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An Abductive Theory of Scientific Method

January 2006
Psychological Methods 10(4):371-88

DOI:10.1037/1082-989X.10.4.371

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PubMed

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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 methodology.

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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

methodology.

Keywords: scientiﬁc method, phenomena detection, theory construction, abductive reasoning,

research methodology

[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:

brian.haig@canterbury.ac.nz

Psychological Methods

2005, Vol. 10, No. 4, 371–388

1082-989X/05/$12.00 DOI: 10.1037/1082-989X.10.4.371

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.

Inductive 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.

Hypothetico-Deductive Method

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-

nisms.

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.

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HAIG

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

its characterization.

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.

Phenomena Detection

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

available.

Table 1

Submethods and Strategies of the Abductive Theory of Method

Phenomena detection

Theory construction

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)

Methods

Initial data analysis

Exploratory data analysis (e.g., stem-and-leaf,

box plot)

Computer-intensive resampling methods (e.g.,

bootstrap, jackknife, cross-validation)

Meta-analysis

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.

373

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-

ary biology.

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

memory).

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-

tistical methods.

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

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HAIG

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).

The ex-

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

techniques.

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

replications.

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.

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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).

Theory Construction

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-

pirical phenomena.

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

three phases.

Theory Generation

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-

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HAIG

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-

tive reasoning.

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.

358).

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

best explanation.

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

remarks.

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

Methodology.

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,

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THEORY OF SCIENTIFIC METHOD

2005; Rozeboom, 1972; Stephenson, 1961).

This charac

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

manifold

is identiﬁed.

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.

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HAIG

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.

Theory Development

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

question.

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).

379

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

S2.

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.

Theory Appraisal

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

380

HAIG

is presented and discussed. Thereafter, inference to the best

explanation is defended as an important perspective on

theory evaluation.

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,

1988).

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

ATOM.

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

consilience.

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

credible.

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

(Thagard, 2000):

1. Symmetry. Explanatory coherence is a symmetric

relation, unlike, say, conditional probability. That

is, two propositions p and q cohere with each other

equally.

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.

381

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-

er.

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

Optimiza

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-

atory coherence.

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

be provided.

Research Problems

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

philosophy.

382

HAIG

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.

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.

383

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

reliabilist grounds.

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

Again

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

384

HAIG

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

385

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.

Conclusion

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

method.

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-

atorily coherent theory of scientiﬁc method.

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Received January 26, 2005

Revision received August 11, 2005

Accepted August 15, 2005 䡲

388

HAIG

A Lost Idea in Psychology: Observation as Starting Point for the Scientific Investigation of Human Behavior

Preprint

Full-text available

Feb 2023

Where are the observations in psychology ? Recurrent criticisms have been addressed to psychology for its excessive focus on hypothesis testing, and its disregard for the descriptive, exploratory and observational approaches commonly found in the other natural sciences (e.g., zoology, ethology, behavioral ecology, behavioral biology, primatology). The dominant belief in the discipline is that science only progresses by testing theories. We first gather these criticisms. Then, we present some major benefits to the use of such observations (e.g., stimulating discovery, stimulating theory building, improving experimental designs, producing more ecologically valid research). Finally, we propose concrete ways to conduct systematic observations of human behaviors in psychology (e.g., direct observation, official statistics and big data, descriptive exploratory studies, preliminary surveys, interviews, testimonies). Thus, the present contribution supports the idea according to which the primary question for psychologists should be ‘what is it that I study’ rather than ‘what is my theoretical framework?’.

Culture and emotion in Paralympic swimming medalists

Article

Full-text available

Dec 2022

The purpose of the present study was to examine if cultural identity was related to the emotional experiences of Paralympic (N = 83) swimmers’ upon winning medals at the 2016 Paralympic Games. Paralympic athletes’ countries (N = 5) were scored on Hofstede’s (2011) six cultural dimensions and athletes who won medals had their facial expressions analyzed to determine levels of basic emotions (i.e., happiness, sadness, anger, surprise, disgust, fear, and neutral) based on Ekman’s (1993) neuro-cultural theory of emotion. After controlling for medal won, and time and place expectation proxies, we found that happiness was negatively associated with long/short term orientation (r = –0.313, p < 0.004) and positively linked to indulgence/restraint (r = 0.210, p < 0.06). The emotion of neutral was positively associated with power distance (r = 0.239, p < 0.032) and long term/short term orientation (r = 0.290, p < 0.009) while being negatively linked to indulgence (r = –0.276, p < 0.013). Based on an abductive theory of the scientific method (Haig, 2005; 2008) we formed plausible theory-based explanations for our findings and concluded, given our study is the first of its nature, that researchers should continue this line of inquiry.

La investigación sobre la inteligencia emocional en los últimos 20 años (2001-2021). Un estudio bibliométrico.

Article

Full-text available

Feb 2023

Objective: This article reports the first science mapping analysis of the emotional intelligence research’s themes, which shows its conceptual structure and scientific evolution. Methods: Science Mapping Analysis Software Tool, a bibliometric science mapping tool based on co-word analysis was applied using a sample of 8.884 research documents published from 2001 to 2021 in the journals indexed in the Journal Citation Reports of the Web of Science. Results: Published research in emotional intelligence concentrated in the following main themes: personality, academic and job performance, life satisfaction, stress and social cognition. These themes become more diversified through years. Adolescents and students were the main samples over the emotional intelligence were studied. Conclusions. The main research themes in emotional intelligence have been changing steadily over time in terms of importance and number of publications. This encourages us to maintain a positive view of the development of research field in emotional intelligence and call to monitoring by using meta-research techniques such as the present bibliometric study.

Open science, closed doors: The perils and potential of open science for research in practice

Article

Full-text available

Jan 2023

This paper advocates for the value of open science in many areas of research. However, after briefly reviewing the fundamental principles underlying open science practices and their use and justification, the paper identifies four incompatibilities between those principles and scientific progress through applied research. The incompatibilities concern barriers to sharing and disclosure, limitations and deficiencies of overidentifying with hypothetico-deductive methods of inference, the paradox of replication efforts resulting in less robust findings, and changes to the professional research and publication culture such that it will narrow in favor of a specific style of research. Seven recommendations are presented to maximize the value of open science while minimizing its adverse effects on the advancement of science in practice.

PRE-SERVICE EFL TEACHERS’ CREATIVE THINKING

Article

Nov 2022

Lamhot Naibaho

This study aims to investigate the pre-service teachers’ creative thinking at the faculty of Teacher Training and Education, Universitas Kristen Indonesia. It is limited to the difference of creative thinking between the male and female students. It was conducted at the Christian University of Indonesia within a period of 3 months from February to May 2021. The research design used was ex-post facto. The research subjects are 36 semester students who were selected purposively. The research instrument used is a standardized essay test. The data taken trough test were analyzed using descriptively, besides using a descriptive quantative, the data of the research were also analyzed using independent t test trough SPSS. The results of this study shows that the faculty of teacher training and education students’ creative thinking skills is at the level of “good” category, which is 63.69%. From a further analysis, it is found that the comparison between male and female students’ creative thinking skills did not have a significant difference. Thus, it can be concluded that male students creative thinking ability are much the same with the female students’ creative thinking ability at the Faculty of Teacher Training and Education, Universitas Kristen Indonesia.Keywords: creative thinking; teaching English; male and female.

Age 4 Predictors of Age 5 Academic Achievement: A Multi-domain Model of Contextual, Parent, and Child Effects

Article

Full-text available

Jan 2023
Child Youth Care Forum

Background Early academic achievement is critical to school readiness and later academic success. Consequently, multi-domain models are needed to identify risk factors and pathways by which they influence early academic achievement.Objective The aim was to advance the development of a multi-domain model of risk factors associated with early academic achievement by examining the effects of contextual, parent, parenting, and child risk and protective factors assessed at age 4 years on reading and mathematics skills at age 5 years.Method The design used structural equation modeling in half-longitudinal models to assess risk factors in a diverse community sample (N = 796) at age 4 and academic achievement at age 5.ResultsModels showing the effects of socioeconomic status, conflict, stress, caretaker depression, parenting, child verbal skills, temperament, and attachment at age 4 years showed good fit for early reading and mathematics achievement. There were numerous significant indirect paths by which distal factors were associated with early academic achievement via caretaker depression, parenting, and child verbal skills for both reading and mathematics.Conclusions In a model including the association of verbal skills and effortful control (EC), the association of EC and early academic achievement is limited. Caretaker depression is not associated with early achievement directly but is associated with achievement indirectly via parenting. Factors associated with child verbal skills were particularly important for both reading and mathematics. Findings clarify the incremental validity of direct associations of multi-domain factors with early academic achievement, and suggest possible pathways between those factors and achievement.

Psychological Constructs as Organizing Principles

Chapter

Full-text available

Jun 2022

Denny Borsboom

Klaas Sijtsma has suggested that psychological constructs, such as those invoked in the study of intelligence, personality, and psychopathology, should be understood as organizing principles with respect to elements of behavior, including item response behavior. In a discussion in the journal Psychometrika , Sijtsma ( Psychometrika, 71 (3), 451–455 (2006)) contrasted this position with the common cause interpretation of Item Response Theory (IRT) models and the associated theory of validity that I had articulated some years earlier (Borsboom, Psychological Review, 111 (4), 1061–1071 (2004)), arguing that this theory of validity was far too strong given the immature status of psychological constructs. In the present chapter, I present an alternative understanding of IRT models in terms of psychometric networks, which is inspired by Sijtsma’s idea of constructs as organizing principles. From the weak premise that psychological constructs organize behaviors, in the sense of identifying behavioral elements that structurally hang together, in the present chapter, I show how one can build up a psychometric approach that can motivate and guide the use of tests in psychology in the absence of strong common cause interpretations.

All we like sheep: The need for reflection and reflexivity in I-O psychology

Article

Mar 2023

Patrick Hyland

In recent years, various I-O psychologists have raised concerns about the state of the field, with some arguing that we are experiencing a collective identity crisis and have lost our way. In this article, I explore why these concerns have emerged by reviewing the development of our field from a philosophy of science perspective. Then I discuss how the concepts of reflection and reflexivity can help us clarify our professional worldviews and find a way forward. I conclude by suggesting ways to incorporate reflection and reflexivity into I-O research and practice. My hope is to spark a conversation about the role that reflection and reflexivity could play in our field.

Street naming and political identity in the postcolonial African city: A social sustainability framework

Article

Feb 2023

Eric Yankson

Street naming in Windhoek, Namibia, traces its political origins to the German colonial period. While the role of toponymy as a tool for political construction in the postcolonial epoch is an important matter of scholarly interest, much work remains to be done on the subject in the sub-Saharan African context. Moreover, the dialectical relationships between political identity and sustainable development have largely not been broached. This study seeks to bridge the noted lacunae by propounding a social sustainability framework for street naming premised on the conceptual tripod of postcolonialism, subaltern geopolitics and memorialization. It employs questionnaires administered to residents, interviews with subject matter experts and planners, as well as field visits, photography and document analysis to dissect the ontological processes, characteristics and meanings encapsulated in Windhoek’s approach to street naming. The social sustainability framework evinces the identity of the postcolonial African city in six main facets: urban toponymy and morphology; urban planning and development; political engagement; social equity and innovation; indigenization and cultural identity; as well as transitional justice. Through a cynosure of Windhoek, a southern African city under-researched in the existing literature, it provides important theoretical and empirical insights regarding the political dynamics of sustainable development.

Abductive Research Methods in Psychological Science

Chapter

May 2022

Brian D. Haig

Making Things Happen

Chapter

Jan 2014

Keith Johnstone

Conceptual Revolutions

Book

Dec 1992

Paul Thagard

Computational Philosophy of Science

Book

Jan 1988

Paul Thagard

Empirical Evaluation and Justification of Methodologies in Psychological Science

Article

Nov 2001

The purpose of this article is to describe a relatively new movement in the history and philosophy of science, naturalism, a form of pragmatism emphasizing that methodological principles are empirical statements. Thus, methodological principles must be evaluated and justified on the same basis as other empirical statements. On this view, methodological statements may be less secure than the specific scientific theories to which they give rise. The authors examined the feasibility of a naturalistic approach to methodology using logical and historical analysis and by contrasting theories that predict new facts versus theories that explain already known facts. They provide examples of how differences over methodological issues in psychology and in science generally may be resolved using a naturalistic, or empirical, approach.

The Logic of Scientific Discovery.

Article

Sep 1977
Syst Zool

Karl R. Popper

Handbook of the Multivariate Experimental Psychology

Article

Mar 1968
AM J PSYCHOL

The Principles of Scientific Thinking

Book

R. Harré

Judgement and Justification.

Article

Mar 1993

An account of the nature of belief, and justified and unjustified belief. The account is teleological: beliefs are justified as the products of cognitive functions established by natural selection. The issue of "realism", of the relation between explanation and the real world is also discussed.

Good science is abductive, not hypothetico-deductive

Article

Jan 1997

W.W. Rozeboom

Dispositions as Causes

Chapter

Jan 2003

Stephen Mumford

An Abductive Theory of Scientific Method

Abstract

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