One-factor-at-a-time method: Difference between revisions

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{{Short description|Method of designing experiments}}

The '''one-factor-at-a-time method,'''<ref>{{Cite journal|lastlast1=Razavi|firstfirst1=Saman|last2=Gupta|first2=Hoshin V.|date=2015|title=What do we mean by sensitivity analysis? The need for comprehensive characterization of "global" sensitivity in Earth and Environmental systems models|journal=Water Resources Research|language=en|volume=51|issue=5|pages=3070–3092|doi=10.1002/2014wr016527|bibcode=2015WRR....51.3070R |issn=0043-1397|doi-access=free}}</ref>, also known as '''one-variable-at-a-time'''<ref>{{cite journal |last1=Karmoker |first1=James Regun |last2=Hasan |first2=Ikramul |last3=Ahmed |first3=Nusrat |last4=Saifuddin |first4=Mohammad |last5=Reza |first5=Md. Selim |title=Development and Optimization of Acyclovir Loaded Mucoadhesive Microspheres by Box – Behnken Design |journal=Dhaka University Journal of Pharmaceutical Sciences |date=1 April 2019 |volume=18 |issue=1 |pages=1-12 |doi=10.3329/dujps.v18i1.41421 |url=https://www.banglajol.info/index.php/JPharma/article/view/41421 |accessdate=9 January 2020}}</ref>, '''OFAT''', '''OF@T''',

'''OFaaT''', '''OVAT''', '''OV@T''', '''OVaaT''', or '''monothetic analysis''' is a method of [[design of experiments|designing experiments]] involving the testing of factors, or causes, one at a time instead of multiple factors simultaneously.

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There exist cases where the mental effort required to conduct a complex multi-factor analysis exceeds the effort required to acquire extra data, in which case OFAT might make sense. Furthermore, some researchers have shown that OFAT can be more effective than [[Fractional factorial design|fractional factorials]] under certain conditions (number of runs is limited, primary goal is to attain improvements in the system, and experimental error is not large compared to factor effects, which must be additive and independent of each other).<ref name=" Friedman, M., and Savage, L. J. (1947), “Planning Experiments Seeking Maxima,” in Techniques of Statistical Analysis, eds. C. Eisenhart, M. W. Hastay, and W. A. Wallis, New York: McGraw-Hill, pp. 365-372.

">Friedman, M., and Savage, L. J. (1947), “Planning Experiments Seeking Maxima,” in Techniques of Statistical Analysis, eds. C. Eisenhart, M. W. Hastay, and W. A. Wallis, New York: McGraw-Hill, pp. 365-372.</ref><ref name= "Cuthbert Daniel">Daniel , C. (1973) ,“One-at-a-Time Plans,” Journal of the American Statistical Association 68, 353-360</ref>

==Disadvantages==

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#OFAT can miss optimal settings of factors.

Designed experiments remain nearly always preferred to OFAT with many types and methods available,<ref>See [[:Category: Experimental design]], at bottom.</ref> in addition to fractional factorials which, though usually requiring more runs than OFAT, do address the three concerns above.<ref name=Czitrom>[https://www.questia.com/googleScholar.qst?docId{{cite journal |last=Czitrom|first=Veronica|authorlink=5001888588 Veronica Czitrom (|year=1999) "|title=One-Factor-at-a-Time Versus Designed Experiments", |journal=American Statistician, |volume=53, |issue=2 |pages=126–131 |doi=10.]<2307/ref>2685731|jstor= 2685731}}<!--formerly http://www.amstat.org/publications/tas/czitrom.pdf--ref> One modern design over which OFAT has no advantage in number of runs is the [[Plackett-Burman design|Plackett-Burman]] which, by having all factors vary simultaneously (an important quality in experimental designs),<ref name=Czitrom /> gives generally [[Efficiency (statistics)|greater precision in effect estimation]].

==See also==