Find the complete four-session schedule here, then return for session access, recordings, supporting resources, quizzes, and certificates as they are released.
All times are Eastern Time. Remaining presenter materials and access links will be added as they are approved.
Data Structures and Statistical Models
9:00 AM–12:00 PM ET
Data analyzed in organizational research frequently violates the basic regression assumption that errors are independent and unrelated to predictors. This tutorial uses examples and simple illustrations to show why this non-independence can be problematic in both cross-sectional and longitudinal data. The tutorial also gives a high-level overview of common ways to address non-independence, with a focus on how addressing non-independence can expand a researcher’s research methods repertoire and strengthen the theory-methods-data link.
Session access coming soon
Programming Languages
1:00 PM–4:00 PM ET
The session provides an introduction to the main languages of data analysis: Python, R, and SQL. Attendees will learn the similarities and differences between the languages, their respective strengths, and the types of tasks for which each is best suited. After introducing the languages, attendees will then get hands-on experience programming in each during an online lab session, in which participants apply the content from the lecture with organizationally relevant exercises. The session will close with a Q&A session and an opportunity to exchange ideas and insights between attendees.
Session access coming soon
Qualitative Data Coding
9:00 AM–12:00 PM ET
In this session, I will discuss techniques to help you analyze textual materials and then see patterns in your coding. In the first part of the session, I will discuss a coding approach that emerged from my own experiences. This approach is based on the metaphor of painting your textual materials. From this, I will discuss and show generic techniques from Computer Aided Qualitative Data Analysis software to help you see patterns in your data. We also will discuss, in brief, non-coding, advanced analytic techniques that can bridge between coding efforts toward theoretical insights for a qualitative research project. For the hands-on activity, we will analyze one interview together. We will practice coding this textual document using techniques identified above. Given that this interview is part of a large set of interviews, we will discuss some analysis ideas to make sense of similar coding efforts across other interviews. Finally, we will discuss other ways to analyze this interview (and other similar interviews) beyond coding.
Session access coming soon
Machine Learning
1:00 PM–4:00 PM ET
This tutorial introduces the fundamental concepts and methods of machine learning, with an emphasis on supervised learning. Participants will examine how machine learning models learn from data, how model complexity affects performance, and how regression-based methods support more advanced techniques such as neural networks. Through conceptual explanations and worked examples, the tutorial will build a practical foundation for understanding model selection, regularization, and prediction. Participants will apply these concepts to a dataset by fitting and comparing regularized regression models, evaluating their performance, and identifying signs of overfitting and underfitting.
Session access coming soon
KEEP LEARNING
Continue beyond the live sessions.
Use CARMA resources to revisit key ideas, test your understanding, and document your progress.
