CARMA's January Short Courses: R, Python, web scraping, and machine learning
CARMA's January 2025 Live Online Short Courses are available for registration at a discounted rate for AOM members through our AOM-CARMA Affiliate Program. At CARMA, we believe that many who need/want to learn research methods prefer live instruction, where questions can be asked and answered in real time about issues and topics related to their research. And we recognize the importance of courses that feature a programming focus, such as those on R, Python, web scraping, and machine learning/predictive modeling.
CARMA's instructors are "The Best In Our Business" and are recognized within the organizational studies and management areas as leading methodological scholars. Our instructors include experts who are current or former Editors/Associate Editors for ORM, AMJ, JAP, and OBHDP. They also include several AOM-RMD Distinguished Career and Early Career award winners. Our courses provide opportunities to advance knowledge and skills, while networking with instructors and fellow participants.
We have four short courses on the topics mentioned scheduled for January 6-9, 2025 (10:00amET-3:00pmET, mon-thurs). Visit each course's web page (links below) and find the course description, instructor bio, and a short video preview by the instructor.
Through the AOM-CARMA Affiliate Program, faculty and students who register by December 16 enjoy the discounted pricing of $400 available to our AOM-CARMA Affiliate Members.
We appreciate your patience as we re-establish direct access to CARMA content following the AOM Member Portal update. While this process continues, we've created an alternative way for AOM members to register and receive their discount.
Review the course list below to find the one that best suits your needs, and once you've decided, click here to register.
· Quantitative Methods: Programming/Technology Focus
o Introduction to Python, Dr. Jason Kiley
§ Python is a general purpose programming language that includes a robust ecosystem of data science tools. These tools allow for fast, flexible, reusable, and reproducible data processes that make researchers more efficient and rigorous with existing study designs, while transparently scaling up to big data designs. This short course focuses on the foundational skills of identifying, collecting, and preparing data using Python. We will begin with an overview, emphasizing the specific skills that have a high return on investment for researchers. Then, we will walk through foundational Python skills for working with data. Using those skills, we will cover collecting data at scale using several techniques, including programmatic interfaces for obtaining data from WRDS, application programming interfaces (APIs) for a wide range of academic and popular data (e.g., The New York Times), web scraping for quantitative and text data, and computer-assisted manual data collections. From there, we will assemble and transform data to produce a ready-for-analysis dataset that is authoritatively documented in both code and comments, and which maintains those qualities through the variable additions, alternative measure construction, and robustness checks common to real projects. By the end of the course, you will have the skills-and many hands–on code examples-to conduct a rigorous and efficient pilot study, and to understand the work needed to scale it up. The course design does not assume any prior training, though reasonable spreadsheet skills and some familiarity with one of the commonly–used commercial statistical systems is helpful. In particular, no prior knowledge of Python is required, and we will cover a general introduction to Python in the beginning of the course content.
o Introduction to R and Data Analysis, Dr. Scott Tonidandel
§ This course will provide a gentle introduction to the R computing platform and the R-Studio interface. We will cover the basics of R such as importing and exporting data, understanding R data structures, and R packages. You will also learn strategies for data manipulation within R (compute, recode, selecting cases, etc.) and best practices for data management. We will work through examples of how to conduct basic statistical analyses in R (descriptive, correlation, regression, T-test, ANOVA) and graph those results. Finally, we will explore user-defined functions in R and lay the groundwork for understanding how to perform more complex analyses presented in other CARMA short courses.
o Machine Learning/Predictive Modeling, Dr. Louis Hickman
§ Organizational research often employs traditional statistical methods, such as linear regression, ANOVA, EFA, CFA, and SEM. However, these methods have limitations. They may not fully capture data complexity, overlook crucial relationships, or fail to optimally predict dependent variables. Additionally, traditional models can be overly complex, fitting well in the sample but failing to generalize to new, independent data due to potential chance capitalization. Machine learning algorithms address these limitations by avoiding underfitting (capturing complexity) and overfitting (cross-validating on new data). Predictive modeling forms, like random forests, LASSO regression, and neural networks, serve these purposes. These models complement traditional approaches in organizational research and may even replace them, especially when the number of variables exceeds the number of cases. This CARMA short course provides a hands-on experience using R and RStudio to analyze predictive models. If you're unfamiliar with R basics, we recommend taking CARMA's introductory R course. We'll use available datasets and pre-developed R code to discuss, run, and interpret various machine learning models. Time permitting, we'll explore methods to compare the performance of these models.
o Web Scraping, Dr. Richard Landers
§ In this course, you will learn how to create novel datasets from information found for free on the internet using only R and your own computer. First, we'll walk through a brief introduction of data source theories, web architecture, web design, and analytic approaches for scraped data. Second, we will explore the collection of unstructured data by directly scraping web pages in several small hands-on projects. Third, we will explore the collection of structured data by learning how to send queries directly to service providers like Reddit and X via their APIs. Finally, we will walk through the various ethical and legal issues to be navigated whenever planning a web scraping project.
CARMA is a non-profit academic center at Texas Tech University now in our 26th year of providing research methods education. For more information about CARMA, visit our website: carmattu.com. To ensure that you don't miss out on any upcoming CARMA events, add subscribe to our CARMA Calendar (subscribe button located at the bottom of the calendar).
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Larry Williams
Professor
Texas Tech University
Lubbock TX
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