7 Emma Tupone
7.1 About the Author
Emma is a graduate student at Brooklyn College whose work reflects a strong foundation in psychological research and data analysis. Her portfolio demonstrates both technical proficiency and the ability to apply reproducible research practices to a wide range of real-world datasets.
7.2 Learning Journey
This portfolio presents a collection of data analysis projects completed as part of a graduate course in Reproducible Psychological Research. Across chapters, Emma applies reproducible workflows in R to diverse datasets, including public health records, socioeconomic indicators, sports analytics, and behavioral data.
Her work highlights core competencies in data cleaning, visualization, statistical inference, and predictive modeling, all integrated through reproducible reporting tools such as :contentReferenceoaicite:0 and Shiny.
7.3 Reflection & Growth
Over the course of the semester, Emma significantly deepened her understanding of both data analysis and research methodology. Through hands-on projects, she built skills in manipulating and cleaning data, visualizing patterns, and applying statistical techniques to answer meaningful research questions.
She developed proficiency using :contentReferenceoaicite:1 to create clear and informative visualizations, and gained experience with statistical methods including t-tests, ANOVAs, chi-square tests, and logistic regression. Beyond execution, she strengthened her ability to interpret results thoughtfully and connect findings to real-world implications.
7.4 Challenges & Problem Solving
Emma encountered several challenges throughout the semester, including:
- Debugging complex R code, particularly when working with large datasets or interactive components
- Interpreting nuanced statistical outputs such as residuals, effect sizes, and model diagnostics
- Synthesizing insights across multiple assignments into cohesive conclusions
Addressing these challenges required persistence, iterative testing, and careful attention to both coding and statistical reasoning.
7.5 Achievements
Emma is especially proud of completing her Bookdown portfolio, which integrates multiple assignments into a cohesive and polished body of work. This project reflects her ability to combine reproducible code, clear visualizations, and well-structured interpretations.
Notable accomplishments include:
- Developing interactive visualizations using Shiny
- Conducting advanced analyses such as logistic regression and chi-square contribution analysis
- Maintaining clarity and consistency across chapters in both code and communication
7.6 Key Takeaways
- Built strong skills in data cleaning, visualization, and statistical modeling
- Developed the ability to interpret and communicate complex results
- Gained experience working with diverse, real-world datasets
- Strengthened reproducible research practices through integrated workflows
7.7 Looking Ahead
Emma leaves the course with increased confidence in her ability to independently explore and analyze data, select appropriate statistical methods, and communicate findings in a clear and professional manner. She is well-prepared to apply these skills in future research, coursework, and professional settings.
“This semester reinforced the value of persistence, curiosity, and attention to detail in research.”