371, 05069–05069. https://doi.org/10.1051/e3sconf/202337105069
Kumar, J., Patel, T., Sugandh, F., Dev, J., Kumar, U., Adeeb, M., Kachhadia, M. P., Puri, P., Prachi, F., Zaman, M. U., Kumar, S., Varrassi, G., & Rehman, A. (2023). Innovative approaches and therapies to enhance neuroplasticity and promote recovery in patients with neurological disorders: A narrative review. Cureus, 15(7). https://doi.org/10.7759/cureus.41914
Liu, Q., & Wang, L. (2020). T-Test and ANOVA for data with ceiling and/or floor effects. Behavior Research Methods, 53. https://doi.org/10.3758/s13428-020-01407-2
Mathur, S., Gawas, C., Ahmad, I. Z., Wani, M., & Tabassum, H. (2023). Neurodegenerative disorders: Assessing the impact of natural vs drug‐induced treatment options. AGING MEDICINE, 6(1), 82–97. https://doi.org/10.1002/agm2.12243
Saliya, C. A. (2022). Relevant statistical concepts. Doing Social Research and Publishing Results, 171–204. https://doi.org/10.1007/978-981-19-3780-4_11
RSCH FPX 7864 Assessment 3 ANOVA Application and Interpretation
Tomasevic, N., Gvozdenovic, N., & Vranes, S. (2020). An overview and comparison of supervised data mining techniques for student exam performance prediction. Computers & Education, 143, 103676. https://doi.org/10.1016/j.compedu.2019.103676
Vale, J., Oliver, M., & Clemmer, R. M. C. (2020). The influence of attendance, communication, and distractions on the student learning experience using blended synchronous learning. The Canadian Journal for the Scholarship of Teaching and Learning, 11(2). https://doi.org/10.5206/cjsotl-rcacea.2020.2.11105
Wysocki, A. C., Lawson, K. M., & Rhemtulla, M. (2022). Statistical control requires causal justification. Advances in Methods and Practices in Psychological Science, 5(2), 251524592210958. https://doi.org/10.1177/25152459221095823