Data Analysis Plan

1. Name the variables and the scales of measurement.

Four variables are as under: 

1. Quiz 1
2. GPA
3. Total
4. Final

Variable 1 (Quiz), Variable 3 (Total), and Variable 4 (Final) are continuous variables because they can take any numerical value within a range. For example, Quiz scores can range from 0 to the maximum number of questions on the quiz, and Final exam scores can range from 0 to the maximum number of questions on the final exam.

Variable 2 (GPA) is also a continuous variable, although it is typically measured on a categorical scale, such as a letter grade (e.g., A, B, C, D, F) or a numerical scale (e.g., 0-4.0). This is because GPA is calculated as an average of grades earned across multiple courses, which can take any numerical value within a range.

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

Is there a significant difference in the mean quiz scores across the three sections of the course?

Null Hypothesis: There is no significant difference in the mean quiz scores across the three sections of the course.

Alternative Hypothesis: There is a significant difference in the mean quiz scores across the three sections of the course.

Testing Assumptions

1. Paste the SPSS output for the given assumption.

1. Summarize whether or not the assumption is met. 

In assessing the normality of the data, we examine the skewness and kurtosis values. A perfectly normally distributed variable has a skewness of 0 and a kurtosis of 3. Hence, data with skewness and kurtosis values close to 0 and 3, respectively, are considered to be normally distributed.

For instance, the Quiz variable exhibits a negative skewness and a kurtosis value of 0.162, indicating a slight leftward skew and a slightly higher peak than a perfectly normal distribution. Nonetheless, the magnitude of these values is relatively small, suggesting that the assumption of normality is not severely violated.

Results and Interpretation

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

1. Interpret statistical results as discussed in the instructions.

According to the inter-correlation matrix, the correlation between quiz 1 and GPA is not statistically significant. The observed correlation coefficient is 0.152 with a p-value of .121, indicating a weak relationship between these variables, which is not deemed statistically significant. Additionally, the effect size is small, with a value of 0.05. Therefore, we do not have sufficient evidence to reject the null hypothesis.

Conversely, a significant positive relationship is observed between the Total Score and the Final Score. The correlation coefficient is highly significant with a p-value of less than 0.001, allowing us to reject the null hypothesis for this correlation.

Furthermore, when analyzing the correlation between the student’s GPA and final exam score, a Pearson Correlation coefficient of 0.379 was observed. This correlation is highly significant with a two-tailed significance level of less than 0.001 and is considered to have a moderate effect size. The moderate effect size indicates a moderate degree of association between GPA and final exam scores, suggesting a meaningful relationship between these two variables in the sample of 105 students.

Statistical Conclusions

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

The analysis conducted on the data indicated a statistically significant difference in the mean quiz scores among students across the three sections of the course. This finding was further supported by a Pearson Correlation analysis, which revealed a strong and statistically significant correlation between quiz 1, total scores, and final scores. However, it is worth noting that the correlation between quiz 1 and GPA was not found to be statistically significant, indicating a weak relationship between these two variables.

PSYC FPX 4700 Assessment 5 Research Report

Moreover, it is essential to recognize that correlation analysis assesses association between variables and does not establish causality. While the significant correlations provide valuable insights into the relationships between the variables, they do not imply causation. Thus, further research or experimental studies would be required to explore any causal connections between the variables in question.

1. Analyze the limitations of the statistical test. 

The limitations of the statistical test used in the analysis include limited generalizability due to the specific sample studied, a limited scope of variables examined, assumptions of normality that may not hold for all variables, and a focus on correlation rather than causality.

1. Provide any possible alternate explanations for the findings and potential areas for future exploration. 

Application

This analytical approach holds promise for its application in psychology, particularly in exploring the connections between interventions and their outcomes. It can be effectively utilized to investigate how different forms of therapy, like Psychodynamic Therapy, relate to various mental health outcomes. For instance, a correlational study conducted by Sanchez et al. (2019) examined the link between college students’ physical activity and emotional quotient.

The significance and potential impact of employing this analysis lie in identifying the most effective form of therapy for specific mental health disorders. By conducting such studies, researchers can gain valuable insights that could ultimately lead to more targeted and efficacious treatment approaches.

References

Cote, C. (2021). 4 types of data analytics to improve decision-making. Business Insights. https://online.hbs.edu/blog/post/types-of-data-analysis

Kelley, K. (2020, May 27). What is Data Analysis? Process, Methods, and Types Explained. Simplilearn.com. https://www.simplilearn.com/data-analysis-methods-process-types-article

Sanchez, J. A., Diez-Vega, I., Esteban-Gonzalo, S., & Rodriguez-Romo, G. (2019). Physical activity and emotional intelligence among undergraduate students: A correlational study. BMC Public Health, 19(1).
https://doi.org/10.1186/s12889-019-7576-5

Vasileiou, K., Barnett, J., Thorpe, S., & Young, T. (2018). Characterising and justifying sample size sufficiency in interview-based studies: Systematic analysis of qualitative health research over a 15-year period. BMC Medical Research Methodology, 18(1), 1–18. https://doi.org/10.1186/s12874-018-0594-7