RSCH FPX 7864 Assessment 2 Correlation Application And Interpretation

RSCH FPX 7864 Assessment 2

Correlation Application and Interpretation 

Student name

RSCH-FPX7864

Capella University

Professor Name

Submission Date

Data Analysis Plan

The data analysis process encompasses methods and mechanisms for collecting and analyzing data, enabling researchers to achieve their research targets. The developed data analysis model allows researchers to perform data analysis on a systematic basis, thus coming up with reliable findings that hold valuable insights (Ngulube, 2023).

The evaluation attempts to identify possible correlations between key student academic achievement variables, such as quiz one performance and final examination performance, past grade point average (GPA), and total points earned. The results of the research study will present significant patterns in student performance that could influence future teaching practices.

The study examines four key variables:

• Total Points: Summative number measure that is the aggregate of total scholarly evaluation scores achieved over the course of the academic term, and is an interval variable. The lowest zero is the lowest score, and the highest cumulative score is the highest score possible.
  
  
• Final Exam Score: The variable is continuous in that it has a cumulative numerical measure indicating the total number of correct answers provided during the final period of evaluation. The scoring scale begins at zero and goes to the maximum limit imaginable.
  
  
• Quiz 1 score: The variable is continuous with the number of correctly chosen answers on the first test. The grading range extends from zero points to the highest possible score.
  
  
• GPA: Academic achievement indicator representing the average of all course grades achieved, computed via a numerical assessment scale extending from 0.00 (minimum threshold) to 4.00 (maximum threshold), and is a continuous variable.

Total-Final Correlation

• Research Question

Is there a correlation between students’ total credit accumulation during the term and their final exam results?

• Hypotheses

Null Hypothesis (H0): There is no correlation between students’ total credit accumulation during the term and their final exam results. H₀: ρ = 0

Alternative Hypothesis (Ha): There is a correlation between students’ total credit accumulation during the term and their final exam results. Hₐ: ρ ≠ 0

Quiz 1 and GPA Correlation

• Research Question

Is there a correlation between students’ GPAs and their performance on Quiz 1?

• Hypotheses

Null Hypothesis (H0): There is no correlation between students’ GPA and their performance on Quiz 1. H₀: ρ = 0

Alternative Hypothesis (Ha): There is a correlation between students’ GPA and their performance on Quiz 1. Hₐ: ρ ≠ 0

Testing Assumptions

• Table 1: Descriptive Statistics

Hypothesis testing is used to statistically verify that prerequisite assumptions have been met to apply the test appropriately, and effect size determination is facilitated by hypothesis testing procedures (Shreffler and Huecker, 2023). The descriptive statistical analysis results of skewness and kurtosis were used to determine the directional distribution patterns of the data. In disseminating the analysis, Quiz 1 had a skewness of -0.851 and a kurtosis of 0.162. In the GPA variable, the skewness obtained was -0.220, and the kurtosis obtained was -0.688 after analysis. On the completion of descriptive analysis, the third variable exhibited a skew of -0.341, with a skewness and kurtosis of -0.277. The analysis of the total variable produced -0.757 and 1.146 for the coefficients of skewness and kurtosis, respectively. Adversarial skewness coefficients affirmed rightward data distribution patterns in the histogram representation.

On the other hand, after analysis, the value of positive kurtosis coefficients indicated that Quiz 1 scores and total points had a higher peakedness attribute. Moreover, descriptive analysis showed that the skew and kurtosis coefficients of all variables fell within the -2 to +2 (acceptable range) normality threshold values. Descriptive statistics verify the normality hypothesis because all the data obtained are within safe normality parameters. We also have data on the variables, which show properties of a normal distribution, helping us justify performing correlation analysis to establish the strength of relationships.

Results & Interpretation

• Table 2: Pearson’s Correlations Between Academic Performance Variables

The Pearson correlation analysis was conducted to research the relationship between the two variables. The correlation coefficient, r, indicated the strength of association in the correlation. The p-value is another component needed in the interpretation of the results. The p-value will either reject or accept the data evidence, in favor of either the null or alternative hypothesis. A correlation analysis of student cumulative points vs. final examination performance gave the following results: r(103) = 0.875, p < .001, and r = 0.875, which indicated that there is a strong positive relationship between the cumulative points and final performance of the student. Furthermore, the null hypothesis was rejected because the obtained p-value falls below the 0.05 significance level, indicating a significant correlation between the cumulative score and final performance.

The student GPA and quiz performance correlation analysis yielded the following results: r(103) = 0.152, p = < 1.21. This indicates a weak association between the two variables. Judging by the context of the results, the p-value is greater than 0.05 and, thus, the null hypothesis cannot be rejected, which, in its turn, points to the absence of a significant association between the variables. The nonsignificant relationship between the variables revealed that Quiz 1 could not be used to predict the GPA that the student would attain in the entire course. The data analysis results indicated that the variables of cumulative points and final examination performance exhibited a stronger level of association than the GPA-Quiz 1 variables.

Statistical Conclusions

The descriptive analysis assists the researcher in testing or assessing the data normality through the analysis of skewness and kurtosis coefficients, both negative and positive. The results of descriptive examination showed that the student GPA and final exam performance had a negative coefficient, which validated the fact that the data were close to normal distribution. The correlation test aids the determination of relationships that exist between GPA-quiz 1 variables and the cumulative final performance attained by students. The p-value and r-analysis will help to interpret the result and define the associations.

The results of correlation analysis between cumulative points of students and their final exam performance were as follows: r(103) = 0.875, with p = 0.001. The r coefficient of 0.875 represents a good association, whereas p < 0.001 represents statistically significant associations between variables. The results showed that good students in the classroom tend to get high grades in the final exam. Moreover, the correlation analysis of student GPA and performance on quiz 1 gave the following results: r(103) = 0.152, p < 1.21. The r coefficient value of 0.152 indicates a weak association between the variable, and the p value of less than 1.21 points to no significant association between Quiz 1 and student GPA performance. Therefore, self-evaluation tools such as quiz 1 scores cannot be used to forecast student GPA because other external factors could contribute to student achievement.

• Limitations

There were various limitations to the correlation analysis. Although strong relationships were determined, causation could not be established because the correlations were bidirectional. Extrinsic factors that may have affected the relationships were not investigated. The testing only looked at direct relationships, not more complicated relationships that might have been identified with multiple regression. The small sample size decreased the statistical power, especially for the relationships between GPA and Quiz 1.

The quantitative research did not consider other qualitative data on student learning practices and teaching methods that may have enhanced understanding. It might have been appropriate to use multiple regression analysis to understand the overall effect of various variables on performance variations (Mizumoto, 2022). Further studies employing wider methods and a variety of data may help reveal the underlying mechanisms behind the academic relationships observed in the original study.

Application

The relationship between medication intake time and patient pain management efficacy can be examined through correlation analysis in nursing practice, which may help establish how timely medication intake affects therapy outcomes. Correlations between staffing ratios and infection prevention rates will be investigated in the intensive care unit to determine how sufficient staffing can be helpful in the provision of quality care, similar to the case of cumulative academic performance and success in the final examination.

Patient education time may predict the level of treatment adherence and can provide insights for healthcare teams about effective education strategies for different population groups (Oliveira et al., 2024). The implementations demonstrate how correlation analysis provides valuable insights in any medical setting by defining a strong relationship between clinical variables, such as staffing rates, medication schedules, and patient education duration, and their outcomes. Medical institutions that rely on research-backed guidelines created based on correlation results have reported improvements in care quality and organizational efficiency (Hashish & Alsayed, 2020).

The ability to identify correlations empowers professional nurses to develop specialized interventions addressing specific performance areas. This enables the development of evidence-based structures for promoting clinical excellence, as evidenced by our findings, which revealed a statistically significant correlation between total points and the final exam (rejected null hypothesis). The relationship between GPA and Quiz 1 was insignificant (failed to reject the null hypothesis). Using statistical correlation results with a caring clinical rationale allows implementing effective strategies with proven methodologies.

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