Webinars

The TSHS Resources Portal (www.causeweb.org/tshs) contains datasets from 13 real health sciences research studies. Each dataset is accompanied by a study description and a data dictionary. Most are linked to a published paper as well. These datasets, plus some extra teaching tools, are peer reviewed and ready for use with your learners. In this webinar, Amy and Carol will walk through what is available and how to get the most out of this resource.

We are living in a world full of multivariate observational data. Qualitative assumptions about the data generating process, operationalised in simple directed acyclic graph can help students to understand multivariate phenomena like Simpson's or Berkson's paradox, confounding and bias. By teaching causal inference the introductory course can overcome the mantra "correlation does not imply causation". The webinar discusses some motivation as well as teaching ideas and the integration in the curriculum.

Over the past 10 years we have adopted a variety of new teaching methods to make both our in-person and our online introductory biostatistics courses more active, relevant and effective. These include the flipped classroom approach, active learning, collaborative answer keys, and group projects using “The Islands”. The virtual world of The Islands, created by Michael Bulmer at the University of Queensland, allows students to actually do research (and statistics) from start to finish by designing, executing, analyzing and reporting the results of a “real” study on virtual people. We have collaborated with Dr. Bulmer to add features to The Islands (such as clinics and hospitals) to facilitate health-related research studies, both experimental and observational. Carrying out an Island study provides students with sometimes painful but nevertheless invaluable experience in many aspects of research, including study design, data collection, teamwork, data analysis, and communicating research results to others. This webinar will describe The Islands and how we use them for student projects and will discuss the benefits and challenges of these projects, both for students and for instructors. Webinar Recap: https://tshsblog.wixsite.com/main/single-post/2020/05/01/TSHS-Spring-Webinar-Recap-Explore-The-Islands

In this webinar, I will discuss a novel oral communication curriculum we developed and use with graduate students to help them communicate their scientific work with others. I'll use examples of how the students leverage elements of storytelling, stage presence, and improvisational skills to more effectively connect with and captivate audiences as they convey their research. We will also explore how these ideas can transfer into our own work, building a shared knowledge of how we can support students' (and our own) development of oral communication skills.

The idea of a "flipped classroom" has been integrated for two years into the introductory biostatistics course required of all Masters of Public Health (MPH) students at the University of Michigan. The course was divided into eight modules, with each module consisting of one or more video lectures and three modes of assessment: a quiz and two in-class projects. The in-class projects consisted of (1) data analysis of contemporary public health data sets using Excel and (2) review of statistical methods and results in manuscripts published recently in the American Journal of Public Health. This talk will review my experiences with the development of the course, with the implementation of the course, and student input received from anonymous end-of-semester evaluations. Registration: Please use the following form to register: https://redcap.hfhs.org/redcap/surveys/?s=4WH8JJ9KYH. The webinar link will be sent to you ahead of the session, and a link to the webinar recording will be sent to you about a week after the session.

Think-aloud interviews with students can be used to detect specific misconceptions and understand how students reason about statistical questions. Data from think-aloud interviews can then be used to develop conceptual assessments, design new teaching strategies, or suggest further experiments to learn how students think about statistics. In this webinar, we will discuss the benefits of using think-aloud interviews to develop conceptual assessments and the experience we have had using think-aloud interviews in two introductory-level statistics courses.

One of the interfaces that SAS® University Edition includes is the popular JupyterLab interface. You can use this open-source interface to generate dynamic notebooks that easily incorporate SAS® code and results into documents such as course materials and analytical reports. The ability to seamlessly interweave code, results, narrative text, and mathematical formulas all into one document provides students with practical experience in creating reports and effectively communicating results. In addition, the use of an executable document facilitates collaboration and promotes reproducible research and analyses. After a brief overview of SAS University Edition, this paper describes JupyterLab, discusses examples of using it to learn data science with SAS, and provides tips. SAS University Edition, which is available at no charge to educators and learners for academic, noncommercial use, includes SAS® Studio, Base SAS®, SAS/STAT®, and SAS/IML® software and some other analytical capabilities. Sponsored by SAS Global Academic Programs

In this talk, we will discuss why causal inference concepts align well with recommendations for introductory statistics courses and propose topics appropriate for such courses. In addition, we will highlight some resources for instructors interested in teaching causal inference, including a classroom activity we developed based on a popular dataset investigating the effects of youth smoking on lung function.

Rich visualizations of data not only helps the analyst with exploring hidden features in data, but is an essential tool in presenting and communicating results bringing the data to life. In this webinar, the presenter will show how the JMP Statistical Discovery Software is an excellent tool to use in the classroom to help students incorporate visualization into their analyses.Sponsored by JMP Academic Programs

This work introduces new assessment tools to measure learning outcomes of students in undergraduate statistics programs (e.g. majors) against the competencies recommended in the (2014) ASA Guidelines for Undergraduate Programs in Statistical Sciences. In short, these assessment tools seek to (1) measure student learning outcomes with respect to program objectives; (2) discover whether students are gaining additional relevant competencies not explicitly included in the program/major through extracurricular experiences; (3) facilitate comparisons across years and institutions to benefit continuous improvement of the program/major. This webinar presents uses and results after piloting with Senior/Capstone undergraduate statistics students shortly before graduation at four different institutions around the US.