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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. This analysis focuses on four variables: Quiz 1, GPA, Final, and Total, all of which are considered continuous variables.

Correlation Analysis

Overall-Final Relationship: 

Research Inquiry: Does a significant relationship exist between the overall points accumulated in the course and the number of correct responses on the final examination? 

Null Proposition: There is no significant relationship between the overall points accumulated in the course and the number of correct responses on the final examination.

Alternative Hypothesis: The alternative hypothesis suggests that there exists a meaningful relationship between the total points accumulated throughout the course and the number of accurate responses provided on the final examination.

GPA-Quiz 1 Correlation:

Investigation Focus: Does a student’s prior GPA correlate significantly with their performance on Quiz 1, measuring the number of correct responses?

Null Assumption (H₀): The correlation between a student’s previous GPA and their Quiz 1 performance, in terms of correct answers, is not significant.

Alternative Hypothesis (H₁): A notable link exists between a student’s prior GPA and their performance on Quiz 1, measured by the number of accurate responses.

Assessing Assumptions

In the provided descriptive statistics, the skewness and kurtosis measures for GPA and the final exam are displayed. Both GPA and final exam skewness values are within the -1 to 1 interval, indicating reasonably balanced distributions. This implies that the data is likely normally distributed.

 

Results & Interpretation

Descriptive Statistics (Table 1):
 
GPA
Total
Quiz 1
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):

Table 2 reveals a slight uptrend of 0.152 between GPA and Quiz 1 scores. Nonetheless, the obtained P-value of 0.212 indicates that this correlation lacks statistical significance. Consequently, we uphold the null hypothesis.

Correlations by Pearson:

 
Quiz 1
GPA
Total
Final

Quiz 1

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 ‘Final’ and ‘Total’ are highly correlated, with a coefficient of 0.875, indicating a strong relationship between them. This correlation is statistically significant, suggesting that 76% of the variation in the ‘Total’ can be explained by the ‘Final.’ Consequently, we reject the null hypothesis.

Likewise, there’s a modest association between GPA and the Final exam, indicated by a correlation coefficient of 0.379. This finding holds statistical importance, indicating that approximately 14% of the fluctuations in GPA can be clarified by variations in the Final exam scores.

Statistical Conclusions

Although there isn’t enough proof to suggest a strong link between GPA and Quiz 1 results, the connections between ‘Final’ and ‘Total’ scores, as well as between GPA and Final scores, show clear statistical significance.

PSY FPX 7864 Assessment 2 Correlation Application and Interpretation

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.

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

PSY FPX 7864 Assessment 2 Correlation Application and Interpretation

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

 

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