Abstract
Imprecise theories do not give enough guidelines for empirical analyses. A paradigmatic shift from linear to curvilinear relationships is necessary to advance management theories. Within the framework of the abductive generation of theories, the authors present a data exploratory technique for the identification of functional relationships between variables. Originating in medical research, the method uses fractional polynomials to test for alternative curvilinear relationships. It is a compromise between nonparametric curve fitting and conventional polynomials. The multivariable fractional polynomial (MFP) technique is a good tool for exploratory research when theoretical knowledge is nonspecific and thus very useful in phenomena discovery. The authors conduct simulations to demonstrate MFP’s performance in various scenarios. The technique’s major benefit is the uncovering of nontraditional shapes that cannot be modeled by logarithmic or quadratic functions. While MFP is not suitable for small samples, there does not seem to be a downside of overfitting the data as the fitted curves are very close to the true ones. The authors call for a routine application of the procedure in exploratory studies involving medium to large sample sizes.
| Original language | English |
|---|---|
| Pages (from-to) | 738-760 |
| Number of pages | 23 |
| Journal | Organizational Research Methods |
| Volume | 18 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Oct 11 2015 |
| Externally published | Yes |
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Keywords
- abductive method
- curvilinear relationships
- fractional polynomials
- non-monotonic curves
ASJC Scopus subject areas
- Management of Technology and Innovation
- Strategy and Management
- Decision Sciences(all)
Cite this
Exploring Curvilinearity Through Fractional Polynomials in Management Research. / Nikolaeva, Ralitza; Bhatnagar, Amit; Ghose, Sanjoy.
In: Organizational Research Methods, Vol. 18, No. 4, 11.10.2015, p. 738-760.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Exploring Curvilinearity Through Fractional Polynomials in Management Research
AU - Nikolaeva, Ralitza
AU - Bhatnagar, Amit
AU - Ghose, Sanjoy
PY - 2015/10/11
Y1 - 2015/10/11
N2 - Imprecise theories do not give enough guidelines for empirical analyses. A paradigmatic shift from linear to curvilinear relationships is necessary to advance management theories. Within the framework of the abductive generation of theories, the authors present a data exploratory technique for the identification of functional relationships between variables. Originating in medical research, the method uses fractional polynomials to test for alternative curvilinear relationships. It is a compromise between nonparametric curve fitting and conventional polynomials. The multivariable fractional polynomial (MFP) technique is a good tool for exploratory research when theoretical knowledge is nonspecific and thus very useful in phenomena discovery. The authors conduct simulations to demonstrate MFP’s performance in various scenarios. The technique’s major benefit is the uncovering of nontraditional shapes that cannot be modeled by logarithmic or quadratic functions. While MFP is not suitable for small samples, there does not seem to be a downside of overfitting the data as the fitted curves are very close to the true ones. The authors call for a routine application of the procedure in exploratory studies involving medium to large sample sizes.
AB - Imprecise theories do not give enough guidelines for empirical analyses. A paradigmatic shift from linear to curvilinear relationships is necessary to advance management theories. Within the framework of the abductive generation of theories, the authors present a data exploratory technique for the identification of functional relationships between variables. Originating in medical research, the method uses fractional polynomials to test for alternative curvilinear relationships. It is a compromise between nonparametric curve fitting and conventional polynomials. The multivariable fractional polynomial (MFP) technique is a good tool for exploratory research when theoretical knowledge is nonspecific and thus very useful in phenomena discovery. The authors conduct simulations to demonstrate MFP’s performance in various scenarios. The technique’s major benefit is the uncovering of nontraditional shapes that cannot be modeled by logarithmic or quadratic functions. While MFP is not suitable for small samples, there does not seem to be a downside of overfitting the data as the fitted curves are very close to the true ones. The authors call for a routine application of the procedure in exploratory studies involving medium to large sample sizes.
KW - abductive method
KW - curvilinear relationships
KW - fractional polynomials
KW - non-monotonic curves
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U2 - 10.1177/1094428115584006
DO - 10.1177/1094428115584006
M3 - Article
VL - 18
SP - 738
EP - 760
JO - Organizational Research Methods
JF - Organizational Research Methods
SN - 1094-4281
IS - 4
ER -