ADVANCED TOPICS · 8 live virtual workshops
Advanced Topic Workshop Series
Eight advanced workshops combine expert instruction, applied work, selected CARMA recordings, discussion, and assessment across quantitative, qualitative, and computational methods.
Active AOM membership is required and will be validated when you sign in.WHAT YOU'LL GAIN
Practical outcomes for your research.
For doctoral students and early-career scholars looking to strengthen and broaden their methodological toolkit.
- Evaluate advanced quantitative, qualitative, and computational approaches for your research.
- Connect method choices to the assumptions, data, and decisions behind a study.
- Practice applying new ideas through lectures, panels, and hands-on activities.
- Extend live learning with selected recordings, discussion, and assessment.
HOW THE SERIES WORKS
Workshop details.
Preliminary schedule · 2026–2027. Additional presenter bios and session details are coming soon.
- In-depth lectures and expert panels
- Hands-on applied components
- Selected recordings, discussion, and assessment
Workshop sessions & presenters
Grounded Theory
In this session, we will explore many of the fundamental aspects of using a grounded theory approach for qualitative projects. We will cover topics such as iteration between data collection and analysis, purposeful and theoretical sampling, the constant comparative method, ensuring trustworthiness, theoretical saturation, and developing emergent theory. Participants will be given some data to analyze and will have the opportunity to discuss their analysis with other participants, keeping in mind topics addressed in the session.
Session access & resources →Multilevel Analysis
This lecture will discuss basic and advanced multilevel analyses, using random coefficient modeling (RCM) in R. The lecture will start by laying out a foundation for analyzing two-level models in RCM, where the dependent variable is at the lower level (Level 1), and predictors are at both the lower level (Level 1) and a higher-level unit (Level 2). The lecture will then apply RCM to multilevel longitudinal models involving growth, and consider more complex models, including 3-level models and models involving contextual influences emanating from multiple units simultaneously. Empirical examples will be provided, along with R codes and data to analyze and apply each of the RCM models discussed.
Session access & resources →Computational Modeling
This workshop introduces participants to computational modeling. Specifically, we discuss and demonstrate the use of computational models as a tool to develop more explicit, transparent, reproducible, and insightful theories for organizational science. To do so, we begin with a discussion to distinguish the difference between narrative construct theorizing (i.e., the dominant form of theory development in organizational science) and computational process theorizing (i.e., the form of theory development promoted in the workshop). After this initial discussion, we collectively work through a specific motivated example to demonstrate how to: program a computational model using software (R, Positron, and GenAI), execute virtual experiments with a developed computational model, and analyze simulated data from a computational model to derive theoretical insights. During this collective work experience, participants actively engage in completing various tasks pertaining to process thinking, programming, simulation, and data analysis. By the end of this workshop, participants will gain an understanding of the importance of computational modeling for effective theory development and how to take the next steps in developing computational models for their own theories of interest.
Session access & resources →Large Language Models
The use of large language models (LLMs) in research represents an extraordinarily broad range of options and opportunities, from consulting ChatGPT like a research assistant to the real-time delivery of LLM-generated content to research participants. Whatever the use case, the diverse new capabilities brought by LLMs bring an equally diverse new set of challenges and risks. In this lecture, we'll first explore what social scientists need to know about the technical foundations of LLMs to make good use of them. Next, we'll explore LLM use cases that can be executed with no or limited technical ability. Finally, we briefly explore more advanced uses of LLMs, including automated qualitative coding and use of LLM embeddings in natural language models. In the application activity, participants will work through the engineering steps needed to execute one of the use cases and describe the risks of the approach taken.
Session access & resources →Interpretive Content Analysis
This applied module engages researchers in the practice of doing advanced qualitative analysis. Focused on interpretive analysis, it focuses on practices of coding and analysis with particular attention paid to the advanced analytic processes that facilitate novel theorizing. Using a combination of learning techniques, including taught sessions and lab sessions (coding and analysis exercises), the module works to demystify the interpretive research process.
Session access & resources →Using AI to Enhance Your Research and Data Analysis
Full session details and preparation information are coming soon.
Session access & resources →Panel analysis
This workshop covers the primary methods used in analyzing panel data, a data structure in which the same units are observed repeatedly over time. We will explore why that structures creates both opportunities and challenges for statistical analysis. Participants will be introduced to pooled OLS, econometric fixed effects and random effects, and mixed effect models as means to analyze panel data, with particular attention paid to how each model treats stable differences across units and separates within-unit from between-unit variation. Participants will learn how to think about model choice using the underlying research question, assumptions about unobserved heterogeneity, and tools such as the Hausman test. The workshop will also emphasize interpretation of model coefficients in common panel models and common mistakes that arise when researchers analyze panel data. At the end, participants will be provided a real world data and sample code to analyze the data using the models discussed in the workshop. The goal will be to conduct analysis and interpret the results in line with the various tests performed and a hypothetical research question that could be explored with the data.
Session access & resources →Introduction to Causal Inference
Organizational researchers often seek to draw causal inferences from observational data. However, this is a challenging task, given that the relevant counterfactual outcomes are by definition unobservable. Observational settings can also involve confounding that further complicates causal inference. To address these issues, a number of methodological approaches have emerged, each with its own set of identifying assumptions. Some approaches adjust for observed differences between units, while others leverage temporal variation, discontinuities, instrumental variables, or other features of the research setting to identify causal effects. Despite the benefits they provide, these approaches support a causal interpretation only when their underlying assumptions hold. Although researchers can often evaluate the plausibility of these assumptions and test some of their observable implications, the identifying assumptions themselves generally cannot be established from the observed data alone. As such, applying these approaches requires careful consideration and evaluation. In this session, we focus on the assumptions that make causal inference possible and on the ways those same assumptions constrain it.
Session access & resources →Learning that supports your research
The member program is designed to help you apply the material, stay in conversation, and show what you learned.
Learn live
Expert-led virtual instruction with room for application, questions, and discussion.
Keep learning on demand
Live-session recordings and selected CARMA archive content let members revisit each topic on their schedule.
Practice together
Hands-on activities and a moderated discussion space connect concepts to members’ own research.
Mark progress
Assessments and completion certificates document participation in the member program.
BEFORE YOU REGISTER
One registration for the series.
Register once to follow the full series, then return to My Learning for your sessions and resources.
- Active AOM membership is required and will be validated during sign-in.
- Register once for the workshop series; individual workshop registration is not required.
- Workshops may be taken individually or followed as a complete advanced series.
- Full presenter bios, session descriptions, and preparation details are coming soon.