- Data Science and R: http://datascience.tntlab.org/
- Web Scraping: http://rlanders.net/scrapy/
- Natural Language Processing: https://github.com/andreakropp/SIOP2017-NLPTutorial
- Reproducible Research Tools: https://github.com/eauer22/SIOP-2019-Master-Tutorial-Creating-Reproducible-and-Interactive-Analyses-with-JupyterLab-and-Binder-.git
Resources from SIOP's TIP (quick introductions created for an IO audience)
- A crash course in machine learning: http://www.siop.org/tip/jan17/crash.aspx
- A crash course in R: http://www.siop.org/tip/july16/crash.aspx
- A crash course in Block Chain: http://my.siop.org/tip/jan18/editor/ArtMID/13745/ArticleID/322/Crash-Course-in-I-O-Technology-A-Crash-Course-in-Blockchain
- A crash course in Web apps and R Shiny: http://my.siop.org/tip/jan18/editor/ArtMID/13745/ArticleID/235/Crash-Course-in-I-O-Technology-A-Crash-Course-in-Web-Applications-and-Shiny
- A crash course in webscraping and APIs: http://my.siop.org/tip/jan18/editor/ArtMID/13745/ArticleID/108/Crash-Course-in-I-O-Technology-A-Crash-Course-in-Web-Scraping-and-APIs
- A crash course on the internet: http://www.siop.org/tip/july17/crash.aspx
- A crash course in Natural Language Processing: http://www.siop.org/tip/april17/crash.aspx
- A crash course in Data Visualization: http://www.siop.org/tip/oct16/crash.aspx
Overview of Data Science:
Landers, R. N., Auer, E. M., Collmus, A. B., & Marin, S. (2019). Data science as a new foundation for insightful, reproducible, and trustworthy social science. In R. N. Landers (Ed.), Cambridge Handbook of Technology and Employee Behavior (pp. 761-789). New York, NY: Cambridge University Press.
Intro to Webscraping in Psychology:
Landers, R. N., Brusso, R. C., Cavanaugh, K. J., & Collmus, A. B. (2016). A primer on theory-driven web scraping: Automatic extraction of big data from the Internet for use in psychological research. Psychological methods, 21(4), 475.
Intro to Machine Learning and AI in IO:
Putka, D. J., Beatty, A. S., & Reeder, M. C. (2018). Modern prediction methods: New perspectives on a common problem. Organizational Research Methods, 21(3), 689-732.
Barney, M. (2019). The Reciprocal Roles of Artificial Intelligence and Industrial-Organizational Psychology. In R. Landers (Ed.), The Cambridge Handbook of Technology and Employee Behavior (Cambridge Handbooks in Psychology, pp. 38-56). Cambridge: Cambridge University Press. doi:10.1017/9781108649636.004
Translation of Machine Learning words into Human Language: http://neoacademic.com/2017/12/07/translating-machine-learning-terms-into-social-science-words/
Big Data in IO:
Landers, R. N., Fink, A. & Collmus, A. B. (in press). Using big data to enhance staffing: Vast untapped resources or tempting honeypot? In J. L. Farr & N. T. Tippins (Eds.), Handbook of Employee Selection. New York, NY: Routledge.
Tonidandel, S., King, E. B., & Cortina, J. M. (Eds.). (2015). Big data at work: The data science revolution and organizational psychology. Routledge.
Guzzo, R. A., Fink, A. A., King, E., Tonidandel, S., & Landis, R. S. (2015). Big data recommendations for industrial–organizational psychology. Industrial and Organizational Psychology, 8(4), 491-508.
Tonidandel, Scott, Eden B. King, and Jose M. Cortina. "Big data methods: Leveraging modern data analytic techniques to build organizational science." Organizational Research Methods 21, no. 3 (2018): 525-547.