AOM-CARMA Affiliate Program Discount Jan 5-8 Live Online Short Courses:

Advance Your Research Skills with CARMA- Learn from the Best in Our Business

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. In solidarity with our academic community, we remain dedicated to expanding access to research methods training-especially during these challenging times. To support students, educators, and researchers, we are offering our lowest prices ever on CARMA's Live Online January Short Courses. Together, we stand united in advancing the methods of science and their resulting  truths.

Spots Open in Five Live Online Short Courses in January 2026!

CARMA's January Live Online Short Courses are designed to help you enhance your research skills with from leading management scholars. Choose from 5 courses spread across the Qualitative and Quantitative focus areas.

Unbeatable Pricing-Register Now!
AOM-CARMA Affiliate Program Members register now for just $300 through December 21; late registration pricing is $400 December 22 – January 2.

Take advantage of our lowest pricing model ever -review the course list below, find the best courses for you, and click here to register.

Qualitative:

·       Generative-AI for Qualitative Research (Dr. Christina Silver) - The CARMA Generative-AI for Qualitative Research course provides both a theoretical and practical understanding of the rapidly developing field of Qualitative-AI. The course begins by mapping the landscape of AI in relation to qualitative research processes, covering the principles, practices and ethics of using these technologies throughout the analytic workflow. This includes both traditional and generative-AI. A range of tools designed to facilitate qualitative research that harness AI in different ways are introduced and students have the opportunity to experiment with a selection of them, using sample data and their own research materials, if appropriate.

The emphasis of the course is to critically reflect on the potential role and appropriate use of AI-tools. Ethical issues are central, along with how to document the use of AI transparently, and best practices for integrating AI with human interpretation in qualitative studies. We also discuss the future of qualitative research in the generative-AI world, reflecting on the impact on methods of these technologies.

Students will leave the course with a clear understanding of the implications of employing AI in qualitative studies and with practical experience of several tools. The qualitative AI space is evolving quickly, so the tools focused on during this course are subject to change, depending on what is available at the time of the course, but will include tools from across the qualitative-AI space. Students will have free access to all the tools used for the purpose of the course, and will be provided access ahead of the first sessions.

·      Publishing Papers with Interview Data (Dr. Heather Vough) - In this course, we will focus on collecting, analyzing, writing, and reviewing papers using interview data. In the first section, we will tackle questions around who to choose as your informants, how to access informants, how to put together an interview protocol, and how to perform interviews. In the next section, we'll explore various ways of analyzing interview data. The third section of the course will emphasize the actual writing up of qualitative data, in other words, how to move from coded data to a written findings section. In the final section of the class, we will discuss reviewing qualitative work as well as common hurdles in the review process on the way to publication. Participants in this course should have at least an idea for an interview-based study in mind and would benefit from having initial data to work with over the course of the short course.

Quantitative:

• Advanced Regression Analysis for Mediation & Moderation (Dr. Justin DeSimone) - This short course features a deep dive into regression analysis with a particular focus on mediation and moderation analyses. The course will balance conceptual explanations, follow-along demonstrations, and discussions about best practices for conducting and interpreting various regression models. Participants should finish the short course with a better understanding of the conceptual and practical considerations involved in regression analysis, especially as related to mediation and moderation modeling. Topics covered include brief review of various forms of correlation, single and multiple regression, and model comparison techniques. This course will then focus on mediated regression, moderated regression, and moderated mediation. Additional advanced topics including response surface analysis and relative importance assessment will also be introduced. For all topics, examples will be discussed and follow-along assignments completed using data and syntax provided by the instructor. This short course uses both Excel and R for demonstrations of these techniques.
  
  
• Introduction to Experience Sampling Methods: Design & Measurement (Dr. Shawn McClean) - Join us for an enlightening workshop on "Experience Sampling: Design and Measurement," where we delve into the dynamic world of within-individual research methodologies. This comprehensive course will guide you through the theoretical underpinnings of experience sampling and related methodologies, offering a deep understanding of its significance in capturing real-time, real-world data. We'll explore the practicalities of study logistics, ensuring you're well-equipped to design and execute studies with precision and efficiency. Dive into the intricacies of survey design, learning how to craft questions that yield meaningful and reliable responses. Additionally, we'll cover effective survey administration techniques, focusing on timing, frequency, and response optimization to ensure high-quality data collection. Whether you're a faculty member or PhD student, with experience in this approach or interested in trying it out, this workshop will provide you with the essential tools and knowledge to excel in the field of within-individual research.
  
  
• Panel Analysis Macro Data (Dr. DJ Schepker) - This short course focuses on the concepts related to analyzing panel data (e.g. multiple, repeated observations on an entity over time). We will start by covering what panel data is, what makes panel data different, and how panel data is structured. After considering data structure and variation, we will cover multiple econometric models that can used to analyze panel data, such as econometric random effects and fixed effects models. We will explore when the use of these models is appropriate and their theoretical meaning. We will also explore specifications across multiple types of models and the use of the hybrid model. Finally, we will conclude with discussions around when the dependence in the data (such as time effects) may be a nuisance to be controlled for versus a variable with explanatory power. Throughout, we will explore how to conduct these models in statistical packages using a real world dataset that enables us to answer a variety of questions related to panel data.

View the Short Course Preview Playlist

Click here to view the full January 2026 Live Online Short Course Preview playlist on CARMA's YouTube Channel.

Dr. Larry Williams, CARMA Director

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Larry Williams
Professor
Texas Tech University
Lubbock TX
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