title: A practical guide to big data research in psychology.
abstract: 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
authors.affiliation: Department of Psychology and Social Behavior, University of California, Irvine.
authors.forename: Eric Evan
authors.initials: EE
authors.lastname: Chen
authors.affiliation: Department of Data and Analytics, Upworthy.
authors.forename: Sean P
authors.initials: SP
authors.lastname: Wojcik
mesh_terms: D057225:Data Mining; D066264:Datasets as Topic; D006801:Humans; D011584:Psychology; D012984:Software