AOM-CARMA January 5-8 Quantitative Short Courses

Advance Your Research Skills with CARMA's Live Online Short Courses!

Quantitative Researchers

CARMA Live Online Short Courses can help management faculty and students learn about the latest techniques for data collection and analysis. Our courses cover ESM and ML/NLP, along with the analysis of panel data and mediation and moderation. Our instructors have editorial, author, and teaching experience and can help you learn how to apply quantitative methods in management research.

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 the 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.

Choose from Our 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 courses spread across the Qualitative and Quantitative focus areas. Details on Quantitative courses are provided below.

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.

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.
  
  
• 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.
  
  
• 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.

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Larry Williams
Professor
Texas Tech University
Lubbock TX
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