Getting Started

Frequently Asked Questions

1. What is statistics education research?

In general, statistics education research focuses on both the instruction and learning of statistics at all levels, using any appropriate research methodology (qualitative and quantitative) that addresses the question being asked. It investigates instructional strategies, student understanding, curricular design, assessment methods, and many other aspects to improve how statistics is taught and learned.

Curiosity initiates research. First, identify areas you are curious about. Then, investigate what's been done on that topic. Next, think of specific research questions you’d like to explore. Collaborating with others—especially those with complementary expertise—can help. Reading journals, attending conferences, and seeking mentorship are also key steps.

Both quantitative and qualitative methods are used, depending on the research question. Quantitative approaches may include surveys, experiments, or analysis of assessment data, while qualitative methods may involve interviews, classroom observations, or analysis of student work. Often, mixed methods are used to provide richer insights.

Potential collaborators can be found at conferences like USCOTS and JSM, through professional organizations such as ASA and ISI, and via online communities like CAUSEweb. Reaching out to colleagues in your department or related disciplines may also lead to productive partnerships.

Key journals include the *Journal of Statistics Education (JSE)*, *Statistics Education Research Journal (SERJ)*, *Teaching Statistics*, and other discipline-based education research journals. Broader education journals sometimes publish relevant work as well.

You can share research by publishing in journals, presenting at conferences, participating in webinars, contributing to CAUSEweb resources, or collaborating with professional organizations. Engaging in outreach and teaching communities also helps disseminate findings.

Mentorship opportunities, collaborative research groups, and professional development workshops are available through CAUSE, ASA sections, and conference initiatives. Some grant programs also provide support for early-career researchers.

Data science education often builds on principles from statistics education, with overlapping concerns in teaching data analysis, visualization, inference, and computational thinking. Research in both areas informs how students develop skills for modern data challenges.

Subscribing to journals, attending conferences, joining mailing lists like CAUSE, and following professional organizations are all good ways to stay up to date. Many researchers also share work on preprint servers and professional networks.

Statistics and data are central to modern life. Improving how statistics is taught and learned ensures students are better prepared to think critically, analyze data, and make informed decisions. Research guides evidence-based improvements to curricula, pedagogy, and assessment.

Statistics Research Topics

Simulation Based Inference

Research topics in SBI Research

Assessment

Research topics in Assessment

Measurement

Research topics in Measurement

Guidelines for Research

Research Priorities Report

Connecting Research to Practice in a Culture of Assessment for Introductory College-level Statistics (PDF file)

Related Webinar:
"Connecting Research to Practice in Education Research" with Dennis Pearl, The Ohio State University

ASA Report

Using Statistics Effectively in Mathematics Education Research (PDF file)

Related Webinar:
"Using Statistics Effectively in Statistics Education Research" with Sterling Hilton, Brigham Young University

Guidelines for Programs

Statistics Education Graduate Programs

Statistics Education Graduate Programs : A report on a workshop funded by an ASA Member Initiative Grant including recommendations endorsed by the ASA Board of Directors.

The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education

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