RSCH FPX 7864 Assessment 2 Correlation Application and Interpretation

Correlation Application and Interpretation

Student name

RSCH-FPX7864

Capella University

Professor Name

Submission Date

Data Analysis Plan

The best methodology of data analysis starts with a systematic approach of data collection and applying methods of analysis to address research purposes. Such an approach can be applied to conduct a systematic assessment of the collected information using such methodological tricks and to produce meaningful findings in research (Tumiran, 2024). The research question aims at determining the possible correlation of some important performance indicators of students such as the marks in the first quiz, the final exam, total course points, and previous grade point average. The study has four variables, namely:

· Score in Test 1: The amount of correct answers given on the first test and scored on in a continuous manner between zero and a potential maximum score.

· Final Exam Score: This is a numeric variable that represents the aggregate correct number of responses given in a final examination that is administered in a continuous form where the scale is zero to the maximum possible score.

· Total Points Earned: This is a continuous variable that is a cumulative total of all the scores of all the assessments taken to date in the academic term, up to a potential maximum score.

· Grade Point Average (GPA): A mathematical rate of former performance on a 4-point sequence of 0.00 to 4.00, an average of all grades on previous course work.

Total-Final Correlation

Research Question

Does the number of total points that the student has earned in a semester have any statistical connection with the performance in the final examination?

Hypotheses

Null Hypothesis (H0): There exists no statistical correspondence that exists between the total points the student attains during one semester and performance in the end examination. H0: r = 0.

Alternative Hypothesis (Ha): There is a statistical relationship present between total points earned by the student in a semester and performance in the final examination. Hₐ: ρ ≠ 0.

Quiz 1 and GPA Correlation

Research Question

Does previous academic achievement in terms of the grade point average have a statistically significant relationship with the performance in the first quiz?

Hypotheses

Null Hypothesis (H0): The previous academic performance, in terms of the grade point average, is not statistically related to the performance in the first quiz. H0: r = 0.

Alternative Hypothesis (Ha): Prior academic performance, based on grade point average, is statistically associated with the performance in the first quiz. Hₐ: ρ ≠ 0.

Table 1: Descriptive Statistics

Data verification is statistically tested to ensure the conditions are satisfied to apply a suitable test to justify the reliability of the results and the reasonableness of the selection of an analytical technique (Dul et al., 2020). The descriptive statistics analysis obtained the values of skew and kurtosis, which were used to determine the skewness directionality in the data. Descriptive testing of Quiz 1 showed skewness of -0.851 and kurtosis of 0.162. As far as the GPA variable is concerned, the calculated skewness was -0.220, and kurtosis was -0.688 after analysis. The third variable had -0.341 skewness with -0.277 kurtosis on completion of descriptive testing. With regard to the total variable, the skewness and kurtosis values were found to be -0.757 and 1.146, respectively. The negative values of skew proved the left-shifted tendencies of data distribution in the histogram display. On the other hand, the positive values of kurtosis after analysis indicated that Quiz 1 scores and total points had high peak characteristics. In addition, descriptive analysis showed that the skewness and kurtosis values of all variables fell within the boundaries of -2 to +2 of normality. The descriptive statistics demonstrate that the assumption of normality is used, as the results obtained meet the reasonable normality parameters. Also, the data about the variables shows that they follow a normal distribution, which justifies continuing with correlation analysis to identify the level of association.

Results & Interpretation

Table 2: Pearson’s Correlations Between Academic Performance Variables

The Pearson correlation analysis was performed to test the association of the two variables. In the correlation, the degree of association was reflected in the correlation coefficient r. The p-value is another element that is important in interpreting the results. The p-value determines whether the data inference is in favor of the null or the alternative hypothesis. The results of correlation analysis between the total points and the final examination score of the learners are presented below: r(103) = 0.875, p <.001. The r was 0.875, meaning that there was a positive relationship between total points and the final score of the learner. Also, the null hypothesis was rejected because the calculated value of p is lower than the level of significance of 0.05 and strongly indicates that there is a correlation between the total score and the final score.

The results of the correlation analysis between the learner’s GPA and the score on quiz 1 produced the following results: r(103) = 0.152, p < 1.21. The r was 0.152, a weak relationship between the variables. Based on the circumstances of the outcome, the p-value of the results is greater than 0.05, and the null hypothesis is, therefore, not rejected; that is, there is no significant relationship between variables. The insignificance of the relationship between the variables meant that Quiz 1 did not predict the GPA of the learner at the end of the entire course. The results of the data analysis reported that the variables of the total point and final examination scores showed a higher level of association than the variables of GPA-Quiz 1.

Statistical Conclusions

The descriptive analysis assists the researcher in evaluating the normality of the data by looking at both the negative and positive values of skewness and kurtosis. The results of the descriptive analysis revealed that student GPA and final exam scores are negative, which supports the fact that the data follows a normal distribution. The correlation test is used to find relationships between the GPA-quiz 1 variables and the overall final scores achieved by learners.

The p-value and the r-analysis can assist in interpreting the outcome and recognising the relationships. These results were obtained through correlation analysis between total points and final exam performance in students: (103) = 0.875, p <.001. The r value of 0.875 indicates that the variables are strongly correlated; the p value of less than 0.001 indicates that the relationships between the variables are statistically significant. Findings indicated that good classroom students generally have high final exam results. Moreover, the correlation analysis between student GPA and quiz 1 showed the following results (103) = 0.152, p < 1.21. The r value of 0.152 shows weak correlation between the variables, whereas p is less than 1.21, which shows that there is no significant correlation between Quiz 1 and student GPA scores. Hence, self-assessment instruments such as quiz 1 results are not able to forecast student GPA because other contextual variables can influence learner performance.

Limitations

There were several significant limitations to the correlation analysis. Although there were strong associations identified, causality could not be determined because the correlations were bidirectional. Potential external variables that might have influenced the associations were not investigated. The analysis focused only on direct correlations without looking at more complex relationships that could have been revealed by multiple regression. The sample size was small, and this minimized the statistical power, especially when testing the GPA-Quiz 1 correlations. The quantitative approach failed to capture additional qualitative data on the learning methods and instructional strategies that students use, which could have been useful. Multiple regression would have given a clue on how different factors work together to influence the variation of the scores (Maulud & Abdulazeez, 2020). More research, with broader methodologies and different data sources, may assist in uncovering the mechanisms behind the academic relationships revealed in the initial research.

Application

Examples of a correlation study in nursing education would involve, among others, a study about the relationship drawn between clinical simulation hour’s exposure and performance on the National Council Licensure Examination of registered nurses, and the contribution of practicum hours towards licensure performance. In the same fashion, a research can be directed towards exploration of a relationship that has been established between the level of staffing of nurses to patients in a health facility and the instances of medication error incidences in a healthcare facility, and this study is a prime example of an attribute similar to performance in quiz 1 as it is an attribute in the determination of performance in other academic contexts. In addition, a correlation study has an opportunity to concentrate on an association that is between telephone follow-up contacts after discharge and readmission in health facilities and this is an example of an attribute similar to performance in quiz 1, and therefore, performance in other contexts is determined.

The below instances show how a correlation analysis can be applied to the lending nurses in order to gain useful information on genuine associations among variables. The studies carried out through the correlation analysis have developed evidence-based projects on the higher patient safety and effective functioning of healthcare facilities founded on evidence generated by positive correlations formed with patient outcomes and healthcare operations (Alwali, 2023). Negative correlations reject the null hypothesis, thus creating support to determine genuine associations in determining the correct evidence practices. The bearing allows the nurses to develop projects that cater to unique environments in solving perpetual problems with nurse models that promote genuine healthcare advances in developing statistics ventures within the context of insider information (Hsin et al., 2025). The correlation studies are essential in various projects in healthcare.

Related assessment for this class: RSCH FPX 7864 Assessment 3