Analysis Researchsubmit One Apa Formatted Word Document For This Ass ✓ Solved
Analysis & Research Submit one APA-formatted Word document for this assignment. 1. Briefly restate your research area of interest. You will use this topic as the basis to select variables for the remaining questions in this assignment. (3 pts.) 2. Pearson Correlation a.
Identify two variables for which you could calculate a Pearson correlation coefficient. Describe the variables and their scale of measurement. (3 pts.) b. Now, assume you conducted a Pearson correlation and came up with a significant positive or negative value. Create a mock r value (for example, .3 or -.2). Report your mock finding in APA style, and interpret the statistic in terms of effect size and R2 while also taking into account the third variable problem and well as direction of causality. (10 pts.) (Note that the text does not use APA style; consult the 6th edition manual.) 3.
Spearman’s Correlation a. Identify two variables for which you could calculate a Spearman’s correlation coefficient. Describe the variables and their scale of measurement. (3 pts.) b. Now, assume you conducted a correlation and came up with a significant positive or negative value. Create a mock r value (for example, .3 or -.2).
Report your mock finding in APA style and interpret the statistic in terms of effect size and R2 while also taking into account the third variable problem and well as direction of causality. (10 pts.) (Note that the text does not use APA style; consult the 6th edition manual.) 4. Partial Correlation vs. Semi-Partial Correlation a. Identify three variables for which you may be interested in calculating either a partial or semi-partial correlation coefficient. (2 pts.) b. Compare/contrast these two types of analyses using your variables and research example.
Which would you use and why? (10 pts.) 5. Simple Regression a. Identify two variables for which you could calculate a simple regression. Describe the variables and their scale of measurement. (3 pts.) b. Which variable would you include as the predictor variable and which as the outcome variable?
Why? (5 pts.) c. What would R2 tell you about the relationship between the two variables? (5 pts.) 6. Multiple Regression a. Identify at least three variables for which you could calculate a multiple regression. Describe the variables and their scale of measurement. (3 pts.) b.
Which variables would you include as the predictor variables and which as the outcome variable? Why? (5 pts.) c. Which regression method would you use and why? (5 pts.) d. What would R2 and adjusted R2 tell you about the relationship between the variables? (5 pts.) 7. Logistic Regression a.
Identify at least three variables for which you could calculate a logistic regression. Describe the variables and their scale of measurement. (3 pts.) b. Which variables would you include as the predictor variables and which as the outcome variable? Why? (5 pts.) c. Which regression method would you use, and why? (5 pts.) d. What would the output tell you about the relationship between the variables? (5 pts.)
Paper for above instructions
1. Research Area of Interest
This research aims to explore the impact of social media use on academic performance among university students. With the advent of digital communication, understanding how various social media platforms affect students' academic outcomes is crucial for both educators and students. The overarching questions investigate whether increased social media use correlates with improved or diminished academic performance.
2. Pearson Correlation
a. Variables
In this analysis, two variables have been selected for Pearson correlation:
- Social Media Use (measured in hours per week, ratio scale).
- Academic Performance (measured as GPA, which is on a 4.0 scale, also a ratio scale).
b. Mock Pearson Correlation
Assuming the Pearson correlation coefficient calculated is \( r = -0.4 \) (p < 0.01), this suggests a significant negative correlation between social media use and academic performance. In terms of effect size, an \( r^2 = 0.16 \) indicates that 16% of the variance in GPA can be explained by the amount of time spent on social media, suggesting a notable impact. A negative correlation indicates that as social media use increases, academic performance tends to decrease.
However, we must consider the third variable problem, such as the influence of study habits or overall mental health, which may simultaneously affect both social media use and GPA. Therefore, causality cannot be strictly inferred from this correlation alone.
3. Spearman’s Correlation
a. Variables
For Spearman’s correlation, the chosen variables are:
- Social Media Engagement (measured by a Likert scale from 1 to 5, ordinal scale).
- Student Motivation (measured by a Likert scale from 1 to 5, ordinal scale).
b. Mock Spearman Correlation
Assuming we found a Spearman correlation coefficient of \( r_s = 0.5 \) (p < 0.01), it suggests a significant positive correlation between social media engagement and student motivation. The effect size implication here, with \( r_s^2 = 0.25 \), indicates that 25% of the variance in student motivation can be explained by social media engagement levels.
Although the correlation shows that as student engagement with social media increases, so does motivation, it’s worth noting the potential third variable issue. For example, students who are intrinsically motivated may engage more on social media to connect with like-minded individuals. Again, this makes it challenging to draw definitive causal conclusions.
4. Partial Correlation vs. Semi-Partial Correlation
a. Variables
Variables selected for analysis are:
- Social Media Use.
- Academic Performance.
- Study Time (measured in hours per week, ratio scale).
b. Comparison
Partial correlation would remove the effect of Study Time from both Social Media Use and Academic Performance to see what direct relationship remains. Conversely, semi-partial correlation would remove Study Time’s influence on Academic Performance while still considering Social Media Use, providing insight into the unique contribution of one variable over the other.
If the goal is to understand how much Social Media Use uniquely influences Academic Performance, then a semi-partial correlation would be more appropriate. By doing so, the relationship can reflect the underlying impact of social media while accounting for controlled study habits.
5. Simple Regression
a. Variables
For simple regression, the variables are:
- Social Media Use (predictor).
- Academic Performance (outcome).
b. Predictor and Outcome Variables
Social Media Use is the predictor variable because we seek to understand how it influences Academic Performance, which is the outcome variable. The logic here follows the traditional approach where the time allocated to social media is presumed to affect GPAs.
c. Interpretation of R²
The R² value would indicate the percentage of the variance in Academic Performance explained by Social Media Use. For instance, if R² = 0.2, this suggests that 20% of the variability in GPA can be accounted for through social media usage, still leaving a majority unaccounted for by this single factor.
6. Multiple Regression
a. Variables
In this case, the variables are:
- Social Media Use.
- Study Time.
- Extracurricular Activities (measured in the number of hours per week, ratio scale).
b. Predictor and Outcome Variables
Here Social Media Use, Study Time, and Extracurricular Activities serve as predictor variables, with Academic Performance as the outcome variable. This model allows for a more comprehensive understanding by examining multiple influences on academic outcomes.
c. Regression Method
I would employ a stepwise regression method due to its ability to determine which predictors significantly contribute to the model while controlling for others.
d. Interpretation of R² and Adjusted R²
In multiple regression, R² would reflect the proportion of total variance explained by all predictors collectively. Adjusted R² adjusts for the number of predictors in the model, providing a more accurate representation where contextually fewer predictors hold greater explanatory power.
7. Logistic Regression
a. Variables
For logistic regression, the variables include:
- Student Engagement (measured on a dichotomous scale: Engaged/Not Engaged).
- Social Media Use.
- Academic Performance Level (dichotomous: Pass/Fail).
b. Predictor and Outcome Variables
The predictor variables would be Social Media Use and Student Engagement, with Academic Performance Level as the outcome variable to determine probabilities of passing based on engagement levels and social media usage.
c. Regression Method
The binary logistic regression method would be employed to analyze the impact of social media and engagement on the likelihood of passing.
d. Output Interpretation
The output would yield odds ratios indicating how changes in predictor variables affect the odds of achieving a particular academic outcome. For example, higher engagement might result in increased likelihood of passing, elucidating the relationships among these variables.
References
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4. McDonald, R.P. (1999). Test Theory: A Unified Treatment. Mahwah, NJ: Lawrence Erlbaum Associates.
5. Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2010). Multivariate Data Analysis (7th ed.). Upper Saddle River, NJ: Pearson.
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9. Hayes, A.F. (2013). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach. New York: Guilford Press.
10. Laerd Statistics (2015). Pearson’s Correlation. Retrieved from https://statistics.laerd.com/statistical-guides/pearsons-correlation-statistical-guide.php