This is where I’ll post information about recent or upcoming presentations.
1 Missing Data
I have been teaching about fundamental issues associated with missing data in research and program evaluation studies. This is a neglected topic in a lot of basic research design and statistical methods courses.
1.1 Upcoming CSTAT Webinar
The third iteration of this presentation will be a webinar designed for a broader audience of MSU graduate students, research staff, and faculty (Pierce, 2026, November 5). The repository will not be publicly available until shortly before the presentation. See below for available materials from related presentations I have already delivered.
This talk will discuss some fundamental concepts and issues related to missing data in research studies. It will cover what missing data are, why we end up with them, and why they matter (i.e., their scientific consequences). The seminar will describe different types of missing data and introduce a structured framework for addressing missing data issues, plus strategies for diagnosing the amount, nature, and patterns of missing data. Finally, we will discuss some ways to prevent missing data and options for obtaining valid, unbiased statistical results even you have missing data. Links to additional learning resources will be shared.
1.2 Recent CSTAT Seminar
The second iteration of this presentation was an in-person seminar (Pierce, 2026, July 20). This version was longer than the previous one due to expanded content. The audience consisted of a small group of Peruvian university faculty, so I experimented with using Google Gemini to translate the materials from English to Spanish. I would appreciate getting feedback from bilingual people about how well that translation actually worked. Both English and Spanish versions of the slides are available in my FMD2026 repository.
This talk will discuss some fundamental concepts and issues related to missing data in research studies. It will cover what missing data are, why we end up with them, and why they matter (i.e., their scientific consequences). The seminar will describe different types of missing data and introduce a structured framework for addressing missing data issues, plus strategies for diagnosing the amount, nature, and patterns of missing data. Finally, we will discuss some ways to prevent missing data and options for obtaining valid, unbiased statistical results even you have missing data. Links to additional learning resources will be shared.
1.3 MSU Program Evaluation Occasional Speaker Series
The first iteration of my missing data presentation (Pierce, 2024, December 5) was designed for students pursuing Master’s degrees in program evaluation. Slides are available in my FMDE2024 repository and a video recording is also available.
This talk will discuss some fundamental concepts and issues related to missing data in program evaluation contexts. We will cover why missing data matters plus types of missing data and how they affect statistical results. Then we will highlight how to describe the nature, scope, and patterns of missing data and how they relate to observed data. Finally, we will discuss some ways to prevent missing data and options for obtaining valid, unbiased statistical results even when there is missing data.
2 Reproducible Research
Another topic I have been teaching about is reproducible research. I have used the following presentations to articulate the principles and practices underlying my approach in the hope that other researchers will start exploring how to do more reproducible work themselves.
2.1 CSTAT Webinars
My fourth and most recent presentation on reproducible research (Pierce, 2025, October 2) had some expanded material compared to the third one (Pierce, 2025, March 6). The most recent slides and example files are available in my CSTAT.RR2025v2 repository. A video recording is also available.
This seminar will introduce the audience to a set of principles, practices, and free, open-source software tools that enable scientists to generate reproducible statistical analyses and reports. We will cover why reproducibility is important, then offer a vision of how to enhance the reproducibility of your work, with concrete steps you can take to achieve that goal. We will discuss tailoring the degree of reproducibility you aim to achieve for a given project, which may vary due to project context or constraints. In terms of software, we will describe how R, RStudio, Quarto, and TinyTex comprise a powerful suite of tools that uses dynamic documents to automate producing fully-formatted reports, manuscripts, or slides complete with narrative text, analysis results, figures, tables, and references. Git and GitHub.com add further value through support for version control and collaboration on the source code for dynamic documents. The session will include conceptual content, examples of dynamic documents, and links to supporting resources the audience can use to accelerate learning how to make their work more reproducible.
2.2 Ann Arbor R Users’ Group
The second iteration of my reproducible research presentation (Pierce, 2024, November 14) was delivered to an audience of R users. Slides and example files are available in my Pierce.AARUG2024 repository.
Abstract
This presentation will discuss the importance of reproducibility and illustrate how R and Quarto are foundational pieces of an integrated set of tools for generating fully-formatted, reproducible reports in a variety of output formats. We’ll cover some key principles and practices that enhance reproducibility.
2.3 American Evaluation Association 2024
The first iteration of my reproducible research presentation (Pierce, 2024, October 21-26) was delivered at a conference focused on program evaluation. Slides and example files are available in my Pierce.AEA2024 repository.
Fully reproducible statistical analyses are ones for which investigators have shared all the materials required to exactly recreate their findings so others can verify them or conduct alternative analyses. That requires sharing the original (usually de-identified) data, supporting documentation, and the software code used to analyze the data. While reproducibility has been described as an attainable minimum standard for trustworthy, credible scientific work; it is not yet well-embedded in evaluators’ professional training. This session will introduce the audience to a set of principles, practices, and free, open-source software tools that enable evaluators to efficiently generate reproducible statistical analyses and evaluation reports. We will cover why reproducibility is important in an evaluation context, then offer a vision of how to improve the reproducibility of your work and suggest concrete steps you can take to achieve that goal. We will discuss tailoring the degree of reproducibility you aim to achieve for a given project, which may vary due to project context or constraints. In terms of software, we will describe how R, RStudio, Quarto, and TinyTex comprise a powerful suite of tools that can generate dynamic documents containing a mix of narrative text along with R code that can be compiled to automate producing a fully-formatted report, manuscript, or set of slides complete with narrative text, analysis results, figures, tables, and references. Git and GitHub.com add further value through support for version control and collaboration on the source code for dynamic documents. The session will include conceptual content, examples of dynamic documents, and links to supporting resources the audience can use to accelerate learning how to make their work more reproducible.
3 Research Case Studies
3.1 MSU College of Osteopathic Medicine
I presented one of my projects to osteopathic medicine students as a research case study (Pierce et al., 2026, March 30). The objective was to describe a pilot study on the TEACH intervention for youth with childhood-onset lupus, based on a recent publication (Cunningham et al., 2025). The talk emphasized some features of the research design we used and methodology decisions we had to make, plus the results we obtained. I aimed to make the content accessible to students with limited prior training in research design. Slides are available in my OST597.TEACH repository.