RSCH 7864 Assessment 2: Correlation Application and Interpretation

Data Analysis Plan

RSCH 7864 Assessment 2 The data analysis plan incorporates the variables quiz 1, GPA, total, and final. Each of these variables represents continuous data since they are measured on a numerical scale and consist of quantitative values. The purpose of this analysis is to examine the relationships among these variables, with a special emphasis on the relationships between total and final scores, along with GPA and Quiz 1 scores. The correlational analysis is used because it is an appropriate statistical technique for assessing the magnitude and direction of the associations between two continuous variables (Bracke, 2024).RSCH 7864 Assessment 2: Correlation Application and Interpretation Through the use of these techniques, the skewness and kurtosis are calculated, which are essential distributional properties that provide information about the nature of the data set. Results were evaluated with Pearson’s correlation coefficients and the corresponding p-values, which indicate the level of statistical significance of the findings.

The first research question addresses the relationship between total and final scores: “Is there a correlation between total and final scores?” The null hypothesis posits that no correlation exists between total and final scores, whereas the alternative hypothesis indicates a positive correlation between the variables. Similarly, the second research question seeks to find a relationship between GPA and quiz 1 scores: “Are GPA and quiz 1 scores related?” The null hypothesis will be that GPA and Quiz 1 scores do not correlate; the alternative hypothesis will be that they are correlated. These hypotheses guide the analysis, bringing more depth into the correlation between these variables and facilitating the interpretation of the data within the framework of the research goals.

Testing Assumption

The skewness and kurtosis values of the variables quiz 1, GPA, total, and final are given in the descriptive statistics table, which is necessary to check the normality assumption of correlation analysis. Quiz 1 has a skewness of -0.704 and a kurtosis of -0.287, while the total variable has a skewness of -0.758 and a kurtosis of 0.657. For GPA, the skewness is -0.096, and the kurtosis is -0.832. The last variable shows skewness at -0.606 and kurtosis at 0.463. All of these values fall within the range of -2.00 to +2.00. Hence, the values do not significantly deviate from normality. More verification steps include dividing skewness and kurtosis values with their respective standard errors, skewness at 0.236, and kurtosis at 0.467, which provide z-scores within the range of -2.00 to +2.00. Thus, none of the variables violated the normality assumption, and therefore data are good enough for Pearson’s correlation analysis.

Results & Interpretation

The Pearson’s correlation table below summarises the relationship between quiz 1, GPA, and total and final scores. The correlation value between total and final scores is 0.659, with a p-value of < 0.001. The p-value is below the threshold of 0.05, so the null hypothesis of no relationship between total and final scores is dismissed, and hence the alternative hypothesis claims the existence of a relationship. This signifies a strong and statistically significant positive correlation between these variables. On the other hand, the relationship between quiz 1 and GPA is 0.142, with a p-value of 0.149. The p-value being exceeding 0.05, the null hypothesis cannot be dismissed, and there is an insignificant relationship between quiz 1 and GPA. This indicates that there are different levels of significance for the variables and that a significant positive correlation exists between total and final scores.

Statistical Conclusions

Pearson relationship is employed to measure the correlation between variables. In total and final scores, the relationship is significant to support the alternative hypothesis that the variables have a positive relationship. It indicates that high total scores are related to high final scores. In Quiz 1 and GPA, the relationship coefficient is 0.142, with a p-value of 0.149, which means that it holds no significant value. The null hypothesis states that there is no correlation between quiz 1 and GPA. Thus, the scores from Quiz 1 do not significantly impact GPA.

The Pearson correlation is used to assess the relationships that exist between the variables. With a correlation of significance between total and final scores, the alternative hypothesis found above explains that there is a positive relation with each other. That means higher total scores are related to higher final scores. However, the correlation between quiz 1 and GPA has a result of 0.142, while the p-value is 0.149. This shows that it is insignificant. A null hypothesis is that quiz 1 has no relationship with GPA. It is thus held that quiz 1 scores do not necessarily and significantly influence GPA.

Application

Correlation plays a vital role in nursing research because it can help to establish relationships between variables that have an impact on patient care and outcomes. For instance, correlating nurse-to-patient ratios with patient recovery times may indicate the required nursing levels for quality care improvement. Similarly, understanding the association of nursing rounds’ frequency with hospital-acquired pressure ulcers could help identify important strategies to avoid pressure ulcers. Since the two variables measure interval data, they are highly suitable for correlational analysis. The understanding of these associations helps in implementing evidence-based practices to improve patient outcomes and optimize the use of healthcare resources.

References

Bracke, S. (2024). Correlation and regression. Springer, 181–200. https://doi.org/10.1007/978-3-662-67446-8_10