PSY FPX 7864 Assessment 2 Correlation Application and Interpretation
Phillip April 23, 2024 No Comments

PSY FPX 7864 Assessment 2 Correlation Application and Interpretation

PSY FPX 7864 Assessment 2 Correlation Application and Interpretation

Name

Capella university

PSY FPX 7864 Quantitative Design and Analysis

Prof. Name

Date

Plan for Data Analysis

Understanding the correlation between past academic performance and current achievements provides valuable insights into the trajectory of student learning. While numerous factors influence a student’s success, their previous Grade Point Average (GPA) serves as a broad indicator of their academic history and capabilities. In this analysis, we focus on four variables: Quiz 1, GPA, Final, and Total, all of which are considered continuous variables.

Correlation Analysis

Total-Final Correlation:

Research Question: Is there a significant correlation between the total points earned in the class and the correct answers on the final exam?

Null Hypothesis: There is no significant correlation between the total points earned in the class and the correct answers on the final exam.

Alternative Hypothesis: There is a significant correlation between the total points earned in the class and the correct answers on the final exam.

GPA-Quiz1 Correlation:

Research Question: Is there a significant correlation between a student’s previous GPA and the number of correct answers on Quiz 1?

Null Hypothesis (H₀): There is no significant correlation between a student’s previous GPA and the number of correct answers on Quiz 1.

Alternative Hypothesis (H₁): There is a significant correlation between a student’s previous GPA and the number of correct answers on Quiz 1.

Assumptions Testing

The descriptive statistics table below presents the skewness and kurtosis levels for GPA and the final exam. The skewness values for both metrics fall within the -1 to 1 range, indicating fairly symmetric distributions. This suggests a normal distribution in the data.

Results & Interpretation

Descriptive Statistics (Table 1):

GPA

Total

Quiz1

Final

Mean 2.862 100.086 7.467 61.838
Std. Dev 0.713 13.427 2.481 7.635
Skewness -0.220 -0.757 -0.851 -0.341
Kurtosis -0.688 1.146 0.162 -0.277

Correlation Matrix (Table 2):

In Table 2, a minor positive correlation of 0.152 exists between GPA and Quiz 1. However, with a P-value of 0.212, this correlation is not statistically significant, leading to the acceptance of the null hypothesis.

Pearson’s Correlations:

Quiz1

GPA

Total

Final

Quiz1 0.152 0.121 0.499
GPA 0.152 0.318 0.379
Total 0.121 0.318 0.875
Final 0.499 0.379 0.875

The strongest correlation is observed between ‘Final’ and ‘Total’, with a coefficient of 0.875. This relationship is statistically significant, indicating that the ‘Final’ accounts for 76% of the variation in the ‘Total’, leading to the rejection of the null hypothesis.

PSY FPX 7864 Assessment 2 Correlation Application and Interpretation

Similarly, a moderate correlation exists between GPA and the Final, with a coefficient of 0.379. This relationship is also statistically significant, suggesting that 14% of the variability in GPA can be explained by the Final scores.

Statistical Conclusions

While there’s insufficient evidence to support a significant correlation between GPA and Quiz 1 scores, the relationships between ‘Final’ and ‘Total’ scores, and between GPA and Final scores, are statistically significant.

Application

Correlation analysis plays a crucial role in investigating relationships, such as those between military service experiences and specific medical conditions among veterans. Such analysis aids in identifying patterns in health outcomes, potentially leading to the recognition of conditions as “presumptive,” simplifying access to benefits and treatment for affected veterans.

References

Betancourt, J. A., et al. (2021). Exploring Health Outcomes for U.S. Veterans Compared to Non-Veterans from 2003 to 2019. Healthcare (Basel, Switzerland), 9(5), 604. doi:10.3390/healthcare9050604

Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE.

Gravetter, F. J., & Wallnau, L. B. (2016). Statistics for the behavioral sciences (10th ed.). Cengage Learning.

PSY FPX 7864 Assessment 2 Correlation Application and Interpretation

Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE Publications.

McHugh, M. L. (2013). The Chi-square test of independence. Biochemia Medica, 23(2), 143-149.