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Workshop 7: Modern Analytics for Data Big and Small

Presenters:

Dan J. Putka, Human Resources Research Organization (HumRRO)
Richard N. Landers, Old Dominion University

Coordinator:

Christopher Rosett, Comcast

Abstract:

This workshop will provide an overview of three “hot” topics in modern analytics that have relevance to I-O psychologists and HR professionals. We’ll discuss developments in: (a) data acquisition and curation (e.g., web scraping), (b) unstructured text processing and analysis (e.g., natural language processing), and (c) predictive modeling (e.g., machine learning).

Full Description:

This workshop will provide an overview of three “hot” topics and in modern analytics that have relevance to I-O psychologists and HR professionals. We’ll begin with a brief overview of the evolution of analytic methods, particularly with respect to the needs of I-O practice. Next, we will review developments in: (a) data acquisition and curation (e.g., web scraping), (b) unstructured text processing and analysis (e.g., natural language processing), and (c) predictive modeling (e.g., machine learning). Within each topic, we’ll follow a common structure that addresses why the topic is important, how to make sense of the possibilities, and tips for realizing quick and significant analytic wins with the methods discussed. For each topic, we will provide a worked mini-example that highlights use of selected methods in the topic area and close the discussion of each topic with an overview of resources where attendees can learn more.

Intended Audience:

Intermediate and general audience at post-graduate level: This workshop is appropriate for I-O psychology and HR professionals who have had graduate-level coursework in statistics and research methods. We assume that attendees will have a basic working knowledge of factor analysis, traditional regression approaches to modeling, and the process of model fitting and evaluation. Familiarity with the R programming language will be helpful, but not essential.

Learning Objectives:

  • Identify situations where modern analytic methods offer advantages over traditional methods
  • Describe developments in data acquisition and curation that can benefit I-O and HR work
  • Identify a basic process for leveraging unstructured text in I-O and HR work
  • Describe and compare a framework for differentiating between predictive modeling methods
  • List key considerations when identifying predicting modeling approaches for a given situation

Presenter Biographies

Dan J. Putka is a Principal Staff Scientist at the Human Resources Research Organization in Alexandria, Virginia. Over the past 16 years, Dan has helped numerous organizations develop, evaluate, and implement assessments to enhance their hiring and promotion processes, and guide individuals to career and job opportunities that fit them well. Complementing his client-centered work, Dan has maintained an active presence in the Industrial-Organizational psychology scientific community, focusing on advancing psychometric and analytic methods that are sensitive to the demands of applied research and practice. Along these lines, he has delivered numerous presentations and invited workshops at national conferences, published a multitude of book chapters and articles in top-tier journals, and serves on the editorial board of multiple scientific journals. Dan is a past-president of the Personnel Testing Council of Metropolitan Washington, and a fellow of APA and three of its divisions to include the Society for Industrial and Organizational Psychology (SIOP; Division 14), APA’s Quantitative and Qualitative Methods Division (Division 5), and the Society for Military Psychology (Division 19). Dan currently serves on SIOP’s committee to revise the Principles for the Validation and Use of Personnel Selection Procedures, and recently served on the international task force to revise the Guidelines and Ethical Considerations for Assessment Center Operations. Dan holds a Ph.D. in I-O Psychology with a specialization in Quantitative Methods from Ohio University.

Richard N. Landers is an Associate Professor of Industrial-Organizational Psychology. He earned his Ph.D. in industrial and organizational (I-O) psychology from the University of Minnesota in 2009. His research program concerns meaningful intersection points between computer science and psychology in the areas of social scientific research methods and several specific areas of I-O Psychology (i.e., hiring and training/development). In terms of professional achievements, he has published in both domain outlets (e.g., Journal of Applied Psychology) and interdisciplinary outlets related to computer science (e.g., Computers in Human Behavior), in addition to serving as Associate Editor of Simulation & Gaming, Computers in Human Behavior, and for ACM SIGCHI. In addition to academic outlets, his research and writing has been featured in Forbes, Business Insider, Science News, Popular Science, Maclean’s, and the Chronicle of Higher Education, among others. In 2016, he was awarded a Certificate of Recognition by SIOP for his research on big data and in 2015 was his university’s nominee for the State Council of Higher Education in Virginia’s Outstanding Faculty Award in the “Rising Star” category.


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