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PSYC FPX4700 Assessment 5 Research Report

Research Report 

The practice of data analysis entails the exploration, transformation, and modeling of data to extract useful insights, draw informed conclusions, and support decision-making processes (Kelley, 2020). It is extensively employed to detect patterns and trends in data sets, providing valuable information for business strategies and decisions. However, to gain meaningful insights, data must first undergo cleaning, preparation, and transformation procedures (Cote, 2021). In this research, we investigate how student demographics and quiz and final exam scores were recorded by teachers in three different sections of a course.

PSYC FPX4700 Assessment 5 Research Report

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.

Descriptive Statistics

 

N

Minimum

Maximum

Mean

Std. Deviation

Skewness

Kurtosis

Statistic

Statistic

Statistic

Statistic

Statistic

Statistic

Std. Error

Statistic

Std. Error

quiz1

105

0

10

7.47

2.481

-.851

.236

.162

.467

gpa

105

1.08

4.00

2.8622

.71266

-.220

.236

-.688

.467

total

105

54

123

100.09

13.427

-.757

.236

1.146

.467

final

105

40

75

61.84

7.635

-.341

.236

-.277

.467

Valid N (listwise)

105

        

 

  1. Summarize whether or not the assumption is met. 

Based on these values, we can examine whether the data are normally distributed. A normally distributed variable has a skewness value of 0 and a kurtosis value of 3. Therefore, if the skewness and kurtosis values are close to 0 and 3, respectively, the data are normally distributed.

PSYC FPX4700 Assessment 5 Research Report

For example, the Quiz variable has a negative skewness and a kurtosis value of 0.162, which indicates that the data are slightly skewed to the left and have a slightly higher peak than a normal distribution. However, the magnitude of the skewness and kurtosis values are relatively small, which suggests that the normality assumption is not severely violated.

Results and Interpretation

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

Correlations

 

quiz1

gpa

total

final

quiz1

Pearson Correlation

1

.152

.797**

.499**

Sig. (2-tailed)

 

.121

<.001

<.001

N

105

105

105

105

gpa

Pearson Correlation

.152

1

.318**

.379**

Sig. (2-tailed)

.121

 

<.001

<.001

N

105

105

105

105

total

Pearson Correlation

.797**

.318**

1

.875**

Sig. (2-tailed)

<.001

<.001

 

<.001

N

105

105

105

105

final

Pearson Correlation

.499**

.379**

.875**

1

Sig. (2-tailed)

<.001

<.001

<.001

 

N

105

105

105

105

**. Correlation is significant at the 0.01 level (2-tailed).

 

  1. Interpret statistical results as discussed in the instructions.

The inter-correlation matrix indicates that the correlation between quiz 1 and GPA is not statistically significant. The inter-correlation matrix shows that the correlation between quiz 1 and GPA is weak, with a correlation coefficient of 0.152 and a p-value of .121, indicating that the relationship between these variables is not statistically significant. Furthermore, the effect size is small, with a value of 0.05. As a result, we fail to reject the null.

A significant positive relationship between the two variables is found, implying that as the Total Score increases, so does the Final Score. With a p-value of less than 0.001, we can reject the null hypothesis for this correlation.

The correlation analysis between the student’s GPA and final exam score revealed a Pearson Correlation coefficient of .379 with a two-tailed significance level of <.001 and a sample size of 105. The effect size for the correlation is classified as moderate, signifying a moderate degree of association between GPA and final exam scores.

Statistical Conclusions

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

The analysis revealed a significant difference in the mean quiz scores of students across the three sections of the course. This was supported by a Pearson Correlation analysis that showed a substantial and statistically significant correlation between quiz 1, total, and final scores. However, the correlation between quiz 1 and GPA was not significant, indicating a weak relationship between the two variables. Additionally, the correlation analysis focused on association rather than causality.

  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. 

Potential directions for future research include exploring the effects of student demographics or instructional strategies on quiz scores. Additionally, enhancing the sample size could lead to more precisely evaluating the relationships between variables (Vasileiou et al., 2018).

PSYC FPX4700 Assessment 5 Research Report

Application

This analytical approach could be applied in psychology to explore the association between different interventions and their outcomes. This approach could be applied to explore the relationship between various forms of therapy, such as Psychodynamic Therapy, and their influence on various mental health outcomes. As an illustration, a correlational study by Sanchez et al. (2019) investigated the relationship between college students’ physical activity and emotional quotient. The importance and potential impact of conducting such an analysis would be identifying the most efficacious form of therapy for a particular mental health disorder.

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 

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