PSYC FPX 4700 Assessment 5 Research Report

Student Name

Capella University

PSYC FPX 4700 Statistics for the Behavioral Sciences

Prof. Name:

Date

Research Report

The process of data analysis involves the exploration, transformation, and modeling of data to extract valuable insights, draw informed conclusions, and support decision-making (Kelley, 2020). It is widely used to identify patterns and trends within datasets, providing essential information for business strategies and decision-making processes. However, to obtain meaningful insights, data must undergo cleaning, preparation, and transformation procedures (Cote, 2021). This study examines how student demographics, quiz scores, and final exam scores were recorded by instructors across three different sections of a course.

Data Analysis Plan

Name the variables and the scales of measurement.

Four variables are as follows:

Variable	Scale of Measurement
Quiz 1	Continuous
GPA	Continuous
Total	Continuous
Final	Continuous

Variables 1 (Quiz), 3 (Total), and 4 (Final) are continuous variables because they can assume any numerical value within a specified range. For instance, Quiz scores can vary from 0 to the maximum number of questions on the quiz, while 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 often measured on a categorical scale, such as letter grades (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.

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

Paste the SPSS output for the given assumption.

Variable	N	Minimum	Maximum	Mean	Std. Deviation	Skewness	Kurtosis
quiz1	105	0	10	7.47	2.481	-0.851	0.162
gpa	105	1.08	4.00	2.862	0.71266	-0.220	-0.688
total	105	54	123	100.09	13.427	-0.757	1.146
final	105	40	75	61.84	7.635	-0.341	-0.277

Valid N (listwise): 105

Summarize whether or not the assumption is met.

To assess 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. Therefore, data with skewness and kurtosis values close to 0 and 3, respectively, are considered to be normally distributed.

For example, the Quiz variable shows a negative skewness and a kurtosis value of 0.162, indicating a slight leftward skew and a peak that is somewhat higher than that of a perfectly normal distribution. However, the magnitude of these values is relatively small, suggesting that the assumption of normality is not significantly violated.

Results and Interpretation

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

	quiz1	gpa	total	final
quiz1	1	0.152	0.797**	0.499**
gpa	0.152	1	0.318**	0.379**
total	0.797**	0.318**	1	0.875**
final	0.499**	0.379**	0.875**	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 considered 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.

In contrast, 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 examining the correlation between students’ GPAs and final exam scores, a Pearson Correlation coefficient of 0.379 was found. This correlation is highly significant, with a two-tailed significance level of less than 0.001, and is regarded as having a moderate effect size. The moderate effect size indicates a reasonable degree of association between GPA and final exam scores, suggesting a meaningful relationship between these two variables within the sample of 105 students.

Statistical Conclusions

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

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

Moreover, it is crucial to understand that correlation analysis evaluates the association between variables but does not establish causality. While the significant correlations offer valuable insights into the relationships among the variables, they do not imply a cause-and-effect relationship. Therefore, additional research or experimental studies would be necessary to investigate any causal links between the variables in question.

Analyze the limitations of the statistical test.

The limitations of the statistical test employed in the analysis include restricted generalizability due to the specific sample studied, a narrow scope of variables examined, assumptions of normality that may not be applicable to all variables, and a focus on correlation rather than causation.

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

Future research could explore various avenues for further investigation. For example, understanding how student demographics or instructional strategies influence quiz scores could yield valuable insights. Additionally, increasing the sample size could enhance the precision of evaluating the relationships between variables (Vasileiou et al., 2018).

Application

This analytical approach shows promise for application in psychology, particularly in examining the connections between interventions and their outcomes. It can be effectively utilized to investigate how different forms of therapy, such as Psychodynamic Therapy, relate to various mental health outcomes. For instance, a correlational study conducted by Sanchez et al. (2019) explored the relationship between college students’ physical activity and emotional intelligence.

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