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An Abductive Theory of Scientiﬁc Method
Brian D. Haig
University of Canterbury
A broad theory of scientiﬁc method is sketched that has particular relevance for the behavioral
sciences. This theory of method assembles a complex of speciﬁc strategies and methods that
are used in the detection of empirical phenomena and the subsequent construction of
explanatory theories. A characterization of the nature of phenomena is given, and the process
of their detection is brieﬂy described in terms of a multistage model of data analysis. The
construction of explanatory theories is shown to involve their generation through abductive,
or explanatory, reasoning, their development through analogical modeling, and their fuller
appraisal in terms of judgments of the best of competing explanations. The nature and limits
of this theory of method are discussed in the light of relevant developments in scientiﬁc
Keywords: scientiﬁc method, phenomena detection, theory construction, abductive reasoning,
[T]he attempt to understand and improve methods, and to do so
via theorizing them, is at the center of an intelligently evolving
cognition (Clifford Hooker, 1987, p. 291)
This article is concerned with scientiﬁc method in the
behavioral sciences. Its principal goal is to outline a broad
theory of scientiﬁc method by making use of selected de-
velopments in contemporary research methodology. The
time now seems right to intensify efforts to assemble knowl-
edge of research methods into larger units of understanding.
Currently, behavioral scientists use a plethora of speciﬁc
research methods and a number of different investigative
strategies when studying their domains of interest. Among
this diversity, the well-known inductive and hypothetico-
deductive accounts of scientiﬁc method have brought some
order to our investigative practices. The former method
speaks to the discovery of empirical generalizations,
whereas the latter method is used to test hypotheses and
theories in terms of their predictive success.
However, although inductive and hypothetico-deductive
methods are commonly regarded as the two main theories of
scientiﬁc method (Laudan, 1981; and, in fact, are sometimes
regarded as the principal claimants for the title of the
deﬁnitive scientiﬁc method), they are better thought of as
restrictive accounts of method that can be used to meet
speciﬁc research goals (Nickles, 1987), not broad accounts
of method that pursue a range of research goals. In fash-
ioning empirical generalizations, the inductive method un-
doubtedly addresses an important part of scientiﬁc inquiry.
However, it is a part only. Of equal importance is the
process of theory construction. Here, however, the hypo-
thetico-deductive method, with its focus on theory testing,
speaks only to one, although important, part of the theory
construction process (Simon, 1977).
The theory of method outlined in this article is a broader
account of scientiﬁc method than either the inductive or
hypothetico-deductive theories of method. This more com-
prehensive theory of method endeavors to describe system-
atically how one can ﬁrst discover empirical facts and then
construct theories to explain those facts. Although scientiﬁc
inquiry is often portrayed in hypothetico-deductive fashion
as an undertaking in which theories are ﬁrst constructed and
facts are then gathered in order to test those theories, this
should not be thought of as its natural order. In fact, scien-
tiﬁc research frequently proceeds the other way around. The
theory of method described here adopts this alternative,
facts-before-theory sequence, claiming that it is a search for
the understanding of empirical phenomena that gives ex-
planatory theory construction its point. With this theory of
method, phenomena exist to be explained rather than serve
as the objects of prediction in theory testing.
Two Theories of Method
Before presenting the proposed theory of scientiﬁc
method, the well-known inductive and hypothetico-deduc-
tive accounts of scientiﬁc method are brieﬂy considered.
I thank Fran Vertue, Tony Ward, and Claire O’Loughlin for
helpful comments on earlier versions of this article.
Correspondence concerning this article should be addressed to
Brian D. Haig, Department of Psychology, University of Canter-
bury, Private Bag 4800, Christchurch, New Zealand. E-mail:
2005, Vol. 10, No. 4, 371–388
Copyright 2005 by the American Psychological Association
1082-989X/05/$12.00 DOI: 10.1037/1082-989X.10.4.371
This serves to deﬁne their proper limits as methods of
science and, at the same time, provide useful contrasts to the
more comprehensive theory of method.
In popular accounts of inductive method (e.g., Chalmers,
1999), the scientist is typically portrayed as reasoning in-
ductively by enumeration from secure observation state-
ments about singular events to laws or theories in accor-
dance with some governing principle of inductive
reasoning. Sound inductive reasoning is held to create and
justify theories simultaneously, so that there is no need for
subsequent empirical testing. Some have criticized this view
of method for placing excessive trust in the powers of
observation and inductive generalization, and for believing
that enumerative induction is all there is to scientiﬁc infer-
ence. In modern behavioral science, the radical behaviorism
of B. F. Skinner is a prominent example of a research
tradition that uses an inductive conception of scientiﬁc
method (Sidman, 1960; Skinner, 1984). Within this behav-
iorist tradition, the purpose of research is to detect empirical
phenomena of learning that are subsequently systematized
by nonexplanatory theories.
Although the inductive method has received considerable
criticism, especially from those who seek to promote a
hypothetico-deductive conception of scientiﬁc inquiry, it
nevertheless stresses, in a broad-brush way, the scientiﬁc
importance of fashioning empirical generalizations. Shortly,
it is shown that the alternative theory of scientiﬁc method to
be presented uses the inductive method in the form of
enumerative induction, or induction by generalization, in
order to detect empirical phenomena.
For more than 150 years, hypothetico-deductivism has
been the method of choice in the natural sciences (Laudan,
1981), and it assumed hegemonic status in 20th century
psychology (Cattell, 1966). Psychology’s textbook presen-
tations of scientiﬁc method are often cast in hypothetico-
deductive form, and the heavy emphasis psychological re-
searchers have placed on testing hypotheses by using
traditional statistical signiﬁcance test procedures basically
conforms to a hypothetic-deductive structure.
The hypothetico-deductive method is standardly portrayed
in minimal terms: The researcher is required to take a hypoth-
esis or a theory and test it indirectly by deriving from it one or
more observational predictions. These predictions are amena-
ble to direct empirical test. If the predictions are borne out by
the data, then that result is taken as a conﬁrming instance of the
theory in question. If the predictions fail to square with the
data, then that fact counts as a disconﬁrming instance of the
theory. Although tacitly held by many scientists, and endorsed
in different ways by prominent philosophers of science (e.g.,
Hempel, 1966; Popper, 1959), the hypothetico-deductive ac-
count of method has been strongly criticized by both philoso-
phers and psychologists (e.g., Cattell, 1966; Glymour, 1980;
Rorer, 1991; Rozeboom, 1999).
The central criticism of the hypothetico-deductive method
is that it is conﬁrmationally lax. This laxity arises from the
fact that any positive conﬁrming instance of a hypothesis
obtained by the hypothetico-deductive method can conﬁrm
any hypothesis that is conjoined with the test hypothesis,
however plausible, or implausible, that conjunct might be.
This criticism has prompted some methodologists (e.g.,
Glymour, 1980; Rozeboom, 1999) to declare that the hypo-
thetico-deductive method is hopeless and should therefore
be abandoned. Although this is a fair assessment of the
conﬁrmational worth of the orthodox account of the hypo-
thetico-deductive method, it should be noted that the
method can be recast in a more sophisticated form and put
to useful effect in hypothesis testing research (Giere, 1983).
Although the hypothetico-deductive method does not ﬁgure
as a method of theory appraisal in the comprehensive theory
of scientiﬁc method presented here, it can play a legitimate
role in hypothesis and theory testing. It should thus be seen
as complementary to the broader theory of method, not a
rival to it. I comment brieﬂy on this matter toward the end
of the article.
The theory of method introduced in the next section is a
broader theory than both the inductive and hypothetico-
deductive theories. However, it should be acknowledged at
the outset that it has its own omissions. Most obviously, the
method begins by focusing on data analysis and thereby
ignores the matters of research design, measurement, and
data collection. This is a limit to its comprehensiveness that
it shares with the two theories of method just canvassed.
Overview of the Broad Theory
According to the broad theory of method, scientiﬁc in-
quiry proceeds as follows. Guided by evolving research
problems that comprise packages of empirical, conceptual,
and methodological constraints, sets of data are analyzed in
order to detect robust empirical regularities, or phenomena.
Once detected, these phenomena are explained by abduc-
tively inferring the existence of underlying causal mecha-
Here, abductive inference involves reasoning from
The term causal mechanism is ambiguous. In the broad theory
of method being proposed, the generation of theories involves
explanatory inference to claims about the existence of causal
entities. It is not until the development of these theories is under-
taken that the mechanisms responsible for the production of their
effects are identiﬁed and spelled out. Also, in this article it is
assumed that the productivity of causal mechanisms is distinct
from the regularities that they explain (Bogen, 2005; but cf.
Woodward, 2003). Of course, this does not preclude the method-
ological use of generalizations that describe natural regularities in
order to help identify the causal mechanisms that produce them.
phenomena, understood as presumed effects, to their theo-
retical explanation in terms of underlying causal mecha-
nisms. Upon positive judgments of the initial plausibility of
these explanatory theories, attempts are made to elaborate
on the nature of the causal mechanisms in question. This is
done by constructing plausible models of those mechanisms
by analogy with relevant ideas in domains that are well
understood. When the theories are well developed, they are
assessed against their rivals with respect to their explanatory
goodness. This assessment involves making judgments of
the best of competing explanations.
An important feature of the broad theory of scientiﬁc
method is its ability to serve as a framework within which
a variety of more speciﬁc research methods can be located,
conjoined, and used. Operating in this way, these otherwise
separate speciﬁc research methods can be viewed as sub-
methods of the parent method. In turn, the submethods
provide the parent theory with the operational bite that helps
it make scientiﬁc inquiry possible. Comprehensive methods
are often constituted by a number of submethods and strat-
egies that are ordered according to an overarching structure
(Ross, 1981). In characterizing the broad theory, I indicate
how a number of speciﬁc research methods are deployed
within its compass. Table 1 contains a variety of research
methods and strategies that can be placed within the struc-
ture of the comprehensive theory of scientiﬁc method. A
number of these are discussed in the exposition of the
method that follows, but most of them are not required for
The majority of submethods selected
for consideration in the article have been chosen primarily
to facilitate the exposition of the processes of phenomena
detection and theory construction without attempting to give
an essential characterization of these processes.
As a theory of scientiﬁc method, the account presented
here obviously endeavors to throw some light on the nature
of scientiﬁc inquiry. It also has some clear implications for
the way research is carried out within its purview. However,
partly because of its incomplete nature, the theory is not
accompanied by a set of instructions for its ready imple-
mentation. Such an accompaniment awaits a fuller account
of the method and would have to be modiﬁed as a function
of the nature of the submethods chosen to operate within it.
Because of the prominence of abductive reasoning in this
broad theory of method, I henceforth refer to it as the
abductive theory of method (ATOM). The exposition of the
method begins with an account of phenomena detection and
then considers the process of constructing explanatory the-
ories. Toward the end of the article, two pairs of important
methodological ideas that feature prominently in ATOM are
examined. The article concludes with a discussion of the
nature and limits of the method.
Scientists and philosophers often speak as though science
is principally concerned with establishing direct relation-
ships between observation and theory. There is empirical
evidence that psychologists speak, and sometimes think, in
this way (Clark & Paivio, 1989), whereas philosophers of
science of different persuasions often say that scientiﬁc
theories are evaluated with respect to statements about
Note, however, that the strategy of analogical modeling is
essential for theory development in the abductive theory of method
and that the theory of explanatory coherence does heavy-duty
work in the abductive theory of method because it is the best
developed method of inference to the best explanation currently
Submethods and Strategies of the Abductive Theory of Method
Theory generation Theory development Theory appraisal
Strategies Abductive methods Strategies Inference to the best
explanationControl for confounds Exploratory factor analysis Analogical modeling
Calibration of instruments Grounded theory method Theory of explanatory
coherenceData analytic strategies Heuristics (e.g., principle
Constructive replication of the common cause)
Initial data analysis
Exploratory data analysis (e.g., stem-and-leaf,
Computer-intensive resampling methods (e.g.,
bootstrap, jackknife, cross-validation)
Note. For the most part, particular methods and strategies subsumed by the abductive theory are appropriate either for phenomena detection or for theory
construction, but not for both. Exceptions include exploratory factor analysis and grounded theory method, both of which have data analytic components
that can contribute to phenomena detection.
THEORY OF SCIENTIFIC METHOD
relevant data (Bogen & Woodward, 1988). Despite what
they say, scientists frequently behave in accord with the
view that theories relate directly to claims about phenom-
ena, such as empirical generalizations, not data, while in
turn, claims about phenomena relate directly to claims about
data. That is, talk of a direct relationship between data and
theory is at variance with empirical research practice, which
often works with a threefold distinction between data, phe-
nomena, and theory.
As just noted, ATOM assigns major importance to the
task of detecting empirical phenomena, and it views the
completion of this task as a requirement for subsequent
theory construction. This section of the article discusses the
process of phenomena detection in psychological research.
First, the distinction between data and phenomena is drawn.
Then, a multistage model of data analysis is outlined. This
model serves to indicate one way in which a variety of
statistical methods available to psychologists can be com-
bined in phenomena detection.
The Nature of Phenomena
Bogen and Woodward (1988; Woodward, 1989, 2000)
have argued in detail that it is claims about phenomena, not
data, that theories typically seek to predict and explain and
that, in turn, it is the proper role of data to provide the
observational evidence for phenomena, not for theories.
Phenomena are relatively stable, recurrent, general features
of the world that, as researchers, we seek to explain. The
more striking of them are often called effects, and they are
sometimes named after their principal discoverer. The so-
called phenomenal laws of physics are paradigmatic cases
of claims about phenomena. By contrast, the so-called fun-
damental laws of physics explain the phenomenal laws
about the relevant phenomena. For example, the electron
theory of Lorentz is a fundamental law that explains Airy’s
phenomenological law of Faraday’s electro-optical effect
(Cartwright, 1983). Examples of the innumerable phenom-
ena claims in psychology include the matching law (the law
of effect), the Flynn effect of intergenerational gains in IQ,
and recency effects in human memory.
Although phenomena commonly take the form of empir-
ical regularities, they comprise a varied ontological bag that
includes objects, states, processes, events, and other features
that are hard to classify. Because of this variety, it is
generally more appropriate to characterize phenomena in
terms of their role in relation to explanation and prediction
(Bogen & Woodward, 1988). For example, the relevant
empirical generalizations in cognitive psychology might be
the objects of explanations in evolutionary psychology that
appeal to mechanisms of adaptation, and those mechanisms
might in turn serve as phenomena to be explained by ap-
pealing to the mechanisms of natural selection in evolution-
Phenomena are frequently taken as the proper objects of
scientiﬁc explanation because they are stable and general.
Among other things, systematic explanations require one to
show that the events to be explained result from the causal
factors appealed to in the explanation. They also serve to
unify the events to be explained. Because of their ephemeral
nature, data will not admit of systematic explanations.
In order to understand the process of phenomena detec-
tion, phenomena must be distinguished from data. Unlike
phenomena, data are idiosyncratic to particular investigative
contexts. Because data result from the interaction of a large
number of causal factors, they are not as stable and general
as phenomena, which are produced by a relatively small
number of causal factors. Data are ephemeral and pliable,
whereas phenomena are robust and stubborn. Phenomena
have a stability and repeatability that is demonstrated
through the use of different procedures that often engage
different kinds of data. Data are recordings or reports that
are perceptually accessible; they are observable and open to
public inspection. Despite the popular view to the contrary,
phenomena are not, in general, observable; they are abstrac-
tions wrought from the relevant data, frequently as a result
of a reductive process of data analysis. As Cartwright
(1983) remarked in her discussion of phenomenal and the-
oretical laws in physics, “the distinction between theoretical
and phenomenological has nothing to do with what is ob-
servable and what is unobservable. Instead the terms sepa-
rate laws which are fundamental and explanatory from those
that merely describe” (p. 2). Examples of data, which serve
as evidence for the aforementioned psychological effects,
are rates of operant responding (evidence for the matching
law), consistent intergenerational IQ score gains (evidence
for the Flynn effect), and error rates in psychological ex-
periments (evidence for recency effects in short-term
The methodological importance of data lies in the fact
that they serve as evidence for the phenomena under inves-
tigation. In detecting phenomena, one extracts a signal (the
phenomenon) from a sea of noise (the data). Some phenom-
ena are rare, and many are difﬁcult to detect; as Woodward
(1989) noted, detecting phenomena can be like looking for
a needle in a haystack. It is for this reason that, when
extracting phenomena from the data, one often engages in
data exploration and reduction by using graphical and sta-
A Model of Data Analysis
In order to establish that data are reliable evidence for the
existence of phenomena, scientists use a variety of method-
ological strategies. These strategies include controlling for
confounding factors (both experimentally and statistically),
empirically investigating equipment (including the calibra-
tion of instruments), engaging in data analytic strategies of
both statistical and nonstatistical kinds, and constructively
replicating study results. As can be seen in Table 1, these
procedures are used in the detection of phenomena, but they
are not used in the construction of explanatory theory (cf.
Franklin, 1990; Woodward, 1989). The later discussion of
the importance of reliability in the process of phenomena
detection helps indicate why this is so.
Given the importance of the detailed examination of data
in the process of phenomena detection, it is natural that the
statistical analysis of data ﬁgures prominently in that exer-
cise. A statistically oriented, multistage account of data
analysis is therefore outlined in order to further characterize
the phenomena detection phase of ATOM. The model pro-
ceeds through the four stages of initial data analysis, ex-
ploratory data analysis, close replication, and constructive
replication. However, it should be noted that, although the
behavioral sciences make heavy use of statistical methods in
data analysis, qualitative data analytic methods can also be
used in the detection of phenomena (cf. Strauss, 1987).
Initial data analysis. The initial examination of data
(Chatﬁeld, 1985) refers to the ﬁrst informal scrutiny and
description of data that is undertaken before exploratory
data analysis proper begins. It involves screening the data
for its quality. Initial data analysis variously involves check-
ing for the accuracy of data entries, identifying and dealing
with missing and outlying data, and examining the data for
their ﬁt to the assumptions of the data analytic methods to
be used. Data screening thus enables one to assess the
suitability of the data for the type of analysis intended.
This important, and time-consuming, preparatory phase
of data analysis has failed to receive the amount of explicit
attention that it deserves in behavioral science education.
Recently, however, the American Psychological Associa-
tion’s Task Force on Statistical Inference (Wilkinson & the
Task Force on Statistical Inference, 1999) recommended
changes to current practices in data analysis that are broadly
in keeping with the goals of initial data analysis. Fidell and
Tabachnick (2003) provided a useful overview of the im-
portance of the work required to identify and correct prob-
lems in data.
It should be clear, even from these brief remarks, that the
initial examination of data is a requirement of successful
data analysis in science, for data that lack integrity can
easily result in the misuse of data analytic methods and the
drawing of erroneous conclusions.
Exploratory data analysis. Exploratory data analysis
uses multiple forms of description and display and involves
descriptive, and frequently quantitative, detective work de-
signed to reveal the structure or patterns in the data under
scrutiny (Behrens & Yu, 2003; Tukey, 1977).
ploratory data analyst is encouraged to undertake an unfet-
tered investigation of the data and perform multiple analy-
ses using a variety of intuitively appealing and easily used
The compendium of methods for the exploration of data
is designed to facilitate both the discovery and the commu-
nication of information about data. These methods are con-
cerned with the effective organization of data, the construc-
tion of graphical displays, and the examination of
distributional assumptions and functional dependencies.
The stem-and-leaf display and the box-and-whisker plot are
two well-known exploratory methods.
Two attractive features of exploratory methods are their
robustness to changes in underlying distributions and their
resistance to outliers in data sets. Exploratory methods with
these two features are particularly suited to data analysis in
the behavioral sciences, where researchers are frequently
confronted with ad hoc data sets on manifest variables that
have been acquired in convenient ways.
Close replication. Successfully conducted exploratory
analyses will suggest potentially interesting data patterns.
However, it will normally be necessary to check on the
stability of the emergent data patterns through use of con-
ﬁrmatory data analysis procedures. Computer-intensive re-
sampling methods such as the bootstrap, the jackknife, and
cross-validation (Efron & Tibshirani, 1993) constitute an
important set of conﬁrmatory procedures that are well suited
to the demands of modern data analysis. Such methods free
us, as researchers, from the assumptions of orthodox statis-
tical theory, and permit us to gauge the reliability of chosen
statistics by making thousands, even millions, of calcula-
tions on many data points. Statistical resampling methods
like these are used to establish the consistency, or reliability,
of sample results. In doing this, they provide us with the
kind of validating strategy that is needed to achieve close
Now that psychology has ﬁnally begun to embrace ex-
ploratory data analysis, one can hope for a corresponding
increase in the companionate use of statistical resampling
methods in order to ascertain the validity of the data patterns
initially suggested by the use of exploratory methods.
Constructive replication. In establishing the existence
of phenomena, it is necessary that science undertake both
close and constructive replications. The statistical resam-
pling methods just mentioned are concerned with the con-
Behrens and Yu suggested that the inferential foundations of
exploratory data analysis are to be found in the notion of abduc-
tion. By contrast, ATOM regards exploratory data analysis as a
descriptive pattern detection process that is a precursor to the
inductive generalizations involved in phenomena detection. Ab-
ductive inference is reserved for the construction of causal explan-
atory theories that are introduced to explain empirical phenomena.
Behrens and Yu’s suggestion conﬂates description and explanation
in this regard.
Statistical resampling methods can be used in a hypothetico-
deductive manner within ATOM in order to test descriptive hy-
potheses that are suggested by exploratory data analytic work.
However, this use of the hypothetico-deductive method should be
distinguished from its use to evaluate explanatory hypotheses and
theories. The latter takes place outside the methodological space
provided by ATOM.
THEORY OF SCIENTIFIC METHOD
sistency of sample results that help researchers achieve
close, or internal, replications. By contrast, constructive
replications are undertaken to demonstrate the extent to
which results hold across different methods, treatments, and
occasions. In other words, constructive replication is a tri-
angulation strategy designed to ascertain the generalizabil-
ity of the results identiﬁed by successful close replication
(Lindsay & Ehrenberg, 1993). Constructive replication, in
which researchers vary the salient conditions, is a time-
honored strategy for justifying claims about phenomena.
In recognition of the need to use statistical methods that
are in keeping with the practice of describing predictable
phenomena, researchers should seek the generalizability of
relationships rather than their statistical signiﬁcance (Ehren-
berg & Bound, 1993)— hence, the need to use observational
and experimental studies with multiple sets of data, ob-
served under quite different sets of conditions. The recom-
mended task here is not to ﬁgure what model best ﬁts a
single set of data but to ascertain whether the model holds
across different data sets. Seeking reproducible results
through constructive replications, then, requires data ana-
lytic strategies that are designed to detect signiﬁcant same-
ness rather than signiﬁcant difference.
The four-stage model of data analysis just outlined assists
in the detection of phenomena by attending in turn to data
quality, pattern suggestion, pattern conﬁrmation, and gen-
eralization. In effect, this process is one of enumerative
induction in which one learns empirically, on a case-by-case
basis, the conditions of applicability of the empirical gen-
eralizations that represent the phenomena. Thus, as noted
earlier, the importance of inductive reasoning shown by the
traditional inductive method is shared by ATOM’s account
of phenomena detection.
It bears repeating that this model of data analysis is
clearly not the only way in which phenomena detection can
be achieved. In addition to the several strategies of phenom-
ena detection mentioned earlier, meta-analysis is a promi-
nent example of a distinctive use of statistical methods by
behavioral scientists to aid in the detection of phenomena.
As is well-known, meta-analysis is widely used to conduct
quantitative literature reviews. It is an approach to data
analysis that involves the quantitative analysis of the data
analyses of primary empirical studies. By calculating effect
sizes across primary studies in a common domain, meta-
analysis helps researchers detect general positive effects (cf.
Schmidt, 1992). By using statistical methods to ascertain the
existence of robust empirical regularities, meta-analysis can
be usefully viewed as the statistical analogue of direct
experimental replication. It is in this role that meta-analysis
currently performs its most important work in science. Con-
trary to the claims made by some of its critics in psychology
(e.g., Sohn, 1996), meta-analysis can be regarded as a
legitimate and important means of detecting empirical phe-
nomena in the behavioral sciences (Gage, 1996).
Detecting empirical phenomena is a major goal of scien-
tiﬁc research, and their successful detection constitutes an
important type of scientiﬁc discovery in its own right.
However, once detected, phenomena serve the important
function of prompting the search for their own understand-
ing. This understanding is commonly met in science by
constructing relevant explanatory theories.
For inductivists, inductively grounded conclusions about
phenomena are of paramount importance. However, al-
though inductivists often subsequently construct theories,
their theories do not provide explanations of phenomena
that appeal to causal mechanisms. Instead, their theories
function as tools or instruments concerned with the descrip-
tion, economical ordering, and prediction of empirical rela-
tionships. For hypothetico-deductivists, theories are said to
be generated amethodologically through free use of the
imagination (Hempel, 1966; Popper, 1959). Theories ob-
tained in this manner are often regarded as explanatory in
nature, but their worth is principally judged in terms of their
predictive success, rather than their ability to explain em-
ATOM, by contrast, maintains that theory construction is
neither inductive nor amethodological. For it, theory con-
struction comprises three methodological phases: theory
generation, theory development, and theory appraisal.
These phases do not occur in a strictly temporal order, for
although theory generation precedes theory development,
theory appraisal begins with theory generation, continues
with theory development, and extends to the comparative
appraisal of well-developed theories. Further, ATOM’s
characterization of theory construction is abductive through
and through: Theory generation, theory development, and
theory appraisal are all portrayed as abductive, or explana-
tory, undertakings, although the form of abduction is dif-
ferent in each case. The account of theory construction that
follows articulates the abductive character of each of the
Abductive inference. This section begins with a general
characterization of the type of abductive reasoning that is
often involved in theory generation. It is followed by a
discussion of the method of exploratory factor analysis that
is presented as a prominent example in psychology of an
abductive method of theory generation. The discussion of
exploratory factor analysis, therefore, serves as an optional
and restricted account of theory generation for ATOM. The
characterizations of abduction and factor analysis are
adapted from Haig (2005).
The basic idea of abductive inference can be usefully
traced back to the American philosopher and scientist
Charles Sanders Peirce (1931–1958). More recent develop-
ments in the ﬁelds of philosophy of science and artiﬁcial
intelligence (e.g., Josephson & Josephson, 1994; Magnani,
2001; Thagard, 1988, 1992) have built on Peirce’s ideas to
signiﬁcantly advance researchers’ understanding of abduc-
Abduction is a form of reasoning involved in both the
generation and evaluation of explanatory hypotheses and
theories. For Peirce (1931–1958), “abduction consists in
studying the facts and devising a theory to explain them”
(Vol. 5, 1934, p. 90). It is “[t]he ﬁrst starting of an hypoth-
esis and the entertaining of it, whether as a simple interro-
gation or with any degree of conﬁdence” (Vol. 6, 1934, p.
Traditionally, abduction was thought to take its place at
the inception of scientiﬁc hypotheses, where it often in-
volves making an inference from puzzling facts to hypoth-
eses that might well explain them. However, there are a
number of different ways in which explanatory hypotheses
can be abductively obtained. In focusing on the generation
of hypotheses, Thagard (1988) helpfully distinguished be-
tween existential and analogical abduction. As he put it,
“Existential abduction postulates the existence of previously
unknown objects, such as new planets, . . . [whereas] ana-
logical abduction uses past cases of hypothesis formation to
generate hypotheses similar to existing ones” (p. 54). Exis-
tential abduction is the type of abduction centrally involved
in the factor analytic generation of explanatory hypotheses.
Later, it is shown that the theory development phase of
ATOM adopts a modeling strategy that involves analogical
abduction, and its approach to comparative theory appraisal
uses a further form of abduction known as inference to the
Existential abduction can be characterized in the follow-
ing general schema:
The surprising empirical phenomenon, P, is detected.
But if hypothesis H were approximately true, and the relevant
auxiliary knowledge, A, was invoked, then P would follow as
a matter of course.
Hence, there are grounds for judging H to be initially plausible
and worthy of further pursuit.
This schematic characterization of existential abduction,
as it occurs within the theory generation phase of ATOM, is
coarse grained and far from sufﬁcient. It should, therefore,
be understood in the light of the following supplementary
First, as indicated in the discussion of phenomena detec-
tion, the facts to be explained in science are not normally
particular events, but empirical generalizations or phenom-
ena, and, strictly speaking, they are not typically observed.
Second, conﬁrmation theory in the philosophy of science,
and the nature of the hypothetico-deductive method in par-
ticular, make it clear that the facts, or phenomena, are
derived not just from the proposed theory but from that
theory in conjunction with accepted auxiliary claims taken
from relevant background knowledge.
Third, the antecedent of the conditional assertion in the
second premise of the argument schema should not be taken
to imply that abductive inferences produce truths as a matter
of course. Although science aims to provide true, or approx-
imately true, theories of the world, the supposition that the
proposed theory be true is not a requirement for the deri-
vation of the relevant facts. All that is required is that the
theory be plausible enough to be provisionally accepted. It
is important to distinguish between truth, understood as a
guiding ideal for science (a goal that we, as scientists, strive
for but never fully reach), and the justiﬁcation of theories,
which is based on epistemic criteria such as predictive
success, simplicity, and explanatory breadth. As proxies for
truth, justiﬁcatory criteria such as these are indicative of
truth, but they are not constitutive of truth.
Fourth, it should be noted that the conclusion of the
argument schema does not assert that the hypothesis itself is
true, only that there are grounds for thinking that the pro-
posed hypothesis might be true. This is a weaker claim that
allows one to think of a sound abductive argument as
delivering a judgment that the hypothesis is initially plau-
sible and worthy of further pursuit. Assessments of initial
plausibility constitute a form of justiﬁcation that involves
reasoning from warranted premises to an acceptance of the
knowledge claims in question. This form of justiﬁcation is
discussed later in the section on ATOM and Scientiﬁc
Fifth, the schema depicting abductive inference focuses
on its logical form only. It is, therefore, of limited value in
understanding the theory construction process unless it is
combined with a set of regulative constraints that enable us
to view existential abduction as an inference, not just to any
conceivable explanation, but to a plausible explanation. The
description of research problems presented later indicates
that the constraints that regulate the abductive generation of
scientiﬁc theories comprise a host of heuristics, rules, and
principles that govern what counts as good explanations.
Exploratory factor analysis. Unfortunately, there is a
dearth of codiﬁed abductive methods available for ready use
in the behavioral sciences. A notable exception is the
method of exploratory factor analysis. Exploratory factor
analysis is designed to facilitate the postulation of latent
variables that are thought to underlie patterns of correlations
in new domains of manifest variables. It does this by using
multiple regression and partial correlation theory to model
sets of manifest or observed variables in terms of linear
functions of other sets of latent, or unobserved, variables.
Although the nature and purpose of exploratory factor anal-
ysis is a matter of some debate, it can plausibly be under-
stood as an abductive method of theory generation (Haig,
THEORY OF SCIENTIFIC METHOD
2005; Rozeboom, 1972; Stephenson, 1961).
terization of the inferential nature of exploratory factor
analysis is seldom given in expositions of the method;
however, it is an interpretation that coheres well with its
general acceptance as a latent variable method.
On this interpretation, exploratory factor analysis facili-
tates the achievement of useful existential abductions, al-
though for this to happen, the method must be used in an
exemplary manner (cf. Fabrigar, Wegener, MacCallum, &
Strahan, 1999; Preacher & MacCallum, 2003) with circum-
spect interpretation of the factors. As noted earlier, existen-
tial abductions enable us, as researchers, to hypothesize the
existence of entities previously unknown to us. The innu-
merable examples of existential abduction in science in-
clude the initial postulation of hidden entities such as atoms,
genes, tectonic plates, and personality traits. In cases like
these, the primary thrust of the initial abductive inferences
is to claims about the existence of theoretical entities
order to explain empirical facts or phenomena. Similarly,
the hypotheses given to us through the use of exploratory
factor analysis postulate the existence of latent variables
such as Spearman’s g and extraversion. It remains for fur-
ther research to elaborate on the ﬁrst rudimentary concep-
tion of these variables.
The factor analytic use of existential abduction to infer
the existence of, say, the theoretical entity g can be coarsely
reconstructed in accordance with the aforementioned
schema for abductive inference along the following lines:
The surprising empirical phenomenon known as the positive
If g exists, and it is validly and reliably measured by a Wechsler
intelligence scale (and/or some other objective test), then the
positive manifold would follow as a matter of course.
Hence, there are grounds for judging the hypothesis of g to be
initially plausible and worthy of further pursuit.
This example serves to illustrate the point that the method
of exploratory factor analysis proper should be taken to
include the factor analyst’s substantive interpretation of the
statistical factors. It is important to realize that the factor
analyst has to resort to his or her own abductive powers
when reasoning from correlational data patterns to under-
lying common causes. Note that the schema for abductive
inference, and its application to the generation of Spear-
man’s hypothesis of g, are concerned with the form of the
arguments involved, not with the actual generation of the
explanatory hypotheses. In each case, the explanatory hy-
pothesis is given in the second premise of the argument. An
account of the genesis of the explanatory hypothesis must,
therefore, be furnished by some other means. It is plausible
to suggest that reasoning to explanatory hypotheses trades
on human beings’ evolved cognitive ability to abductively
generate such hypotheses. Peirce (1931–1958) himself
maintained that the human ability to engage readily in
abductive reasoning was founded on a guessing instinct that
has its origins in evolution. More suggestively, Carruthers
(2002) maintained that our ability, as humans, to engage in
explanatory inference is almost certainly largely innate, and
he speculated that it may be an adaptation selected for
because of its crucial role in the ﬁtness-enhancing activities
of our ancestors such as hunting and tracking. Whatever its
origin, an informative methodological characterization of
the abductive nature of factor analytic inference must appeal
to the scientist’s own psychological resources as well as
those of logic.
Exploratory factor analysis, then, can usefully function as
a submethod of ATOM by being located in that theory’s
context of theory generation. Although it exempliﬁes well
the character of existential abduction, exploratory factor
analysis is clearly not an all-purpose method for abductively
generating explanatory hypotheses and theories. With its
focus on common factors, it can properly serve as a gener-
ator of elementary theories only in those multivariate do-
mains where there are common causal structures.
Understood in the context of theory generation, methods
of existential abduction like exploratory factor analysis
should not be expected to achieve highly developed and
well-validated scientiﬁc theories. At best, they deliver ru-
dimentary theories that have initial plausibility. It is impor-
tant to realize that these abductive methods enable us to
justify the initial plausibility of the theories they spawn. The
very process of the abductive generation of theories has a
bearing on the ﬁrst determinations of their worth, in that we
appeal to the soundness of the abductive arguments used in
the introduction of theories in order to evaluate their early
epistemic promise (cf. Whitt, 1992).
Relatedly, the nascent theories bequeathed us by methods
like exploratory factor analysis postulate the existence of
hidden causal mechanisms, but they do not provide an
informative characterization of their nature. Such theories
have the status of dispositional theories in that they provide
us with oblique characterizations of the properties we at-
tribute to things by way of their presumed effects under
Some take exploratory factor analysis to be a data analytic
method, only. My principal reason for assigning a theory genera-
tion role to exploratory factor analysis is based on the belief that
factors are best regarded as latent common causes and that infer-
ence to such causes is abductive in nature (Haig, 2005).
The term entity is used as a catch-all ontological term that
covers a miscellany of properties that includes states, processes,
and events. Although existential abductions in exploratory factor
analysis are to properties expressed as the values of variables, not
all existential abductions need take this form.
The positive manifold is a term that is sometimes used to refer
to the striking, and well-established, fact that almost all different
tests of ability correlate positively with one another to a signiﬁcant
degree. Despite its historical link to Spearman’s theory of general
intelligence, the positive manifold can be taken as evidence for the
existence of two or more factors.
speciﬁed conditions (cf. Mumford, 1998). A move beyond
the rudimentary nature of their dispositional characteriza-
tion requires subsequent elaboration. It is to a strategy for
developing such theories that I now turn.
Models in science. The standard inductive and hypo-
thetico-deductive views of scientiﬁc method give little at-
tention to the process of theory development. The use of
traditional inductive method leads to theories that are orga-
nized summaries of their constituent empirical generaliza-
tions, and the orthodox hypothetico-deductive method as-
sumes that hypotheses and theories emerge fully formed,
ready for immediate testing.
In contrast to these two theories of scientiﬁc method,
ATOM is concerned with the development of explanatory
theories. As just noted, the theories it generates through
existential abduction are dispositional in nature, and ex-
plicit provision has to be made for their development
before they are systematically evaluated against rival
theories with respect to their explanatory goodness. As
noted earlier, ATOM recommends that this be done by
building analogical models of the causal mechanisms in
There is a long-held view (e.g., Duhem, 1914/1954), still
popular in some quarters, that analogical models are dis-
pensable aids to formulating and understanding scientiﬁc
theories. This negative view of the cognitive value of ana-
logical models in science contrasts with the positive view
that they are an essential part of the development of theories
(cf. Campbell, 1920; Harre´, 1976; Hesse, 1966). Contem-
porary studies of scientiﬁc practice, including philosophy of
science, frequently accord analogical models a genuine,
indispensable, cognitive role in science (e.g., Abrantes,
1999; Giere, 1988; Harre´, 1988).
Science uses different types of models for different pur-
poses. For example, iconic models
are constructed to pro
vide a good resemblance to the object or property being
modeled, mathematical models offer an abstract symbolic
representation of the domain of interest, and analogue mod-
els express relevant relations of analogy between the model
and the reality being represented. Harre´ (1970) contains a
useful taxonomy of this variety. Although it is acknowl-
edged that there is a need to use a variety of different
modeling strategies in science, ATOM adopts the strategy
of using analogical models to help develop explanatory
theories. Because analogical modeling is a strategy that
increases the content of explanatory theories, its reasoning
takes the form of analogical abduction.
Analogical modeling. The idea that analogical models
are important in the development of scientiﬁc theories can
be traced back to Campbell (1920). Although analogies are
not always used in scientiﬁc explanation, their role in theory
development within ATOM is of central importance. The
need for analogical modeling within ATOM stems from two
features of its characterization of theory generation. First, as
with exploratory factor analysis, the abductive generation of
theories takes the form of existential abduction, through
which the existence of theoretical entities is postulated.
Therefore, an appropriate research strategy is required to
learn about the nature of these hidden entities. For this, the
strategy of analogical modeling is used to do the required
elaborative work. Second, recall that the postulation of
theoretical entities through existential abduction confers an
assessment of initial plausibility on those postulations.
However, for claims about those latent entities to have the
status of genuine knowledge, further evaluative work has to
be done. The construction of appropriate analogical models
serves to assess the plausibility of our expanded understand-
ing, as well as to expand our understanding of those entities.
For ATOM, theory development expands our knowledge
of the nature of our theories’ causal mechanisms. This is
achieved by using the pragmatic strategy of conceiving of
these unknown mechanisms in terms of what is already
familiar and well understood. Well known examples of
models that have resulted from this strategy are the molec-
ular model of gases, based on an analogy with billiard balls
in a container; the model of natural selection, based on an
analogy with artiﬁcial selection; and the computational
model of the mind, based on an analogy with the computer.
To understand the nature of analogical modeling, it is
helpful to distinguish between a model, the source of the
model, and the subject of the model (Harre´, 1976; Hesse,
1966). From the known nature and behavior of the source,
one builds an analogical model of the unknown subject or
causal mechanism. If we take the biological example just
mentioned, Darwin fashioned his model of the subject of
natural selection by reasoning by analogy from the source of
the known nature and behavior of the process of artiﬁcial
selection. In this way, analogical models play an important
creative role in theory development. However, this role
requires the source, from which the model is drawn, to be
different from the subject that is modeled. For example, the
modern computer is a well-known source for the modeling
of human cognition, though our cognitive apparatus is not
generally thought to be a real computer. Models in which
the source and the subject are different are sometimes called
paramorphs. Models in which the source and the subject are
the same are sometimes called homoeomorphs (Harre´,
More precisely, iconic models are constructed as representa
tions of reality, real or imagined. In ATOM they stand in for the
hypothesized causal mechanisms. Although representations, iconic
models are themselves things, structures, or processes that corre-
spond in some way with things, structures, or processes that are the
objects of modeling. They are, therefore, the sorts of things sen-
tences can be about (Harre´, 1976).
THEORY OF SCIENTIFIC METHOD
1976). The paramorph can be an iconic, or pictorial, repre-
sentation of real or imagined things. It is iconic paramorphs
that feature centrally in the creative process of theory de-
velopment through analogical modeling.
In evaluating the aptness of an analogical model, the
analogy between its source and subject must be assessed,
and for this one needs to consider the structure of analogies.
The structure of analogies in models comprises a positive
analogy in which the source and subject are alike, a negative
analogy in which the source and subject are unlike, and a
neutral analogy where we have no reliable knowledge about
matched attributes in the source and subject of the model.
The negative analogy is irrelevant for purposes of analogi-
cal modeling. Because we are essentially ignorant of the
nature of the hypothetical mechanism of the subject apart
from our knowledge of the source of the model, we are
unable to specify any negative analogy between the model
and the mechanism being modeled. Thus, in considering the
plausibility of an analogical model, one considers the bal-
ance of the positive and neutral analogies (Harre´, 1976).
This is where the relevance of the source for the model is
spelled out. As is shown in the next section, ATOM sub-
scribes to a view of comparative theory appraisal that takes
good analogies as a criterion of explanatory worth.
Analogical reasoning is important in science and clearly
lies at the inferential heart of analogical modeling. How-
ever, as noted above, because the theories fashioned by
ATOM are explanatory theories, the analogical models in-
volved in theory development will involve explanatory an-
alogical reasoning, that is, analogical abduction. The rea-
soning involved in analogical abduction can be simply
stated in the form of a general argument schema as follows:
Hypothesis H* about property Q was correct in situation S1.
Situation S1 is like the situation S2 in relevant respects.
Therefore, an analogue of H* might be appropriate in situation
Darwin’s theory or model of natural selection, and the
other aforementioned analogical models, can plausibly be
construed to be based on analogical abduction. The general
argument for analogical abduction just given can be rewrit-
ten in simpliﬁed form for Darwin’s case as schema follows:
The hypothesis of evolution by artiﬁcial selection was correct in
cases of selective domestic breeding.
Cases of selective domestic breeding are like cases of the natural
evolution of species with respect to the selection process.
Therefore, by analogy with the hypothesis of artiﬁcial selection,
the hypothesis of natural selection might be appropriate in
situations where variants are not deliberately selected for.
The methodology of modeling through analogical abduc-
tion is quite well developed and provides a general, though
useful, source of guidance for behavioral scientists. Instruc-
tively for psychology, Harre´ (Harre´ & Secord, 1972) fol-
lowed his own account of analogical modeling to construct
a rule model of microsocial interaction in social psychol-
ogy. Here, Goffman’s (1969) dramaturgical perspective
provides the source model for understanding the underlying
causal mechanisms involved in the production of ceremo-
nial, argumentative, and other forms of social interaction.
Thus far, it has been suggested that, for ATOM, the
epistemic worth of hypotheses and theories generated by
existential abduction are evaluated in terms of their initial
plausibility and that these assessments are subsequently
augmented by judgments of the appropriateness of the anal-
ogies that function as source models for their development.
However, with ATOM, well-developed theories are ap-
praised further with respect to a number of additional cri-
teria that are used when judgments about the best of com-
peting explanatory theories are made.
Contemporary scientiﬁc methodology boasts a number of
general approaches to the evaluation of scientiﬁc theories.
Prominent among these are the hypothetico-deductive
method, which evaluates theories in terms of predictive
success; Bayesian accounts of conﬁrmation, which assign
probabilities to hypotheses via Bayes’s theorem; and infer-
ence to the best explanation, which accepts a theory when it
is judged to provide a better explanation of the evidence
than its rivals do. Of these three approaches, the hypo-
thetico-deductive method is by far the most widely used in
psychology (cf. Cattell, 1966; Rorer, 1991; Rozeboom,
1999). Despite occasional urgings (e.g., Edwards, Lindman,
& Savage, 1963; Lee & Wagenmakers, 2005; Rorer, 1991),
psychologists have been reluctant to use Bayesian statistical
methods to test their research hypotheses, preferring instead
to perpetuate the orthodoxy of classical statistical signiﬁ-
cance testing within a hypothetico-deductive framework.
Despite the fact that inference to the best explanation is
frequently used in science, and extensively discussed in the
philosophy of science, it is virtually unheard of, let alone
used, to appraise theories in psychology.
True to its name, ATOM adopts an abductive perspective
on theory evaluation by using a method of inference to the
best explanation. It is shown shortly that, in contrast to the
hypothetico-deductive method, ATOM adopts an approach
to inference to the best explanation that measures empirical
adequacy in terms of explanatory breadth, not predictive
success, and, in contrast with Bayesianism, it takes theory
evaluation to be an exercise that focuses directly on expla-
nation, not a statistical undertaking in which one assigns
probabilities to theories. The basic justiﬁcation for using
inference to the best explanation when evaluating explana-
tory theories is that it is the only method researchers have
that explicitly assesses such theories in terms of the scien-
tiﬁc goal of explanatory worth.
In considering theory evaluation in ATOM, the idea of
inference to the best explanation is introduced. Then, a
well-developed method of inference to the best explanation
is presented and discussed. Thereafter, inference to the best
explanation is defended as an important perspective on
Inference to the best explanation. In accordance with its
name, inference to the best explanation is founded on the
belief that much of what we know about the world is based
on considerations of explanatory worth. Being concerned
with explanatory reasoning, inference to the best explana-
tion is a form of abduction. As mentioned earlier, it involves
accepting a theory when it is judged to provide a better
explanation of the evidence than its rivals do. In science,
inference to the best explanation is often used to adjudicate
between well-developed, competing theories (cf. Thagard,
A number of writers have elucidated the notion of infer-
ence to the best explanation (e.g., Day & Kincaid, 1994;
Lipton, 2004; Thagard, 1988). The most prominent account
is due to Lipton, who suggested that inference to the best
explanation is not an inference to the “likeliest” explanation,
but to the “loveliest” explanation, where the loveliest ex-
planation comprises the various explanatory virtues such as
theoretical elegance, simplicity, and coherence; it is the
explanatory virtues that provide the guide to inference about
causes in science. However, the most developed formula-
tion of inference to the best explanation as a method of
theory evaluation was provided by Thagard (1992). Tha-
gard’s formulation of inference to the best explanation
identiﬁes, and systematically uses, a number of evaluative
criteria in a way that has been shown to produce reliable
judgments of best explanation in science. For this reason it
is adopted as the method of choice for theory evaluation in
The theory of explanatory coherence. Thagard’s (1992)
account of inference to the best explanation is known as the
theory of explanatory coherence (TEC). According to TEC,
inference to the best explanation is centrally concerned with
establishing relations of explanatory coherence. To infer
that a theory is the best explanation is to judge it as more
explanatorily coherent than its rivals. TEC is not a general
theory of coherence that subsumes different forms of co-
herence such as logical and probabilistic coherence. Rather,
it is a theory of explanatory coherence in which the prop-
ositions hold together because of their explanatory relations.
Relations of explanatory coherence are established
through the operation of seven principles. These principles
are symmetry, explanation, analogy, data priority, contra-
diction, competition, and acceptability. The determination
of the explanatory coherence of a theory is made in terms of
three criteria: consilience, simplicity, and analogy (Thagard,
1988). I next consider the criteria, and then the principles.
The criterion of consilience, or explanatory breadth, is the
most important criterion for choosing the best explanation.
It captures the idea that a theory is more explanatorily
coherent than its rivals if it explains a greater range of facts.
For example, Darwin’s theory of evolution explained a wide
variety of facts that could not be explained by the accepted
creationist explanation of the time. Consilience can be static
or dynamic. Static consilience judges all the different types
of facts available. Dynamic consilience obtains when a
theory comes to explain more classes of fact than it did at
the time of its inception. A successful new prediction that is
also an explanation can often be taken as a sign of dynamic
The notion of simplicity that Thagard (1988) deemed the
most appropriate for theory choice is a pragmatic notion that
is closely related to explanation; it is captured by the idea
that preference should be given to theories that make fewer
special or ad hoc assumptions. Thagard regarded simplicity
as the most important constraint on consilience; one should
not sacriﬁce simplicity through ad hoc adjustments to a
theory in order to enhance its consilience. Darwin believed
that the auxiliary hypotheses he invoked to explain facts,
such as the gaps in the fossil record, offered a simpler
explanation than the alternative creationist account.
Finally, analogy is an important criterion of inference to
the best explanation because it can improve the explanation
offered by a theory. Thus, as noted in the earlier discussion
of analogical modeling, the explanatory value of Darwin’s
theory of natural selection was enhanced by its analogical
connection to the already understood process of artiﬁcial
selection. Explanations are judged more coherent if they are
supported by analogy to theories that scientists already ﬁnd
Within TEC, each of the three criteria of explanatory
breadth, simplicity, and analogy are embedded in one or
more of the seven principles. Thagard (1992, 2000) formu-
lated these principles in both formal and informal terms.
They are stated here informally in his words as follows
1. Symmetry. Explanatory coherence is a symmetric
relation, unlike, say, conditional probability. That
is, two propositions p and q cohere with each other
2. Explanation. (a) A hypothesis coheres with what it
explains, which can either be evidence or another
hypothesis. (b) Hypotheses that together explain
some other proposition cohere with each other. (c)
The more hypotheses it takes to explain some-
thing, the lower the degree of coherence.
3. Analogy. Similar hypotheses that explain similar
pieces of evidence cohere.
4. Data Priority. Propositions that describe the re-
sults of observations have a degree of acceptability
on their own.
THEORY OF SCIENTIFIC METHOD
5. Contradiction. Contradictory propositions are in-
coherent with each other.
6. Competition. If p and q both explain a proposition,
and if p and q are not explanatorily connected,
then p and q are incoherent with each other (p and
q are explanatorily connected if one explains the
other or if together they explain something).
7. Acceptance. The acceptability of a proposition in a
system of propositions depends on its coherence
with them. (p. 43)
Limitations of space preclude a discussion of these prin-
ciples; however, the following points should be noted. The
principle of explanation is the most important principle in
determining explanatory coherence because it establishes
most of the coherence relations. The principle of analogy is
the same as the criterion of analogy, where the analogy must
be explanatory in nature. With the principle of data priority,
the reliability of claims about observations and generaliza-
tions, or empirical phenomena, will often be sufﬁcient
grounds for their acceptance. The principle of competition
allows noncontradictory theories to compete with each oth-
Finally, with the principle of acceptance, the overall
coherence of a theory is obtained by considering the pair-
wise coherence relations through use of Principles 1– 6.
The principles of TEC combine in a computer program,
ECHO (Explanatory Coherence by Harmany
tion), to provide judgments of the explanatory coherence of
competing theories. This computer program is connectionist
in nature and uses parallel constraint satisfaction to accept
and reject theories based on their explanatory coherence.
The theory of explanatory coherence has a number of
virtues that make it an attractive theory of inference to the
best explanation: It satisﬁes the demand for justiﬁcation by
appeal to explanatory considerations rather than predic-
tive success; it takes theory evaluation to be a compara-
tive matter; it can be readily implemented by, and indeed
is instantiated in, the computer program, ECHO, while
still leaving an important place for judgment by the
researcher; and it effectively accounts for a number of
important episodes of theory assessment in the history of
science. In short, TEC and ECHO combine in a success-
ful method of explanatory coherence that enables re-
searchers to make judgments of the best of competing
explanatory theories. Thagard (1992) is the deﬁnitive
source for a detailed explication of the theory of explan-
Psychology is replete with competing theories that might
usefully be evaluated with respect to their explanatory co-
herence. Durrant and Haig (2001) hinted at how two com-
peting theories of language evolution might be judged in
terms of their explanatory coherence. However, examples of
the full use of TEC to appraise the best of competing
explanatory theories in the behavioral sciences have yet to
A number of authors (e.g., Haig, 1987; Laudan, 1977;
Nickles, 1981) have stressed the value of viewing scientiﬁc
inquiry as a problem-solving endeavor. It will be recalled
that the overview of ATOM indicated the method’s com-
mitment to the notion of a research problem. This acknowl-
edgment of the importance of research problems for inquiry
contrasts with the orthodox inductive and hypothetico-de-
ductive accounts of method, neither of which speaks of
problem solving as an essential part of its characterization.
In an effort to depict scientiﬁc inquiry as a problem-
solving endeavor, ATOM uses a constraint-inclusion view
of research problems (Haig, 1987; Nickles, 1981). The idea
of problems as constraints has been taken from the problem-
solving literature in cognitive psychology (Simon, 1977)
and groomed for a methodological role. Brieﬂy, the con-
straint-inclusion theory depicts a research problem as com-
prising all the constraints on the solution to that problem,
along with the demand that the solution be found. With the
constraint-inclusion theory, the constraints do not lie out-
side the problem but are constitutive of the problem itself;
they actually serve to characterize the problem and give it
structure. The explicit demand that the solution be found is
prompted by a consideration of the aims of the research, the
pursuit of which is intended to ﬁll the outstanding gaps in
the problem’s structure.
Note that all relevant constraints are included in a prob-
lem’s formulation. This is because each constraint contrib-
utes to a characterization of the problem by helping to rule
out some solutions as inadmissible. However, at any one
time, only a manageable subset of the problem’s constraints
will be relevant to the speciﬁc research task at hand. Also,
by including all the constraints in the problem’s articulation,
the problem enables the researcher to direct inquiry effec-
tively by pointing the way to its own solution. In a very real
sense, stating the problem is half the solution!
The constraint-inclusion account of problems stresses the
fact that in good scientiﬁc research, problems typically
evolve from an ill-structured state and eventually attain a
degree of well-formedness such that their solution becomes
possible. From the constraint-inclusion perspective, a prob-
In the principles of symmetry and competition, p and q are to
be understood as propositions (hypotheses or evidence statements)
within a theory (system of propositions).
The spelling of Harmany is deliberate, being a tribute to
Gilbert Harman (1965), who coined the term inference to the best
explanation and introduced the corresponding idea to modern
lem will be ill-structured to the extent that it lacks the
constraints required for its solution. Because the most im-
portant research problems will be decidedly ill-structured,
we can say of scientiﬁc inquiry that its basic purpose is to
better structure our research problems by building in the
various required constraints as our research proceeds. It
should be emphasized that the problems dimension of
ATOM is not a temporal phase to be dealt with by the
researcher before moving on to other phases such as ob-
serving and hypothesizing. Instead, the researcher deals
with scientiﬁc problems all the time; problems are gener-
ated, selected for consideration, developed, and modiﬁed in
the course of inquiry.
Across the various research phases of ATOM there will
be numerous problems of varying degrees of speciﬁcity to
articulate and solve. For example, the successful detection
of an empirical phenomenon produces an important new
constraint on the subsequent explanatory efforts devised to
understand that constraint; until the relevant phenomenon,
or phenomena, are detected, one will not really know what
the explanatory problem is. Of course, constraints abound in
theory construction. For example, constraints that regulate
the abductive generation of new theories will include meth-
odological guides (e.g., give preference to theories that are
simpler, and that have greater explanatory breadth), aim-
oriented guides (e.g., theories must be of an explanatory
kind that appeals to latent causal mechanisms), and meta-
physical principles (e.g., social psychological theories must
acknowledge humankind’s essential rule-governed nature).
The importance of research problems, viewed as sets of
constraints, is that they function as the “range riders” of
inquiry that provide ATOM with the operation force to
guide inquiry. The constraints themselves comprise relevant
substantive knowledge as well as heuristics, rules, and prin-
ciples. Thus, the constraint inclusion account of problems
serves as a vehicle for bringing relevant background knowl-
edge to bear on the various research tasks subsumed by
ATOM and Scientiﬁc Methodology
Before concluding the article, I want to identify and
brieﬂy discuss two important methodological ideas that are
part of the deep structure of ATOM. These ideas are pre-
sented in two contrasts: (a) generative and consequentialist
methodology and (b) reliabilist and coherentist justiﬁcation.
Generative and Consequentialist Methodology
Modern scientiﬁc methodology promotes two different
research strategies that can lead to justiﬁed knowledge
claims. These are known as consequentialist and generative
strategies (Nickles, 1987). Consequentialist strategies jus-
tify knowledge claims by focusing on their consequences.
By contrast, generative strategies justify knowledge claims
in terms of the processes that produce them. Although
consequentialist strategies are used and promoted more
widely in contemporary science, both types of strategy are
required in an adequate conception of research methodol-
ogy. Two important features of ATOM are that it is under-
written by a methodology that promotes both generative and
consequentialist research strategies in the detection of phe-
nomena, and generative research strategies in the construc-
tion of explanatory theories.
Consequentialist reasoning receives a heavy emphasis in
behavioral science research through use of the hypothetico-
deductive method, and null hypothesis signiﬁcance testing,
and structural equation modeling within it. Consequentialist
methods reason from the knowledge claims in question to
their testable consequences. As such, they confer a retro-
spective justiﬁcation on the theories they seek to conﬁrm.
In contrast to consequentialist methods, generative meth-
ods reason from warranted premises to an acceptance of the
knowledge claims in question. Exploratory factor analysis is
a good example of a method of generative justiﬁcation. It
affords researchers generative justiﬁcations by helping them
reason forward from established correlational data patterns
to the rudimentary explanatory theories that the method
generates. As noted earlier, it is judgments of initial plau-
sibility that constitute the generative justiﬁcations afforded
by exploratory factor analysis. Generative justiﬁcations are
forward looking because they are concerned with heuristic
appraisals of the prospective worth of theories.
Reliabilist and Coherentist Justiﬁcation
In addition to embracing both generative and consequen-
tialist methodologies, ATOM uses two different theories of
justiﬁcation, although it does so in a complementary way.
These approaches to justiﬁcation are known as reliabilism
and coherentism. Reliabilism asserts that a belief is justiﬁed
to the extent that it is acquired by reliable processes or
methods (e.g., Goldman, 1986). For example, under appro-
priate conditions, beliefs produced by perception, verbal
reports of mental processes, and even sound argumentation
can all be justiﬁed by the reliable processes of their produc-
tion. ATOM makes heavy use of reliability judgments be-
cause they furnish the appropriate type of justiﬁcation for
claims about empirical phenomena.
For example, as noted
earlier, statistical resampling methods such as the bootstrap,
and the strategy of constructive replication, are different
sorts of consistency tests through which researchers seek to
establish claims that data provide reliable evidence for the
existence of phenomena.
The use of reliability as a mode of justiﬁcation, or validation,
differs from the normal psychometric practice in which reliability
and validity are presented as contrasts. However, the use of con-
sistency tests to validate knowledge claims on reliabilist grounds is
widespread in science.
THEORY OF SCIENTIFIC METHOD
By contrast with reliabilism, coherentism maintains that a
belief is justiﬁed in virtue of its coherence with other
accepted beliefs. One prominent version of coherentism,
explanationism, asserts that coherence is determined by
explanatory relations and that all justiﬁcation aims at max-
imizing the explanatory coherence of belief systems (Lycan,
1988). However, the claim that all justiﬁcation is concerned
with explanatory coherence is too extreme, as the existence
of reliabilist justiﬁcation makes clear.
It should be emphasized that, although reliabilism and
explanationism are different and are often presented as
rivals, they do not have to be seen as competing theories of
justiﬁcation. ATOM adopts a broadly coherentist perspec-
tive on justiﬁcation that accommodates both reliabilism and
explanationism and allows for their coexistence, comple-
mentarity, and interaction. It encourages researchers ﬁrst to
seek and accept knowledge claims about empirical phenom-
ena based solely on reliabilist grounds, and then to proceed
to construct theories that will explain coherently those
claims about phenomena. Thus, when using TEC, one is
concerned with delivering judgments of explanatory coher-
ence, but TEC’s principle of data priority presupposes that
the relevant empirical generalizations have been justiﬁed on
Further, the acceptability of the claims about phenomena
will be enhanced when they coherently enter into the ex-
planatory relations that contain them. Alternatively, the
explanatory coherence, speciﬁcally the explanatory breadth,
of a theory will be reduced as a consequence of rejecting a
claim about a relevant phenomenon that was initially ac-
cepted on insufﬁcient reliabilist grounds.
Discussion and Conclusion
This concluding section of the article brieﬂy comments on
the nature and limits of ATOM and its implications for
research practice. In doing so, it also makes some remarks
about the nature of science.
Phenomena Detection and Theory Construction
Recognition of the fundamental importance of the distinc-
tion between empirical phenomena and explanatory theory
suggests the need to differentiate between empirical
progress and theoretical progress in science. The successful
detection of a phenomenon is a major achievement in its
own right, and it is a signiﬁcant indicator of empirical
progress in science. (The importance of phenomena detec-
tion in science is underscored by the fact that more Nobel
prizes are awarded for the discovery of phenomena than for
the construction of explanatory theories.) From the perspec-
tive of ATOM, theoretical progress is to be understood in
terms of the goodness of explanatory theories as determined
by TEC. Arguably, behavioral science methodology has
placed a heavier professional emphasis on the description of
empirical regularities than on the construction of explana-
tory theories. However, ATOM takes phenomena detection
and theory construction to be of equal worth.
The characterization of phenomena given earlier in the
article helps correct two widely held misunderstandings of
science. First, it makes clear that taking the distinction
between observation and theory to be of fundamental meth-
odological importance prevents one from being able to
conceptualize properly the process of phenomena detection.
This holds whether or not one subscribes to a hard-and-fast
observation–theory distinction, or whether one accepts a
relative observation–theory distinction and the ambiguous
idea of theory ladenness that goes with it. To correctly
understand the process of phenomena detection, one needs
to replace the observation–theory distinction with the three-
fold distinction between data, phenomena, and theory.
This suggested replacement also serves to combat the
tendency to overemphasize the importance of observation as
a source of evidence in science. For it is phenomena, not
data, that typically serve as evidence for theories. Moreover,
although data serve as evidence for phenomena, their per-
ceptual qualities in this role are of secondary importance.
Methodologically speaking, what matters in science is not
the phenomenal or experiential qualities of perception but
whether or not perception is a reliable process (Woodward,
1989). It is for this reason that reliable nonhuman measure-
ment techniques are just as important as human perceptual
techniques in detecting phenomena.
Generally speaking, the implications of ATOM’s account
of phenomena detection for research practice in the behav-
ioral sciences is consistent with a number of recent propos-
als for improving researchers’ data analytic practices. In
particular, the model of data analysis outlined in this article
reinforces the importance now accorded exploratory data
analysis in psychology (Behrens & Yu, 2003). In addition,
it highlights the need to recognize that computer-intensive
resampling methods are a valuable source of pattern con-
ﬁrmation—a point oddly ignored by the American Psycho-
logical Association’s Task Force on Statistical Inference
(Wilkinson & the Task Force on Statistical Inference,
1999). Of interest, at a general level, the acknowledgment of
phenomena detection as a distinctive research undertaking
in its own right enables behavioral scientists to endorse the
inductivism of radical behaviorist methodology but eschew
its instrumentalist prescriptions for theorizing and postulate
latent causal mechanisms instead. This constructive part of
radical behaviorism is an account of phenomena detection
that can be found in the biological sciences (Sidman, 1960).
As such, it deserves a wider adoption in the behavioral
sciences than is currently the case.
ATOM’s account of theory construction is at variance
with the way many behavioral scientists understand theory
construction in science. Most behavioral scientists probably
use, or at least endorse, a view of theory construction that is
strongly shaped by the guess-and-test strategy of the hypo-
thetico-deductive method. In contrast with this prevailing
conception of scientiﬁc method, ATOM asserts that (a)
theory generation can be a logical, or rational, affair, where
the logic takes the form of abductive reasoning; (b) theory
development is an important part of theory construc-
tion—an undertaking that is stiﬂed by an insistence on
immediate testing; and (c) empirical adequacy, understood
as predictive success, is not by itself an adequate measure of
theory goodness, there being a need to use additional virtues
to do with explanatory worth.
ATOM’s three phases of theory construction have vary-
ing degrees of application in the behavioral sciences. Cod-
iﬁed methods that generate theories through existential ab-
duction are rare. The use of exploratory factor analysis to
postulate common causes is a striking exception, although
the explicit use of this method as an abductive generator of
elementary plausible theory is rarely acknowledged.
Grounded theory method (Strauss, 1987), which is increas-
ingly used in behavioral research, can be regarded as an
abductive method that helps generate theories that explain
the qualitative data patterns from which they are derived.
However, it does not conﬁne itself to existential abduction,
and it imposes weaker constraints on the abductive reason-
ing permitted by the researcher than does exploratory factor
analysis. The earlier suggestion that as human beings, we
have an evolved cognitive ability to abductively generate
hypotheses leads to the plausible suggestion that scientists
frequently reason to explanatory hypotheses without using
codiﬁed methods to do so. Two prominent examples in the
behavioral sciences are Chomsky’s (1972) publicly ac-
knowledged abductive inference to his innateness hypothe-
sis about universal grammar, and Howard Gardner’s
(Walters & Gardner, 1986) self-described use of “subjective
factor analysis” to postulate his multiple intelligences. Also,
it is likely that behavioral scientists use some of the many
heuristics for creative hypothesis generation listed by
McGuire (1997) in order to facilitate their abductive rea-
soning to hypotheses.
The strategy of analogical modeling is sometimes used in
the various behavioral sciences to develop theories. This is
not surprising, given that many of the proposed causal
mechanisms in these sciences are theoretical entities whose
natures can only be got at indirectly using such a modeling
strategy. However, there is little evidence that the behav-
ioral sciences explicitly incorporate such a strategy into
their methodology and their science education practices.
Given the importance of such a strategy for the expansion of
explanatory theories, methodologists in the behavioral sci-
ences need to promote analogical modeling as vigorously as
they have promoted structural equation modeling. Structural
equation modeling provides knowledge of causal networks.
As such, it does not so much encourage the development of
detailed knowledge of the nature of the latent variables as it
speciﬁes the range and order of causal relations into which
such variables enter. By contrast, analogical modeling seeks
to provide more detailed knowledge of the causal mecha-
nisms by enumerating their components and activities.
These different forms of knowledge are complementary.
Inference to the best explanation is an important approach
to theory appraisal that has not been explicitly tried in the
behavioral sciences. Instead, hypothetico-deductive testing
for the predictive success of hypotheses and theories holds
sway. TEC, which is the only codiﬁed method of inference
to the best explanation, can be widely used in those domains
where there are two or more reasonably well-developed
theories that provide candidate explanations of relevant
phenomena. By acknowledging the centrality of explanation
in science, one can use TEC to appraise theories with
respect to their explanatory goodness. It is to be hoped that
behavioral science education will soon add TEC to its
concern with cutting-edge research methods.
The Scope of ATOM
Although ATOM is a broad theory of scientiﬁc method, it
should not be understood as a fully comprehensive account.
ATOM is a singular account of method that is appropriate
for the detection of empirical phenomena and the subse-
quent construction of postulational theories, where those
theories purportedly refer to hidden causal mechanisms, and
where their causes are initially given a rudimentary, dispo-
sitional characterization. However, in dealing with explan-
atory theories in which the causal mechanisms referred to
are more directly accessible than theoretical entities, re-
searchers do not have to use a strategy of analogical mod-
eling in order to provide a more informative characteriza-
tion of their theories. The use of functional brain imaging
techniques, such as functional magnetic resonance imaging,
in order to map neuronal activity in the brain is a case in
point. Further, although the evaluation of theories in terms
of explanatory criteria deserves a heavy weighting in sci-
ence, inference to the best explanation will not always be an
appropriate, or a sufﬁcient, resource for evaluating theories.
For example, although predictive success has probably been
overemphasized in both scientiﬁc methodology and practice
(Brush, 1995), it nevertheless remains an important criterion
of a theory’s worth. It, may, therefore, be sought in a
modiﬁed hypothetico-deductive strategy that corrects for
the conﬁrmational inadequacies of its simple form.
Like all theories of scientiﬁc method, ATOM is norma-
tive in the sense that it advises researchers of what to do in
a limited number of research contexts. However, it is im-
portant to stress that the normative force of ATOM is
conditional in nature. More precisely, its recommendations
are subjunctive conditionals that take the form “If you want
THEORY OF SCIENTIFIC METHOD
to reach goal X, then use strategy Y.” The justiﬁcation for
pursuing goal X rests with the researcher; it is not to be
found in ATOM. Laudan (1996) argued in detail for the
conditional nature of methodological recommendations, and
Proctor and Capaldi (2001) recently commended his view of
methodology to psychologists.
ATOM aspires to be a coherent theory that brings to-
gether a number of different research methods and strategies
that are normally considered separately. The account of
phenomena detection offered is a systematic reconstruction
of a practice that is common in science but that is seldom
presented as a whole in methodological writings. The ab-
ductive depiction of theory construction endeavors to make
coordinated sense of the way in which science sometimes
comes to obtain knowledge about the causal mechanisms
that ﬁgure centrally in the understanding of the phenomena
that they produce. With rare exceptions, the abductive gen-
eration of elementary plausible theory, the strategy of ana-
logical modeling, and the method of inference to the best
explanation are all yet to receive explicit consideration in
psychology and the other behavioral sciences—but see Ro-
zeboom (1999), Harre´ and Secord (1972), and Eﬂin and
Kite (1996), respectively. ATOM serves to combine these
methodological resources in a broad theory of scientiﬁc
The question of whether ATOM is a genuinely coherent
theory of method remains to be answered. Although it is a
fairly comprehensive account of method, and although it
seems to capture a natural order of scientiﬁc inquiry, further
development is required before its cohesiveness can be
properly judged. My hope is that, upon fuller explication,
ATOM might be shown in a reﬂexive way to be an explan-
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Received January 26, 2005
Revision received August 11, 2005
Accepted August 15, 2005 䡲