The Quantitative Analysis Center Apprenticeship
I attended the 2-month Quantitative Analysis Center Summer Apprenticeship (QAC apprenticeship) at Wesleyan University in 2024. This immersive program provided hands-on research experience, where I collaborated with faculty on active projects, learning and applying data analysis tools, research methodologies, advanced statistical techniques, and data visualization skills.
Before the QAC apprenticeship, my research mainly involved correlational analyses using SPSS, which I was comfortable with but began to feel limited by. My lack of exposure to R, a tool gaining traction in the psychology field, felt like a barrier to advancing my research skills. Additionally, the research methods I had previously used were relatively straightforward and similar in design, often focused on basic statistical analyses. While these projects were valuable learning experiences, I had become increasingly eager to expand my understanding of more advanced research methodologies.
The apprenticeship provided me with the opportunity to break out of these routines and engage with a wider range of methodologies, which helped me see the depth and versatility required in high-level research. This experience allowed me to gain hands-on experiences in a variety of statistical tools and techniques, which I applied across multiple types of analyses, as detailed below:
- Gained proficiency in SPSS, R, SAS, and Stata
- Conducted hypothesis testing using t-tests, ANOVA, and Chi-Square tests
- Performed regression analysis, including linear, logistic, post-estimation methods, 2SLS, and simultaneous equation systems
- Applied advanced statistical models: mixed and hierarchical linear models, ordered and multinomial logit, survival analysis, count models, and time series models
- Learned specialized techniques, such as cluster analysis, principal component analysis (PCA), factor analysis, and path analysis
The apprenticeship taught me that effective research requires aligning methods with specific research questions and understanding the importance of model selection, such as when to use linear models versus more complex ones like multilevel or hierarchical models, based on the data structure and assumptions. By the end of the apprenticeship, I had developed a more critical and flexible approach to research methodology, appreciating that the choice of method should always be guided by the research question and the data at hand.
