The massive volume of data that now covers a wide variety of human behaviors offers researchers in psychology an unprecedented opportunity to conduct innovative theory- and data-driven field research. This article is a practical guide to conducting big data research, covering data management, acquisition, processing, and analytics (including key supervised and unsupervised learning data mining methods). It is accompanied by walkthrough tutorials on data acquisition, text analysis with latent Dirichlet allocation topic modeling, and classification with support vector machines. Big data practitioners in academia, industry, and the community have built a comprehensive base of tools and knowledge that makes big data research accessible to researchers in a broad range of fields. However, big data research does require knowledge of software programming and a different analytical mindset. For those willing to acquire the requisite skills, innovative analyses of unexpected or previously untapped data sources can offer fresh ways to develop, test, and extend theories. When conducted with care and respect, big data research can become an essential complement to traditional research. (PsycINFO Database Record

When the human mind is free to roam, its subjective experience is characterized by a continuously evolving stream of thought. Although there is a technique that captures people's streams of free thought-free association-its utility for scientific research is undermined by two open questions: (a) How can streams of thought be quantified? (b) Do such streams predict psychological phenomena? We resolve the first issue-quantification-by presenting a new metric, "forward flow," that uses latent semantic analysis to capture the semantic evolution of thoughts over time (i.e., how much present thoughts diverge from past thoughts). We resolve the second issue-prediction-by examining whether forward flow predicts creativity in the lab and the real world. Our studies reveal that forward flow predicts creativity in college students (Study 1) and a representative sample of Americans (Study 2), even when controlling for intelligence. Studies also reveal that membership in real-world creative groups-performance majors (Study 3), professional actors (Study 4) and entrepreneurs (Study 5)-is predicted by forward flow, even when controlling for performance on divergent thinking tasks. Study 6 reveals that forward flow in celebrities' social media posts (i.e., on Twitter) predicts their creative achievement. In addition to creativity, forward flow may also help predict mental illness, emotional experience, leadership ability, adaptability, neural dynamics, group productivity, and cultural success. We present open-access online tools for assessing and visualizing forward flow for both illustrative and large-scale data analytic purposes.

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Duarte et al. are right to worry about political bias in social psychology but they underestimate the ease of correcting it. Both liberals and conservatives show partisan bias that often worsens with cognitive sophistication. More non-liberals in social psychology is unlikely to speed our convergence upon the truth, although it may broaden the questions we ask and the data we collect.