Description

The purpose of the assignment is to have students develop and execute a research project in applied statistics using large publicly available data sets. The work of the assignment includes hypothesis generation, data visualization, statistical testing, and analysis. In addition to writing an analytic narrative of their work, students will be required to disseminate their work by presenting it at a public poster session on campus. This assignment can be classified as undergraduate research embedded in the curriculum.

Background and Context

This assignment is for the Applied Statistics course for Business and Economics majors but could be adapted for a variety of statistics courses in other departments where large data sets are available for use. This is the semester project and the assignment requires students to demonstrate competency in the learning objectives of the course (attached with rubric). It is one of the final pieces of work that students submit during the semester and it reaches into material from the entire course. All of the expectations in the assignment are scaffolded into the course material earlier in the semester.

This assignment is designed to be an undergraduate research experience embedded into the curriculum. One of the things that my colleagues and I have learned is that research experiences need to be introduced early and integrated into the curriculum to have the maximum impact on students’ intellectual development. This assignment requires students to prepare and present a data research project that focuses on a significant business or economic question for research. This is a core experience for students before they take on larger research projects in the upper division courses.

Students most often enroll in this course during their sophomore year. Although business and economics majors dominate the room, many students throughout the institution take the course because it fulfills a general education requirement for them. Students at this level are often uncomfortable with the idea that they are responsible for designing and executing a research project that includes serious data analysis with unknown outcomes. This is why the frequent check-ins are part of the assignment design. Periodically during the semester, students are required to report progress and problems encountered in their work. Students also struggle with cutting large data sets from sites like the Census, Department of Education, IPUMS, and the Bureau of Labor statistics. Earlier assignments during the semester provide them repeated practice in navigating data sites.

Even though a statistics course is usually thought of as tied to Intellectual Skills and Quantitative Fluency, this assignment and the entire course has a deeper focus on Applied Learning and Collaborative Learning. The assignment and the course fit a model of undergraduate research embedded in the curriculum.

Alignment and Scaffolding

This is the capstone assignment for the introductory applied statistics course. It builds on students’ successfully completing assignments and tasks earlier in the semester. The assignments during the semester enable students to build the skills necessary to complete the final assignment. These assignments include learning how to cut and clean a data set so it can be analyzed, and learning how to visualize and describe data. The capstone assignment also demonstrates how an assignment about t-tests can be expanded into a semester project.

Reflections

This assignment is adapted from an assignment I used for several years when teaching Geographic Information Science (GIS). I will be using it for the first time in the applied statistics class this fall as I return to teaching after 5 years. The core of the assignment differs little from the original assignment except that, in its present form, students do not need to construct mapped results of their analysis as part of the assignment. I found this approach to be effective in getting students more deeply into the material because they had to produce a final product for their peers to inspect. As you might expect, good students do well at this assignment. However, I have found that, for the most part, marginal students do acceptable-to-good work and that they often exceed my expectations.

Students have often struggled with finding a topic. Mostly because there is just so much data out there, they get lost in the voluminous options. This is why I have included an idea sheet (with data sources). I am hoping this will help them focus a bit more. The other thing that students struggle with is envisioning what an acceptable final project might look like. I find that peppering lecture/discussion time with examples of successful past projects along with potentially successful projects is helpful.

One thing that has been helpful and you can observe in the syllabus is that the entire course is “front-loaded.” I ask more of the students early on in the semester and I “let up” on them later in the semester when they are working on their project. There is less new material in the final weeks, we more focus on discussion of how to apply what they learned early in the semester. The case study sessions that are listed on the syllabus allow me to bring in a new case study, or open it up to the students to talk about their projects and have them discuss problems or issues they may be having with the work.

It is also helpful to have an experienced student as an extra guide for students. This could be a student supplemental instructor, teaching assistant or tutor (depending on the instruction). Students will seek out their peer more often than they will seek the instructor. A good student assistant can provide information and perspective to the instructor about things that should be reviewed again in upcoming classes.

Although students use and are familiar with a whole range of technology, they are most often adept at being a consumer of technology rather than people who make technology bend to their will. This is a transition point for them. Short videos that help them use the technology are popular and useful. You can make your own or find many on YouTube.

Rubric(s)

1. Rubric
2. Syllabus