RSCH FPX 7864 Assessment 4 Data Analysis and Application Template
RSCH FPX 7864 Assessment 4 Data Analysis and Application Template
Name
Capella university
RSCH-FPX 7864 Quantitative Design and Analysis
Prof. Name
Date
Data Analysis Plan
This study aims to provide a comprehensive review by examining a dataset from the “grades.jasp” file to evaluate the potential impact of review session participation on students’ final exam scores. Specifically, the study compares the final exam scores of students who attended review sessions against those who did not. The primary objective is to determine whether this difference is statistically significant (Tomasevic et al., 2020). Key study variables include “Review” and “Final.” The “Review” variable is categorical, with values representing students who attended (1) and those who did not (2). The “Final” variable is continuous and represents the total number of correct answers on the final exam.
Research Question and Hypotheses
The study seeks to answer the following research question: Does attending a review session improve students’ final exam performance? To test this, two hypotheses are established. The null hypothesis (H₀) states that students who attended review sessions have no significant difference in their final exam scores compared to those who did not attend. Conversely, the alternative hypothesis (H₁) suggests that attending a review session has a significant effect on students’ final exam scores.
Identification of Variables
The independent variable in this study is review session attendance, which categorizes students into those who attended and those who did not (Vale et al., 2020). The dependent variable is final exam scores, a continuous variable that represents the total number of correct answers. These variables are essential for determining the effect of review session participation on academic performance. The independent variable (review session attendance) is manipulated across groups, while the dependent variable (final exam score) is the measured outcome.
Testing Assumptions
For accurate statistical analysis, specific assumptions must be tested. This includes evaluating group variances using Levene’s test, which determines whether the assumption of homogeneity of variance is met. If Levene’s test results in a non-significant p-value (p > .05), the assumption holds, allowing for standard statistical tests. If the p-value is significant (p < .05), indicating a violation of homogeneity, alternative statistical approaches such as Welch’s t-test may be necessary (Saliya, 2022). Ensuring this assumption is met enhances the reliability of inferential statistics, particularly the validity of t-tests. If variances are significantly different, statistical outcomes may be misleading, necessitating adjustments to the analysis.
Results & Interpretation
The study compared final exam scores between students who attended and those who did not attend review sessions. The first group (n = 50) had an average final score of 61.545 with a standard deviation of 7.356, while the second group (n = 55) had an average final score of 62.160 with a standard deviation of 7.993. Statistical analysis using a t-test revealed no statistically significant difference between the two groups (t = -0.41, p = 0.68). While students who attended review sessions performed slightly better (M = 62.2, SD = 7.993), this difference was not statistically significant (Kuldoshev et al., 2023). These findings suggest that review sessions had only a minor and inconclusive effect on final exam performance.
Statistical Conclusions
The results of the t-test indicate that there was no significant difference in mean final exam scores between students who attended and those who did not attend review sessions. The two-tailed t-test produced a t-value of -0.41 and a p-value of 0.68, exceeding the typical significance threshold (p < .05) (Liu & Wang, 2020). Although students who attended review sessions had slightly higher scores, the observed difference was not statistically significant. Consequently, the null hypothesis cannot be rejected, suggesting that review session attendance did not substantially impact final exam performance.
Limitations
Several limitations may have influenced the study outcomes. The sample size (n = 105) may not have been large enough to detect small but meaningful differences (Tomasevic et al., 2020). Additionally, external validity concerns arise due to potential differences in students’ academic backgrounds, motivation levels, and learning habits. The study also lacks control over confounding variables, such as prior knowledge, engagement in coursework outside review sessions, and variations in instructional quality (Wysocki et al., 2022). These factors could have influenced final exam scores, limiting the study’s internal validity. Future research should consider these elements to better understand the relationship between review sessions and academic performance.
Application
The independent samples t-test is widely applicable in biostatistics and clinical research. For instance, in neurological research, this statistical method could compare the efficacy of two treatments for neurodegenerative disorders like Alzheimer’s disease. One group might receive a pharmaceutical intervention, while another undergoes cognitive rehabilitation therapy (Mathur et al., 2023). The dependent variable, in this case, would be a cognitive improvement score measured through standardized cognitive assessments. Understanding the effectiveness of different treatment approaches through statistical analysis can help optimize patient care and therapeutic strategies in clinical practice (Kumar et al., 2023).
Table Representation
| Section | Key Information | Reference(s) |
|---|---|---|
| Data Analysis Plan | Compares final exam scores between students who attended review sessions and those who did not. Evaluates statistical significance of differences. | Tomasevic et al., 2020 |
| Research Question | Does attending a review session improve students’ final exam performance? | – |
| Hypotheses | H₀: No significant difference in scores between attendees and non-attendees. H₁: Attending review sessions significantly impacts exam performance. |
– |
| Variables | Independent: Review session attendance (Yes/No). Dependent: Final exam score (Total correct answers). |
Vale et al., 2020 |
| Testing Assumptions | Evaluates homogeneity of variance using Levene’s test (p > .05 = assumption met; p < .05 = assumption violated). Uses t-test or Welch’s t-test. | Saliya, 2022 |
| Results | Mean final scores: – Attendees: 62.2 (SD = 7.993) – Non-attendees: 61.545 (SD = 7.356). No significant difference (t = -0.41, p = 0.68). |
Kuldoshev et al., 2023 |
| Statistical Conclusions | The t-test showed no significant improvement in final scores due to review sessions. The null hypothesis is not rejected. | Liu & Wang, 2020 |
| Limitations | Small sample size, external validity concerns, uncontrolled confounding variables (e.g., motivation, prior knowledge, teaching quality). | Tomasevic et al., 2020; Wysocki et al., 2022 |
| Application | Uses t-test in clinical research (e.g., comparing Alzheimer’s treatments). Helps determine cognitive improvement and guide patient care. | Mathur et al., 2023; Kumar et al., 2023 |
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 4 Data Analysis and Application Template
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