In this article, we introduce functional data analysis (FDA), a set of statistical tools developed to study information on curves or functions. We review fundamentals of the methodology along with previous applications in other business disciplines to highlight the potential of FDA to managerial science. We provide details of the three most commonly used FDA techniques, including functional principal component analysis, functional regression, and functional clustering, and demonstrate each by investigating measures of firm financial performance from a panel data set of the 1,000 largest U.S. firms by revenues from 1992 to 2008. We compare results obtained from FDA with hierarchical linear modeling and conclude by outlining ideas for future micro- and macro-level organizational research incorporating this methodology.
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