The Skill Every Researcher Needs: R Programming

In today’s data-driven education landscape, the ability to analyze, interpret, and act on complex data has become a foundational skill for researchers, evaluators, and education professionals. From large-scale assessments to classroom-level interventions, educational data is abundant and increasingly nuanced. Professionals are expected to move beyond surface-level insights and engage in deeper analysis that can inform instruction, policy, and program design.

This growing demand has made programming languages such as R essential tools in modern educational research and evaluation. The course ESPY 494: Introduction to R Programming Language, part of the Online Master of Education in Measurement, Evaluation, Statistics and Assessment (MESA) program at UIC, introduces students to these capabilities through a structured, hands-on approach. The course is taught by Professor Qingli Lei, who earned her PhD in Educational Studies and a master’s degree in Statistics from Purdue University. As a researcher in special education with a focus on mathematics learning difficulties, she uses R to analyze large-scale datasets and examine how various factors influence student outcomes. Her work includes published research on mathematics learning and student outcomes, in which she examines how factors such as mathematics anxiety and self-concept influence mathematics performance.

That research perspective carries directly into the course, where students learn not just how to run analyses, but how to use data to answer meaningful research and evaluation questions. Dr. Lei shares how this course builds both technical confidence and practical data skills.

Why R Programming Matters in Research

Professor Qingli Lei explains that learning R enables professionals to go beyond the limitations of traditional tools such as Excel and SPSS. “R allows users to go beyond basic analysis and engage in more advanced, scalable, and reproducible work,” she notes. While many professionals are familiar with conducting analyses such as ANOVA in point-and-click software, R opens the door to more complex modeling techniques.

For example, structural equation modeling, a method commonly used in educational research, can be implemented more efficiently in R. “You can specify multiple models to represent the conceptual pathways of a project,” she explains. This flexibility allows researchers to test ideas more efficiently and makes the analytic process more transparent.

Another key advantage is reproducibility. “When collaborators read your code, they can understand what you are doing,” Professor Lei says. By embedding comments directly into scripts, researchers can clearly communicate their analytical process, which is much harder to achieve in click-based tools.

What Students Learn in the Introduction to R Programming Language Course

The course is designed as an introductory yet comprehensive experience that builds a strong foundation in R for educational data analysis. According to Professor Lei, “This course provides students with a strong foundation in R programming for educational data analysis from setting up the R environment to analyzing real datasets.”

Students begin by learning how to set up their environment, then move on to analyzing data step by step. Because the course is hands-on, students actively work with data rather than just learn theory. By the end of the course, students are expected to produce outputs that meet professional standards. “They may even be able to produce publication-ready outputs using R,” she explains.

Three core competencies are emphasized throughout the course:

 1. Data management and preprocessing

“The data is not always ready,” Professor Lei notes. Students learn how to import data from files, clean it, and restructure it when needed. This is a critical step, as real-world datasets often require preparation before analysis can begin.

2. Statistical analysis

Students develop the ability to run a wide range of statistical analyses. “You can conduct t-tests, correlations, ANOVA, linear regression, and even nonlinear regression,” she explains. Most models can be implemented in R, giving students flexibility when working with different types of data.

3. Data visualization and reporting

Students also learn how to present their findings effectively. “You can create meaningful visualizations and generate reports using professional standards,” she says. This helps students communicate results clearly and credibly.

How Students Apply R to Real Data Challenges

A defining part of the course is the opportunity to work directly with real datasets and research questions. In the second half of the course, students move beyond foundational skills and begin applying what they have learned in a more open-ended way.

Professor Qingli Lei explains that students either bring their own data or work with datasets provided in the course. “If they don’t have their dataset, I will provide real data,” she says. These datasets reflect real educational contexts and allow students to explore meaningful questions. For example, students may analyze student performance data to examine how different variables relate to learning outcomes. These variables can include demographic factors and measures of student motivation.

Students also encounter one of the most common challenges in data work. “Data is messy,” she explains. “You have a lot of missing values, and data often come in different formats.” Before any analysis can begin, students must clean and organize their datasets.

Students are also encouraged to think critically about which variables matter. “You need to decide which variables are most important based on the literature,” she explains. This adds another layer to the work, combining technical skills with research knowledge. By the end of the project, students are not just running analyses; they are also developing their own approaches. They identify which variables have a meaningful impact and interpret their findings. “We want to examine the statistical significance of different variables,” Professor Lei says.

Through this process, students gain experience that closely reflects real research and evaluation work.

Professor Qingli Lei’s Advice for Students New to R Programming

Professor Lei acknowledges that “learning programming can feel challenging at the beginning,” especially for students without a technical background. However, the course is designed to support beginners and provide consistent opportunities for practice. At the start, students rely more on instruction. As the course progresses, they begin to explore more independently.

Students are also supported through regular practice sessions. “We have weekly lab hours where we write code and work through hands-on exercises together,” she says. These sessions allow students to ask questions, debug their code, and work through challenges in real time. The goal is to combine instruction with practice so students can build both understanding and confidence in using R for real research and evaluation work.

Disclaimer

Please note: ESPY 494 is a 2-credit elective course and is not required to complete the MESA degree. For students who are dependent on financial aid, a minimum of 5 credit hours per semester is typically required for students in the M.Ed. in MESA program. In some cases, students may combine a 4-credit course with ESPY 494 (2 credits) to meet this requirement. Students should confirm eligibility with their financial aid advisor.