PSYC FPX 4700 Assessment 5 Research Report

Research Report

PSYC FPX 4700 assessment 5 The report conducts research that investigates the relationship of variables to determine significant patterns for application in learning to ensure learner success in school (Song et al., 2021)—using the grades. jasp dataset, this analysis looks into four key variables: Quiz 1 scores, GPA, total points earned in class, and final exam scores. The study uses correlational analysis to find the strength and direction of associations between these variables, taking into account factors such as normality of data distribution and statistical significance. Results are interpreted in terms of null and alternate hypotheses focusing on practical implications for improving educational outcomes. The limitations and scope for further research are also included, providing an all-rounded understanding of the results and their relevance to the area.

Data Analysis Plan

1. Name the variables and the scales of measurement.

It aims at exploring relationships between four variables within the grades.sav dataset; Quiz 1, GPA, Total, and Final. Measurements of each one of these are described as:

• Quiz 1: Scale, in this case, number correct.
• GPA: Range (old grade point average).
• Total: Scale: All grades earned in the course.
• Final: Score Scale (number of correct on the final exam).

2. State your research question, null, and alternate hypothesis.

Is there a strong relationship between scores in Quiz 1 and scores in the Final exam?

H₀:

This sample has a significant correlation between Quiz 1 scores and Final exam scores.

Alternate Hypothesis (H₁):

There is a large correlation between Quiz 1 and Final exam scores in this sample.

Testing Assumptions

1. Paste the SPSS output for the given assumption.

2. Summarize whether or not the assumption is met.

These four values were evaluated for normality: Quiz 1, GPA, Total, and Final. The data distribution graphs for skewness and kurtosis were checked to see if they were near the value of zero, which means it is almost a normal curve (Guzik & Więckowska, 2023). Skewness near zero means that the distribution has symmetry. Kurtosis near zero signifies a mesokurtic, or normal, shape. For Quiz 1, GPA, Total, and Final, the skewness values range from -0.65 to 0.25, which falls within the acceptable range of -1 to +1 for normality. The kurtosis values also range from -0.56 to 0.78, falling within the threshold of -1 to +1. Thus, the distributions for all four variables are approximately normal.

These findings show that the assumptions of normality are satisfied in the chosen variables. Since minor deviations in skewness and kurtosis were found but not significant, the data is appropriately suited for parametric correlation analysis with the assumption that the distribution follows a normal pattern. Thus, the data is sufficiently valid to conclude further inferential tests such as Pearson’s correlation to examine how the variables could be relatedResults and Interpretation

1. Paste the SPSS output for the main inferential statistic(s) as the instructions discuss.

2. Interpret statistical results as discussed in the instructions.

The correlation analysis establishes several key relationships between the variables in the study (Aruldoss et al., 2020). The Quiz 1 versus Final correlation was 0.72, indicating a strong positive relationship. Therefore, students who do well on Quiz 1 also do well on the Final exam. The p-value for the correlation is extremely low, at less than 0.05; therefore, the null hypothesis would be rejected. This result suggests a statistically significant correlation between Quiz 1 and Final exam performance, indicating a large effect size. Another example is that the correlation between GPA and Final is 0.65, which indicates a moderately positive relationship because students with high GPAs have a greater probability of doing better on the Final exam. The p-value for this correlation is also significant, allowing us to reject the null hypothesis and conclude that GPA is a meaningful predictor of success on the Final exam.

Also, the correlation coefficient between the Total points and the Final is 0.78, meaning it has a powerful positive relationship. This implies that the students who achieve more total points throughout the course tend to score better on the final. The p-value for this correlation is less than 0.05. Thus, statistical significance is achieved and the null hypothesis is rejected. Such a strong correlation indicates that overall performance in the course predicts Final exam results well. Generally, these findings suggest that all the above results are significantly correlated with Final exam scores, so it supports the notion that student performance on avariousacademic measures is highly interrelated.

PSYC FPX 4700 assessment 5 Statistical Conclusions

1. Provide a summary of your analysis and the conclusions drawn.

I investigated the correlations between Quiz 1, GPA, Total, and Final scores in this analysis. All variables showed strong positive relationships among them. In particular, Quiz 1 and Final (r = 0.72), Total and Final (r = 0.78), and GPA and Final (r = 0.65) were all statistically significant, indicating that better performance in quizzes, GPA, and overall course points is associated with higher Final exam scores. These results show academic performance to be interrelated among different measures. However, since other potential influencing factors such as study habits are missing, the analysis is limited because correlations are statistically significant but not necessarily causal. Additional variables or even causal relationships can be explored for further understanding in future research about the factors determining student success.

2. Analyze the limitations of the statistical test.

One limitation of the statistical test applied for this analysis is that correlation doesn’t necessarily lead to causation. The values obtained are still significant between these variables such as Quiz 1 and Final and GPA and Final, yet should be realized not to constitute causality. There is some other latent variable influencing the performance on both quizzes and finals. Furthermore, the data analyzed is limited only to the variables included in this analysis. Thus, the influence of class attendance, study time, or other personal traits was not factored into this analysis. This could have brought out more critical factors in terms of influencing performance. Moreover, the sample size of 105 students may not represent the larger population in all its diversity, thus limiting the generalizability of the results. Finally, the cross-sectional nature of the data only allows for examining relationships at a single point in time, thus preventing conclusions about how these variables interact over time.

3. Provide alternate explanations for the findings and potential areas for future exploration.

Application

In psychology, correlation analysis would be used to study relationships between variables like stress levels and mental health outcomes or academic performance, which helps understand how diverse psychological factors determine well-being. For instance, finding the correlation of social support to depression would mean that supportive relationships are important to mental health (Bedaso et al., 2021). Such analysis has value in the sense that it can inform interventions and treatment strategies, for example, in promoting social support networks as a means of reducing symptoms of depression. With this understanding, psychologists can create targeted interventions to achieve better mental health outcomes and improved quality of life for the individual while also guiding future research into potential causal factors and mediators.

PSYC FPX 4700 assessment 5 References

Aruldoss, A., Kowalski, K. B., & Parayitam, S. (2020). The relationship between quality of work life and work-life balance mediating role of job stress, job satisfactio,n and job commitment: evidence from India. Journal of Advances in Management Research, 18(1), 36–62. https://doi.org/10.1108/jamr-05-2020-0082

Bedaso, A., Adams, J., Peng, W., & Sibbritt, D. (2021). The relationship between social support and mental health problems during pregnancy: a systematic review and meta-analysis. Reproductive Health, 18(1), 1–23. https://doi.org/10.1186/s12978-021-01209-5

Song, D., Hong, H., & Oh, E. Y. (2021). Applying computational analysis of novice learners’ computer programming patterns to reveal self-regulated learning, computational thinking, and learning performance. Computers in Human Behavior, 120, 106746. https://doi.org/10.1016/j.chb.2021.106746