“Good science simply transforms good theory into sound operationalizations and then makes robust inferences through meticulous observations and analyses. This is the time to think about properly wielding the Big Data sword to transform organizational research into organizational science. Think Big.”
— Wenzel & Van Quaquebeke (2018)
I. Big Data Analytics
Scientific psychology aims to both explain and predict human behavior. While these goals are philosophically compatible, they are not always aligned in practice...
II. Statistical/Machine Learning Methods
Machine learning methods are generally categorized as supervised or unsupervised learning...
III. Key Concepts in Machine Learning
- Overfitting: When a model fits the training data too well and fails to generalize.
- Resampling Methods: Cross-validation, bootstrap sampling, etc.
- Regularization: Techniques like Lasso and Ridge to reduce model complexity.
IV. Learning Resources for Big Data in R
- ISLR: An Introduction to Statistical Learning
- R for Data Science
- Text Mining with R
- The Elements of Statistical Learning
V. Real-World Examples
- Predicting job performance and turnover (Sajjadiani et al., 2019)
- Leadership effectiveness using ML (Spisak et al., 2019)
- Predicting replicability of experiments (Altmejd et al., 2019)
- Facebook Likes & personality prediction (Youyou et al., 2015)
- Gamified hiring assessments (Arctic Shores, 2019)
- Network analysis (Menezes et al., 2019)
- Text mining in management (Kobayashi et al., 2018)
- Dynamic computational modeling (Weinhardt & Vancouver, 2012)
VI. Why Big Data in Psychology & Management?
- Integrates prediction with theory-driven explanation
- Uncovers deep behavioral patterns
- Enables theory development
- Introduces replicable statistical principles
- Reduces reliance on p-values
VII. Limitations of Big Data Analysis
- Doesn’t always account for nested or longitudinal data structures
- Less robust to noise and measurement error
- Lacks direct causal inference capacity
References
- Wenzel & Van Quaquebeke (2018). Organizational Research Methods, 21(3), 548–591
- Yarkoni & Westfall (2017). Perspectives on Psychological Science, 12(6), 1100–1122
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