RSCH FPX 7864 Assessment 2 Data Analysis and Application

Name

Capella University

RSCH 7864: Quantitative Design and Analysis 

Prof Name

October, 2024

Data Analysis Plan

The data analysis plan of this assessment is determined by the relationship between four key variables from the data set being studied: To remove plagiarism and ensure originality, you can rephrase the sentence as follows: Quiz 1 scores, GPA, total class points, and final exam results. These are all continuous factors, so they are numerical data related to student assessment performance. To determine the nature of these relationships, we will use correlational analysis. We will focus our attention on the relationship between class points and scores on the final exams, as well as on the relationship and scores for Quiz 1. The correlational analysis will allow us to examine whether changes in one variable depend on changes in another and to what extent such relations exist. The ultimate aim is to see the kind of relationship these variables have with each other, whether it is a positive or negative correlation or whether they are simply unrelated, and whether their interdependence is positive or negative.

Our research will be guided by two important research questions. First, we are going to test the hypothesis: Are total class points significantly correlated with final exams? The null hypothesis is that the two variables are uncorrelated, and the alternative hypothesis is that the two have a positive correlation. The second question of research had to do with how GPA is related to Quiz 1; again the null hypothesis was that these were not related and the alternative hypothesis was that the two had a positive correlation. Using Pearson’s Correlation Analysis, these two hypotheses would be tested with the results interpreted using the correlation coefficient, degrees of freedom, and the p-value (Staggs, 2019). Testing these hypotheses enables one to know whether the variables are linked, how strong the links may be, and other things that they can provide regarding knowledge informed to insights about student performance when these assessments are made of different types. Student scores on quizzes will be analyzed for their relation to overall performance and GPA.

Testing Assumptions

The table for the descriptive statistics provides data for Quiz 1, GPA, class points earned overall, and final exams and displays several important metrics. The skewness values also provide information about the shapes of these distributions. On Quiz 1 the skewness is at -0.500, so it has a moderate left skew, meaning many students scored much lower on this test. The skew of the GPA is 0.200, which indicates a slight right-skewed distribution, where most students’ GPAs are found on the bottom of the scale and only a few students have higher GPAs as outliers. Total class points skewness is -0.250, and scores on the final exams are -0.400, indicating that some students might have performed much worse than their classmates.

Kurtosis values provide additional information regarding the tail of the distribution. The kurtosis for Quiz 1 scores is 0.300, indicating a slightly platykurtic distribution, and is flatter than a normal distribution indicating fewer extreme scores. The kurtosis for total class points is 1.100, so the distribution is leptokurtic, with more data in the tail- and therefore suggests even greater variability among students’ scores. The kurtosis of the GPA stands at -0.300, meaning a flatter distribution with less variability around the mean. Finally, the final exam scores have a kurtosis of -0.100, which also points out a relatively flat distribution. Therefore, this signifies that the student performance in the final exam has been more consistently distributed within the group. These statistics are essential for establishing the normality assumptions considered necessary when carrying out correlation analyses in subsequent stages of this evaluation.

Results & Interpretation

Pearson’s Correlations

p < .05, ** p < .01, *** p < .001

From Pearson’s correlation table above, we see all the relations between Quiz 1 scores, GPA, total class points, and scores on the final exam. From the data, the correlation coefficient between Quiz 1 and GPA was 0.203 with a p-value of 0.210 indicating that there existed no significant relation between the variables and thus we could not reject the null hypothesis. Hence, it seems that the scores of the students on Quiz 1 have no significant impact on their GPA.

However, total class points relate extremely well with final exam scores as is evident by Pearson’s r value being 0.798 and also a p-value of < 0.001. This high positive correlation indicates that the more total points students get, the higher their final exam scores seem to be; thus, we can reject the null hypothesis for these two variables as well. The correlation between Quiz 1 and total class points has a value of 0.687 (p < 0.001), and in this case, it is very high. As a conclusion, the correlation between GPA and total class points is 0.342 (p < 0.01), indicating a moderate positive association. Overall, the strong correlations between total points, final score, and Quiz 1 indicate a close relationship; however, that of Quiz 1 with GPA remains poor.

Statistical Conclusions

Pearson correlation analysis was conducted to explore the interaction of variables in the table. The result of the analysis is nonsignificant, indicating no relationship exists between scores from Quiz 1 and GPA. This indicates the performance on the quiz does not seem to influence overall academic achievement. This was a dramatic contrast to the strong positive relationship between total class points and final exam scores. Overall, this supports the alternative hypothesis that a significant relationship exists between the two factors. The null hypothesis posits that there is no connection between Quiz 1 scores and GPA, as well as the total points earned in the class. Appear to be closely related to the final performance, making constant academic effort across tests important.

Application

Correlation analysis, therefore, can be very instrumental in improving patient care and operational efficiency in healthcare systems. For example, relationship studies between patient demographics and adherence to a prescribed treatment plan could help reveal trends in identifying patients at greater risk of non-compliance. This would thus be used in formulating targeted interventions. In this case, one could offer specific education programs or support systems that are best suited to increase patient engagement and adherence. Additionally, correlating patient satisfaction scores with clinical outcomes would allow for the further development of understanding how patient perceptions of care affect their recovery processes. With such correlations, health organizations may implement evidence-based practices that promote the quality of care as well as the patient experience, leading to improved health outcomes and the use of resources.

RSCH FPX 7864 Assessment 2 Conclusion

In conclusion, the statistical analysis in this paper has pointed out some fundamental relationships that exist in the key variables of the healthcare context (Yildiz et al., 2021). Hence, the high positive correlation between total points and final scores supports the alternative hypothesis because a higher number of points will logically correspond to a better result. On the contrary, the insignificance of Quiz 1 and GPA suggests that the null hypothesis is in line with the results that initial performance cannot predict an individual’s success in total examinations in this case. It may be utilized to evaluate, in a healthcare setting, whether the outcome from several areas, such as how productive the staff can be relative to patients’ satisfaction or health outcome. Awareness of these patterns enables healthcare professionals to better plan strategies aimed at improving patient care and organizational performance.

RSCH FPX 7864 Assessment 2 References

Staggs, V. S. (2019). Pervasive errors in hypothesis testing: Toward better statistical practice in nursing research. International Journal of Nursing Studies, 98, 87–93. https://www.sciencedirect.com/science/article/abs/pii/S0020748919301713?via%3Dihub

Yildiz, B., Yildiz, H., & Ayaz Arda, O. (2021). Relationship between work-family conflict and turnover intention in nurses: A meta‐analytic review. Journal of Advanced Nursing, 77(8). https://onlinelibrary.wiley.com/doi/10.1111/jan.14846

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