Regularities in data from factorial experiments
Regularities in Data from Factorial
XIANG LI,1 NANDAN SUDARSANAM,2 AND DANIEL D. FREY1,2
Massachusetts Institute of Technology, 1Department of Mechanical Engineering; and 2Engineering Systems
Division, Cambridge, Massachusetts 02139
This paper was submitted as an invited paper resulting from the "Understanding Complex Systems"
conference held at the University of Illinois–Urbana Champaign, May 2005
Received May 3, 2005; revised March 4, 2006; accepted March 6, 2006
This article documents a meta-analysis of 113 data sets from published factorial experiments. The studyquantiﬁes regularities observed among factor effects and multifactor interactions. Such regularities are knownto be critical to efﬁcient planning and analysis of experiments and to robust design of engineering systems. Threepreviously observed properties are analyzed: effect sparsity, hierarchy, and heredity. A new regularity isintroduced and shown to be statistically signiﬁcant. It is shown that a preponderance of active two-factorinteraction effects are synergistic, meaning that when main effects are used to increase the system response, theinteraction provides an additional increase and that when main effects are used to decrease the response, theinteractions generally counteract the main effects. 2006 Wiley Periodicals, Inc. Complexity 11: 32– 45, 2006
Key Words: design of experiments; robust design; response surface methodology
nisms. The authors have carried out meta-analysis of 113
cover regularities arising in natural, artiﬁcial, and so-
science and engineering disciplines. The goal was to identify
cial systems and to identify their underlying mecha-
and quantify regularities in the experimental data regardingthe size of factor effects and interactions among factors.
These regularities appear to arise from the interplay of thephysical behavior of the systems and the knowledge of the
Corresponding author: Daniel D. Frey, Department of Me-
experimenters. Therefore our results should be interesting
chanical Engineering and Engineering Systems Division,
to a broad range of investigators in complex systems includ-
Massachusetts Institute of Technology, 77 Massachusetts Av-
ing engineers, statisticians, physicists, cognitive scientists,
enue, Cambridge, MA 02139 (e-mail: email@example.com)
and social scientists.
2006 Wiley Periodicals, Inc., Vol. 11, No. 5
This article is organized as follows: Section 2 presents the
experiment . In the 2k
design, the main effect of a factor
motivation for the study and provides some necessary back-
is computed by averaging of all the responses at each
ground in the Design of Experiments; Section 3 describes
level of that factor and taking the difference.
the research methodology; Section 4 gives an example of the
Interaction: The failure of a factor to produce the same
analysis using one of our data sets; Section 5 presents the
effect at different levels of another factor . An interac-
results of the meta-analysis; Section 6 presents an investi-
tion that can be modeled as arising from the joint effect of
gation of nonlinear transformation of the responses and its
two factors is called a two-factor interaction. Similarly,
inﬂuence on the regularities; and Section 7 presents con-
three-factor interactions and higher order interactions
clusions and suggestions for future research.
may be deﬁned.
2.2. Why Are Regularities in Experimental DataImportant?
2.1. What is Design of Experiments and Why Is It
Based on experience in planning and analyzing many ex-
periments, practitioners and researchers in DOE have iden-
Experimentation is an important activity in design of sys-
tiﬁed regularities in the interrelationships among factor ef-
tems. Most every existing engineering system was shaped by
fects and interactions. Such regularities are frequently used
a process of experimentation including preliminary investi-
to justify experimental design and analysis strategies .
gation of phenomena, subsystem prototyping, and system
This section reviews three regularities noted in the DOE
veriﬁcation tests. Major, complex systems typically require
literature describing their nature, origins, and inﬂuence on
thousands of experiments . Consequently, experimenta-
theory and practice. These regularities are effect sparsity,
tion is a signiﬁcant driver of development cost and time to
hierarchical ordering, and effect heredity.
market. There is pressure to drive down the resource re-
Effect sparsity refers to the observation that number of
quirements of experimentation, especially in commercially
relatively important effects in a factorial experiment is gen-
erally small . This is sometimes called the Pareto Principle
The mathematical and scientiﬁc discipline of Design of
in Experimental Design,
based on analogy with the obser-
Experiments (DOE) seeks to provide a theoretical basis for
vations of the 19th century economist Vilfredo Pareto, who
experimentation across many domains of inquiry. Com-
argued that, in all countries and times, the distribution of
monly articulated goals of DOE include: making scientiﬁc
income and wealth follows a logarithmic pattern resulting
investigation more effective and reliable ; efﬁcient pro-
in the concentration of resources in the hands of a small
cess and product optimization ; and improvement of
number of wealthy individuals.
system robustness to variable or uncertain ambient condi-
Effect sparsity appears to be a phenomenon character-
tions, internal degradation, manufacturing, or customer use
izing the knowledge of the experimenters more so than the
proﬁles [4 – 6]. The use of DOE in engineering appears to be
physical or logical behavior of the system under investiga-
rising as it is frequently disseminated through industry "Six
tion. Investigating an effect through experimentation re-
Sigma" programs, corporate training courses, and university
quires an allocation of resources—to resolve more effects
typically requires more experiments. Therefore, effect spar-
This article relies on several concepts and terms from
sity is in some sense an indication of wasted resources. If
DOE. To make the discussion clear to a broad audience of
the important factor effects could be identiﬁed during plan-
investigators in complex systems, the following deﬁnitions
ning, then those effects might be investigated exclusively,
resources might be saved, and only signiﬁcant effects wouldbe revealed in the analysis. But experimenters are not nor-
Response: An output of the system to be measured in an
mally able to do this. Effect sparsity is therefore usually
evident, but only after the experiment is complete and the
Factor: A variable that is controlled by the experimenter
data have been analyzed.
to determine its effect on the response.
Researchers in DOE have devised means by which the
Active factor: A factor that experiments reveal to have a
sparsity of effects principle can be exploited to seek efﬁcien-
signiﬁcant effect on the system response.
cies. Many experiments are designed to have projective
Level: The discrete values a factor may take in an exper-
properties so that when dimensions of the experimental
space are collapsed, the resulting experiment will have de-
Full factorial experiment: An experiment in which every
sired properties. For example, the fractional factorial 23⫺1
possible combination of factor levels is tested. In a system
design may be used to estimate the main effects of three
factors, each having two levels, the full factorial
, and C
. As Figure 1 illustrates, if any of the three
experiment is denoted as the 2k
dimensions associated with the factors is collapsed, the
Main effect: The individual effects of each factor in an
resulting design becomes a full factorial 22 experiment in
2006 Wiley Periodicals, Inc.
the remaining dimension . Latin Hypercube Sampling
has become popular for sampling computer simulations ofengineering systems, suggesting that its projective proper-ties provide substantial practical advantages for engineeringdesign. Although effect sparsity is widely accepted as auseful regularity, better quantiﬁcation seems to be needed.
Reliance on effect sparsity has led to strong claims aboutsingle array methods of robust design, but ﬁeld investiga-tion have shown that crossed arrays give better results .
Degrees of reliance on effect sparsity may be the root causeof some disagreements about methodology in robust de-sign.
Hierarchical ordering (sometimes referred to as simply
"hierarchy") is a term denoting the observation that maineffects tend to be larger on average than two-factor inter-actions, two-factor interactions tend to be larger on averagethan three-factor interactions, and so on . Effect hierar-chy is illustrated in Figure 2 for a system with four factors A
, and D
. Figure 2 illustrates a case in which hierarchy isnot strict—for example, that some interactions (such as thetwo-factor interaction AC
) are larger than some main effects(such as the main effect of B
The phenomenon of hierarchical ordering is partly due
to the range over which experimenters typically explorefactors. In the limit that experimenters explore smallchanges in factors and to the degree that systems exhibitcontinuity of responses and their derivatives, linear effectsof factors tend to dominate. Therefore, to the extent thathierarchical ordering is common in experimentation, it isdue to the fact that many experiments are conducted for thepurpose of minor reﬁnement rather than broad-scale explo-ration.
The phenomenon of hierarchical ordering is also partly
determined by the ability of experimenters to transform theinputs and outputs of the system to obtain a parsimoniousdescription of system behavior . For example, it is wellknown to aeronautical engineers that the lift and drag ofwings is more simply described as a function of wing areaand aspect ratio than by wing span and chord. Therefore,when conducting experiments to guide wing design, engi-
The projective property of the fractional factorial 23⫺1 design of an
neers are likely to use the product of span and chord (wing
area) and the ratio of span and chord (the aspect ratio) asthe independent variables. Therefore, one might say thatthe experimenters have performed a nonlinear transforma-
the two remaining factors. Projection, in effect, removes a
tion of input variables (span and chord) before conducting
factor from the experimental design once it is known to
the experiments. In addition, after conducting the experi-
have an insigniﬁcant effect on the response. Projective
ments, further transformations might be conducted on the
properties of fractional factorial experiments can enable an
response variable. In aeronautics, lift and drag are often
investigator to carry out a full factorial experiment in the
transformed into a nondimensional lift and drag coefﬁ-
few critical factors in a long list of factors without knowing
cients by dividing the measured force by dynamic pressure
a priori which of the many factors are the critical few.
and wing area. It is also common in statistical analysis of
Similarly, Latin Hypercube Sampling enables an experi-
data to apply transformations such as a logarithm as part of
menter to sample an n
-dimensional space so that, when n
exploration of the data. A key aspect of hierarchical ordering
1 dimensions collapse, the resulting sampling is uniform in
is its dependence on the perspective and knowledge of the
2006 Wiley Periodicals, Inc.
The hierarchy and heredity among main effects and interactions in a system with four factors A
, and D
experimenter as well as conventions in reporting data. It is
face methodology, high-resolution experiments (e.g., cen-
important in assessing regularities in published experimen-
tral composite designs) are frequently used with a small
tal data that we do not alter the data as it was presented in
number of factors only after screening and gradient-based
any ways that affect its hierarchical structure. Section 4 will
search bring the response into the neighborhood where
provide some exploration of this issue.
interactions among the active factors are likely. Effect he-
Effect hierarchy has a substantial effect on the resource
redity can also provide advantages in analyzing data from
requirements for experimentation. A full factorial 2k
experiments with complex aliasing patterns, enabling ex-
iment allows one to estimate every possible interaction in a
perimenters to identify likely interactions without resorting
system with k
two-level factors, but the resource require-
to high-resolution designs .
ments grow exponentially as the number of factors rises. A
The effect structures listed above have been identiﬁed
saturated, resolution III
fractional factorial design allows
through long experience by the DOE research community
one to estimate main effects in a system with k
and by practitioners who plan, conduct, and analyze exper-
factors with only k
⫹ 1 experiments, but the analysis may be
iments. The effect structures ﬁgure prominently in discus-
seriously compromised if there are large interaction effects
sion of DOE methods, including their theoretical underpin-
in the system. Better quantiﬁcation of effect hierarchy
nings and practical advice on their use. However, effect
seems to be needed to guide choice between these alterna-
structures have not been quantiﬁed by formal empirical
tives and the many other options for experimental planning.
methods. Further, there has been little effort to search for
For example, the degree to which systems exhibit hierarchy
other regularities that may exist in experimental data across
has been shown to strongly determine the effectiveness of
many domains. These gaps in the literature motivated the
robust design methodologies . If such decisions among
investigation described in the next sections.
robust design methods can be based on empirical studies,further efﬁciencies may be possible.
3. RESEARCH METHODOLOGY
Effect heredity (sometimes referred to as "inheritance")
The present study was performed using a set of 46 pub-
implies that, in order for an interaction to be signiﬁcant, at
lished engineering experiments that includes 113 responses
least one of its parent factors should be signiﬁcant . This
in all. A General Linear Model was used to estimate factor
regularity can strongly inﬂuence sequential, iterative ap-
effects in each data set and the Lenth method was used to
proaches to experimentation. For example, in response sur-
identify active effects. Then, across the set of 113 responses,
2006 Wiley Periodicals, Inc.
the model parameters and the relevant conditional proba-
linear combination of functions of the experimental factors.
bilities were analyzed. Details of the approach are given in
In DOE, the GLM often takes a form of a polynomial. If the
the following seven subsections.
experiment uses only two levels of each factor, then anappropriate model should include only selected polynomial
3.1. The Set of Experimental Data
terms resulting in the following equation:
We assembled a set of 46 full factorial 2k
experiments pub-lished in academic journals or textbooks [16 – 60]. The ex-
periments come from a variety of ﬁelds including biology,
兲 ⫽ ␤ ⫹ 冘 ␤ ⫹ 冘 冘 ␤
chemistry, materials, mechanical engineering, and manu-
2, . . , x n
facturing. The reason we used full factorial designs is thatwe did not want to assume
the existence of any given effect
structure in this investigation, we want to test
it and quan-
ijkx ix jx k
· · · ⫹ . (1)
it. Full factorial experiments allow all the interactions in
a system to be estimated. The reason that we used two-levelexperiments is that they are much more common in the
The term ␤ is a constant that represents the mean of the
literature than other full factorial experiments and we
response. The terms ␤ quantify the main effects of the
wanted a large sample size.
on the system response. The terms ␤ determine
Many of the 46 experiments contain several different
the two-factor interactions involving factors x
responses since a single set of treatments may affect many
ilarly, terms ␤
quantify the three-factor interactions. In
different observable variables. Our set of 46 experiments
two-level designs, the input variables are frequently normal-
includes 113 responses in all. Table 1 provides a complete
ized into coded levels of ⫺1 and ⫹1. Given this normaliza-
list of these responses. Table 2 summarizes some relevant
tion, the sizes of the coefﬁcients ␤ can be compared directly
facts about the overall set. For example, Table 2 reveals that
to assess the relative inﬂuence of the factor effects.
the vast majority of the experiments had either 3 or 4 fac-tors. The number of main effects and interactions are also
3.3. The Lenth Method for Effect Analysis
listed, but this is not based on analysis of the data, but only
An effect in an experiment is the observed inﬂuence of a
on the number of effects resolvable by the experimental
factor or combination of factors on a response. An effect is
design. It is notable that the data set includes 569 two-factor
sometimes said to be "active" if it is judged to be a signiﬁ-
interactions and only 383 three-factor interactions because
cant effect by one of various proposed statistical tests.
the 54 responses from 23 designs each contribute only one
Among the commonly used test for "active" effects are the
potential three-factor interaction. Note that the one re-
Normal Plot (or Half-Normal Plot) method , Box-Meyer
sponse from a 27 experiment contributes 35 potential three-
method , and the Lenth method . In this investigation,
factor interactions that represent about 9% of the potential
the Lenth method was selected because it is applicable to
three-factor interactions in the entire set.
unreplicated factorial experiments, because it is computa-
All the experimental data in this research were recorded
tionally simple, and because it can be automated without
in our database in the form they were originally reported in
applying many arbitrary assumptions. In the Lenth method,
the literature. No nonlinear transformations were per-
a plot is made of the numerical values of all effects and a
formed before entry into the database nor were nonlinear
threshold for separating active and inactive effects is calcu-
transformations conducted during the meta-analysis pre-
lated based on the standard error of effects. In the ﬁrst step,
sented in Section 5; therefore the regularities we report in
a parameter s
that section are regularities in data as they are presented by
experimenters. As it is widely known in the statistics com-munity, nonlinear transformation of the response can
1.5 ⫻ median
sometimes lead to more parsimonious models and reduceactive interactions. Therefore, to explore how nonlinear
where ␤ includes all estimated effects including main effects
transformations affect regularities, we conducted a fol-
and interactions ␤ , ␤ , . . , ␤ , . . Then the pseudo stan-
low-up study using the same methods, but performing the
dard error (PSE
) and margin of error of the effects are
analysis of the data after a log transform was applied (these
deﬁned to be, respectively,
results are in Section 6). This issue of transformation of datais also brieﬂy explored via an example in Section 4.
⫽ 1.5 ⫻ median
3.2. The General Linear Model
The General Linear Model (GLM) is frequently used in sta-tistics. The GLM represents the response of a system as a
Margin of Error ⫽ t
2006 Wiley Periodicals, Inc.
List of the Responses Subjected to Meta-analysis
Engineering System [Ref.]
Engineering System [Ref.]
Remediating aqueous heavy metals 
Finish turning 
Epitaxial layer growth 
Limestone effects 
Processing of incandescent lamps 
Init. setting time
Final setting time
Cr toxicity and L. nimor 
Wood sanding oper. 
Cherry removal rate
Glass ﬁber composites 
Maple removal rate
Pine removal rate
Cherry surface rough
Solvent extraction of cocaine 
Maple surface rough
Plasma spraying of ZrO2 
Oak surface rough
Pine surface rough
Grinding of silicon wafers 
Post-exp. bake in x-ray mask fab. 
Concrete mix hot clim. 
EDM of carbide composites 
Color-improved lamps 
Polymerization of microspheres 
Machinability study 
Diffusion welding 
Fine grinding 
Max grinding force
Max motor current
Ball burnishing of an ANSI 1045 
Grinding cycle time
Abrasive wear of Zi-Al alloy 
Leaching of manganese 
Surface morphology of ﬁlms 
Aqueous SO2 leaching 
Ident. of radionuclide 
Crystal growth 
Pilot plant ﬁltration rate 
Friction measurement machine 
Frict coeff val.
Chl and tetracycline 
Frict coeff ﬂuct.
Detonation spray process 
Erosion durability 
Antifungal antibiotic 
Antifungal antibio. act.
Production of surfactin 
Xylitol production 
Steam-exp. laser-printed paper 
Thermal fatigue of PWBs 
Hydrosilylation of polypropylene 
Wire EDM process 
Solid polymer electrolyte cells 
Simulation of earth moving sys. 
Fractionation of rapeseed lecithin 
Wet clutch pack 
Deter. of reinforced concrete 
*This experiment was not a full factorial design, but contained a full factorial design as a subset. Only the full factorial settings were used in themeta-analysis.
2006 Wiley Periodicals, Inc.
A Summary of the Set of 113 Responses and the Potential Effects Therein
is the 0.975th quantile of the t
4. Calculate the conﬁdence intervals (␣ ⫽ 0.05) for the
is the statistical degrees of freedom. Lenth  sug-
percentages of potential effects that are active. As some
gests that the degrees of freedom should be one third of the
of the active numbers of interactions are very small, we
total number of effects.
construct exact two-sided conﬁdence intervals based on
The margin of error for effects is deﬁned to provide
the binomial distribution.
approximately 95% conﬁdence. A more conservative mea-sure, the simultaneous margin of error (SME
) is also deﬁned
3.5. Method for Quantifying Hierarchy
To test and quantify effect hierarchy, we compared the sizeof main effects with that of two-factor interactions, and the
size of two-factor interactions with that of three-factor in-teractions. As the responses in different data sets are in
different units, we need to normalize them in order to makecomparisons. We choose to make an afﬁne transformation
共1 ⫹ 0.951/m
so that the minimum response and maximum response in
each experiment were each, respectively, 0 and 100. Thisnormalization was only required in our assessment of hier-
is the total number of effects. In the Lenth method,
archy and did not inﬂuence our assessment of other regu-
it is common to construct a bar graph showing all effects
larities discussed in this article. The following steps sum-
with reference lines at both the margin of error and at the
marize the procedure we used to assess hierarchy:
simultaneous margin of error. In this article, we needed toselect one consistent criterion of demarcation between ac-
1. Normalize the responses of each experiment by means of
tive and inactive effects. We judged it was more appropriate
an afﬁne transformation so that they all range over the
to use the margin of error as the criterion in study of full
same interval [0, 100].
factorial experiments and that the alternative simultaneous
2. For each experiment, estimate all the main effects and
margin of error criterion is more appropriate for screening
interactions as described in Section 3.2.
3. Use conventional statistical tools such as box-plots to
analyze the absolute values of the main effects, two-
3.4. Method for Quantifying Effect Sparsity
factor interactions, and three-factor interactions.
To quantify effect sparsity in the set of data, we used the
4. Calculate the ratio between main effects and two-factor
interactions, two-factor interactions and three-factor in-teractions.
1. For each experiment, estimate all the main effects and
interactions as described in Section 3.2.
2. Apply the Lenth method and label each effect as either
3.6. Method for Quantifying Heredity
active or inactive as described in Section 3.3.
To quantify heredity in the set of data, we analyzed proba-
3. Categorize the effects into main effects, two-factor inter-
bilities and conditional probabilities of effects being active.
actions, and three-factor interactions, etc. Calculate the
Following the deﬁnitions and terminology of Chipman et al.
percentage of active effects within each category.
, we deﬁne p
as the probability that a main effect is
2006 Wiley Periodicals, Inc.
active and deﬁne a set of conditional probabilities for two
is active兩neither A
is active兲 (7)
is active兩either A
is active兩both A
Extending the terminology of Chipman et al. , we
deﬁned conditional probabilities for three-factor interac-tions as follows:
is active兩none of A
are active兲 (10)
A wet clutch pack (adapted from Lloyd ).
is active兩one of A
is active兲 (11)
is active兩two of A
are active兲 (12)
4. Calculate the percentage of inactive two-factor interac-
tions that are synergistic and antisynergistic.
is active兩all of A
are active兲. (13)
5. Calculate 95% conﬁdence intervals for the synergistic
and antisynergistic percentages using the binomial dis-
On the basis of these deﬁnitions, we estimate the con-
ditional probabilities as the frequencies observed in our setof 113 responses and associated factor effects.
4. AN ILLUSTRATIVE EXAMPLE FOR A SINGLE DATASET
3.7. Method for Quantifying Asymmetric Synergistic
Before presenting the meta-analysis of the complete data-
base of 113 responses, it is helpful to observe how the
We use the term "asymmetric synergistic interaction struc-
method discussed in Section 3 reveals the effect structures
ture" (ASIS) to describe the degree to which the signs of
evident in a single data set. Lloyd  published a full
main effects provide information about the likely signs of
factorial (27) experiment regarding drag torque in disen-
interaction effects. Given the GLM described in Section 2.2,
gaged wet clutches. A wet clutch, such as the one depicted
a synergistic two-factor interaction will satisfy the inequality
in Figure 3, is a device designed to transmit torque from an
␤ ␤ ␤ ⬎ 0 and an antisynergistic two-factor interaction will
input shaft that is normally connected to a motor or engine
satisfy the inequality ␤ ␤ ␤ ⬍ 0. To evaluate the null hy-
to an output (which in Figure 3 is connected to the outer
pothesis that synergistic two-factor interactions and anti-
case). When a wet clutch pack is disengaged, it should
synergistic two-factor interactions are equally likely, we fol-
transmit no torque and thereby create no load on the motor.
lowed these steps:
In practice, wet clutch packs result in a nonzero drag torque
Step 1: For each response
resulting in power losses.
The study in  was conducted at Raybestos Manhattan
1. Estimate the main effects and interactions for each re-
Inc., a designer and manufacturer of clutches and clutch
sponse as described in Section 3.2.
materials. The experiment was designed to assess the inﬂu-
2. Label each two-factor interaction as either synergistic or
ence of various factors on power loss and was likely a part of
antisynergistic according to our deﬁnition.
a long-term effort to make improvements in the design ofclutches. The factors in the study were oil ﬂow ( A
Step 2: Carry out statistics on the set of 113 responses.
), spacer plate ﬂatness (C
), friction materialgrooving (D
), oil viscosity (E
), friction material (F
1. Calculate the percentage of all two-factor interactions
rotation speed (G
). Most of these factors are normally under
that are synergistic and antisynergistic.
the control of the designer; however, some of these variables
2. Use the Lenth method to discriminate between active
such as oil viscosity might vary substantially during opera-
effects and inactive effects.
tion and therefore were probably included in the study to
3. Calculate the percentage of active two-factor interactions
assess there inﬂuence as noise factors. However, for the
that are synergistic and antisynergistic.
purpose of the experiment, it must have been the case that
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The Main Effects from the Clutch Case Study
The Main Effects from the Clutch Case Study Using a log Transform
Drag Torque (ft lbs)
all these factors were brought under the control of theexperimenter to a substantial degree. Each factor was varied
strongly a function of the number of factors involved.
between two levels and the drag torque was measured as
Among main effects, 5 of 7 are active. Among two-factor
the response. The complete results of the full factorial ex-
interactions, 9 of 21 are active. Among three-factor inter-
periment are too lengthy to present here, but the main
actions, only 7 of 35 are active.
effects and active two-factor interactions as determined by
Effect inheritance is strongly indicated. The four largest
the Lenth method are presented in Tables 3 and 4. This is
two-factor interactions involved two factors both with
slightly different from Lloyd's analysis in the original article
active main effects. Of the remaining ﬁve two-factor in-
because there he simply assumed effects of order 4 or higher
teractions, all involved at least one active main effect.
were all insigniﬁcant.
The hypothesized regularity, ASIS, was strongly evident.
Every major effect structure under investigation in this
Seven of nine active two-factor interactions meet the
study can be observed in this data set:
criterion because the sign of the interaction effect equalsthe sign of the product of the participating main effects.
Effect sparsity is strongly indicated in the sense that there
This example raises an important point about ASIS. Many
are 127 effects estimable within this experiment, but only
ﬁnd the regularity to be surprising because, in their ex-
21 were active, 5 main effects, 9 two-factor interactions,
perience, a response becomes increasingly difﬁcult to
and 7 higher order interactions. Effect sparsity is only
further improve as successive improvements are made.
weakly indicated by the main effects since 5 out of 7 were
ASIS is not necessarily inconsistent with this general
active in the study, but is strongly indicated among inter-
trend. In this example, to reduce drag torque, the main
actions since only 14 of 122 possible interactions were
effects suggest that both oil ﬂow ( A
) and grooving (D
should be set to coded levels of ⫺1. However, the signif-
Effect hierarchy is strongly indicated because the propor-
interaction would lead to far less reduction of
tion of potential effects that actually prove to be active is
drag torque than one would expect from the linear model.
In fact, the interactions will most likely determine thepreferred level of D
rather than the main effect.
The Active Two-Factor Interactions from the Clutch Case Study
Nonlinear transformations of responses can strongly af-
fect regularities in data. To illustrate this, we applied a log
Drag Torque (ft lbs)
transformation to the drag torque of the wet clutch packand repeated our analysis of the data. The main effects and
active two-factor interactions as determined by the Lenth
method are presented in Tables 5 and 6. For this particular
data set, the log transform failed to improve the hierarchical
ordering of the data. The number of active two-factor inter-
actions actually increased from 9 to 12. It is also important
to note that in the original data, the synergistic interactions
were more numerous, and in the transformed data the
synergistic and antisynergistic interactions are equally rep-resented. This motivated an effort to assess the inﬂuence of
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Figure 4 depicts a box plot of the absolute values of factor
effects for each of three categories: main effects, two-factorinteractions, and three-factor interactions. The median of
The Active Two-Factor Interactions from the Clutch Case Study Using
main effect strength is about four times larger than the median
strength of two-factor interactions. The median strength oftwo-factor interactions is more than two times larger than the
median strength of three-factor interactions. However, Figure
4 also reveals that many two- and three-factor interactions
were observed that were larger than the median main effect.
Again, the trends in this study support the principle of hierar-
chy, but suggest caution in its application.
Table 8 presents the conditional probabilities of observ-
ing active effects. This data strongly support the effect he-
redity principle. Whether the factors participating in an
interaction have active main effects strongly determines the
likelihood of an active interaction effect. It is noteworthy
that, under some conditions, a two-factor interaction is
about as likely to be active as a main effect. In addition, it isobserved that, under the right conditions, a three-factorinteraction can be fairly likely to be active, but still only half
transformations on ASIS through a second meta-analysis
as likely as a main effect.
reported in Section 6.
Table 9 presents the results of our investigation into
ASIS. First, it is noteworthy that about two-thirds of all
5. RESULTS OF META-ANALYSIS OF 133 DATA SETS
two-factor interaction are synergistic. The conﬁdence inter-
The methods described in Section 3 were applied to the set
vals for that percentage do not include 50%, so we can reject
of 113 responses from published experiments (Table 1).
the null hypothesis that the two percentages might be equal.
Some of the main results of this meta-analysis are summa-
Further, it is of practical signiﬁcance that the percentage of
rized in Table 7. The main effects were not very sparse, with
synergistic effects is much higher among active two-factor
more than one third of main effects classiﬁed as active.
interactions than among all two-factor interactions.
However, only about 7.4% of all possible two-factor inter-actions were active. The percentage drops steadily as the
6. ADDITIONAL INVESTIGATION OF THE LOG
number of factors participating in the interactions rise.
Thus, Table 7 tends to validate both the effect sparsity
The analysis in Section 5 is based on the data from experi-
principle (especially as applied to interactions) and also
ments as originally published without any nonlinear trans-
tends to validate the hierarchical ordering principle. How-
formations. However, response transformations are com-
ever, this study also supports a caution in applying effect
mon in analysis of experimental data. For background on
sparsity and hierarchy. For example, if about 2.2% of three-
good practice, see Wu and Hamada  who describe eight
factor interactions are active (as Table 7 indicates), then
commonly used transformations. One motivation for trans-
most experiments with seven factors will contain one or
forming data is variance stabilization. Another is generation
more active three-factor interactions.
of a more parsimonious model with fewer higher order
Percentage of Potential Effects in 113 Experiments That Were Active as Determined by the Lenth Method
No. of active effects
Percentage of effects that were active (%)
Conﬁdence intervals (␣ ⫽ 0.05) on the percentage
of effects that were active (%)
2006 Wiley Periodicals, Inc.
Box plot of absolute values for main effects, two-factor interactions, and three-factor interactions.
terms. To provide a rough sense of how such transforma-
cantly different from 50%. An analysis of two-factor inter-
tions affect the regularities reported here, we focused on
action synergies on the log transformed data can be found
just one commonly employed transformation, the loga-
in Table 10. Therefore, we conclude that the newly reported
rithm. Of the 107 data sets that could be subject to this
regularity of ASIS is a property of data as they are reported
transformation (those containing only positive response
by their experimenters (usually in physical dimensions) and
values), it was found that log transformation resulted in
is not generally persistent under nonlinear transformations
more parsimonious models for 13 responses (meaning that
of the reported data. ASIS is a function of the physical
the number of active effects were reduced), whereas theuntransformed data produced more parsimonious modelsin 28 cases. In the other 66 responses, the number of sig-
niﬁcant effects was unaffected by the use of this transfor-mation. In addition, we observed that in both the full set of
Synergistic and Antisynergistic Two-Factor Interactions in 113 Exper-
107 transformed responses and in the smaller set of 13 more
parsimonious transformed responses, the proportion ofsynergistic and antisynergistic responses was not signiﬁ-
All two-factor interactions
The Conditional Probabilities of Observing Active Effects Based Meta-
(␣ ⫽ 0.05) (%)
analysis of 113 Experiments
Active two-factor interactions
(␣ ⫽ 0.05) (%)
2006 Wiley Periodicals, Inc.
for the purpose of an extended study of system regularities and
analyzed using the methods described here. Such an effortwould be resource intensive, but it would guard against po-
Synergistic and Antisynergistic Interactions in 107 Experiments Whose
tential biases introduced by studying only those systems on
Responses Were Transformed Using a Logarithm
which full factorial experiments have already been conducted.
One major outcome of this work is validation and quan-
tiﬁcation of previously known regularities. All three regular-
ities commonly discussed in the DOE literature (effect spar-
transformed data sets
sity, hierarchy, and heredity) were conﬁrmed as statistically
signiﬁcant. However, many investigators will ﬁnd that, ac-
cording to this study, these regularities are not as strong as
they previously supposed. Although effect sparsity and hi-
C.I. (␣ ⫽ 0.05) (%)
erarchy are statistically signiﬁcant trends, exceptions to
these trends are not unlikely, especially given the large
number of opportunities for such exceptions in complex
systems. The data presented here suggest that a system with
four factors is more likely than not to contain a signiﬁcant
␣ ⫽ 0.05) (%)
13 data sets that
interaction given that 7.4%(4) ⫹ 2.2%(4) ⬎ 50%. The data
also suggest that a system with a dozen factors is likely to
contain around 10 active interactions with roughly equal
numbers of two-factor interactions and three-factor inter-
actions since 7.4%(12) ⬇ 2.2%(12) ⬇ 5. These observations
may be important in robust parameter design. It is known
that robust design relies on the existence of some two-factor
C.I. (␣ ⫽ 0.05) (%)
interactions for its effectiveness. However, some three-fac-
tor interactions may interfere with robust design, depend-
ing on which method is used. For example, ﬁeld compari-
sons of single array methods and crossed array methods
C.I. (␣ ⫽ 0.05) (%)
have revealed that crossed arrays are more effective. Thishas led to the conjecture that single arrays rely too stronglyon effect sparsity . The meta-analysis in this articlesuggests that the problem may be more closely related to
systems and whatever transformations experimenters actu-
effect hierarchy. Depending on the number of factors,
ally use before reporting the data, but may be altered by
three-factor interactions may be more numerous than two-
factor interactions. Any robust design method that relies onstrong assumptions of effect hierarchy is likely to give dis-
7. CONCLUSIONS AND FUTURE WORK
appointing results unless some effective steps are taken to
The results presented here must be interpreted carefully. It is
reduce the likelihood of these interactions through system
important to acknowledge the many inﬂuences on the data
design, response deﬁnition, or factor transformations.
that we subjected to meta-analysis. This investigation was
Another beneﬁt may arise from this study because it
entirely based on two-level full factorial experiments pub-
quantiﬁes effect heredity. Bayesian methods have been pro-
lished in journals and textbooks. Full factorial experiments are
posed for analyzing data from experiments with complex
most likely to be conducted for systems that have already been
aliasing patterns . These methods require prior proba-
investigated using less resource intensive means. For example,
bilities for the parameters given in Table 8 ( p , p , and so
it is common practice to use a screening experiment before
on). A hypothesis for future investigation is that using the
using a higher resolution design. A speciﬁc consequence is
results in Table 8 in concert with Bayesian methods will
that all the estimates of percentages of active effects in Table 2
provide more accurate system models than the same meth-
may be inﬂated. If the screening stage has ﬁltered out several
ods using previously published parameter estimates.
inactive factors, then the experiments with the remaining fac-
Another major outcome of this study is identiﬁcation
tors are more likely to exhibit active effects of all kinds. In order
and quantiﬁcation of ASIS—a strong regularity not previ-
to characterize the structure of a larger population of systems
ously identiﬁed in the literature. It was shown that about
on which experiments have been conducted, responses could
80% of active two-factor interactions are synergistic, mean-
be selected at random from many engineering domains, and
ing that ␤ ␤ ␤ ⬎ 0. The consequences of ASIS for engineer-
then full factorial experiments might be carried out speciﬁcally
ing design require further discussion. In cases wherein
2006 Wiley Periodicals, Inc.
larger responses are preferred, procedures that exploit main
effects are likely to enjoy additional increases due to active
The ﬁnancial support of the National Science Foundation
two-factor interactions even if those interactions have not
(award 0448972) and the support of the Ford/MIT Alliance are
been located or estimated. By contrast, in cases wherein
greatly appreciated. The comments of an anonymous reviewer
smaller responses are preferred, procedures that exploit
proved helpful in clarifying the presentation of this research.
main effects to reduce the response are likely to be penal-ized by increases due to active two-factor interactions. The
discussion of ASIS and its relationship to improvement ef-
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thedigest Primary Care Society for GastroenterologySample Article Cancer focus: Screening acceptability: more than the public can swallow?Pancreatic cancer: tracking a silent killer Beyond our scope:Endoscopy special Functional illness training - Treating constipation Patients deserve more In my early years in practice I looked after a young woman with primary liver
Circular 57 pinfish, eel, sea trout, tilapia, sturgeon, and striped bass (Inglis et al. 1993). Strep has also been Streptococcus is a genus of bacteria containing isolated from a variety of ornamental fish, including some species that cause serious diseases in a rainbow sharks, red-tailed black sharks, rosey number of different hosts. A major identifying