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2017 Master Tutorials

The nine Master Tutorials are sponsored by the Society for Industrial and Organizational Psychology, Inc. (SIOP) and presented as part of the 32nd Annual Conference.

These sessions are designed to appeal to practitioners and academics at a post-graduate level. There is no additional cost to attend any Master Tutorial beyond the cost of basic conference registration. There are no known conflicts of interest or commercial support regarding these sessions and their presenters.


Each Master Tutorial session provides 1.5 continuing education credits for psychology purposes.

 SIOP is approved by the American Psychological Association to sponsor continuing education for psychologists. SIOP maintains responsibility for this program and its content.


Modern Methods for I-O Psychologists: An Interactive Tutorial in R  Thursday, April 27, 1:30pm - 2:50pm
What is Machine Learning? Foundations and Introduction to Useful Methods  Thursday, April 27, 3:30pm - 4:50pm
Automated Data Collection: An Introduction to Web Scraping with Python Friday, April 28, 11:30am - 12:50pm
Natural Language Processing and Text Mining for I-O Psychologists Saturday, April 29, 8:00am - 9:20am
Executive Succession: Potential to Perform or Perform to Potential? Saturday, April 29, 10:00am - 11:20am
Making Research Reproducible: Tutorial for Reproducible Research with R Markdown Saturday, April 29, 10:00am - 11:20am
R Shiny: Using Apps to Support I-O Research Saturday, April 29, 10:00am - 11:20am
Using New metaBUS Functions to Facilitate Systematic Reviews and Meta-Analyses Saturday, April 29, 3:00pm - 4:20pm
Data Visualization with R Saturday, April 29, 3:00pm - 4:20pm

Modern Methods for I-O Psychologists: An Interactive Tutorial in R

Thursday, April 27, 2017
1:30pm - 2:50pm
Northern Hemisphere E1

Presenters:

Allen Goebl, Korn Ferry Institute / University of Minnesota
Jeff Jones, Korn Ferry
Sarah Semmel, Twitter

Abstract:

Advances in statistics and I-O psychology have led to a variety of new techniques for designing hiring systems (e.g., Dominance Analysis, Lasso, Pareto Optimality). In this interactive
tutorial we will discuss several of these methods and show how they can be coded in R. All R materials will be made available on goo.gl/osusL7.

Full Description:

Advances in statistics and I-O psychology have led to a variety of new techniques for designing hiring systems (e.g., Dominance Analysis, Lasso, Pareto Optimality). Unfortunately, the ability to leverage these techniques often depends on specialized software/training. The goal of this interactive tutorial is to overcome this limitation by showing participants how to implement these techniques in R. The targeted audience members for this tutorial are intermediate R users who are interested in learning about methodology typically not taught in an intro statistics course. All R materials will be made available on goo.gl/osusL7.

Learning Objectives:

  • Demonstrate and evaluate the relative importance of predictors using several of the most prevalent statistical methods.
  • Recognize and critique the controversy surrounding the attribution of “importance” to predictors.
  • Implement and apply the most popular methods of regularized regression.
  • Assess the subgroup differences corresponding to a composite of predictors and the adverse impact associated with a selection system.

Presenter Biographies:

Allen Goebl is a Manager at the Korn Ferry Institute and a PhD student at the University of Minnesota. He specializes in developing software for analytics and research and is proficient in a variety of scripting languages including R, Javascript, and Python. Allen is the lead author and maintainer of the iopsych R package and has contributed to several other open source projects. His research focuses primarily on refining the statistical and psychometric methods used in employee selection.

Jeff Jones is a Senior Manager of Analytics at Korn Ferry where he specializes in psychometrics, analytics, and research. He is one of the core psychometricians leading efforts in the design of new tools and scoring algorithms. Moreover, as part of his role, he uses his computational skills to develop applications that are used in demand generation, talent analytics, dashboard design, and automation. He received his Ph.D. at the University of Minnesota in Psychometrics and Quantitative Psychology.

Sarah Semmel is a People Scientist at Twitter where she works on a variety of projects focusing on areas such as employee engagement, performance management, and competency modeling. She is finishing up her Ph.D. at University of Minnesota in Industrial/Organizational psychology with a focus on Quantitative psychology. She has previously worked as a contractor on Facebook’s People Analytics team and as an intern for PDRI and Amazon. Sarah spends most of her time working with data and is passionate about using new and novel methods to provide insights into how organizations and employees function.


What is Machine Learning? Foundations and Introduction to Useful Methods

Thursday, April 27, 2017
3:30pm - 4:50pm
Asia 5

Presenters:

Sarah Semmel, Twitter
Jeff Jones, Korn Ferry
Allen Goebl, Korn Ferry Institute / University of Minnesota

Abstract:

Machine learning is a set of analytic tools that enable us to derive meaningful insight from big data. This tutorial will provide an introduction to the fundamentals of machine learning and specific useful methods for those who are interested in becoming more involved with machine learning.

Full Description:

As big data continues to be a popular theme in I-O and the press, it is important to delve into the analytic tools that enable us to derive meaningful insights from big data. Machine learning is a collection of methods that can be applied to various types of datasets in order to produce predictions or derive insights about the structure of our data. This tutorial will provide an introduction to the fundamentals of machine learning for those who have a basic understanding of statistics and are interested in becoming more involved with these types of analyses.

Learning Objectives:

  • Describe the core vocabulary and concepts related to machine learning.
  • Explain and summarize the difference between supervised and unsupervised learning.
  • Identify and select what type of approach is appropriate for a given question and data structure.
  • Recognize the basic structures and purposes of select machine learning approaches including ridge regression and deep learning.

Presenter Biographies:

Sarah Semmel is a People Scientist at Twitter where she works on a variety of projects focusing on areas such as employee engagement, performance management, and competency modeling. She is finishing her Ph.D. at University of Minnesota in Industrial/Organizational psychology with a focus on Quantitative psychology. She has previously worked as a contractor on Facebook’s People Analytics team and as an intern for PDRI and Amazon. Sarah spends most of her time working with data and is passionate about using new and novel methods to provide insights into how organizations and employees function.

Jeff Jones is a Senior Manager of Analytics at Korn Ferry where he specializes in psychometrics, analytics, and research. He is one of the core psychometricians leading efforts in the design of new tools and scoring algorithms. Moreover, as part of his role, he uses his computational skills to develop applications that are used in demand generation, talent analytics, dashboard design, and automation. He received his Ph.D. at the University of Minnesota in Psychometrics and Quantitative Psychology.

Allen Goebl is a Manager at the Korn Ferry Institute and a PhD student at the University of Minnesota. He specializes in developing software for analytics and research and is proficient in a variety of scripting languages including R, Javascript, and Python. Allen is the lead author and maintainer of the iopsych R package and has contributed to several other open source projects. His research focuses primarily on refining the statistical and psychometric methods used in employee selection.


Automated Data Collection: An Introduction to Web Scraping with Python

Friday, April 28, 2017
11:30am - 12:50pm
Northern Hemisphere E3

Presenter:

Ivan Hernandez, DePaul University

Abstract:

This interactive session guides participants on how to collect data from the web using the python programming language. A 10-line process of web scraping is demonstrated, and this method is flexible enough to provide the foundation for participants to scrape data on their own, from a multitude of websites.

Full Description:

This tutorial offers an approachable, simple, yet powerful framework for collecting data from websites to help facilitate I-O research. Attendees gain insight into the python programming language, and how HTML is structured for a foundation of collecting data from a multitude of websites. Participants then gain hands-on experience using python and the BeautifulSoup library to scrape and parse job attribute data from the O*NET. Participants can use these skills to automate data collection, allowing data to be collected at scale and self-update.

Learning Objectives:

  • Describe python and Jupyter Notebooks
  • Understand HTML and how webpages are structured
  • Demonstrate how to automatically download websites and parse the data using python

Presenter Biography:

Ivan Hernandez is a Professional Lecturer in the Industrial Organizational Division of the Psychology Department at DePaul University. He completed his doctorate degree in Industrial Organizational Psychology at the University of Illinois in Urbana-Champaign, where his research focused on computational social science applied to organizational behavior including absenteeism and job satisfaction. Following graduate school, he was a collaborative post-doctoral researcher at Northwestern University and the Georgia Institute of Technology with the SONIC and DELTA laboratories where he researched group-level dynamics using computational approaches such as text mining, agent-based modeling, and data scraping. He also supervised the NASA data science internship program at Northwestern University. Ivan has also served as the instructor for the part time data science course at General Assembly in Chicago, IL, where he gave professional training on statistical and machine learning methodologies.


Natural Language Processing and Text Mining for I-O Psychologists

Saturday, April 29, 2017
8:00am - 9:20am
Asia 4

Presenters:

Allison B. Yost, CEB
Andrea Kropp, CEB
Cory Kind, CEB

Abstract:

Advances in Natural Language Processing (NLP) are unlocking novel workplace research opportunities and ushering in text-based analytical solutions. This session teaches essential text mining techniques and principles via three I-O-specific cases studies – employee surveys, personality detection and resume-based selection algorithms – and will include reproducible code in R.

Full Description:

It is hard to imagine a job that involves neither speaking nor writing, yet researchers studying workplace performance and attitudes seldom analyze the verbatim words that are exchanged between employees and customers. This is usually because analyzing natural language requires a specialized skillset and toolkit. The presenters in this session will teach natural language processing and text mining essentials using case studies from their own workplace language research. Tutorials such as this one which introduce organizational psychologists to new methods and demonstrate how those methods can be applied within their own field are essential to their professional development.

Learning Objectives:

  • Identify organizational research questions well-suited for text analysis and surface the available sources of words/text at an organization.
  • List the pros and cons of various pre-processing steps, such as speech-to-text conversion, translation to other languages, fuzzy matching or error correction.
  • Define the vocabulary associated with basic NLP transformations and give an example of each, (e.g., Tokenizing, Stemming, Unigrams and n-gram counting, & Part-of-speech tagging).
  • Define terms for more advanced text mining techniques and give an example of the type of analysis that would use each approach (e.g., Entity recognition, Topic classification, Sentiment classification, Pattern learning, & Relationship extraction).
  • Describe common open-source methods for conducting text analyses.
  • List educational resources to become more proficient at text analysis.
  • List questions that all vendors offering NLP-based solutions should be able to answer about how their service works.

Presenter Biographies:

Allison Yost is a Research Scientist in CEB’s Talent Management Labs where she leads research on Leadership and Employee Engagement. Prior to joining CEB, Allison worked in the Talent Management Analytics and Solutions group at Marriott International and as a Personnel Research Psychologist with the U.S. Office of Personnel Management. Allison earned her Ph.D. in Industrial-Organizational Psychology from the George Washington University and M.A. in Quantitative Psychology from James Madison University.

Andrea Kropp is a Senior Research Scientist at CEB in the Talent Management Labs division who oversees several exploratory research efforts aimed at extracting patterns and meaning from spoken and written language in the workplace. A self-taught data scientist, she has been involved with many of CEB’s R&D and product development efforts in human capital measurement, benchmarking and prediction since 2002. Andrea is also a successful entrepreneur, having founded and profitably sold a search engine marketing company that applied innovative analysis of online language patterns to attract traffic to client websites. Andrea holds a B.S. in Chemistry from the University of California at Irvine and an M.S. in Physical Chemistry from the University of Michigan.

Cory Kind is a Research Scientist in the Talent Management Labs division at CEB. Her work focuses on analyzing unstructured text data using R and Python. She has experience with a wide variety of text applications, including entity resolution, sentiment analysis, content tagging, topic modeling, and constituency trees, as well as both classical statistical modeling and data-driven machine learning approaches for prediction. Cory holds an A.B. History and Literature degree from Harvard University and a Master of Information and Data Science degree from the University of California at Berkeley.


Executive Succession: Potential to Perform or Perform to Potential?

Saturday, April 29, 2017
10:00am - 11:20am
Southern Hemisphere II

Presenter:

Thomas W. Mason, TWMason

Abstract:

This tutorial will offer an applied approach to judging potential for executive succession and suggestions for making senior talent reviews more engaging for line leaders, along with a discussion of how judgments of potential can tie directly to efficient and specific development planning. Audience discourse will be encouraged.

Full Description:

This tutorial takes an applied view of potential for executive succession and suggests specific ways attendees can design their own measures. We will challenge the conjecture that performance and potential are substantially different concepts, and take a hard look at the logic and usefulness of relying on either traditional predictors or performance to gauge executive potential. The session will include ideas for making talent reviews more engaging for line leaders, and a discussion of how judgments of potential can tie directly to efficient and specific development planning. The agenda includes plenty of time for audience discourse.

Learning Objectives:

  • Compare the utility of performance and potential measures for the executive succession process
  • Develop specific observable criteria for vetting, comparing and developing potential executive successors
  • Facilitate talent reviews in the language of line managers and their businesses
  • Plan development assignments based on opportunity to perform, not just opportunity to learn

Presenter Biography:

Thomas W. Mason (PhD, University of Tennessee, industrial and organizational psychology, statistics minor), a licensed psychologist, is President of TWMason, a consulting firm specializing in executive leadership, including succession, assessment, development, coaching and team effectiveness. Before founding TWMason in 2012, Tom worked for Jeanneret & Associates in Houston and PDI Ninth House in Houston and Chicago. His consulting experience covers the breadth of I-O psychology and all levels of organizations. He has worked with clients on five continents and has experience as general manager and regional executive. He has taught frequently in I-O programs as adjunct or visiting faculty. 


Making Research Reproducible: Tutorial for Reproducible Research with R Markdown

Saturday, April 29, 2017
10:00am - 11:20am
Asia 4

Presenters:

Boris Yanovsky, Facebook
Ryan Derickson, National Center for Organization Development
Katerine Osatuke, National Center for Organization Development

Abstract:

This interactive session will serve as a gentle introduction to creating collaborative, reproducible research using R Markdown. Participants will learn to build dynamic documents – embedded with outputs, code, and graphical visualizations – for sharing and communicating their analysis results with others.

Full Description:

Replication and reproducibility is the cornerstone of scientific inquiry and the need for this type of research process has never been greater. Concerns regarding issues such as replication, data fabrication, and ethical misconduct have recently come to surface in the media. One way we can address these challenges is by ensuring our research is transparent and our data and analyses are accessible. In this tutorial, participants will learn how to generate collaborative and reproducible research projects using the Markdown program in R. This open-source software allows users to elegantly document all data analysis steps alongside notes, summaries, and graphs. All materials, including code and reproducible examples, are available at http://github.com/yanovskyb/siop.

Learning Objectives:

  • Describe the basic features of R Markdown.
  • Utilize R Markdown for preparing data that is accessible, replicable, and reproducible.
  • Format research for publication with CSS.

Presenter Biographies:

Boris Yanovsky earned his Master's degree in Industrial-Organizational Psychology from Xavier University in Cincinnati, OH, in September 2009. He is currently pursuing a doctorate in Quantitative Research Methods from the University of Cincinnati. As a researcher with the Department of Veterans (VA) National Center for Organization Development (NCOD), some of the work in which Mr. Yanovsky has recently been involved include instrument development, validation, and reporting, in addition to providing analytic support and data products to help inform decision making. Mr. Yanovsky has interests in organizational research and assessment, which includes work in such areas as psychometrics and measurement, leadership development, survey design, and program evaluation.

Ryan Derickson is a PhD candidate in quantitative research methods at the University of Cincinnati, and a researcher at the National Center for Organization Development. His background is Industrial-Organizational Psychology (MA from Xavier University). His professional interest is helping leaders use data to make better decisions and create healthier organizations. To that end, his current work focuses on novel IRT models for response bias, tree-based machine learning, and data visualization. 

Dr. Katerine Osatuke earned her doctorate in psychology from Miami University in Oxford, Ohio. As part of her doctoral requirements, she completed a clinical internship at the Cincinnati VA Medical Center in Cincinnati, OH and several clinical training rotations in medical and community settings. Dr. Osatuke is a licensed clinical psychologist. She has interests and research background in models of psychological change, including how change is defined, empirically measured, and tracked across time. She co-authored 53 publications and over 13 dozen presentations at national and international conferences on various aspects of clinical and organizational change. Dr. Osatuke is currently a Health Scientist and Research Supervisor for the VHA National Center for Organization Development. She provides data analytic support to a variety of VHA management initiatives and organizational assessments and interventions at VHA facilities nationwide.   


R Shiny: Using Apps to Support I-O Research

Saturday, April 29, 2017
10:00am - 11:20am
Northern Hemisphere A3

Presenters:

Samantha Holland, DCI Consulting Group, Inc.
Jennifer Green, George Mason University
Hannah M. Markell, George Mason University
Frank Bosco, Virginia Commonwealth University

Abstract:

Even researchers just beginning to use the R statistics platform can make simple web-ready Shiny apps that make their research and results more accessible to colleagues and lay people alike. Attendees will be exposed to motivating examples of Shiny apps and learn the basic concepts behind application development.

Full Description:

Dashboards for data analytics and visualizations are increasingly common in organizations. They allow us to easily explore data, enhance reproducibility of research, and facilitate complex analyses. RStudio’s Shiny package is one enablement to make web-based applications for these purposes and more. The purpose of this session is to demonstrate that even researchers who are just beginning to use R can make simple web-ready Shiny apps that make their research and results more accessible to colleagues and lay people alike. Attendees will be exposed to motivating examples of Shiny apps and learn the basic concepts behind application design and deployment.

Learning Objectives:

  • Explain the purpose of R Shiny and its use in an I-O context.
  • Describe the basic structure and core building components of Shiny applications to facilitate design.
  • Utilize Shiny commands to augment existing R code in order to translate basic R script to a web-ready application.
  • Apply Shiny concepts to research projects, client work, and/or I-O problems.

Presenter Biographies:

Samantha Holland is a Consultant at DCI Consulting Group whose work centers primarily on employee selection validation and consulting on best practices. She received Ph.D. in Industrial-Organizational Psychology at George Mason University, where her core research areas included leadership perceptions, social network analysis, and research methods. More recently, Samantha has developed Shiny applications including a dashboard for exploring SIOP productivity and social networks as well as tools to facilitate calculation of rater agreement and data transformations.

Jennifer P. Green is a doctoral candidate in the Industrial-Organizational psychology program at George Mason University. Her research interests include statistical techniques, research methods, and the interaction of personality and situations. She has published in Organizational Research Methods and the Journal of Management. Recently, she created Shiny applications to deploy tools for researchers testing structural equation or confirmatory factor models.

Hannah Markell is a second year PhD student in the Industrial-Organizational Psychology at George Mason University. Her research interests include examining the intersection of work and family, and examining how statistics and research methods are used and reported in our field. Hannah recently led the development of an R Shiny calculator that can be used to calculate latent product factor loadings for a two-way interaction latent product indicator.

Frank Bosco (Ph.D., 2011, University of Memphis) is an Assistant Professor of Management at Virginia Commonwealth University’s School of Business. His research spans the areas of human resource management, organizational behavior, and organizational research methods. Dr. Bosco is especially interested in employee staffing (e.g., employee selection), cognitive ability testing, meta-analysis, big data, open science, and approaches for summarizing entire scientific literatures. Dr. Bosco is co-Founder of metaBUS.org, a cloud-based search and summary platform containing over one million findings related to I-O research.


Using New metaBUS Functions to Facilitate Systematic Reviews and Meta-Analyses

Saturday, April 29, 2017
3:00pm - 4:20pm
Asia 4

Presenters:

Jasmine Khosravi, metaBUS
Colin Lee, University of Calgary
Frank A. Bosco, Virginia Commonwealth University
Piers Steel, University of Calgary

Abstract:

The metaBUS platform provides web-based tools for finding, curating, synthesizing, and disseminating I-O research. We demonstrate an updated interface for facilitating meta-analyses drawing on a collection of over 1,000,000 correlations reported in 28 I-O journals from 1980-current. We engage attendees by demonstrating and providing access to the online platform (http://metabus.org/portal).

Full Description:

We demonstrate metaBUS tools for locating and synthesizing research findings. Locate tools reveal the entire collection of research with correlational data on the concept from over 1,000,000 correlations from 1980-current in 28 applied psychology journals. Users can examine any concept as it relates to all other concepts in the database, generate reference lists, and access links to full manuscripts and construct descriptions. Synthesis tools enable instant meta-analyses between combinations of over 4,000 concepts in a searchable taxonomy. We demonstrate how to conduct moderator analyses, customize concepts, enter additional data to an analysis, and store your work in your MyMetaBUS account.

Learning Objectives:

  • Describe the updated metaBUS database structure, protocols, and functionality
  • Explain the hierarchical taxonomy of constructs
  • Demonstrate how to conduct an instant meta-analysis using metaBUS, including moderator analyses, filters, exclude, reverse-code, etc.
  • Utilize metaBUS as a starting point to add data to generate systematic reviews, including literature search capabilities, building concepts, and creating work groups
  • Discuss limitations of the metaBUS approach

Presenter Biographies:

Dr. Jasmine Khosravi is a recent graduate from the doctoral program in I-O psychology at Bowling Green State University, and is currently the post-doc for metaBUS. While Jasmine does not have any specific research interests, she is passionate about the application of scientific research and bridging the pervasive “scientist-practitioner gap”. She holds a PhD in I-O Psychology from BGSU, an MA in I-O Psychology from University of West Florida, and a BA in Psychology from Butler University.

Dr. Colin Lee is a postdoctoral fellow at the Haskayne School of Business, University of Calgary, Canada. He conducts research on topics at the intersection of HR, OB, and Careers. He leverages recent developments in data extraction, normalization, and processing in order to provide insight into how people can be matched to work, and to develop tools that help with the dissemination of academic knowledge. He received his Ph.D. from the Rotterdam School of Management, Erasmus University, the Netherlands. He holds an MPhil in Business Research from the Erasmus University and an MSc and BSc degree in Interdisciplinary Social Science from Utrecht University, the Netherlands.

Dr. Piers Steel received his PhD from the University of Minnesota and is a professor in Organizational Behavior, the Distinguished Research Chair in Advanced Business Leadership at the Canadian Centre for Advanced Leadership in Business, and a fellow of both APS and SIOP. He received several awards for teaching, service and research, including the George A. Miller award from the APA, given to the more important research article in the last five years. He has received media coverage in venues ranging from the New York Times to the New Yorker and Scientific America.

Frank Bosco is an assistant professor of management at Virginia Commonwealth University and co-Founder of metaBUS.org. His research spans the areas of human resource management, organizational behavior, and organizational research methods. Frank is especially interested in employee staffing (e.g., employee selection), cognitive ability testing, meta-analysis, big data, open science, and approaches for summarizing entire scientific literatures. His research appears in outlets such as Journal of Applied Psychology, Journal of Management, Organizational Research Methods, Personnel Psychology, and Science. He holds a doctorate in business from the University of Memphis.


Data Visualization with R

Saturday, April 29, 2017
3:00pm - 4:20pm
Southern Hemisphere I

Presenters:

Adam Beatty, Human Resources Research Organization (HumRRO)
Jeff Jones, Korn Ferry
Alexander Schwall, Development Dimensions International, Inc.

Abstract:

The computer language R offers powerful methods to communicate research results. This session will offer a tutorial to prepare data, create publication-ready data visualizations, and to publish results on interactive websites. Bring your laptop (optional) for this interactive session and download session materials in advance.

Full Description:

Scientists and practitioners alike have to communicate their research for it to have an impact, and an increasingly sophisticated audience is requiring more polished and refined data visualizations. The statistical computer language R puts powerful tools into the hands of scientists and practitioners to effectively communicate with the consumers of their research. This session will give a hands-on and step-by-step tutorial to create publication-ready data visualizations and to publish results on interactive websites.

Learning Objectives:

  • Utilize R software and preparing the data
  • Describe how to explore and visualize data with ggplot2
  • Create web-based (browser-based) graphs and figures with shiny.

Presenter Biographies:

Adam Beatty is a Senior Scientist in HumRRO’s Assessment Research and Analysis (ARA) program and received his Ph.D. in Industrial/Organizational Psychology from the University of Minnesota. His technical activities include developing and validating selection measures, job analysis, and analyzing and managing data to support a wide range of projects and clients. He is viewed as an organizational resource on R at HumRRO, and has been using R for almost ten years.

Jeff Jones is a Senior Manager of Analytics at Korn Ferry where he specializes in psychometrics, analytics, and research. He is one of the core psychometricians who leads efforts in designing new tools and scoring algorithms. Moreover, as part of his role, he uses his computational skills to develop applications that are used in demand generation, talent analytics, dashboard design, and automation. He received his Ph.D. at the University of Minnesota in Psychometrics and Quantitative Psychology. 

Alexander Schwall is a Senior Consultant at Development Dimensions International. His main task is to develop new assessment and testing products. He also internally consults and advises consultants and sales associates. A central part of his roles is to automate data analysis using R, and to create dashboards and data visualizations to enrich products and increase client value. He received a Master’s Degree from the Technical University, Aachen, Germany and a Ph.D. in I/O Psychology from The Pennsylvania State University.


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