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• Operationalisation

Last updated 22 Mar 2021

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This term describes when a variable is defined by the researcher and a way of measuring that variable is developed for the research.

This is not always easy and care must be taken to ensure that the method of measurement gives a valid measure for the variable.

The term operationalisation can be applied to independent variables (IV), dependent variables (DV) or co variables (in a correlational design)

Examples of operationalised variables are given in the table below:

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• Operationalisation | A Guide with Examples, Pros & Cons

Operationalisation | A Guide with Examples, Pros & Cons

Published on 6 May 2022 by Pritha Bhandari . Revised on 10 October 2022.

Operationalisation means turning abstract concepts into measurable observations. Although some concepts, like height or age, are easily measured, others, like spirituality or anxiety, are not.

Through operationalisation, you can systematically collect data on processes and phenomena that aren’t directly observable.

• Self-rating scores on a social anxiety scale
• Number of recent behavioural incidents of avoidance of crowded places
• Intensity of physical anxiety symptoms in social situations

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Table of contents

Why operationalisation matters, how to operationalise concepts, strengths of operationalisation, limitations of operationalisation, frequently asked questions about operationalisation.

In quantitative research , it’s important to precisely define the variables that you want to study.

Without transparent and specific operational definitions, researchers may measure irrelevant concepts or inconsistently apply methods. Operationalisation reduces subjectivity and increases the reliability  of your study.

Your choice of operational definition can sometimes affect your results. For example, an experimental intervention for social anxiety may reduce self-rating anxiety scores but not behavioural avoidance of crowded places. This means that your results are context-specific and may not generalise to different real-life settings.

Generally, abstract concepts can be operationalised in many different ways. These differences mean that you may actually measure slightly different aspects of a concept, so it’s important to be specific about what you are measuring.

If you test a hypothesis using multiple operationalisations of a concept, you can check whether your results depend on the type of measure that you use. If your results don’t vary when you use different measures, then they are said to be ‘robust’.

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There are three main steps for operationalisation:

• Identify the main concepts you are interested in studying.
• Choose a variable to represent each of the concepts.
• Select indicators for each of your variables.

Step 1: Identify the main concepts you are interested in studying

Based on your research interests and goals, define your topic and come up with an initial research question .

There are two main concepts in your research question:

• Social media behaviour

Step 2: Choose a variable to represent each of the concepts

Your main concepts may each have many variables , or properties, that you can measure.

For instance, are you going to measure the  amount of sleep or the  quality of sleep? And are you going to measure  how often teenagers use social media,  which social media they use, or when they use it?

• Alternate hypothesis: Lower quality of sleep is related to higher night-time social media use in teenagers.
• Null hypothesis: There is no relation between quality of sleep and night-time social media use in teenagers.

Step 3: Select indicators for each of your variables

To measure your variables, decide on indicators that can represent them numerically.

Sometimes these indicators will be obvious: for example, the amount of sleep is represented by the number of hours per night. But a variable like sleep quality is harder to measure.

You can come up with practical ideas for how to measure variables based on previously published studies. These may include established scales or questionnaires that you can distribute to your participants. If none are available that are appropriate for your sample, you can develop your own scales or questionnaires.

• To measure sleep quality, you give participants wristbands that track sleep phases.
• To measure night-time social media use, you create a questionnaire that asks participants to track how much time they spend using social media in bed.

After operationalising your concepts, it’s important to report your study variables and indicators when writing up your methodology section. You can evaluate how your choice of operationalisation may have affected your results or interpretations in the discussion section.

Operationalisation makes it possible to consistently measure variables across different contexts.

Scientific research is based on observable and measurable findings. Operational definitions break down intangible concepts into recordable characteristics.

Objectivity

A standardised approach for collecting data leaves little room for subjective or biased personal interpretations of observations.

Reliability

A good operationalisation can be used consistently by other researchers. If other people measure the same thing using your operational definition, they should all get the same results.

Operational definitions of concepts can sometimes be problematic.

Underdetermination

Many concepts vary across different time periods and social settings.

For example, poverty is a worldwide phenomenon, but the exact income level that determines poverty can differ significantly across countries.

Reductiveness

Operational definitions can easily miss meaningful and subjective perceptions of concepts by trying to reduce complex concepts to numbers.

For example, asking consumers to rate their satisfaction with a service on a 5-point scale will tell you nothing about why they felt that way.

Lack of universality

Context-specific operationalisations help preserve real-life experiences, but make it hard to compare studies if the measures differ significantly.

For example, corruption can be operationalised in a wide range of ways (e.g., perceptions of corrupt business practices, or frequency of bribe requests from public officials), but the measures may not consistently reflect the same concept.

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Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalisation .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

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Operationalization

Operationalization definition.

Examples of Operational Definitions

Imagine a researcher who is interested in helping curb aggression in schools by exploring if aggression is a response to frustration. To answer the question, the researcher must first define “aggression” and “frustration,” both conceptually and procedurally. In the example of frustration, the conceptual definition may be obstruction of goal-oriented behavior, but this definition is rarely specific enough for research. Therefore, an operational definition is needed that identifies how frustration and aggression will be measured or manipulated. In this example, frustration can be operationally defined in terms of responses to the question: How frustrated are you at this moment? The response options can be (a) not at all, (b) slightly, (c) moderately, and (d) very. The researcher could then classify people as frustrated if they answered “moderately” or “very” on the scale.

The researcher must also operationalize aggression in this particular study. However, one challenge of developing an operational definition is turning abstract concepts into observable (measurable) parts. For example, most people will agree that punching another person in the face with the goal of causing pain counts as an act of aggression, but people may differ on whether teasing counts as aggression. The ambiguity about the exact meaning of a concept is what makes operationalization essential for precise communication of methodological procedures within a study. In this particular example, aggression could be operational-ized as the number of times a student physically hits another person with intention to harm. Thus, having operationally defined the theoretical concepts, the relation between frustration and aggression can be investigated.

The Pros and Cons of Operationalization

Operationalization is an essential component in a theoretically centered science because it provides the means of specifying exactly how a concept is being measured or produced in a particular study. A precise operational definition helps ensure consistency in interpretation and collection of data, and thereby aids in replication and extension of the study. However, because most concepts can be operationally defined in many ways, researchers often disagree about the correspondence between the methods used in a particular study and the theoretical concept. In addition, when definitions become too specific, they are not always applicable or meaningful.

References:

• Emilio, R. (2003). What is defined in operational definitions? The case of operant psychology. Behavior and Philosophy, 31, 111-126.
• Underwood, B. J. (1957). Psychological research. New York: Appleton-Century-Crofts.
• Social Psychology Research Methods

Conceptual engineering and operationalism in psychology

• Original Research
• Open access
• Published: 14 July 2021
• Volume 199 , pages 10615–10637, ( 2021 )

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• Elina Vessonen   ORCID: orcid.org/0000-0002-5931-6267 1  

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This paper applies conceptual engineering to deal with four objections that have been levelled against operationalism in psychology. These objections are: (i) operationalism leads to harmful proliferation of concepts, (ii) operationalism goes hand-in-hand with untenable antirealism, (iii) operationalism leads to arbitrariness in scientific concept formation, and (iv) operationalism is incompatible with the usual conception of scientific measurement. Relying on a formulation of three principles of conceptual engineering, I will argue that there is a useful form of operationalism that does not fall prey to these four objections.

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1 Introduction

Conceptual engineering and operationalism are linked by content and by history. In terms of content, both are concerned with the appropriate formulation of (scientific) concepts. While the details and the domain of applicability of conceptual engineering are debated, it is agreed that conceptual engineering involves the creation and revision of concepts so as to make them appropriate for a given task (Cappelen, 2018 ). For instance, an appropriately engineered concept of truth might be such that it performs relevant explanatory work while avoiding paradoxes that traditional concepts of truth tend to cause (Scharp, 2013 ). Operationalism, on the other hand, is concerned with the formulation of concepts that are appropriate for a specific task, namely, measurement or testing (Chang, 2009 ). For instance, the operationalization of the concept of well-being requires characterizing well-being so that it is defined in terms of a measurement operation or otherwise measurable terms—the choice between these options, and the ontological status of the resulting concept depends on how strict a form of operationalism one subscribes to.

As for the historical link, both operationalism and conceptual engineering have connections to logical empiricism and Rudolf Carnap. The connection to logical empiricism is most obvious in the case of conceptual engineering. Most authors trace the origins of conceptual engineering to the work of Rudolf Carnap—indeed the term “conceptual engineering” reportedly first appears in Richard Creath’s introduction to a book containing correspondence between Carnap and Willard van Orman Quine (Creath, 1990 ). In his 1950 book, Carnap sketched the method of explication, which involves the re-characterization of scientific concepts by balancing four criteria a useful concept ought to fulfill (in Carnap’s view): exactness, similarity to prior usage, fruitfulness and simplicity (Carnap, 1950b ). The method of explication is regularly viewed as an example of, or a precursor to conceptual engineering (e.g. Scharp, 2013 ).

The connection between operationalism and Carnap is more indirect. The first and the most famous explicit characterization of operationalism is due to Nobel Prize winning physicist Percy W. Bridgman, whose ideas on the operational definition of scientific concepts drew the interest of experimental psychologists and logical empiricists in the 1930s and 1940s (Chang, 2009 ; Green, 1992 ). Footnote 1 Bridgman’s focus on concepts defined in testable and measurable terms resonated with logical empiricists, some of whom weighed in on debates about operationalism alongside Bridgman and experimental psychologists (Hempel, 1954 ). While Carnap did not contribute explicitly to these debates, he seems nonetheless to have been on the radar when operationalism was formulated and adopted in psychology (Stevens, 1935 ).

Notwithstanding these connections and similarities (which certainly do not exhaust the relevant historical links), conceptual engineering and operationalism have had very different trajectories in academic research, and specifically within philosophy. Operationalism received quite a bit of attention in its heydays in the 1930s and the 1940s, and arguably had a significant impact on the way researchers started to approach the measurement of psychological attributes (Michell, 1990 ). But the main participants to debates on operationalism—Bridgman, a handful of logical empiricists and experimental psychologists—found fatal flaws in each other’s formulations of operationalism and were unable to agree on how the approach should be applied. By mid-1950s, Bridgman felt that he had “created a Frankenstein” (Bridgman in (Frank, 1956 ), quoted in (Chang, 2009 )), and most philosophers abandoned operationalism, perhaps partly because it was viewed as an extension or a close relative of logical empiricism (Green, 1992 ).

Although several authors have claimed that contemporary psychological measurement continues to proceed along operationalist lines in practice (Lovett & Hood, 2011 ; Maul & McGrane, 2017 ), operationalism qua philosophy has fallen in disrepute. Accordingly, we find contemporary authors writing that operationalism has been “almost uniformly rejected as philosophically unworkable” (Maul, 2017 , p. 60) and that operationalism is an “erroneous philosophy of science” (Meehl, 1995 , p. 267). Two notable exceptions to these condemnations are the works of Hasok Chang ( 2009 , 2017 ) and Uljana Feest ( 2010 , 2012 ), who argue that when appropriately interpreted, operationalism continues to have value for philosophy as well as for science.

Conceptual engineering, by contrast, has been a silent—or at least an unnamed—approach until very recently. Although the approach was reportedly named already in the 1990s (Creath, 1990 ), and something like it has probably been around way before Carnap explicated explication, it is only in the last decade or so that conceptual engineering has become a popular topic in metaphilosophy. One reason for this might be that philosophers have been more interested in analyzing concepts than revising them (or at least it has been common for philosophers to conceive of their activities in these terms). Accordingly, conceptual analysis has been thought of as the method of analytic philosophy (e.g. Scharp, 2013 ). In recent years, conceptual engineering, and the closely related ameliorative analysis (Haslanger, 2000 ), have been explicitly applied to various concepts such as truth and gender. Footnote 2 With increased attention, various challenges and criticisms have also been levelled against conceptual engineering. It is still fair to say that conceptual engineering is not yet a fully formed, single methodology.

So, we have two frameworks that are broadly speaking related to concept formation, one of which has fallen in disrepute, while the other one has only recently become the target of rigorous formulation, investigation and debate. In this paper I am going to discuss the relationship between these two frameworks, operationalism and (Carnapian) conceptual engineering. Focusing on operationalism as it manifests in psychology, I’ll argue that many of the major challenges levelled against operationalism can be reasonably countered if we adopt the perspective of conceptual engineering. In other words, central aspects of (a particular form of) conceptual engineering help redeem (a particular form of) operationalism. This has implications for contemporary psychology, because psychological measurement is frequently thought to have problematic operationalist commitments. Although conceptual engineering might not be necessary for redeeming operationalism, I argue that it does offer one compelling way to defend operationalism in psychology. To my knowledge, the implications of conceptual engineering on psychological operationalism have not been explored before.

I mentioned above that I will apply a particular form of conceptual engineering to counter certain criticisms against operationalism. What if one does not buy this formulation of conceptual engineering? While my discussion will not satisfy such a person that (some of) the problems of operationalism have now been resolved, the discussion at least helps us pinpoint metaphilosophical sources of disagreement about operationalism. In other words, we will be able to trace a disagreement about operationalism to a more general, methodological dispute about concept formation. This might help us avoid talking past each other in debates about operationalism.

The structure of the paper is the following. Section  2 describes operationalism. Section  3 enumerates four common objections to operationalism. Section  4 describes conceptual engineering. Section  5 contains an analysis of operationalism in terms of conceptual engineering. Section  6 concludes with remarks on how the present argument helps contemporary psychology.

2 Operationalism

2.1 what is it.

Operationalism stands for multiple things (Vessonen, 2021 ). For some it is a theory of meaning: it tells us what it means to say that “Maya is depressed”. For others it is a research heuristic: it tells us how to study whether or not Maya is depressed. For yet others operationalism is a metaphysical thesis: it tells us what depression is, for example, that depression is a bodily condition (cf. Flanagan, 1980 ). Because operationalism has many meanings, it is easy to misunderstand the type of operationalism a particular author is defending or critiquing. Indeed, there are papers arguing that S. S. Stevens’ operationalism was widely misunderstood (Feest, 2005 ) and that Bridgman did not subscribe to the views that are often attributed to him (Chang, 2017 ).

Due to their large number, I am not going to go over all possible formulations of operationalism here. Rather, I will focus on operationalism qua the belief that some scientific concepts can and should be defined in terms of a test operation. Footnote 3 For example, intelligence can be defined in terms of an IQ-test, depression in terms of an interviewer-administered rating scale such as the Hamilton Depression Rating Scale, and well-being in terms of a life satisfaction questionnaire.

This rendition might strike as too lenient or local to count as genuine operationalism. Some scholars think that operationalism denotes a universally applied position, which says that all scientific (or psychological) concepts should always be defined in terms of operations. As mentioned, in the history of science, various methodologies and philosophies have been called operationalism (Chang, 2009 ; Feest, 2005 ). Who is to say which definition of operationalism is the correct one, or the one we ought to be interested in? I don’t think there is any way to identify the correct definition of operationalism, but I do have a suggestion for what constitutes a notion of operationalism that is worth discussing. Firstly, when the aim is to understand and improve present-day psychological methodology, the focus should be on forms of operationalism that actually occur in scientific practice. Second, given these aims, we should not waste time on forms of operationalism that are so trivial that no one objects to them. I choose to focus on what one might call local operationalism here—the belief that scientific concepts can and sometimes should be defined in terms of a test operation—for just these reasons. As I will argue below, local operationalism is actually practiced in psychology. Therefore, an analysis of local operationalism matters for real life science. Second, local operationalism does face objections, as I will show in Sect.  3 . Hence there is actually something to defend here. Thus, in this paper operationalism denotes local operationalism.

There is another form of psychological operationalism with which I should juxtapose the above described local operationalism. Feest ( 2005 ) argues that some of the best-known operationalist psychologists, S. S. Stevens and Edward Tolman, subscribed to a form of operationalism that does not fall prey to the typical objections to operationalism. The notion of operationalism that Feest analyses is different from the one I focus on here. Hence Feest’s case, while important, does not salvage local operationalism as described above. Local operationalism needs a defense of its own.

According to Feest ( 2005 , p. 133), when Stevens and Tolman devised operational concepts, they were “partially and temporarily specifying their usage of certain concepts by saying which kinds of empirical indicators they took to be indicative of the referents of the concepts.” I take this to mean that operationalists’ concepts have test-independent referents—e.g. the concept of well-being refers to something that is independent of us testing it—and the purpose of operational definitions is to specify the experimental set up in which that referent manifests itself. Feest ( 2005 , p. 136) elaborates with reference to Stevens’ contention that “to experience is, for the purpose of science, to react discriminately”:

“Did he mean by this that the expression experience has the same meaning as discriminative behavior? Did he mean that the presence of discriminative behavior is always a necessary and sufficient condition for the correct application of the term experience? Based on his research, I think that this is clearly not what he has in mind. […] The question, for him, is how to “get at” particular kinds of phenomenal experience in an experimental context. His assertion is that this can only be done via the behavior of the organism—i.e., that in an experiment, discriminative behavior is a necessary condition for attributing conscious experience to an organism.”

Feest’s analysis of Stevens’ operationalism, and its resistance to common objections, is compelling. But this notion of operationalism, which Feest calls methodological operationalism, is different from the one I am dealing with here. If Stevens had been a local operationalist in my sense of the word, he would have subscribed to the view that experience does mean discriminative behavior. Or in terms of a specific “experience”: a local operationalist would say that e.g. happiness means responding in a certain way to a happiness questionnaire. Here the operational definition does not refer to a test that captures the referent of the concept of happiness, but rather the referent of the concept of happiness is behavior in response to the test. Later we will see that the local operationalist can accept the co-existence of various characterizations of happiness, but for the purposes of research, the local operationalist does reduce happiness to a test.

Why would anyone defend local operationalism, if the philosophically less demanding methodological operationalism is available and can be shown to be useful? It seems to me that methodological operationalists face epistemic issues that other forms of operationalism avoid. In particular, if operational definitions encode the experimental set-ups in which referents of the target concept manifest, it can always be asked whether the set-up really does capture the intended referent. For example, if one were to state that a specific IQ test “gets at” the referent of the concept of intelligence, it is legitimate to demand evidence that the test really does get at that referent. Whereas if intelligence is defined in terms of an IQ test, there is no epistemic gap between the referent of the concept of intelligence and the IQ test—the referent of the concept of intelligence is nothing more than responses to an IQ test. Local operationalism allows safe passage from test results to claims about the target concept by defining away the gap that separates them in methodological operationalism.

2.2 Local operationalism in the wild

Practicing research psychologists rarely take any explicit stance on operationalism. Rather, they routinely engage in the act of operationalization, which is described in many textbooks of psychological measurement. In their textbook, Evans and Rooney ( 2011 ) describe operationalization as an activity that turns a conceptual hypothesis into a research hypothesis. A conceptual hypothesis is an intangible claim such as “Outgoing people have higher well-being than people who keep to themselves” and a research hypothesis is a claim about test outcomes such as “People who score high on standard test E of extraversion give higher ratings of well-being on test W than do people who score low on E.” Here “ratings on test T” is an operational conception of well-being, because it defines well-being in terms of a test, which is a type of scientific operation.

The fact that a researcher operationalizes happiness in terms of a rating on test H does not imply that the researcher believes there is nothing more to happiness than the way people respond to test W. One common, epistemically innocent way to make sense of the act of operationalization is this: the operational concept of well-being gets linked back to the “true” concept of well-being when researchers validate test W. For instance, if the true concept of well-being is “frequent positive affect and infrequent negative affect”, then the researcher needs to ensure that test W tracks this concept by validating the test. Understood in this way, operationalization amounts to making the intended, non-operational target concept measurable, rather than reducing the concept of interest to a more superficial, test-specific concept.

Now, it is true that psychologists routinely validate their measures: they check correlations between the proposed test and tests of related concepts, study relations between different items (i.e. questions) within the test, and perform factor analysis or an item response theoretic analysis to model the structure of responses to the measure (for an overview of these methods, see Markus & Borsboom, 2013 ; Nunnally & Bernstein, 1994 ). Footnote 4 But does this guarantee that the measure tracks the intended, non-operationally-defined target concept? Many psychometricians, methodologists and philosophers of science have argued that the typical methods of validation in psychology are simply insufficient to establish that a psychological test tracks the concept of interest (Alexandrova & Haybron, 2016 ; Borsboom, 2006 ; McClimans et al., 2017 ). For example, Andrew Maul ( 2017 ) showed in a series of studies that fake psychological tests, that is, tests that e.g. ask questions about non-existent psychological properties, would be classified as valid under common criteria for assessing the properties of a test. Why might tests fall short of tracking a test-independent attribute? Maul et al. ( 2013 ) argue that when the methods of validation that are routinely used in contemporary psychology were originally formulated, their creators were heavily influenced by logical empiricism and behaviorism. Some validation practices might not allow inferences to the non-operational concept of interest, because they were not even intended to warrant these kinds of inferences.

Whatever the reason for their existence, what do the shortcomings of validation methods imply regarding the role of operationalism in psychology? If the validation methods are insufficient for ensuring that the measure tracks a non-operational concept, then claims made on the basis of those measures are superficial claims about test-behavior, not about the concept of interest. In other words, claims about the relation between well-being and outgoingness would simply be claims about two kinds of test results. It may be that this slip from claims about non-operational concepts to claims about operational concepts is sometimes accidental: a researcher is unaware of the fact that the validation does not establish a sufficient link between the test and the non-operational concept of interest. On the other hand, it may be that in the absence of (knowledge about) superior methods, research psychologists have simply accepted that the best one can do is to characterize the target concept in terms of a test.

Above I have presented two interpretations of how operationalism might slip into psychology: by accident or as an inevitable consequence of lack of (knowledge about) better methods. But there are other, more radical interpretations as well. For instance, Joel Michell ( 1997 , 2008 ) has criticized psychological measurement extensively, arguing that ever since S. S. Stevens popularized operationalism, psychologists have conceptualized measurement in operationalist terms. In other words, operationalism is not an accidental slip, but rather the rewriting of non-operational concepts in operational terms is the accepted framework for psychological measurement. For the purposes of this paper, we need not settle once and for all the manner in which operationalism slips or arrives to psychology. It suffices to note that psychologists at least sometimes make claims where a concept that has a broad, non-operational meaning gets reduced to an operationally defined concept.

3 Objections to operationalism

In this section I will introduce some common objections levelled against operationalism, focusing on criticisms of the type of operationalism that is (allegedly) practiced in psychology. The four objections I consider are:

operationalism leads to harmful proliferation of concepts,

operationalism goes hand-in-hand with untenable anti-realism,

operationalism leads to arbitrariness in scientific concept formation, and

operationalism is incompatible with the usual conception of scientific measurement.

3.1 Proliferation

In debates about operationalism, one often hears the charge that operationalism leads to harmful proliferation of concepts (e.g. Hempel, 1966 ; Hull, 1968 ; Leahey, 1980 ; Maul et al., 2013 ). If different operations each define a new concept, scientists will drown in concepts—or so the objection goes. The prospect of drowning in concepts sounds odd and undesirable on its own, but it also has more tangible, harmful consequences. Firstly, it is inefficient to produce more and more concepts in situations where one or two could do the job. Second, an ever-increasing stock of operational concepts is a threat to comparability. If researchers have two tests of well-being, and one of them is strongly positively correlated with income while the other is not, are they to conclude that well-being both is and is not associated with income? They might have to accept that conclusion, if they believe that both tests define a distinct concept of well-being.

3.2 Antirealism

It is often thought that operationalism is an antirealist view (Lovett & Hood, 2011 ; Maul & McGrane, 2017 ). Footnote 5 It is, in other words, thought that operationalists deny the test-independent existence of the attributes their measures apparently pertain to and/or they deny the possibility of epistemic access to a test-independent attribute. On this view, for example, operationalists define depression in terms of a test because they believe either (i) that there is nothing more to depression than the way people behave in depression tests (i.e. there is no independent cause to these symptoms), or (ii) that there is no way to access the test-independent determinant of that behaviour. By test-independent attribute I mean an attribute that can be fully characterized without mentioning the operation used to measure it. For example, cognitive assessment of satisfaction with one’s life (whether or not it is observed) is a test-independent attribute, and the concept that denotes that attribute is a non-operational concept. On the other hand, self-reports in response to the Satisfaction with Life Scale is a test-dependent attribute (or property, or quality, whatever term one likes), and it is denoted by an operational concept.

There are, of course, philosophical defences of antirealism. But many people believe that science, including psychology, ought to study entities and phenomena that exist (in some sense) independent of scientists’ tests and measures. To such people, information about how people answer to questions about their moods seems like a pale and useless shadow compared to substantive knowledge about what drives those test responses, e.g. depression qua independent, potentially causally efficacious aspect or property of the human mind. Of course, it might be that some terms psychologists use do not have any test-independent referents—perhaps there is no unified, causally efficacious attribute or process that underlies the symptoms that depression tests inquire about. But that is a matter of investigation. To “go operationalist”(and thereby antirealist) prior to such an investigation is unnecessary defeatism, the critic would say.

3.3 Arbitrary concepts

The third critique flows from the second. According to this objection, if some of the targets of psychological research—happiness, depression, personality and so on—have no test-independent reality, then nothing constraints the formation of those concepts, or the construction of the measures meant to capture those concepts. Another way to state this problem is to say that if there is no truth about what, say, well-being is, then nothing stops researchers from defining well-being in whatever (operational) way pleases them. Andrew Maul and Joshua McGrane make this argument in their 2017 paper. In response to Hand ( 2016 ), who argues that the operationalist underpinnings of (some parts of) psychological measurement do not imply that anything goes, Maul and McGrane ( 2017 , p. 2) assert that “when measurement is freed from any reality constraints, the inevitable outcome is (as repeatedly demonstrated throughout the social and psychological sciences and Hand’s volume) that anything does go.”

An anything goes -approach to concept formation would certainly be a disaster. Most importantly, if researchers can define well-being and intelligence in any way they want, then they can also force almost any empirical claim to come out true. Well-being measure W fails to correlate with income in a convenient manner? No problem! Just revise the measure, and the corresponding operational concept of well-being, until the desired correlation emerges. This is ideology, not science. Furthermore, unconstrained concept formation certainly exacerbates problem number i), concept proliferation.

3.4 Non-measurement

Several authors have also noted that operationalism seems to be unable to accommodate common notions of what measurement is or requires. For instance, Donald Gillies ( 1972 ) argued that strict operationalists cannot make sense of claims such as “Type A thermometers measure temperature more accurately than Type B thermometers”. If every measure defines its target concept, there is no way scientists can compare two measures on their ability to track common target values. Put in another way, operationalism seems unable to accommodate the idea that some measurement results “contain” more error than others. The objection, then, is that an approach to measurement that cannot make sense of error and accuracy is simply not science (Michell, 1990 ).

This objection is interesting, because it reveals a tension or an inconsistency in psychological measurement: on the one hand, it is characterized by operationalist tendencies; on the other hand, psychologists take steps to analyze and reduce sources of error in measurement. If, as per objection ii), operationalism is an antirealist view, then psychologists’ efforts to get rid of error are puzzling. If there is nothing scientists’ measures are trying to represent or track correctly, what is there to be in error about? Footnote 6

4 Three aspects of Carnapian conceptual engineering

Many challenges have been levelled against conceptual engineering in recent years. Due to the open questions and as yet unanswered criticisms, there are various ways to conceptualize conceptual engineering, and not all of them are compatible with each other. In this section, I am going to outline three aspects of conceptual engineering that I draw from the work of Rudolf Carnap (for a thorough discussion of Carnapian explication see Brun, 2016 ).

Here are the three Carnapian insights I use to try to rescue operationalism from some of its critics:

concept formation requires the balancing of (epistemic) values;

concept formation is constrained both from the side of human interest and from the side of reality; and

concept formation is an iterative process involving conceptual pluralism.

As mentioned in the introduction, I think the analysis that follows is valuable even if the reader finds one or more of these three principles controversial. In that case, while my discussion will not satisfy such a person that the problems of operationalism have now been resolved, the discussion allows us to pinpoint the metaphilosophical source of disagreement about operationalism. For instance, in Sect.  5 I will argue against objection (iii), which concerns the alleged anything goes -nature of operationalist concept formation, by appealing to principle (a), which states that concept formation involves the balancing of epistemic values. This allows us to pinpoint at least one reason for why researchers disagree on whether operationalism is an anything goes -methodology. But before re-analyzing operationalism, let me introduce each conceptual engineering principle in turn.

4.1 Values in concept formation

When Carnap ( 1950b ) laid out his method of conceptual engineering, he suggested that the re-characterization of inexact, pre-theoretic or currently used scientific concepts should be done in light of the following criteria: similarity to the pre-existing concept, exactness, fruitfulness and simplicity. The similarity criterion says, in effect, that there must be some continuity between the connotations and/or denotations of the pre-existing concept and those of the proposed explication—otherwise the exercise would amount to changing the subject (see Strawson, 1963 and Carnap’s response in the same volume). Exactness means connecting the new concept to a network of pre-existing concepts by means of a definition or another mode of characterization. Fruitfulness, arguably the most important of the criteria, requires that the concept is useful for the formulation of scientific generalizations. I call these criteria epistemic values, because they specify properties of concepts scientists tend to find valuable.

While these criteria might have sufficed for Carnap’s project of explicating probability, they may need to be complemented or replaced by other values in other contexts. For instance, in sciences that deal with thick or value-laden concepts, such as the science of well-being, scientists might need criteria such as impartiality or avoidance of value-imposition to form an appropriate concept (Alexandrova, 2016 ). Likewise, some authors have argued that while many scientific concepts are not useful for generalizations, they are still fruitful in other ways (Shepherd & Justus, 2015 ). Luckily, we need not settle which precise values are acceptable in the context of concept formation. It suffices to note that conceptual engineers balance different values when formulating new concepts.

4.2 Preferences meet reality

The Carnapian focus on values might make concept formation look like an activity that is all about what scientists want from their concepts, not about what reality, Footnote 7 or empirical evidence that testifies for reality, says about those concepts. And indeed, Carnap often expressed his belief that people should tolerate various linguistic or conceptual systems, and grant scientists “ the freedom to use any form of expression which seems useful to them” (Carnap, 1950a , p. 40). Is conceptual engineering precisely the kind of antirealist arbitrariness that operationalists seem to engage in?

The role of empirical evidence comes to view when we zoom in on the word “useful” in the above quotes, and interpret it in terms of Carnap’s criteria of explication. Remember that according to Carnap, a scientific concept should be fruitful in the sense that it is useful for the formulation of many empirical generalizations (or logical theorems) ( 1950b , p. 7). But what does it take for a concept to be useful for the formulation of such generalizations? Presumably the extension of that concept should have causal or other regular relationships with other entities or attributes. For instance, suppose that a particular definition of depression says that depression is a collection of five different mood-related symptoms. This concept allows us to formulate (non-trivial) generalizations, if reliable methods of investigation show robust relations between the specified depressive symptoms on the one hand and, say, proposed causes and treatments of those symptoms on the other. In other words, confirming that this concept of depression is fruitful requires empirical investigation of relations between the denotation of the proposed concept and other entities, attributes and processes. Footnote 8

In the Carnapian vision, then, while scientists formulate and choose concepts according to their judgment of the fulfillment of certain epistemic values, those judgments are informed by, and revised in light of empirical evidence about the properties and behavior of the extension of the proposed concept. But one might still ask: which one has the upper hand? If scientists’ preferences do not cohere with the results of empirical enquiry, shouldn’t truth prevail over preferences?

Now, there are many ways in which the results of empirical enquiry and the conceptual preferences of scientists rub against each other. I cannot provide a manual for dealing with each situation here. But let me note that empirical evidence often leaves room for various conceptual choices. For instance: suppose one believes that a collection of symptoms counts as a disease only if those symptoms are caused by a unified biological mechanism. Suppose further that after decades of searching, researchers cannot find any such mechanism behind depressive symptoms, but rather, the symptoms seem to be caused by a messy network of social and biological processes. Does this mean that empirical evidence has shown that depression cannot be conceptualized as a disease—that it would be unscientific to believe that depression is a disease?

Well, no. As Quine taught decades ago, one can make legitimate adjustments in various nodes of the web of scientific beliefs. In this case, researchers can revise the notion that a unified biological mechanism is a necessary condition for something to count as a disease. Or they can limit the number of symptoms they count as essential to depression and see if they can find a mechanism that underlies variation in those symptoms. There is nothing about the above-described empirical results that prohibits either of these conceptual moves. To decide which move is the most appropriate, scientists need to consider other evidence and trade-offs between epistemic values: Would the narrower concept of depression be too dissimilar to the pre-theoretic notion of depression? Would the broadened concept of disease be too complicated? Would disease cease to be a fruitful concept? As these considerations illustrate, empirical evidence and human judgment are inextricably intertwined in conceptual engineering.

4.3 Iteration and pluralism

The above outlined interplay of explications and empirical investigations fits neatly with Carnap’s idea of concept formation as a stepwise, iterative process. In the 1950 book, he writes:

“[…] the historical development of the language is often as follows: a certain feature of events observed in nature is first described with the help of a classificatory concept; later a comparative concept is used instead of or in addition to the classificatory concept; and, still later, a quantitative concept is introduced.” ( 1950b , p. 12).

Carnap uses the concept of temperature as an example. He suggests that temperature was initially a qualitative concept denoting sensation-based judgments of hot and cold but then gradually developed into the present-day quantitative concept via a comparative conceptualization. In this process, the new concept is built on the old one, in the sense that it retains some of the meanings or usages of the prior concept, but also improves it in terms of certain epistemic values, especially fruitfulness. While Carnap does not make reference to historical evidence, the philosophico-historical work of Hasok Chang ( 2004 ) has shown that the development of the scientific concept of temperature can indeed be understood as a kind of iterative process of self-correction and enrichment of earlier concepts.

Now, it is unrealistic to think that all scientists in a given field at a given time subscribe to one characterization of the central concept of that field. It is equally unrealistic to think that they revise that characterization synchronously in exactly the same way. Instead, a scientific field often contains a plurality of conceptions that are denoted by the same term and that share some connotations. For instance, well-being has been conceptualized both in terms of preference satisfaction and in terms of the ratio of positive to negative affect. While these concepts are different and would likely lead to different kinds of empirical generalizations, they are both explications of well-being, that is, of what is good for a person (Tiberius, 2006 ).

This kind of plurality of side-by-side existing, potentially competing conceptualization is not only a reality, but also likely to occur whenever Carnapian conceptual engineering is exercised. This is because scientists are likely to make trade-offs between epistemic values in different ways, leading them to propose and work with different kinds of explications. For instance, to avoid imposition of values, a researcher might need a rather intricate, multidimensional concept of well-being, while a scientist who needs to measure well-being on a quantitative scale probably prefers a unidimensional concept. Furthermore, different researchers might take different aspects of the shared pre-theoretic notion as their raw material when explicating a scientific concept. For example, one researcher may focus on the centrality of low mood in lay conceptions of depression, while others regard complexity and multidimensionality as a key aspect of depression.

The upshot is that when practicing conceptual engineering, scientists are likely to have to deal with a plurality of side-by-side existing explications, each indexed to the same or similar term. But how does one manage a multitude of concepts? Carnap believed in a kind survival of the fittest when it comes to navigating and managing conceptual pluralism. He wrote:

“Let us grant to those who work in any special field of investigation the freedom to use any form of expression which seems useful to them; the work in the field will sooner or later lead to the elimination of those forms which have no useful function.” ( 1950a , p. 40).

While it is too optimistic to think that this process never misses a useful concept, or that it always converges on the most useful concept(s), iterative revisions and the assessment of concepts in terms of epistemic values puts at least some breaks on the expansion of the library of concepts.

With this final piece of the conceptual engineering framework in hand, we are ready to apply these insights to operationalism. In the next section, I will go over the four problems I identified earlier and argue that thinking in terms of conceptual engineering helps resolve criticisms levelled against operationalism. While it remains the case that not all forms of operationalism can be defended (at least not by the below arguments), a conceptual engineering approach reveals a form of operationalism that does not crumble under the most common criticisms.

The following discussion also reveals that contemporary psychology already contains measure validation practices that would allow researchers to implement the principles of conceptual engineering. I will use these practices as examples of how operationalist conceptual engineering would work in practice. But let me emphasize that these examples do not mean that contemporary psychologists already think in terms of conceptual engineering or the principles outlined above. As mentioned earlier, there is considerable disagreement regarding what different validation methods are intended to establish and what they manage to establish in reality. I will discuss the implications of conceptual engineering for contemporary psychology in Sect.  6 .

5 Conceptual engineering redeems operationalism

5.1 values, proliferation and comparability.

The first charge against operationalism was that it leads to uncontrollable proliferation of concepts. Isn’t it terribly inefficient for each measure to define its own concept? How can scientists ever compare such measures? In the conceptual engineering edition of operationalism, assessment of concepts in terms of epistemic values ensures that concepts are not formed arbitrarily. Thus, operational concepts of depression will proliferate only in so far as each demonstrably serves a useful function. Furthermore, the process of iterative revisions ensures that old operational concepts are replaced by new ones that fare better in terms of epistemic values. This kind of revision already takes place: the process of constructing a measure in psychology is characterized by proposing test items (i.e. questions), testing their usefulness or validity, deleting poor items and re-testing the resulting revised measure (e.g. de Vet et al., 2011 ). So, the proliferation of operational concepts is not necessarily as wild as one might suspect.

What about comparability? If each measure of well-being defines its own concept, then it is possible that different scientists will arrive at very different, even contradictory generalizations on the basis of different measures. But this problem is mitigated by the fact that one of the criteria for a useful concept is similarity to a pre-theoretic or earlier scientific concept. In order to ensure conceptual continuity, and to avoid the objection that one is changing the subject, every operational concept of well-being should incorporate some pre-theoretic connotations of well-being. Indeed, this kind of conceptual continuity can be established by at least one already existing method of measure validation. To ensure so called face validity, researchers ask experts and laypeople to assess whether or not test questions pertain to the intended concept. For instance, when Tennant et al. validated a mental health and well-being questionnaire called the Affectometer 2, they asked focus groups about whether or not they thought the questions on the Affectometer 2 pertain to mental health and well-being (Tennant et al. 2006 ). While such a process does not ensure that different measures of mental well-being capture exactly the same aspects of mental well-being, it is likely to rule out gross misalignments in generalizations made on the basis of each measure.

Another way to build comparability between two operational concepts is to bypass the question of content and focus on statistical correspondences. For instance, Zimmerman et al. ( 2004 ) looked at how subjects responded to two depression measures, the Hamilton Depression Rating Scale (HDRS) and the Montgomery Åsberg Depression Rating Scale (MADRS), and then used linear regression to see how scores on MADRS correspond to scores on HDRS. In particular, Zimmerman et al. sought the MADRS total score that corresponds to HDRS scores equal or below 7, because in depression research this latter score is often treated as an indicator of remission from depression. Now, it is well-known (and easy to check) that HDRS and MADRS conceptualize depression in somewhat different ways. For example, HDRS lays much more emphasis on somatic symptoms than MADRS. The study of Zimmerman et al. can therefore be understood as an attempt to build comparability between MADRS-based claims about remission and HDRS-based claims about remission despite the fact that the two measures do not capture the same concept of depression.

By providing the above examples I do not mean to suggest that these methods have no downsides or blind spots. The point is just that researchers can build some types of comparability into their measures, even if they accept that the measures define different operational concepts. The problems of proliferation and comparability are not insurmountable.

5.2 Antirealism, caution, and the import of empirical evidence

Consider a fictional intelligence researcher in the early twentieth century. She has constructed a test that requires the test-taker to solve various problems: there are memory-related tasks, vocabulary tests, mathematical puzzles, tests of inter-personal skills, and so on. Suppose the intended long-term goal of the scientist is to measure intelligence understood in non-operational terms, but she does not know what kind of test-independent attribute or property determines success on these kinds of tests. In other words, she does not know whether test success is driven by a specific, unified cognitive skill, a cluster of such skills, a physical property of the brain, learned skills, test-wiseness, acculturation, or some other unobservable, but stable and easily characterizable property. What should she say when people ask what her measure measures? One option is to say that, for now, she defines intelligence in terms of the test. Intelligence is what the test tests, she argues, echoing Edwin G. Boring’s ( 1923 ) famous dictum.

How is this a solution rather than a nasty cop-out? The answer is in the above phrase “for now”. Because the researcher is cautious, she does not want to hazard a guess, even a relatively educated one, at what kind of test-independent attribute causes or determines test responses—indeed, some of the early developers of intelligence tests were reluctant to guess what underlies differences between test takers scores on such tests (see e.g. quotes in Michell ( 2012 )). In other words, since she does not understand the nature of what her measure captures, she cannot say whether it can be legitimately characterized as intelligence. But the researcher still thinks that the tests she has selected reflect pre-theoretic connotations of intelligence, that is, that there is continuity between the operational concept of intelligence and pre-theoretic connotations of intelligence. Indeed, intelligence connotes problem-solving, so a test involving problem-solving has a certain continuity with pre-theoretic views of intelligence. Furthermore, the researcher believes her measure has potential to be developed, and that an appropriately revised measure might well capture something that can legitimately be denoted with a non-operational concept of intelligence. For these reasons, the label “intelligence” is appropriate for the operational concept the test battery defines, even though the researcher does not know what kind of test-independent process or entity determines test responses.

In a situation such as the one outlined above, operationalism does not entail antirealism. In other words, the act of defining the target attribute in terms of a test does not signal (i) a denial of the test-independent existence of intelligence, or (ii) a denial of the possibility of epistemic access to the determinants of test responses. Rather, this kind of operationalism is a temporary solution motivated by caution. What the caution-driven operationalist is implicitly saying is this: there might be a defensible conception of intelligence that has a test-independent referent, but I do not yet know what that concept would look like. Furthermore, that concept might be capturable by empirical means, but I do not yet know what those means are. To avoid unwarranted inferences, I will go for an operational notion of intelligence.

Caution-driven operationalism utilizes (at least) two Carnapian principles: iteration and the idea that human judgment and pushback from reality are intertwined in the process of concept formation. To see this, consider how the revision of a concept of intelligence might go. The first revisions will likely be based on things like correlations between tests, stability of test results over time, and other such “operation-level” properties, that is, properties that do not in and of themselves say very much about what kind of test-independent attribute determines test results. Footnote 9 But revision of the measure in light of evidence of stability and correlations between tests might help researchers zoom in on that determinant in the long run. This is because one explanation of why test results are stable over time is that the test captures the same test-independent attribute on each administration. Likewise, one explanation of why two tests correlate is that they measure the same attribute. If researchers are lucky, the triangulation of various types of evidence, and revision of the measure in light of that evidence, might produce knowledge about the nature of the determinant of test-responses (on triangulation, see e.g. Kuorikoski & Marchionni, 2016 ). Through these iterative steps, then, scientists might gradually gain enough confidence to make the step from a cautious operational concept to a bold non-operational concept.

It is not a novel idea that validation can help researchers zoom in on what exactly a test measures. My aim here is just to give an interpretation of these standard validation activities in terms of conceptual engineering, and to thereby formulate a version of operationalism that is not doomed to antirealism. Vis-à-vis antirealism, the point of the above (admittedly oversimplified) depiction of operationalist conceptual engineering is this. In this process, every conceptual revision—that is, every Carnapian epistemic iteration—whether it is from an operational concept to another operational one or from an operational concept to a non-operational one, involves a combination of human judgment and empirical evidence that testifies for reality. When doing the operational-to-operational-move, a researcher may well admit that test-independent properties exist and that they exert an influence on the way their measure behaves. But she chooses not to formulate her concept in terms of test-independent properties, because she judges that the evidence is not sufficient to warrant claims about what the relevant test-independent properties are like. Test-independent reality does push back, but the researcher chooses to not describe that reality when characterizing her target concept. This kind of caution-driven operationalism is perfectly compatible with realism and may indeed end up morphing into non-operational measurement in the long run.

These points about epistemic iteration are meant to illustrate that operationalists can accept the existence and possibility of epistemic access to test-independent entities, and hence need not be anti-realists. But do the tools psychologists currently use allow researchers to bootstrap their way from operational concepts to non-operational one? I do think that factor models, think aloud -studies, expert interviews, reliability analyses and other psychometric techniques allow scientists to gradually form a more coherent picture of the nature of the test-independent phenomena tests track. But this takes decades. For instance, in the last century, intelligence researchers’ have made considerable progress regarding the nature of the entity or process that determines IQ test responses, but multiple characterizations of the nature of intelligence continue to be compatible with the existing evidence base (Van Der Maas et al., 2006 ). Luckily, I do not need to come up with an instant psychometric method for uncovering test-independent attributes here. In fact, the difficulty of working one’s way from operational to non-operational concepts is good news for caution-driven operationalism. Because the epistemic road to credible non-operational concepts is so treacherous, it is good that researchers have a defensible operationalist position to fall back on, when the alternative is a very uncertain inference to a test-independent phenomenon.

5.3 Vetting values, respecting reality

The arbitrariness charge is now easy to rebut. I have already argued that conceptual engineering inspired operationalism uses epistemic values to assess proposed concepts. Concepts that fare poorly in this assessment are discarded, which means that it is not the case that anything goes. But doesn’t the problem come back at the level of values? If we allow scientists to determine what properties a concept should have, and any property goes, then aren’t researchers back to drowning in arbitrary concepts? I believe that scientific education, peer-review, criticism and public debate will make sure that not any value goes when it comes to concept formation. That is, the processes through which scientists justify their concepts to colleagues (and in some cases to the public) will tend to weed out poor valuative arguments for concepts. For instance, if a scientist discards the value of continuity (i.e. Carnap’s similarity) when explicating well-being, other scientists and laypeople are likely to reject the resulting notion as irrelevant to the science of well-being.

What about Maul and McGrane’s ( 2017 , p. 2) argument that “when measurement is freed from any reality constraints, the inevitable outcome is […] that anything [goes]”? Above I showed that operationalists need not deny the existence of, or the possibility of epistemic access to non-operationally characterized psychological qualities. I also argued that reality seeps in when researchers assess concepts in terms of values. That is to say, when scientists revise concepts in light of different epistemic values, they are often “consulting reality” whether or not they allow themselves to make claims about the nature of that reality. For instance, when researchers investigate statistical associations between two operational concepts of well-being, the real reasons that drive people to respond similarly or dissimilarly to each questionnaire will tend to show up in the association scientists observe (unless it is confounded, but of course there are techniques for handling that). Operationalism in the conceptual engineering mode is not freed from the constraints of reality, and therefore Maul and McGrane’s argument does not apply.

5.4 Precision, accuracy, and steps in between

The final criticism I will deal with states that operationalism is suspicious, because it cannot make sense of two central aspects of scientific measurement: error and accuracy. Now, I think it is true that operationalists cannot, strictly speaking, evaluate the accuracy of their own measures, because they want to refrain from claims about what test-independent attribute their measure tracks. Put in another way, operationalists are always measuring their target concept by definition, and thus there is nothing they can be in error about. But even though operationalists cannot assess accuracy, they can evaluate precision—that is, the consistency of results when numerous measurements are taken under similar conditions. Luckily the psychometric theory of reliability is really a theory of precision or repeatability (Cronbach et al., 1963 ; Kline, 1998 , p. 26; Nunnally & Bernstein, 1994 , p. 248), which means that operationalists have their tools cut out for them.

More ambitiously, operationalists can take stabs at the assessment of accuracy as soon as they dare to take off their operationalist hats and propose non-operational versions of their operational concepts. Recall, again, that operationalists can admit that something test-independent underlies their measure—their scientific claims are just not about that something, at least not yet. With this admission, operationalists might occasionally be tempted to propose a non-operational revision of the operational concept, and then proceed to check for accuracy. For example, suppose an operationalist researcher hypothesizes that her well-being related questionnaire tracks differences in people’s cognitive judgment of how life is going for them overall. One of the first steps in checking for accuracy is to ensure that people indeed engage in such a cognitive judgment when they answer the questionnaire. One way to assess this is to do a think-aloud -study, where subjects speak out loud their thought process when they complete the questionnaire (cf. Al-Janabi et al., 2013 ). If the thinking process matches the hypothesized process, researchers might then go on to investigate how accurately the measure tracks changes in that process. With mounting evidence, they might eventually be convinced that the questionnaire accurately tracks a non-operational conception of well-being, namely, differences in people’s cognitive judgment of how life is going for them overall.

6 Conclusion and implications for psychology

I have argued that the perspective of conceptual engineering allows us to formulate an operationalism that is able to resist some of the main criticisms previously levelled against it. This operationalism is not motivated by antirealism but rather by caution. Its proponents revise their concepts iteratively in light of evidence of the fulfillment of (epistemic) values, which are vetted in scientific discourse. These iterations are also informed by the way a test-independent property or properties influences the behavior of the measure that is under construction and validation. But the operationalist, being operationalist, is unwilling to extend their inferences to the nature of test-independent properties when those properties are poorly understood. Still, the operationalist project might turn into a non-operationalist project if at some point evidence warrants sufficient confidence in characterizing the target concept in non-operational terms.

Although I have not argued for it here, I think that this kind of operationalism is needed in psychology. The reason is two-fold. With all the bad press operationalism has received, a scientist who wants their work respected is better of being vague about what their measure measures instead of declaring operationalist commitments. Indeed, an oft-cited methodology article claims that “[b]eing precise does not make us operationalists”, which would be an unnecessary assurance, if vague definitions were not sometimes used to pre-empt accusations of operationalism (Wilkinson, 1999 , p. 597). But vagueness leads to confusion. And as I have argued, there is debate about whether or not psychologists’ bread-and-butter validation methods reliably ensure that measures indeed track non-operational concepts. I think that a defensible form of operationalism might help us unravel this confusion, because it gives psychologists license to be open about the potential limits of their measures and concepts. My concrete proposal, then, is that researchers should openly admit to operationalism—or otherwise come up with an argument to support a richer, test-independent reading of the claims they make. The rhetorical move where operational conceptions magically give rise to non-operational claims does not serve psychology well.

Another, related reason for the need for operationalism is that in psychology it is incredibly hard to establish the claim that a measure tracks an acceptable, non-operational concept. The reasons for this are numerous, but consider the following, for example:

People learn, remember, tire, and get bored, which makes it difficult to construct a measure that reliably tracks the same attribute from one administration to another.

People react to testing, complicating the inference from the testing situation to “the real world”.

People lie, exaggerate and know themselves poorly, which biases test responses.

Different people might interpret questions differently, which threatens comparability.

The processes underlying psychological phenomena, such as depression, are likely so complicated that it is hard to enumerate and take into account all the factors that are relevant to measurement.

Many people have a stake in the battle for how psychological properties, such as intelligence, are conceptualized. Therefore, the adequacy of any proposed measure is likely to be questioned by someone .

Psychologists and psychometricians have of course developed an impressive array of tools to overcome these problems, but many concepts still resist measurement in non-operationalist terms. Operationalist concepts might in some cases be the best scientists can have. Those concepts can serve useful functions while also functioning as stepping stones in efforts to create measures of non-operational concepts. An operational conception of depression, for instance, can be useful for the prediction of remission and relapse. An operational concept of well-being can tell researchers something about how a divorce or a promotion changes people’s lives.

Naturally, while researchers are still building non-operational concepts, the usage of operational concepts comes with a threat of confusion of its own. If scientists use various conceptualizations of well-being, they might end up with differing views of how well-being is affected by divorce or a promotion. The tools of comparability building discussed in Sect.  5 mitigate this somewhat but might not resolve it entirely. The least scientists can do, I think, is that when they make scientific claims on the basis of a given operational concept, they clearly index the claim to the relevant measure. For example, a claim about the relation between income and well-being would take the form: “Well-being is associated with income in such and such a manner, when well-being is construed in terms of satisfaction of preferences as expressed in questionnaire Q.” This may seem cumbersome, but similarly precise definitions are already considered methodological best practice in psychology (Wilkinson, 1999 ).

It can be argued that Peirce ( 1878 ) and Watson ( 1913 ) characterized something similar without using the name operationalism.

For a comparison of the two appropaches, see Dutilh Novaes ( 2020 ).

The exact definition of an operation is debated. In this paper we can focus on relatively obvious cases such as paper-and-pencil tests. See Bridgman ( 1959 ) for a classification.

I shall use the term validation in this broad sense throughout the paper.

Antirealism (and realism) comes in many forms but I will focus on the styles most relevant to operationalism, as described in (Lovett & Hood, 2011 ). See Trout ( 1998 ) on types of realism.

Borsboom, Mellenbergh and van Heerden ( 2003 ) make a similar point when they argue that a psychometric activity known as latent variable modelling cannot be understood in operationalist terms (albeit they use the word constructivism).

By reality I shall mean the things, attributes and processes that push back when scientists use empirical means to probe the nature of the extension of a proposed concept. In other words, reality is what signals when scientists ask the internal question: Given that I have defined concept C in terms of X, what can I say about the denotation of C on the basis of the empirical evidence? Following Salmon ( 1994 ), I believe that these kinds of questions would have been acceptable and meaningful to Carnap, and that it is therefore not wrong to call my branch of conceptual engineering “Carnapian”. On external and internal questions, see (Carnap, 1934 ).

A philosophical assumption behind this description is that characterizations of concepts usually do not exhaust all stable properties of the denotation of that concept. For instance, the concept “an overall cognitive judgment of how life is going for an individual” denotes a certain process or attribute, but it is still possible to be surprised by empirical evidence about that process—say, the neural phenomena underlying that judgment, the effect of age on that judgment, and so on.

Two tests might correlate e.g. because (i) they are biased in the same way, ii) they tap two distinct, causally related attributes, (iii) they tap two distinct, accidentally related attributes, or (iv) they track different aspect of a multidimensional attribute.

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I thank three anonymous reviewers for their helpful comments. I am also grateful to participants of the Concept Formation in the Natural and Social Sciences -workshop (University of Zurich, 2018) for conversations and comments.

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Vessonen, E. Conceptual engineering and operationalism in psychology. Synthese 199 , 10615–10637 (2021). https://doi.org/10.1007/s11229-021-03261-x

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Operational Hypothesis

An Operational Hypothesis is a testable statement or prediction made in research that not only proposes a relationship between two or more variables but also clearly defines those variables in operational terms, meaning how they will be measured or manipulated within the study. It forms the basis of an experiment that seeks to prove or disprove the assumed relationship, thus helping to drive scientific research.

The Core Components of an Operational Hypothesis

Understanding an operational hypothesis involves identifying its key components and how they interact.

The Variables

An operational hypothesis must contain two or more variables — factors that can be manipulated, controlled, or measured in an experiment.

The Proposed Relationship

Beyond identifying the variables, an operational hypothesis specifies the type of relationship expected between them. This could be a correlation, a cause-and-effect relationship, or another type of association.

The Importance of Operationalizing Variables

Operationalizing variables — defining them in measurable terms — is a critical step in forming an operational hypothesis. This process ensures the variables are quantifiable, enhancing the reliability and validity of the research.

Constructing an Operational Hypothesis

Creating an operational hypothesis is a fundamental step in the scientific method and research process. It involves generating a precise, testable statement that predicts the outcome of a study based on the research question. An operational hypothesis must clearly identify and define the variables under study and describe the expected relationship between them. The process of creating an operational hypothesis involves several key steps:

Steps to Construct an Operational Hypothesis

• Define the Research Question : Start by clearly identifying the research question. This question should highlight the key aspect or phenomenon that the study aims to investigate.
• Identify the Variables : Next, identify the key variables in your study. Variables are elements that you will measure, control, or manipulate in your research. There are typically two types of variables in a hypothesis: the independent variable (the cause) and the dependent variable (the effect).
• Operationalize the Variables : Once you’ve identified the variables, you must operationalize them. This involves defining your variables in such a way that they can be easily measured, manipulated, or controlled during the experiment.
• Predict the Relationship : The final step involves predicting the relationship between the variables. This could be an increase, decrease, or any other type of correlation between the independent and dependent variables.

By following these steps, you will create an operational hypothesis that provides a clear direction for your research, ensuring that your study is grounded in a testable prediction.

Evaluating the Strength of an Operational Hypothesis

Not all operational hypotheses are created equal. The strength of an operational hypothesis can significantly influence the validity of a study. There are several key factors that contribute to the strength of an operational hypothesis:

• Clarity : A strong operational hypothesis is clear and unambiguous. It precisely defines all variables and the expected relationship between them.
• Testability : A key feature of an operational hypothesis is that it must be testable. That is, it should predict an outcome that can be observed and measured.
• Operationalization of Variables : The operationalization of variables contributes to the strength of an operational hypothesis. When variables are clearly defined in measurable terms, it enhances the reliability of the study.
• Alignment with Research : Finally, a strong operational hypothesis aligns closely with the research question and the overall goals of the study.

By carefully crafting and evaluating an operational hypothesis, researchers can ensure that their work provides valuable, valid, and actionable insights.

Examples of Operational Hypotheses

To illustrate the concept further, this section will provide examples of well-constructed operational hypotheses in various research fields.

The operational hypothesis is a fundamental component of scientific inquiry, guiding the research design and providing a clear framework for testing assumptions. By understanding how to construct and evaluate an operational hypothesis, we can ensure our research is both rigorous and meaningful.

Examples of Operational Hypothesis:

• In Education : An operational hypothesis in an educational study might be: “Students who receive tutoring (Independent Variable) will show a 20% improvement in standardized test scores (Dependent Variable) compared to students who did not receive tutoring.”
• In Psychology : In a psychological study, an operational hypothesis could be: “Individuals who meditate for 20 minutes each day (Independent Variable) will report a 15% decrease in self-reported stress levels (Dependent Variable) after eight weeks compared to those who do not meditate.”
• In Health Science : An operational hypothesis in a health science study might be: “Participants who drink eight glasses of water daily (Independent Variable) will show a 10% decrease in reported fatigue levels (Dependent Variable) after three weeks compared to those who drink four glasses of water daily.”
• In Environmental Science : In an environmental study, an operational hypothesis could be: “Cities that implement recycling programs (Independent Variable) will see a 25% reduction in landfill waste (Dependent Variable) after one year compared to cities without recycling programs.”

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Operationalization

Operationalization is the process of strictly defining variables into measurable factors. The process defines fuzzy concepts and allows them to be measured, empirically and quantitatively.

This article is a part of the guide:

• Null Hypothesis
• Research Hypothesis
• Defining a Research Problem
• Selecting Method

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• 1 Scientific Method
• 2.1.1 Null Hypothesis
• 2.1.2 Research Hypothesis
• 2.2 Prediction
• 2.3 Conceptual Variable
• 3.1 Operationalization
• 3.2 Selecting Method
• 3.3 Measurements
• 3.4 Scientific Observation
• 4.1 Empirical Evidence
• 5.1 Generalization
• 5.2 Errors in Conclusion

For experimental research , where interval or ratio measurements are used, the scales are usually well defined and strict.

Operationalization also sets down exact definitions of each variable, increasing the quality of the results, and improving the robustness of the design .

For many fields, such as social science, which often use ordinal measurements, operationalization is essential. It determines how the researchers are going to measure an emotion or concept, such as the level of distress or aggression.

Such measurements are arbitrary, but allow others to replicate the research, as well as perform statistical analysis of the results.

Fuzzy Concepts

Fuzzy concepts are vague ideas, concepts that lack clarity or are only partially true. These are often referred to as " conceptual variables ".

It is important to define the variables to facilitate accurate replication of the research process . For example, a scientist might propose the hypothesis :

“Children grow more quickly if they eat vegetables.”

What does the statement mean by 'children'? Are they from America or Africa. What age are they? Are the children boys or girls? There are billions of children in the world, so how do you define the sample groups?

How is 'growth' defined? Is it weight, height, mental growth or strength? The statement does not strictly define the measurable, dependent variable .

What does the term 'more quickly' mean? What units, and what timescale, will be used to measure this? A short-term experiment, lasting one month, may give wildly different results than a longer-term study.

The frequency of sampling is important for operationalization , too.

If you were conducting the experiment over one year, it would not be practical to test the weight every 5 minutes, or even every month. The first is impractical, and the latter will not generate enough analyzable data points.

What are 'vegetables'? There are hundreds of different types of vegetable, each containing different levels of vitamins and minerals. Are the children fed raw vegetables, or are they cooked? How does the researcher standardize diets, and ensure that the children eat their greens?

The above hypothesis is not a bad statement, but it needs clarifying and strengthening, a process called operationalization.

The researcher could narrow down the range of children, by specifying age, sex, nationality, or a combination of attributes. As long as the sample group is representative of the wider group, then the statement is more clearly defined.

Growth may be defined as height or weight. The researcher must select a definable and measurable variable, which will form part of the research problem and hypothesis.

Again, 'more quickly' would be redefined as a period of time, and stipulate the frequency of sampling. The initial research design could specify three months or one year, giving a reasonable time scale and taking into account time and budget restraints.

Each sample group could be fed the same diet, or different combinations of vegetables. The researcher might decide that the hypothesis could revolve around vitamin C intake, so the vegetables could be analyzed for the average vitamin content.

Alternatively, a researcher might decide to use an ordinal scale of measurement, asking subjects to fill in a questionnaire about their dietary habits.

Already, the fuzzy concept has undergone a period of operationalization, and the hypothesis takes on a testable format.

The Importance of Operationalization

Of course, strictly speaking, concepts such as seconds, kilograms and centigrade are artificial constructs, a way in which we define variables.

Pounds and Fahrenheit are no less accurate, but were jettisoned in favor of the metric system. A researcher must justify their scale of scientific measurement .

Operationalization defines the exact measuring method used, and allows other scientists to follow exactly the same methodology. One example of the dangers of non-operationalization is the failure of the Mars Climate Orbiter .

This expensive satellite was lost, somewhere above Mars, and the mission completely failed. Subsequent investigation found that the engineers at the sub-contractor, Lockheed, had used imperial units instead of metric units of force.

A failure in operationalization meant that the units used during the construction and simulations were not standardized. The US engineers used pound force, the other engineers and software designers, correctly, used metric Newtons.

This led to a huge error in the thrust calculations, and the spacecraft ended up in a lower orbit around Mars, burning up from atmospheric friction. This failure in operationalization cost hundreds of millions of dollars, and years of planning and construction were wasted.

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Independent and Dependent Variables

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

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In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations . One is called the dependent variable, and the other is the independent variable.

In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome. Essentially, the independent variable is the presumed cause, and the dependent variable is the observed effect.

Variables provide the foundation for examining relationships, drawing conclusions, and making predictions in research studies.

Independent Variable

In psychology, the independent variable is the variable the experimenter manipulates or changes and is assumed to directly affect the dependent variable.

It’s considered the cause or factor that drives change, allowing psychologists to observe how it influences behavior, emotions, or other dependent variables in an experimental setting. Essentially, it’s the presumed cause in cause-and-effect relationships being studied.

For example, allocating participants to drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable).

In a well-designed experimental study , the independent variable is the only important difference between the experimental (e.g., treatment) and control (e.g., placebo) groups.

By changing the independent variable and holding other factors constant, psychologists aim to determine if it causes a change in another variable, called the dependent variable.

For example, in a study investigating the effects of sleep on memory, the amount of sleep (e.g., 4 hours, 8 hours, 12 hours) would be the independent variable, as the researcher might manipulate or categorize it to see its impact on memory recall, which would be the dependent variable.

Dependent Variable

In psychology, the dependent variable is the variable being tested and measured in an experiment and is “dependent” on the independent variable.

In psychology, a dependent variable represents the outcome or results and can change based on the manipulations of the independent variable. Essentially, it’s the presumed effect in a cause-and-effect relationship being studied.

An example of a dependent variable is depression symptoms, which depend on the independent variable (type of therapy).

In an experiment, the researcher looks for the possible effect on the dependent variable that might be caused by changing the independent variable.

For instance, in a study examining the effects of a new study technique on exam performance, the technique would be the independent variable (as it is being introduced or manipulated), while the exam scores would be the dependent variable (as they represent the outcome of interest that’s being measured).

Examples in Research Studies

For example, we might change the type of information (e.g., organized or random) given to participants to see how this might affect the amount of information remembered.

In this example, the type of information is the independent variable (because it changes), and the amount of information remembered is the dependent variable (because this is being measured).

For the following hypotheses, name the IV and the DV.

1. Lack of sleep significantly affects learning in 10-year-old boys.

IV……………………………………………………

DV…………………………………………………..

2. Social class has a significant effect on IQ scores.

DV……………………………………………….…

3. Stressful experiences significantly increase the likelihood of headaches.

4. Time of day has a significant effect on alertness.

Operationalizing Variables

To ensure cause and effect are established, it is important that we identify exactly how the independent and dependent variables will be measured; this is known as operationalizing the variables.

Operational variables (or operationalizing definitions) refer to how you will define and measure a specific variable as it is used in your study. This enables another psychologist to replicate your research and is essential in establishing reliability (achieving consistency in the results).

For example, if we are concerned with the effect of media violence on aggression, then we need to be very clear about what we mean by the different terms. In this case, we must state what we mean by the terms “media violence” and “aggression” as we will study them.

Therefore, you could state that “media violence” is operationally defined (in your experiment) as ‘exposure to a 15-minute film showing scenes of physical assault’; “aggression” is operationally defined as ‘levels of electrical shocks administered to a second ‘participant’ in another room.

In another example, the hypothesis “Young participants will have significantly better memories than older participants” is not operationalized. How do we define “young,” “old,” or “memory”? “Participants aged between 16 – 30 will recall significantly more nouns from a list of twenty than participants aged between 55 – 70” is operationalized.

The key point here is that we have clarified what we mean by the terms as they were studied and measured in our experiment.

If we didn’t do this, it would be very difficult (if not impossible) to compare the findings of different studies to the same behavior.

Operationalization has the advantage of generally providing a clear and objective definition of even complex variables. It also makes it easier for other researchers to replicate a study and check for reliability .

For the following hypotheses, name the IV and the DV and operationalize both variables.

1. Women are more attracted to men without earrings than men with earrings.

I.V._____________________________________________________________

D.V. ____________________________________________________________

Operational definitions:

I.V. ____________________________________________________________

2. People learn more when they study in a quiet versus noisy place.

I.V. _________________________________________________________

D.V. ___________________________________________________________

3. People who exercise regularly sleep better at night.

Can there be more than one independent or dependent variable in a study?

Yes, it is possible to have more than one independent or dependent variable in a study.

In some studies, researchers may want to explore how multiple factors affect the outcome, so they include more than one independent variable.

Similarly, they may measure multiple things to see how they are influenced, resulting in multiple dependent variables. This allows for a more comprehensive understanding of the topic being studied.

What are some ethical considerations related to independent and dependent variables?

Ethical considerations related to independent and dependent variables involve treating participants fairly and protecting their rights.

Researchers must ensure that participants provide informed consent and that their privacy and confidentiality are respected. Additionally, it is important to avoid manipulating independent variables in ways that could cause harm or discomfort to participants.

Researchers should also consider the potential impact of their study on vulnerable populations and ensure that their methods are unbiased and free from discrimination.

Ethical guidelines help ensure that research is conducted responsibly and with respect for the well-being of the participants involved.

Can qualitative data have independent and dependent variables?

Yes, both quantitative and qualitative data can have independent and dependent variables.

In quantitative research, independent variables are usually measured numerically and manipulated to understand their impact on the dependent variable. In qualitative research, independent variables can be qualitative in nature, such as individual experiences, cultural factors, or social contexts, influencing the phenomenon of interest.

The dependent variable, in both cases, is what is being observed or studied to see how it changes in response to the independent variable.

So, regardless of the type of data, researchers analyze the relationship between independent and dependent variables to gain insights into their research questions.

Can the same variable be independent in one study and dependent in another?

Yes, the same variable can be independent in one study and dependent in another.

The classification of a variable as independent or dependent depends on how it is used within a specific study. In one study, a variable might be manipulated or controlled to see its effect on another variable, making it independent.

However, in a different study, that same variable might be the one being measured or observed to understand its relationship with another variable, making it dependent.

The role of a variable as independent or dependent can vary depending on the research question and study design.

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Accueil Numéros 15-2 Varia The operationalization of general...

The operationalization of general hypotheses versus the discovery of empirical laws in Psychology

L’enseignement de la méthodologie scientifique en Psychologie confère un rôle paradigmatique à l’opérationnalisation des « hypothèses générales » : une idée sans rapport précis à l’observation concrète se traduit par la tentative de rejeter une hypothèse statistique nulle au profit d’une hypothèse alternative, dite de recherche, qui opérationnalise l’idée générale. Cette démarche s’avère particulièrement inadaptée à la découverte de lois empiriques. Une loi empirique est définie comme un trou nomothétique émergeant d’un référentiel de la forme Ω x  M ( X ) x  M ( Y ), où Ω est un ensemble d’événements ou d’objets datés dont certains états dans l’ensemble M ( Y ) sont par hypothèse impossibles étant données certaines conditions initiales décrites dans l’ensemble M ( X ) . Cette approche permet de préciser le regard que l’historien des connaissances peut porter sur les avancées descriptives et nomothétiques de la Psychologie empirique contemporaine.

Psychology students learn to operationalise ’general hypotheses’ as a paradigm of scientific Psychology : relatively vague ideas result in an attempt to reject the null hypothesis in favour of an alternative hypothesis, a so-called research hypothesis, which operationalises the general idea. Such a practice turns out to be particularly at odds with the discovery of empirical laws. An empirical law is defined as a nomothetic gap emerging from a reference system of the form Ω x  M ( X ) x  M ( Y ), where Ω is a set of events or dated objects for which some states in the set M ( Y ) are hypothetically impossible given some initial conditions depicted in the set M ( X ). This approach allows the knowledge historian to carefully scrutinise descriptive and nomothetic advances in contemporary empirical Psychology.

Texte intégral

I wish to express my thanks to Nadine Matton and Éric Raufaste for their helpful comments on a previous version of this article. This work was funded in part by the ANR-07-JCJC-0065-01 programme.

1 This article is the result of the author’s need to elaborate on the persistent dissatisfaction he feels with the methodology of scientific research in Psychology, and more precisely with his perception of the way in which it is taught. It would indeed be presumptuous to present the following criticism as being a criticism of the methodology of scientific research in Psychology as a whole, since the latter is a notion which is too all-encompassing in its scope to serve as a precise description of the diversity of research practice in this vast field. The source of this dissatisfaction is to be found in what [Reuchlin 1992, 32] calls the ‘distance’ between ‘general theory’ and a ‘specific, falsifiable hypothesis’. A certain form of academism shapes the approach to scientific research in Psychology according to a three-stage process for the formulation of hypotheses e.g., [Charbonneau 1988]. When they write the report of an empirical study, researchers in Psychology must supply the grounds for their research by introducing a so-called general (or theoretical) hypothesis, then show how they have tested this hypothesis by restating it as a so-called operational (or research) hypothesis. In principle, this restatement should involve data analysis, finalised by testing at least one inferential statistical hypothesis, the so-called null hypothesis.

2 As a socially regulated procedure, the sequencing of theoretical, operational and null hypotheses—which we refer to here as operationaliza-tion —may not pose scientific problems to researchers who are mainly concerned with adhering to a socio-technical norm. The sense of dissatisfaction arises when this desire for socio-technical compliance is considered in the light of the hope (albeit an admittedly pretentious or naïve hope) of discovering one or more empirical laws, i.e. demonstrating at least one, corroborated general empirical statement, [Vautier 2011].

3 With respect to the discovery of empirical laws, operationalization may be characterised as a paradigm, based on a ‘sandwich’ system, whose workings prove to be strikingly ineffective. The ‘general hypothesis’ (the uppermost layer of the ‘sandwich’ system) is not the statement of an empirical law, but a pre-referential statement, i.e. a statement whose empirical significance has not (yet) been determined. The null hypothesis test (the lower layer of the ‘sandwich’) binds the research procedure to a narrow, pragmatic decision-making approach amid uncertainty— rejection or acceptance of the null hypothesis—which is not germane to the search for empirical laws if the null hypothesis is not a general statement in the strict sense of the term, i.e. held to be true for all the elements in a given set. Between the external layers of the ’sandwich’ system lies the psychotechnical and statistical core of the operationalization paradigm, i.e. the production of psychological measurements to which the variables required for the formulation of the operational hypothesis are linked. Again, the claim here is not that this characterization of research procedure in Psychology applies absolutely universally ; however, operationalization as outlined above does appear to be sufficiently typical of a certain orthodoxy to warrant a thorough critical analysis.

4 This paradigm governs an approach which is destined to establish a favourable view of ‘general hypotheses’ inasmuch as they have psy-chotechnical and inferential support. However, the ideological interest of these statements does not automatically confer them with nomothetic import. Consequently, one cannot help wondering whether the rule of operationalization does not in fact serve to prevent those who practise it from ever discerning a possible historical failure of orthodox Psychology to discover its own empirical laws, by training the honest researcher not to hope for the impossible. After all, we are unlikely to worry about failing to obtain something which we were not looking for in the first place. We shall see that an empirical law consists precisely of stating an empirical impossibility, i.e. a partially deterministic falsifiable statement. As a result, we have inevitably come to question psychological thought as regards the reasons and consequences of an apodictic approach to probabilistic treatment of the empirical phenomena which it is investigating.

5 This article comprises four major parts. First of all, we shall illustrate operationalization on the basis of an example put forward by [Fernandez & Catteeuw 2001]. Next, we shall identify two logical and empirical difficulties which arise from this paradigm and demonstrate that they render it unsuitable for the discovery of empirical laws, then detail the logical structure of these laws. Lastly, we shall identify some methodological guidelines which are compatible with an inductive search for partial determinisms.

1 An example of operationalization : smoking cessation and anxiety

6 [Fernandez & Catteeuw 2001, 125] put forward the following sequence :

General hypothesis : undergoing smoking cessation tends to increase anxiety in smokers rather than reduce it.

Operational hypothesis : smokers undergoing smoking cessation are more prone to anxiety than non-cessation smokers.

Null hypothesis : there is no difference between anxiety scores for smokers undergoing smoking cessation and non-cessation smokers.

7 This example can be expanded so as to offer more opportunities to engage with the critical exercise. There is no difficulty in taking [Fernandez & Catteeuw 2001] operational hypothesis as a ‘general hypothesis’. Their formulation specifies neither the empirical (nominal) meaning of the notion of smoking cessation, nor the empirical (ordinal or quantitative) significance of the notion of anxiety, even though it makes reference to the ordinal operator more prone to anxiety than  ; lastly, the noun smokers signifies only an indefinite number of people who smoke.

8 The researcher may have given themselves a set of criteria which is sufficient to decide whether, at the moment when they examine an individual, the person is a smoker or not, and if they are a smoker, another set of criteria sufficient to decide whether or not they are undergoing smoking cessation. These sets of criteria allow the values for two nominal variables to be defined, the first attributing the value of smoker or non-smoker, and the second, which is conditional on the status of ‘smoker’, attributing the value of undergoing cessation or non-cessation. However, the statistical definition of the ’undergoing cessation’ variable requires a domain, i.e. elements assigned a value according to its codomain, the (descriptive) reference system of the variable : {undergoing cessation, non-cessation}. The researcher may circumscribe the domain to pairs (smoker, examination date) which they already have obtain or will obtain during the course of their study, and thus define a so-called independent nominal variable.

9 They then need to specify the function which assigns an anxiety score for each (smoker, examination date) pair, in order to define the ’anxiety score’ statistical variable, taken as the dependent variable. The usual solution for specifying such a function consists in using the answers to an anxiety questionnaire to determine this score, according to a numerical coding rule for the responses to the items on the questionnaire. Such procedures, in which standardised observation of a verbal behaviour is associated with the numerical coding of responses, constitute one of the fundamental contributions of psychotechnics (or psychological testing) to Psychology ; it enables anxiety means conditional on the values of the independent variable to be calculated, whence the operational hypothesis : smokers undergoing smoking cessation are more anxious than non-cessation smokers.

10 The operational hypothesis constitutes a descriptive proposition whose validity can easily be examined. However, to the extent that they consider their sample of observations to be a means of testing a general hypothesis, the researcher must also demonstrate that the mean difference observed is significant, i.e. rejects the null hypothesis of the equality of the means for the statistical populations composed of the two types of smokers, using a probabilistic procedure selected from the available range of inferential techniques, for instance Student’s t -test for independent samples. Only then can the operational hypothesis, considered in the light of the two statistical populations, acquire the status of an alternative hypothesis with respect to the null hypothesis.

11 Now, let us restate the sequence of hypotheses put forward by [Fernandez & Catteeuw 2001] thus :

General hypothesis : smokers undergoing smoking cessation are more anxious than non-cessation smokers

Operational hypothesis : given a pair of variables (‘undergoing cessation’, ‘anxiety score’), mean anxiety conditional on the undergoing cessation value is greater than mean anxiety conditional on the non-cessation value.

Null hypothesis : the two conditional means are equal.

2 Operationalization criticised

12 The example which we have just developed is typical of operational-ization in Psychology, irrespective of the experimental or correlational nature [Cronbach 1957, 1975] of the study. In this section, we make two assertions by dealing with the operationalization approach in reverse : (i) the empirical relevance of the test of the null hypothesis is indeterminate (ii) the statistical fact of a mean difference has no general empirical import.

2.1 The myth of the statistical population

13 To simplify the discussion, let us suppose that the researcher tests the null hypothesis of the equality of two means using Student’s t procedure. The issue at stake in the test from a socio-technical point of view is that by qualifying the difference observed as a significant difference, the cherished notation “p < .05” or “p < .01” may be included in a research paper. The null hypothesis test has been the subject of purely statistical criticisms e.g., [Krueger 2001], [Nickerson 2000] and it is not within the scope of this paper to draw up an inventory of these criticisms. In the empirical perspective under examination here, the problem is that this type of procedure is nothing more than a rhetorical device, insofar as the populations to which the test procedure is applied remain virtual in nature.

14 In practice, the researcher knows how to define their conditional variables on the basis of pairs : (smoker undergoing cessation, examination date) and (non-cessation smoker, examination date), assembled by them through observation. But what is the significance of the statistical population to which the inferential exercise makes reference ? If we consider the undergoing cessation value, for example, how should the statistical population of the (smoker undergoing cessation, examination date) pairs be defined ? Let us imagine a survey which would enable the anxiety score for all the human beings on the planet with the status of ‘smoker undergoing smoking cessation’ to be known on a certain date each month in the interval of time under consideration. We would then have as many populations as we have monthly surveys ; we could then consider grouping together all of these monthly populations to define the population of observations relating to the ‘cessation’ status. There is not one single population, but rather a number of virtual populations. The null hypothesis is therefore based on a mental construct. As soon as this is defined more precisely, questions arise as to its plausibility and the interest of the test. Indeed, why should a survey supply an anxiety variable whose conditional means, subject to change, are identical ?

15 Ultimately, it appears that the null hypothesis test constitutes a decision-making procedure with respect to the plausibility of a hypothesis devoid of any determined empirical meaning. The statistical inference used in the operationalization system is an odd way of settling the issue of generality : it involves deciding whether the difference between observed means may be generalised, even if the empirical meaning of this generality has not been established.

2.2 The myth of the average smoker

16 The difference between the two anxiety means may be interpreted as the difference between the degree of anxiety of the average smoker undergoing cessation and the degree of anxiety of the average non-cessation smoker, which poses two problems. Firstly, the discrete nature of the anxiety score contains a logical dead-end, i.e. the use of an impossibility to describe something which is possible. Let us assume an anxiety questionnaire comprising five items with answers scored 0, 1, 2 or 3, such that the score attributed to any group of 5 responses will fall within the sequence of natural numbers (0, 1, 15). A mean score of 8.2 may indeed ‘summarise’ a set of scores, but cannot exist as an individual score. Consequently, should we wish to use a mean score to describe a typical smoker, it must be recognised that such a smoker is not possible and therefore not plausible. As a result, the difference between the two means cannot be used to describe the difference in degrees of anxiety of the typical smokers, unless it is admitted that a typical smoker is in fact a myth.

17 Let us now assume that the numerical coding technique enables a continuous variable to be defined by the use of so-called analogue response scales. The score of any smoker is by definition composed of the sum of two quantities, the mean score plus the deviation from the mean, the latter expressing the fact that the typical smoker is replaced in practice by a particular specimen of the statistical population, whose variable nature is assumed to be random—without it appearing necessary to have empirical grounds for the probability space on which this notion is based. In these conditions, the mean score constitutes a parameter, whose specification is an empirical matter inasmuch as the statistical population is actually defined. An empirical parameter is not, however, the same thing as an empirical law.

3 Formalization of an empirical law

• 2  This is a more general and radical restatement of the definition given by [Piaget 1970, 17] of the (...)

18 According to the nomothetic perspective, scientific ambition consists in discovering laws, i.e. general implications 2 A general implication is a statement in the following form :

which reads thus “for any x of A , if p ( x ) then q ( x )”, where x is any component of a given set A , and p (•) and q (•) are singular statements. This formalization applies without any difficulty to any situation in which the researcher has a pair of variables ( X , Y ), from a domain Ω n  = { ω i , i  =   1, …, n }, whose elements w are pairs (person, observation date). The codomain of the independent variable X is a descriptive reference system of initial conditions M ( X ) = ( x i , i  = 1, …, k }, whilst the dependent variable, Y , specifies a value reference system, M ( Y ) = ( y i , i  = 1, …, l }, the effective observation of which depends, by hypothesis, on the independent conditions. Thus, the onto-logical substrate of an empirical law is the observation reference system Ω x  M ( X ) x  M ( Y ), where Ω ⊃ Ω is an extrapolation of Ω n  : any element of Ω is, as a matter of principle, assigned a unique value in M ( X ) x  M ( Y ) by means of the function ( X , Y ).

19 Two comments arise from this definition. Firstly, as noted by [Popper 1959, 48], “[natural laws] do not assert that something exists or is the case ; they deny it”. In other words, they state a general ontological impossibility in terms of Ω x  M ( X ) x  M ( Y ) : a law may indeed by formulated by identifying the initial conditions α ( X ) ⊂  M ( X ) for which a non-empty subset β ( Y ) ⊂  M ( Y ) exists such that,

This formulation excludes the possibility of X ( ω ) ∈  α ( Y ) and Y ( ω ) ∈ ∁ β ( Y ) being observed, where ∁ β ( Y ) designates the complementary set β ( Y ) with respect to M ( Y ). Making a statement in the form of (2) amounts to stating a general empirical fact in terms of Ω n , and an empirical law in terms of Ω, by inductive generalisation. This law can be falsified, simply by exhibiting an example of what is said to be impossible in order to falsify it. The general nature of the statement stems from the quantifier ∀ and its empirical limit is found in the extension of Ω. The law may then be corroborated or falsified. If it is corroborated, it is possible to measure its degree of corroboration by the number of observations applying to it, i.e. by the cardinality of the equivalence class formed by the antecedents of α ( X )—the class is noted Cl Ω n/X [ α ( X )].

20 The second comment relates to the notion of partial determinism. The mathematical culture passed on through secondary school teaching familiarises honest researchers with the notion of numerical functions y  =  f ( x ), which express a deterministic law, i.e. that x being given, y necessarily has a point value. If the informative nature of the law is envisaged in negative terms [Dubois & Prade 2003], the necessity of the point is defined as the impossibility of its complement. In the field of humanities [Granger 1995], seeking total determinisms appears futile, but this does not imply that there is no general impossibility in Ω x  M ( X ) x  M ( Y ) and therefore no partial determinism. The fact that partial determinism may not have a utility value from the point of view of social or medical decision-making engineering has nothing to do with its fundamental scientific value. The subject of nomothetic research therefore appears in the form of a ‘gap’ in a descriptive reference system, this gap being theoretically interpreted as the effect of a general ontological impossibility. This is why in teaching, a methodology to support the nomothetic goal of training student researchers to ’search for the impossible’ is called for.

4 How to seek the impossible

21 Discovery of a gap in the descriptive reference system involves the discovery of a general empirical fact, from which an empirical law is inferred by extending the set of observations Ω n to an unknown phe-nomenological field Ω ⊃ Ω n (e.g. future events). A general empirical fact makes sense only with reference to the descriptive reference system M ( X ) x  M ( Y ). Practically speaking, dependent and independent variables are multivariate. Let X  = ( X 1 , X 2 , ..., X p ) be a series of p independent variables and M ( X ) the reference system of X  ; M ( X ) is the Cartesian product of the p  reference systems M ( X i ), i  = 1, …, p . Similarly, let Y  = ( Y 1 , ..., Y q ) be a series of q  dependent variables and M ( Y ) the reference system of Y . The descriptive reference system of the study is therefore :

Thus the contingency table (the rows of which represent the multivari-ate values of X , and the columns the multivariate values of Y ) can be defined. Observation readings are then carried out so that the cells in the contingency table are gradually filled in... or remain empty.

22 Two cases must be distinguished here. The first corresponds to the situation in which the researcher is completely ignorant of what is happening in their observation reference system, in other words, they do not have any prior observations. They therefore have to carry out some kind of survey in order to learn more. Knowing what is happening in the reference system means knowing the frequency of each possible state. It does not involve calling on the notion of probability (the latter being firmly in the realm of mathematical mythology) since it would involve knowing the limit of the frequency of each cell in the contingency table as the number of observations ( n ) tends towards infinity.

• 3  “But in terms of truth, scientific psychology does not deal with natural objects. It deals with te (...)

23 A nomothetic gap arises when there is at least one empty cell in at least one row of the contingency table, when the margin of the row (or rows) is well above the cardinality of M (Y ). It is possible to identify all the gaps in the reference system only if its cardinality is well below the cardinality of lln, n. This empirical consideration sheds light on a specific epistemological drawback in Psychology : not only are its descriptive reference systems not given naturally, as emphasised by [Danziger 1990, 2], 3 but in addition the depth of constructible reality is such that its cardinality may be gigantic—so much so that discussing what is happening in an observation reference system cannot be achieved in terms of sensible intuition. The fact is that the socio-technical norms which shape the presentation of the observation techniques used in empirical studies do not refer either to the notion of descriptive reference system or the necessity of plotting the cardinality card[ M ( X ) x  M ( Y )] against the cardinality of the set of observations, card(Ω n ) =  n . If the quotient card[ M ( X ) x  M ( Y )]/ n is not much lower than 1, planning to carry out an exhaustive examination of the nomothetic gaps in the descriptive reference system is unfeasible. This does not prevent the researcher from working on certain initial conditions α ( X ), but in such cases it must nonetheless be established that dividing the number of values of M ( Y ) by the cardinality of the class Cl Ω n/ X [ α ( X )] of antecedents of α ( X ) in Ω n gives a result which is far less than 1.

24 Let us now present the second case, for which it is assumed that the researcher has been lucky enough to observe the phenomenon of a gap, whose ’coordinates’ in the descriptive reference system of the study are [ α ( X ), ∁ β ( Y )]. The permanent nature of this gap constitutes a proper general hypothesis. This hypothesis should be tested using a targeted observation strategy. Indeed, accumulating observations in l is of interest from the point of view of the hypothesis if these observations are such that : —  X ( ω ) ∈  α ( X ), in which case we seek to verify that Y ( ω ) ∈  β ( Y ), —  Y ( ω ) ∈ ∁ β ( Y ), in which case we seek to verify that X ( ω ) ∈ ∁ α ( X ).

This approach to observation is targeted, and indeed makes sense, in that it focuses on a limited number of states : the researcher knows exactly what they are looking for. It is the very opposite of blindly reproducing an experimental plan or survey plan.

25 When a counterexample is discovered, i.e. ω e exists such that X ( ω e ) ∈  α ( X ) and Y ( ω e ) ∈ ∁ β ( Y ), this observation falsifies the general hypothesis. The researcher can then decide either to reject the hypothesis or to defend it. If they decide to defend it, they may restrict the set of conditions α ( X ), or try to find a variable X p +1 which modulates verification of the rule. Formally speaking, this modulating variable is such that there is a strict non-empty subset of M ( X p +1 )—let this be γ ( X p +1 )—such that :

Irrespective of how they revise the original hypothesis, they will have to restrict its domain of validity with respect to the—implicit—set of possible descriptive reference systems. A major consequence of revising the law by expanding the descriptive reference system of initial conditions is resetting the corroboration counter, since the world being explored has been given an additional descriptive dimension : this is the reference system Ω x  M ( X 1 ) x  M ( Y ), where X 1  = ( X , X p +1 ).

4.1 Example

26 Without it being necessary to develop the procedure presented here in its entirety, we can illustrate it using the example of smokers’ anxiety. The problem consists of restating the ’general hypothesis’ as a statement which is (i) general, properly speaking, as understood in (1) –, and (ii) falsifiable. We may proceed in two stages. Firstly, it is not necessary to talk in terms of reference systems to produce a general statement. Expressing the problem in terms of the difference between two means is not relevant to what is being sought ; however, the idea according to which any smoker undergoing cessation becomes more anxious may be examined, along the lines of the ’general hypothesis’ described by [Fernandez & Catteeuw 2001]. This idea is pre-referential inasmuch as we are unable to define a smoker, a smoker undergoing cessation, or a person who is becoming more anxious.

27 Since we cannot claim to be able to actually settle these issues of definition, we shall use certain definitions for the purposes of convenience. Let U be a population of people and T a population of dates on which they were observed. Let Ω n be a subset of U  x  T  x  T such that, for any triplet ω  = ( u , t 1 , t 2 ), u is known on dates t 1 and t 2 in terms of their status as : — a non-smoker, a smoker undergoing cessation or a non-cessation smoker — and their state of anxiety, for instance with reference to a set of clinical signs, of which the person is asked to evaluate the intensity on date t , using a standard ‘state-anxiety’ questionnaire.

28 It can be noted that the set Ω n is a finite, non-virtual set, in that a person u whose smoker status is not known on date t 1 or t 2 for example, constitutes a triplet which does not belong to this set. According to our approach to the statistical population, it is not necessary for the observations to be the result of applying a specific random sampling technique. Since Ω n constitutes a set of known observations from the point of view of the descriptive reference system, it is a numbered set, to which new observations can be added over time ; whence the notation Ω nj (read “j-mat”), where n j stands for the cardinality of the most recent update to the set of observations.

• 4  It may be noted that an observation p such that X j ( p ) = ( n f, f 2 ) is not plausible ; this relates t (...)

29 We can then define the following variables X j and Y j , from the subset P j of Ω nj , which includes the triplets ( u , t 1 , t 2 ) such that t 2  –  t 1  =  d , where d is a transition time (e.g. 2 days). The variable X j matches any component of P j with an image in M ( X j ) = { n f, f 1 , f 2 } x { n f, f 1 , f 2 } where n f, f 1 and f 2 signify ‘non-smoker’, ‘non-cessation smoker’ and ‘smoker undergoing cessation’ respectively. Let us call α ( X j ) the subset of M ( X j ) including all the pairs of values ending in f 2 which do not begin with f 2 and take an element p  ∈  P j , : the proposition ‘ X j ( p ) ∈  α ( X j )’ means that in the period during which they were observed, person u had been undergoing smoking cessation for two days whereas they have not been before. 4

30 The dependent variable Y j must now be defined. Let us assume that for any sign of anxiety, we have a description on an ordinal scale (i.e., a Likert scale). Anxiety can then be described as a multivariate state varying within a descriptive reference system A . Consider A  x  A  ; in this set a subset β ( Y j ) can be defined which includes changes in states defined as a worsening of the state of anxiety. The variable Y j can then be defined, which, for each p  ∈  P j , corresponds to a state in M ( Y j ). The proposition ‘ Y j ( p ) ∈  β ( Y j )’ signifies that in the period during which they were observed, person u became more anxious. Lastly, the general hypothesis can be formulated in terms which ensure that it may be falsified :

31 We have just illustrated an apparently hypothetical-deductive approach ; but in fact it is an exploratory procedure if the community is not aware of any database enabling a nomothetic gap to be identified. Let us assume that the work of the researcher leads to the provision of a database Ω 236 for the community and that sets α ( X j ) and β ( Y j ) are defined after the fact, such that at least one general fact may be stated. The community with an interest in the general fact revealed by this data may seek new supporting or falsifying observations in order to help update the database.

32 If a researcher finds an individual v , with q  = ( v , t v 1 , t v 2 ) and t v 2  –  t v 1  =  d , such that X j ( q ) ∈  α ( X j ) and Y j ( q ) ∈ ∁ β ( Y j ), this means that there is a smoker who has been undergoing cessation for two days, whose anxiety has not worsened. Let us assume that the researcher investigates whether the person was already ‘very anxious’ ; they may suggest that rule (5) should be revised so as to exclude people whose initial clinical state corresponds to certain values in the reference system A . This procedure usually consists in restricting the scope of validity of the general hypotheses.

5 Discussion

• 5  [Meehl 1967] noted several decades ago that the greater the ‘experimental precision’, i.e. sample (...)

33 Operationalization in Psychology consists in restating a pre-referential proposition in order to enable the researcher to test a statistical null hypothesis, the rejection of which enables the ‘general hypothesis’ to be credited with a certain degree of acceptability. 5 Using an example taken from [Fernandez & Catteeuw 2001], we have shown that the aim of such a procedure is not the discovery of empirical laws, i.e. the discovery of nomothetic gaps in a reference system. We shall discuss two consequences of our radical approach to seeking empirical laws in an observation reference system Ω x  M ( X ) x  M ( Y ). The first relates to the methodology for updating the state of knowledge in a field of research, the second to the probabilistic interpretation of accumulated observations.

34 The state of knowledge in a given field of research can be apprehended in practical terms by means of a list of m so-called scientific publications. Let us call this set composed of specialist literature Lm and let Zj be an element in this list. The knowledge historian can then ask the following question : does text Zj allow an observation reference system of the type Ω n  x  M ( X ) x  M ( Y ) to be defined ? Such a question can only be answered in the affirmative if it is possible to specify the following :

n   >  0 pairs ( u , t ),

p  > 0 reference systems enabling the description of the initial conditions affecting the n pairs ( u , t ),

q   >  0 reference systems enabling the description of states affecting the n pairs ( u , t ) according to the initial conditions in which they are found.

35 Specifying a descriptive reference system consists in identifying a finite set of mutually exclusive values. Not all the description methods used in Psychology allow such a set to be defined ; for example, a close examination of the so-called Exner scoring system [Exner 1995] for verbatims which may be collected for any [Rorschach 1921] test card did not enable us to determine the Cartesian product of the possible values. And yet, to find a gap in a reference system, this reference system must be constituted, so as to form a stabilised and objective descriptive framework. Faced with such a situation, a knowledge historian would be justified in describing a scientific era in which research is based on such a form of descriptive methodology as being a pre-referential age.

• 6  We cannot simply classify the sources of score-subjectivity as measurement errors in the quantitat (...)

36 With regard to the matter of the objectivity of a descriptive reference system, we shall confine ourselves to introducing the notion of score-objectivity. Let P   =  ( p i , i   =  1, …, z } be a set of Psychologists and ω j  ∈ Ω. ( X , Y ) i ( ω j ) is the value of ( ω j ) in M ( X ) x  M ( Y ) as determined by the Psychologist p i . We may say that M ( X ) x  M ( Y ) is score-objective relative to P if ( X , Y ) i ( ω j ) depends only on j for all values of j . If a descriptive reference system is not score-objective, an event in Ω x  M ( X ) x  M ( Y ) which occurs in a gap cannot categorically be interpreted as a falsifying observation, since it may depend on a particular feature of the way the reporting Psychologist views it. Unless and until the descriptive definition of an event is regulated in a score-objective manner, the nomothetic aspiration appears to be premature, since it requires the objective world to be singular in nature. 6 Only once a descriptive reference system has been identified may the knowledge historian test its score-objectivity experimentally.

• 7  This type of database, established by merging several databases, has nothing to do with the aggreg (...)

37 The historian might well discover that a field of research is in fact associated with the use of divergent description reference systems. Their task would then be to connect these different fields of reality by attempting to define the problem of the correspondence between the impossibilities identified in the field R a and the impossibilities identified in the field R b —which assumes such identification is possible. Given a certain descriptive reference system of cardinality c, the historian may evaluate its explorability and perhaps note that certain description reference systems are inexplorable. Concerning explorable reference systems, they could perhaps try to retrieve data collected during the course of empirical studies, constitute an updated database, and seek nomothetic gaps in it. 7

38 Let us now move on to the second point of this discussion. If the reference system is explorable and assumed to be score-objective, it may be that each of its possible states has been observed at least once. In this case, the descriptive reference system is sterile from the nomothetic point of view and this constitutes a singular observation fact : everything is possible therein. In other words, given an object in a certain initial state, nothing can be asserted regarding its Y -state. This does not prevent the decision-making engineer from wagering on the object’s Y -state based on the distribution of Y -states, conditioned by the initial conditions in which the object is found. These frequencies may be used to measure ’expectancies’, but they do not form a basis on which to deduce the existence of a probability function for these states. Indeed, defining a random variable Y or Y | X requires the definition of a probability space on the basis of the possible states M ( X ) x  M ( Y ). In order to be probabilistic, such a space requires a probability space established on the basis of Ω e.g. [Renyi 1966]. Since Ω is a virtual set, adding objective probabilities to it is wishful thinking : seeing ( X , Y ) as a pair of random variables constitutes an unfalsifiable interpretation. Since such an interpretation is nonetheless of interest for making decisions, the existence of a related law of probability being postulated, the probability of a given state may be estimated on the basis of its frequency. The higher the total number of observations, the more accurate this estimation will be, which is why a database established by bringing together the existing databases is of interest. With the advent of the internet, recourse to probabilistic mythology no longer requires the inferential machinery of null-hypotheses testers to be deployed ; it rather requires the empirical stabilization of the parameters of the mythical law.

39 We conclude this critical analysis with a reminder that scientific research in Psychology is also aimed at the discovery of empirical laws. This requires two types of objectives to be distinguished with care : practical objectives, which focus on decision amid uncertainty, and nomoth-etic objectives, which focus on the detection of empirical impossibilities. Has so-called scientific Psychology been able to discover any empirical laws, and if so, what are they ? From our contemporary standpoint, this question is easy to answer in principle—if not in practice.

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2  This is a more general and radical restatement of the definition given by [Piaget 1970, 17] of the notion of laws. For him laws designate “relatively constant quantitative relations which may be expressed in the form of mathematical functions”, “general fact” or “ordinal relationships, [...] structural analyses, etc. which are expressed in ordinary language or in more or less formalized language (logic, etc.)”.

3  “But in terms of truth, scientific psychology does not deal with natural objects. It deals with test scores, evaluation scales, response distributions, series lists, and countless other items which the researcher does not discover but rather constructs with great care. Conjectures about the world, whatever they may be, cannot escape from this universe of artefacts.”

4  It may be noted that an observation p such that X j ( p ) = ( n f, f 2 ) is not plausible ; this relates to the question of the definition of the state of cessation and does not affect the structure of the logic.

5  [Meehl 1967] noted several decades ago that the greater the ‘experimental precision’, i.e. sample size, the easier it is to corroborate the alternative hypothesis.

6  We cannot simply classify the sources of score-subjectivity as measurement errors in the quantitative domain [Stigler 1986], since most descriptive reference systems in Psychology are qualitative ; diverging viewpoints for the same event described in a certain descriptive reference system represent an error, not of measurement, but of definition.

7  This type of database, established by merging several databases, has nothing to do with the aggregation methodology of ‘meta-analyses’ based on the use of statistical summaries e.g., [Rosenthal & DiMatteo 2001].

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Stéphane Vautier , «  The operationalization of general hypotheses versus the discovery of empirical laws in Psychology  » ,  Philosophia Scientiæ , 15-2 | 2011, 105-122.

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Stéphane Vautier , «  The operationalization of general hypotheses versus the discovery of empirical laws in Psychology  » ,  Philosophia Scientiæ [En ligne], 15-2 | 2011, mis en ligne le 01 septembre 2014 , consulté le 02 juin 2024 . URL  : http://journals.openedition.org/philosophiascientiae/656 ; DOI  : https://doi.org/10.4000/philosophiascientiae.656

Stéphane Vautier

OCTOGONE-CERPP, Université de Toulouse (France)

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