Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 693-721 |
| Number of pages | 29 |
| Journal | Organizational Research Methods |
| Volume | 15 |
| Issue number | 4 |
| DOIs | |
| State | Published - Dec 2012 |
| Externally published | Yes |
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Keywords
- firm performance
- functional clustering
- functional data analysis
- functional principal component analysis
- functional regression
ASJC Scopus subject areas
- Management of Technology and Innovation
- Strategy and Management
- Decision Sciences(all)
Cite this
Introducing Functional Data Analysis to Managerial Science. / Dass, Mayukh; Shropshire, Christine.
In: Organizational Research Methods, Vol. 15, No. 4, 12.2012, p. 693-721.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Introducing Functional Data Analysis to Managerial Science
AU - Dass, Mayukh
AU - Shropshire, Christine
PY - 2012/12
Y1 - 2012/12
N2 - 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.
AB - 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.
KW - firm performance
KW - functional clustering
KW - functional data analysis
KW - functional principal component analysis
KW - functional regression
UR - http://www.scopus.com/inward/record.url?scp=84873809207&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84873809207&partnerID=8YFLogxK
U2 - 10.1177/1094428112457830
DO - 10.1177/1094428112457830
M3 - Article
AN - SCOPUS:84873809207
VL - 15
SP - 693
EP - 721
JO - Organizational Research Methods
JF - Organizational Research Methods
SN - 1094-4281
IS - 4
ER -