AOM-CARMA June 2026 Live Online Short Courses:
Agentic Coding, Web Scraping, Data Viz, ML/NLP, Python, R
CARMA (Consortium for the Advancement of Research Methods & Analysis) is a non-profit academic center at Texas Tech University, proudly celebrating 26 years of providing top-tier research methods education. We are excited to continue our partnership with AOM through our Affiliate Program, which offers access to some of CARMA's many education resources. We want to let AOM members know that we have six great courses focusing on data technology: Agentic Coding, Web Scraping, Data Viz, ML/NLP, Python, and R. Details on these courses and 17 others are provided below.
Unbeatable Pricing-Register Now!
To support students, educators, and researchers, we are offering our lowest prices ever on CARMA's Live Online June Short Courses. AOM-CARMA Affiliate Program Members register now for just $300 through April 26; registration pricing will be $350 April 27 – May 10. Take advantage of our lowest pricing model ever -review the course list below, find the best courses for you, and register now!.
Choose from 23 Live Online Short Courses in June 2026 Offered Across Two Sessions!
CARMA's June Live Online Short Courses are built to strengthen your research skills alongside leading management scholars. You can choose from 23 courses offered across two sessions, spanning four focus areas: Data Technology, Advanced Methods & Analysis, Introductory Methods & Analysis, and Qualitative Methods. Session I runs June 1–4, followed by Session II on June 8–11.
View the Short Course Preview Playlist
View the full June 2026 Live Online Short Course Preview playlist on CARMA's YouTube Channel.
Data Technology Courses
Session I
Agentic Coding Tools for Researchers: A Practical Introduction (Dr. Justin Frake)
Agentic coding tools like Claude Code, Cursor, and Codex operate directly in your codebase, execute multi-step tasks, and maintain context across a project. Researchers can use them for ideation, downloading and cleaning data, running analyses, drafting papers, and many other tasks that make up the daily work of empirical research. This short course introduces these tools and shows how to integrate them into that work.
The course begins by explaining what makes agentic tools distinct from conversational AI and why that distinction matters for research workflows. Participants will then set up a working environment, including configuration files like CLAUDE.md and agents.md that shape how an agent behaves within a project.
From there, the course covers the broader ecosystem of plugins, MCPs, and skills that extend what agents can do. Participants will install pre-built plugins and skills relevant to empirical research, then learn to build custom skills that reflect their own analytic preferences, coding conventions, and writing voice.
The course also addresses how to work with agents responsibly. This includes breaking research tasks into structured plans, validating those plans before execution, and writing test scripts that verify agent output.
Participants will apply what they learn to research projects using real data and tasks drawn from empirical social science.
Python Tools for Management Research (Dr. Jason Kiley)
Researchers increasingly rely on Python not just for collecting and preparing data, but for the broader ecosystem of tools that make that work faster, more reproducible, and easier to share. This course focuses on that ecosystem: the modern tools that academic researchers and data scientists use every day, taught in a way that is accessible to participants with no prior Python or programming experience.
We will begin with the Python foundations needed to work confidently with the tools that follow. From there, we will dig into Polars, a modern and high-performance alternative to Pandas for working with tabular data, with particular strengths for larger and more complex datasets. We will also spend time on tools for keeping research projects reproducible, well-documented, and organized, including version control with Git and GitHub, reproducible computing environments, and modern project and environment management. We will wrap up with Quarto, a powerful tool for creating manuscripts, presentations, and other documents that embed live code, so that tables, figures, and results are generated directly from your data and update automatically. Notably, most of the tools and workflows we cover are applicable beyond Python, including to R-based research.
No prior knowledge of Python or programming is required; the course is designed to bring all participants to a common foundation before we dive into the tools that are its focus. Participants who have completed Introduction to Python for Research will find the Python basics segments a familiar refresher, while the focus on tools will make the overall course about 80 percent new content.
Web Scraping: Data Collection and Analysis (Dr. Richard Landers)
In this course, you will learn how to create novel datasets from information found for free on the internet using only R and your own computer. First, we'll walk through a brief introduction of data source theories, web architecture, web design, and analytic approaches for scraped data. Second, we will explore the collection of unstructured data by directly scraping web pages in several small hands-on projects. Third, we will explore the collection of structured data by learning how to send queries directly to service providers like Reddit and X via their APIs. Finally, we will walk through the various ethical and legal issues to be navigated whenever planning a web scraping project.
Session II
Data Visualization (Dr. Goran Kuljanin)
This CARMA short course provides a comprehensive introduction to reproducible data visualization with R and Positron. During this course, students will learn how to: (1) create analytical projects, (2) write analytical notebooks, (3) wrangle data for visualizations, (4) decide between different kinds of visualizations for specific analytical goals, (5) apply visualization principles to create insightful visualizations, (6) program data visualizations with ggplot2 and other R packages, (7) iterate on initial data visualizations, (8) visualize cross-sectional, time series, network, and geographic data, (9) develop static, animated, interactive, and reactive data visualizations, and (10) write user-defined functions to develop many data visualizations at once. Throughout the course, students engage in active learning by completing tasks associated with lectures to develop their data visualization knowledge and skills.
Machine Learning/Natural Language Processing (Dr. Louis Hickman)
Organizational and psychological research increasingly uses language data to measure variables and test hypotheses in novel ways. This revolution has been brought on by the availability of open source tools for analyzing language data (e.g., speech, emails, earnings call transcripts, social media content). We will use Python to equip students with skills and example code for using a variety of natural language processing (NLP) methods for converting text data to quantitative data, including traditional, count-based approaches to NLP (dictionaries, n-grams), word embeddings (e.g., word2vec), document embeddings (e.g., BERT), and large language models (LLMs; e.g., GPT, Llama). We will learn how to use these NLP approaches: to estimate similarity among different entities, build predictive models for measuring constructs, to fine tune document embedding models and LLMs, and how to use LLMs to measure variables without training data. Overall, students will come away with a variety of tools for applying NLP in organizational research, while also learning about a variety of papers that have used NLP in organizational research, including micro and macro research and ranging from industrial psychology and human resources topics (e.g., selection and assessment) to organizational behavior/psychology topics.
R Tools for Management Research (Dr. Scott Tonidandel)
This course will provide a gentle introduction to the R computing platform and the R-Studio interface. We will cover the basics of R such as importing and exporting data, understanding R data structures, and R packages. You will also learn strategies for data manipulation within R (compute, recode, selecting cases, etc.) and best practices for data management. We will work through examples of how to conduct basic statistical analyses in R (e.g. descriptive, correlation, regression). We will then progress to some intermediate topics such as graphing and plotting in R, table creation, and leveraging R workflows for research. Finally, we will explore user-defined functions in R and lay the groundwork for understanding how to perform more complex analyses presented in other CARMA short courses.
Other Session I Offerings: (Displayed as divided by content areas)
- Advanced Methods and Analysis
- Introductory Methods and Analysis
- Qualitative Methods
Other Session II Offerings: (Displayed as divided by content areas)
- Advanced Methods and Analysis
- Introductory Methods and Analysis
o Introduction to Meta-Analysis (Dr. Dana Joseph)
o Measurement Development, Evaluation, and Adaptation (Dr. Lisa Schurer Lambert)
Dr. Larry Williams, CARMA Director
------------------------------
Larry Williams
Professor
Texas Tech University
Lubbock TX
------------------------------