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DTSTART;TZID=America/New_York:20261006T090000
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SUMMARY:Concepts & Tools for Research Methods: Data Structures and Statistical Models
DESCRIPTION:Delivery: Live and virtual \n\n\n\nConnect the way research data is structured to the statistical models used to analyze it. Designed primarily for doctoral students building their methods foundation \n\n\n\nWhat to Expect\n\n\n\nData 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. \n\n\n\nPresenter\n\n\n\nDr. Paul Bliese\, University of South Carolina \n\n\n\nPaul D. Bliese is the Department Chair and Jeff B. Bates Chaired Professor in the Management Department at the Darla Moore School of Business at the University of South Carolina. He received a Ph.D. from Texas Tech University. After graduate school\, he worked for 22 years at the Walter Reed Army Institute of Research and retired as a Colonel. Throughout his career\, Dr. Bliese has led efforts to use statistical methods to answer complex organizational problems and advance theory and practice. He developed and maintains the multilevel package for R and has published in a variety of journals\, including the Academy of Management Journal\, the Journal of Applied Psychology\, the Journal of Management\, Organizational Research Methods\, and Organization Science. He served as an Associate Editor for the Journal of Applied Psychology from 2010 to 2017 and as the Editor in Chief for Organizational Research Methods from 2017 to 2021. In 2024\, he began a new role as the first Deputy Editor for Research Methods for the Academy of Management Journal. \n\n\n\nRegister for the tutorial series\n\n\n\nOne registration includes all four live tutorials\, their released resources\, and related program activities. \n\n\n\n\nLogin to register
URL:https://www.aom.org/event/data-structures-and-statistical-models/
CATEGORIES:Research Methods Events
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261008T130000
DTEND;TZID=America/New_York:20261008T160000
DTSTAMP:20260924T165811Z
CREATED:20260916T201131Z
LAST-MODIFIED:20260924T165811Z
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SUMMARY:Concepts & Tools for Research Methods: Programming Languages
DESCRIPTION:Delivery: Live and virtual \n\n\n\nSee how programming languages support transparent\, repeatable research workflows. Designed primarily for doctoral students building their methods foundation \n\n\n\nWhat to Expect\n\n\n\nThe 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. \n\n\n\nPresenter\n\n\n\nDr. Ivan Hernandez\, Virginia Tech \n\n\n\nDr. Ivan Hernandez is an Associate Professor and Area Head of Industrial-Organizational Psychology at Virginia Tech. He is currently an Associate Editor for the Journal of Applied Psychology and an editorial board member for Organizational Research Methods and Technology\, Mind\, and Behavior. His work has been published in leading research journals\, including Industrial-Organizational Psychology: Perspectives on Science and Practice\, Organizational Research Methods\, Psychological Methods\, Behavior Research Methods\, Personnel Psychology\, and the Journal of Applied Psychology. He served as team lead of the winning team in the 2023 SIOP Machine Learning Competition and co-organized the 2024\, 2025\, and 2026 SIOP Machine Learning Competitions. \n\n\n\nRegister for the tutorial series\n\n\n\nOne registration includes all four live tutorials\, their released resources\, and related program activities. \n\n\n\n\nLogin to register
URL:https://www.aom.org/event/programming-languages/
CATEGORIES:Research Methods Events
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DTSTART;TZID=America/New_York:20261012T090000
DTEND;TZID=America/New_York:20261012T120000
DTSTAMP:20260924T165752Z
CREATED:20260916T202755Z
LAST-MODIFIED:20260924T165752Z
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SUMMARY:Concepts & Tools for Research Methods: Qualitative Data Coding
DESCRIPTION:Delivery: Live and virtual \n\n\n\nBuild a practical foundation for organizing\, coding\, and interpreting qualitative material. Designed primarily for doctoral students building their methods foundation \n\n\n\nWhat to Expect\n\n\n\nIn 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. \n\n\n\nPresenter\n\n\n\nDr. Anne Smith\, University of Tennessee \n\n\n\nAnne Smith (BS\, University of Virginia; PhD\, MBA\, University of North Carolina) is the King & Judy Rogers Professor in Business\, Management & Entrepreneurship Department\, Haslam College of Business\, University of Tennessee. Her research addresses qualitative research practices as well as how organizations change and\, many times\, fail. Anne served as an Associate Editor at Academy of Management Journal (qualitative methods) and has continued to serve on the editorial board of Organization Research Methods (ORM) for over a decade\, with five years as an Associate Editor. She has been a guest co-editor for three ORM special feature topics. Anne also has co-edited Research Methodology in Strategy and Management Emerald book series (2019-2021). Her research has been published in journals such as Organization Science\, Journal of Applied Behavioral Science\, Organization Studies\, ORM\, Qualitative Research in Organizations and Management\, Journal of Management\, Entrepreneurship: Theory & Practice\, Journal of Business Research\, and Journal of Management Inquiry. \n\n\n\nRegister for the tutorial series\n\n\n\nOne registration includes all four live tutorials\, their released resources\, and related program activities. \n\n\n\n\nLogin to register
URL:https://www.aom.org/event/qualitative-data-coding/
CATEGORIES:Research Methods Events
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261014T130000
DTEND;TZID=America/New_York:20261014T160000
DTSTAMP:20260924T165745Z
CREATED:20260916T201955Z
LAST-MODIFIED:20260924T165745Z
UID:10002121-1791982800-1791993600@www.aom.org
SUMMARY:Concepts & Tools for Research Methods: Machine Learning
DESCRIPTION:Delivery: Live and virtual \n\n\n\nExplore where machine learning fits into contemporary organizational research. Designed primarily for doctoral students building their methods foundation \n\n\n\nWhat to Expect\n\n\n\nThis 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. \n\n\n\nPresenter\n\n\n\nDr. Louis Hickman\, Virginia Tech \n\n\n\nDr. Louis Hickman is an Assistant Professor of Management at Virginia Tech\, a Visiting Scholar at Amazon\, and a Senior Fellow at University of Pennsylvania’s Wharton People Analytics. He holds an M.S. in Computer and Information Technology\, specializing in natural language processing\, and a Ph.D. in Industrial-Organizational Psychology. His research focuses on applications and implications of machine learning\, natural language processing\, and artificial intelligence in selection\, assessment\, and training and development. His research won the Personnel Psychology best paper award and twice won the Society for Industrial-Organizational Psychology’s Jeanneret Award for Excellence in the Study of Individual or Group Assessment. \n\n\n\nRegister for the tutorial series\n\n\n\nOne registration includes all four live tutorials\, their released resources\, and related program activities. \n\n\n\n\nLogin to register
URL:https://www.aom.org/event/machine-learning/
CATEGORIES:Research Methods Events
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