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Is Construct Validation Valid?
Anna Alexandrova and Daniel M. Haybron*y
What makes a measure of well-being valid? The dominant approach today, construct
validation, uses psychometrics to ensure that questionnaires behave in accordance with
background knowledge. Our first claim is interpretive—construct validation obeys a
coherentist logic that seeks to balance diverse sources of evidence about the construct
in question. Our second claim is critical—while in theory this logic is defensible, in
practice it does not secure valid measures. We argue that the practice of construct val-
idation in well-being research is theory avoidant, favoring a narrow focus on statistical
tests while largely ignoring relevant philosophical considerations.
1. Introduction. What makes a measure of well-being valid? A major proj-
ect in today’s social and medical sciences is measurement of happiness, life
satisfaction, and perceived quality of life using self-reports. When question-
naires used to elicit these reports obey the principles of psychometrics, they
are considered to be valid measurement tools. Central to this project is con-
struct validation—a method for checking the consilience of questionnaires
with the background knowledge about the property in question.
In this article we focus on construct validation of measures of self-reported
states relevant to well-being. There is perhaps more to well-being than sub-
jective states such as happiness or satisfaction, but we put this concern aside.
How an agent feels and judges their life is undoubtedly relevant to their over-
all well-being—any theorist accepts that much. So evaluating standard mea-
surement tools for detecting these feelings and judgments is important regard-
*To contact the authors, please write to: Anna Alexandrova, Department of History
and Philosophy of Science, University of Cambridge, Free School Lane, Cambridge
CB2 3RH, UK; e-mail: a.a.alexandrova@gmail.com. Daniel M. Haybron, College
of Arts and Sciences, Department of Philosophy, Saint Louis University, Verhaegen
Hall, 3634 Lindell Blvd., St. Louis, MO 63108; e-mail: haybrond@slu.edu.
yThe authors are equally and jointly responsible for the contents. They thank the anon-
ymous referees, Valerie Tiberius, Colin DeYoung, and Elina Vessonen for valuable com-
ments.
Philosophy of Science, 83 (December 2016) pp. 1098–1109. 0031-8248/2016/8305-0038$10.00
Copyright 2016 by the Philosophy of Science Association. All rights reserved.
1098
less of our philosophical persuasion on the nature of well-being. The real
question is whether construct validation evaluates these questionnaires in a
fair way.
Our first claim is an explicit statement of the logic of the process, some-
thing philosophers have not done so far. Construct validation, we argue, fol-
lows a coherentist spirit according to which measures are valid to the extent
that they cohere with theoretical and empirical knowledge about the states
being measured. In theory this is a defensible approach to measurement, but
in practice the current procedures of validation do not respect all sources of
knowledge about well-being, and this is our second claim. Construct vali-
dation is in fact dangerously theory avoidant, failing to respect a core com-
mitment of any plausible theory of well-being, namely, that well-being is a
normative category. This constraint implies that measures of subjective states
relevant to well-being need to be judged on their normative validity in addi-
tion to other characteristics. The current almost exclusive attention to the sta-
tistical correlations between questionnaires and questionnaire items does not
provide sufficient constraints to weed out weak measures. We close with a sug-
gestion for how construct validation can be improved.
2. What Is Construct Validation?.The first order of business is to get
clear on the logic behind the procedure. The psychometric tradition in
the social sciences has historically specialized in tests and questionnaires
for detecting unobservable attributes such as intelligence and personality
traits. Today for virtually all researchers who wish to measure any attribute
on the basis of self-reports or performances in tests, psychometric valida-
tion remains the obligatory procedure. The practitioners of the new science
of well-being—psychologists, sociologists, clinical scientists—have also
embraced questionnaires and, with that, psychometric validation.
Questionnaires used in well-being research range from gauging a per-
son’s feeling (“How anxious do you feel?”) to gauging their judgments
(“Is your life going well according to your priorities?”) to gauging their per-
ception of facts deemed important (“Do you feel in control of your circum-
stances?”). They can be longer or shorter and administered through various
media. Some well-known questionnaires include the Satisfaction with Life
Scale (SWLS; Diener et al. 1985), the Positive and Negative Affect Scale
(PANAS; Watson, Clark, and Tellegen 1988), and the Nottingham Health
Profile (Hunt et al. 1981), which measure life satisfaction, happiness, and
health-related quality of life, respectively.
Validation of these scales follows a typical pattern described in measure-
ment textbooks and articles on validation (Simms 2008; de Vet et al. 2011).
First, researchers define the construct to be measured by elaborating its
scope and limits. This is the conceptual stage in which the meaning of
the concepts in question is discussed, invoking anything from philosophical
IS CONSTRUCT VALIDATION VALID? 1099
theories to untutored intuitions to dictionary definitions. For example, the
scope of happiness is often deemed to be positive and negative affect, while
the scope of satisfaction with life is deemed a cognitive judgment about
one’s conditions and goals. In the second stage, researchers choose a mea-
surement method (a questionnaire, a test, or a task), select the items (what
questions? what tasks?), and settle on the scoring method. In the third and
final stage, the instrument is tested for its validity. We focus on this last step,
because it is supposed to discipline all the free philosophizing that happens
in the earlier stages with the hard tools of psychometrics. What are those
tools?
It is hard to speak of a psychometric method in general because the meth-
ods are numerous and constantly evolving.
1
But in the case of well-being
measures, validation frequently involves factor analysis: when hundreds
of subjects fill out the same questionnaire, perhaps several times over a pe-
riod, it is possible to observe the correlations between responses to different
items. These correlations are then used to show that there are one or more
clusters of items called ‘factors’that account for the total information. Sci-
entists speak of factor analysis as extracting “a manageable number of latent
dimensions that explain the covariation among a larger set of manifest var-
iables”(Simms 2008, 421).
2
Explanation is here used in an entirely phe-
nomenological sense as saving the phenomena (the phenomena being the
total data generated by administering the questionnaire in question), rather
than stating the causes of the phenomena. For example, the SWLS is a pop-
ular five-item Likert scale for measuring the cognitive aspect of subjective
well-being, that is, the extent to which subjects judge their life to be satis-
factory. Factor analysis identified all five items to be measuring the same
latent variable because a single factor accounted for 66% of the variance
in the data (Diener et al. 1985). Other scales may turn out to gauge more
than one dimension.
The next step of the testing stage is to check that the behavior of these
factors accords with other things scientists know about the object in ques-
tion. In the case of subjective well-being, this knowledge includes how peo-
ple evaluate their lives and surroundings, what behavior results from these
evaluations, and what other people who know the subjects say about them.
For example, the aforementioned SWLS, according to its authors, earned
1. Sawilowsky (2007) summarizes the state of the art.
2. There is a difference between exploratory and confirmatory factor analysis (see de
Vet et al. 2011, 169–72, among other places). The former is used to reduce the number
of items in a questionnaire by identifying the one(s) that best predict the overall ratings.
The latter, on the other hand, tests that the factors that best summarize the data also con-
form with a theory of the underlying phenomenon if there is one. This distinction is not
important for the present argument.
1100 ANNA ALEXANDROVA AND DANIEL M. HAYBRON
construct validity when Diener and his colleagues compared responses on
the SWLS to responses on other existing measures of subjective well-being
and related constructs such as affect intensity, happiness, and domain satis-
faction. The findings confirmed their expectation that SWLS scores corre-
late highly with those measures that also elicit a judgment on subjective
well-being and less so with measures that focus only on affect or self-
esteem or other related but distinct notions. One piece of evidence in favor
of SWLS was that the scores of 53 elderly people from Illinois correlated
well to the ratings this same population received in an extended interview
about “the extent to which they remained active and were oriented toward
self-directed learning”(Diener et al. 1985, 73). How strong was the corre-
lation? It was r50.43, which is adequate by the standards of the discipline.
Since 1985, SWLS has continued to be scrutinized for its agreement with
the growing data about subjective well-being. Individual judgments of life
satisfaction have been checked against the reports of informants close to the
subjects (Schneider and Schimmack 2009). Proponents of SWLS argue that
it exhibits a plausible relationship with money, relationships, suicide, and
satisfaction with various domains of life, such as work and living condi-
tions.
3
Now we are in a position to formulate a logic for psychometric validation
that we believe captures these practices:
Implicit Logic. A measure Mof a construct Cis validated to the extent that
Mbehaves in a way that respects three sources of evidence:
1. Mis inspired by a plausible theory of Cspecified in stage 1.
2. Subjects reveal Mto track Cthrough their questionnaire answering
behavior.
3. Other knowledge about Cis consistent with variations in values of
Macross contexts.
The first condition captures the role of philosophizing about the nature of C
in the first stage of measure development. There are no strong criteria for
what makes a conception of Cplausible and how elaborate it should be.
The second condition specifies the assumption behind factor analysis.
4
The
third acknowledges that scientists go beyond the merely internal analysis of the
scale: a valid measure correlates with indicators that our background knowl-
edge says it should and does not correlate with indicators that it shouldn’t. To-
3. See Diener et al. (2008, 74–93) for summary and references.
4. For convenience we are focusing on the practice of factor analysis, even though not all
construct validation procedures that concern us involve it—e.g., the validation of single-
item measures.
IS CONSTRUCT VALIDATION VALID? 1101
gether the three conditions capture what it takes for a measure to be declared
valid,
5
but they do not explain the reasons why this inference works. So the
next step is to evaluate Implicit Logic.
3. Construct Validation Is Good in Theory. Construct validation as de-
scribed above conceives of measurement as part of theory development and
validation as part of theory testing. On the original proposal formulated in
the classic 1955 article by Lee Cronbach and Paul Meehl, construct valida-
tion consists in testing the nomological network of hypotheses in the neigh-
borhood of the construct in question (Cronbach and Meehl 1955). To mea-
sure x, we need to know how xbehaves in relation to other properties and
processes that are systematically connected with xby lawlike regularities.
Something like this view is still the consensus: “To determine whether a
measure is useful, one must conduct empirical tests that examine whether
the measure behaves as would be expected given the theory of the underly-
ing construct”(Diener et al. 2008, 67).
We believe that this vision of measure validation is defensible. Its spirit is
remarkably similar to the coherentist vision that characterizes recent work
on measurement of physical quantities (Chang 2004; van Fraassen 2008;
Tal 2013). These philosophers emphasize that the outlines of the concept
in question, be it temperature or time, and the procedure for detecting it
are settled not separately but iteratively, checking and correcting one against
another. Similarly in our case, the initial philosophical judgment about the
nature of happiness or quality of life is coordinated with other constraints
such as the statistical features of the questionnaires and the background knowl-
edge about behavior, related indicators, and ratings of informants. The result-
ing measurement tools can be deemed valid to the extent that they accommo-
date all evidence.
The above vision appears to contrast with cases where measurement
starts with a set of observable relations (e.g., rigid rods of different lengths,
or choices of different goods by an agent) and proceeds via axioms to nu-
merical structures (such as a sequence of real numbers to represent length or
utility function). The latter picture is often associated with the representa-
tional theory of measurement. According to this, a measure is valid if there
is a demonstrated homomorphism between an observable relation and a nu-
merical relational structure (Krantz et al. 1971). The economic approach to
welfare measurement via gross domestic product and other economic indi-
cators seems to follow this logic because it relies, in part, on axioms that
5. There are, of course, other kinds of validity. We concentrate on construct because
among measurement theorists the consensus seems to be that construct validity encom-
passes all other types of validity, such as criterion, predictive, discriminant, and content
validity (Strauss and Smith 2009).
1102 ANNA ALEXANDROVA AND DANIEL M. HAYBRON
relate preferences to utility. Some commentators conclude that since the
psychometric approach does not rely on axioms, it is therefore not in keep-
ing with the representational theory (Angner 2009).
We make no such claims. It may well be that the psychometric approach
is not a tradition of its own and that it too needs something that has played
the role of axioms in the representational theory.
6
Perhaps step 1 of our Im-
plicit Logic aims at this goal by delineating the bounds of the concept in
question. All we claim is that the ideal behind construct validation is to for-
mulate reliable scales that accord with background knowledge. If this pro-
cess works, it should be enough for measurement. But does it?
4. Construct Validation in Practice. Things look worse in practice than
in theory. Although questionnaires are validated against a broad range of
evidence, psychometricians are selective about what counts as evidence
in favor of or against construct validity. We see two problems that illustrate
this selectivity. First, the existing data used to validate questionnaires do not
provide sufficient constraints to weed out the poor ones. Secondly, a legit-
imate source of evidence about the nature of states relevant to well-being—
philosophical theorizing—is either never used or else overridden by statis-
tical considerations. These are the two senses in which construct validation
is theory avoidant, sacrificing valid theoretical knowledge for statistics for
no good reason.
As step 3 of our Implicit Logic shows, researchers base judgments of va-
lidity mainly on whether the measure in question exhibits plausible-seeming
correlations with relevant-seeming variables. This is not unreasonable, since
correlational data are themain source of empirical evidence at hand, and there
is something of a chicken-and-egg problem in that, if we already knew exactly
what correlations a measure should exhibit, we might not have much need for
the measure. One piece of evidence that a well-being measure is valid, for in-
stance, might be that it correlates to some significant degree with money. But
then, on the other hand, the correlation between well-being and money may be
precisely one of the things we hope to find out using the measure. Psycho-
metricians have their work cut out for them.
It makes sense, then, that validation procedures should be flexible and
holistic: we see whether, on balance, the measure behaves in a way that
makes sense. While correlations with any given variable might prove to
be surprising, the overall pattern of correlations should not, in general, be
too much of a surprise. When we do get broadly unexpected results—as
might have been the case, for instance, when research seemed to indicate
that happiness was so strongly prone to adaptation as to be nearly immuta-
6. See Cartwright and Bradburn (2011) on the importance of representation in social
measurement, where concepts are often fuzzy and multitudinous.
IS CONSTRUCT VALIDATION VALID? 1103
ble (Lykken and Tellegen 1996)—then either we need some theoretical
framework to make sense of it, say, that happiness is strongly governed
by homeostatic mechanisms that keep the individual hovering around a given
“set point,”or we should suspect that the measures are not in fact valid (or
that the results are otherwise spurious).
The trouble is that what counts as a “plausible correlation”is a rather elas-
tic quantity, both vague and open to the interpretive predilections of the in-
vestigator, whose judgment in the matter may be less than impartial. The
problem is particularly acute in well-being research, where it can seem as
if nearly everything correlates substantially with nearly everything else.
Moreover, commonsense views of well-being tend to be both expansive
and incoherent; it is only somewhat exaggerated to say that just about any-
thing one might care to venture about well-being—money buys happiness,
money doesn’t buy happiness—is already part of the folklore.
Take a long list of variables that seem like they might be related to well-
being—money, relationships, health, education, work, and so on. Imagine
two measures, A and B, each of which correlates substantially with nearly
all of these variables, while also differing greatly in what those correlations
are. One suggests that relationships are more strongly related to well-being
than money, while the other has the reverse implication, and so forth. It
seems entirely possible that both measures could reasonably be deemed
to exhibit “plausible correlations”and generally pass as valid measures of
well-being. It is also possible that one of those measures is in fact valid, while
the other is not: A gets the correlations essentially right, while B gets them
wrong.
This sort of scenario is not merely a theoretical possibility. Recent stud-
ies have found that life evaluation and affect measures of well-being give
importantly different results, and some researchers have taken the differ-
ences to indicate that life evaluation metrics (such as the SWLS) are supe-
rior on the grounds that they are, or are claimed to be, more sensitive to life
circumstances—generally, correlating more strongly with quantities that
have traditionally interested policymakers such as income, governance,
freedom, and so on (Helliwell, Layard, and Sachs 2012). One question,
to which we will return shortly, is whether the affect measures in question
are themselves well designed. More pertinent for current purposes is this:
why should we assume that the better measure must correlate more strongly
with those variables? Suppose that hedonism, one of the main theories of
well-being in the literature, is in fact correct. In that case, perhaps the best in-
terpretation of the data is that well-being isn’t very sensitive to life circum-
stances. (Of course, those variables, like good governance, might matter a
great deal for reasons of justice, or some other reason.)
Alternatively, perhaps the “life circumstances”on which these research-
ers are focusing just aren’t the ones that matter most for well-being. An im-
1104 ANNA ALEXANDROVA AND DANIEL M. HAYBRON
portant article discussing data from the same global survey, for example,
reports that while life evaluation metrics do indeed track “material prosper-
ity”more strongly, the affect measures better correlate with what the au-
thors call “psychosocial prosperity”: whether people reported being treated
with respect in the previous day, had friends to count on, learned something
new, did what they do best, or chose how their time was spent (Diener et al.
2010). It would not be eccentric to suggest that these are just the sorts of
variables that seem most obviously to matter for well-being, and to which
good measures of well-being ought to be sensitive. Perhaps, then, it is the
affect measure, and not life evaluation, that offers a more meaningful pic-
ture of well-being.
Or perhaps not. Our point is not to endorse or critique either sort of mea-
sure.
7
There may be other reasons to favor life evaluation measures, and
there are differences in the data sets being used by these investigators that
we cannot assess here. The point is just to illustrate how two prominent
measures could both be deemed valid measures of well-being by prevailing
standards, though they have very different statistical properties—and, cru-
cially, statistical tests alone cannot tell us which is the superior instrument.
We need to appeal to theoretical considerations as well: what conception of
well-being is relevant here? Given our best understanding of human well-
being, what sorts of factors should a good measure correlate most strongly
with? Is the measure that more closely tracks money and stuff likely to be a
better indicator of well-being than one that tracks relationships and mean-
ingful work? If we do not take these theoretical questions seriously, ideally
before testing our instruments, we risk settling on whatever measures are
most convenient, most congenial to our personal views, or simply ours,
and not someone else’s.
One form of theory avoidance, then, can lead us to focus on the wrong
correlations, or have the wrong ideas about what the right correlations are:
the statistical data alone do not provide sufficient constraints to allow us to
assess the validity of a measure. In a second form, theory avoidance can
have us measuring the wrong variables altogether, because our instruments
are insufficiently grounded in theoretical considerations that might provide
a rationale for their design. We illustrate with an example of a popular af-
fect questionnaire known as PANAS. PANAS assesses the relative preva-
lence of positive over negative mood and is commonly used to measure the
affective dimensions of subjective well-being. This 20-item questionnaire
7. We may be seeming to mix apples and oranges here, as life evaluation and affect mea-
sures aren’t even supposed to be measures of the same construct. In fact, however, this is
not entirely true: while their proximal concerns are quite distinct, both are often posited
and deployed more fundamentally as general metrics of well-being, aimed at giving a
rough snapshot of overall welfare.
IS CONSTRUCT VALIDATION VALID? 1105
asks subjects to rate themselves on whether they feel enthusiastic, inter-
ested, excited, strong, alert, proud, active, determined, attentive, inspired,
and so on (Watson et al. 1988). All these items have passed factor analysis
and other standard psychometric tests. But note that absent from this list are
cheerfulness, joy, laughter, sadness, depression, tranquillity, anxiety, stress,
weariness—emotions that are intuitively far more central to a happy psy-
chological state and to well-being. This is because the authors of PANAS
arrived at the list of items by testing a long list of English mood terms and
paring it down via factor analysis, so that a longer list would not yield ap-
preciably different results.
Such a procedure allows investigators to avoid hard theoretical questions
about which taxonomy of emotional states to employ, or which states are
most relevant to well-being. But for the same reason, there is little reason
to expect such a method to yield a sound measure of well-being, or even
of emotional well-being. Rather, what is being assessed, roughly, is the
number of English mood terms that apply to the respondent—or rather,
the number of terms from a list of words that survived factor analysis.
But, first, this leaves the measure prey to the vagaries of common English
usage and folk psychology—potentially important emotional phenomena
may not be prominent in the vocabulary of a given language, or may not
be correctly classified as emotional, and so may be omitted from the mea-
sure. Of particular concern here are relatively diffuse background states—
anxiety, stress, peace of mind (not on the list)—that are quite important for
well-being yet easily overlooked, resulting in a kind of “streetlight”prob-
lem where we end up looking where the light is best, rather than where
the keys are.
Second, some states are presumably more important for well-being than
others; feelings of serenity or joy (not on the list) probably count for more
than feeling “attentive”or “alert”(on the list), and indeed some of the
PANAS items might barely deserve inclusion at all, if our interest is in as-
sessing well-being. Yet a term like “attentive”might exhibit quite distinctive
correlations and thus make it on the list, while other more salient terms are
left by the wayside.
The worries here essentially amount to saying that you can’t get the right
measure without attending to theoretical considerations—namely, what do
our best theories tell us are the emotional states that might matter for well-
being? For example, one of the authors recently proposed an account of
emotional well-being, or happiness, that divides emotional states into three
broad types—representing functional responses to different types of well-
being-relevant information regarding matters of security, opportunity, and
success—and further posits emotional well-being as a central element in
an account of well-being (Haybron 2008, 2013). Whether or not that taxon-
omy is the right one to employ in well-being measures, some such account
1106 ANNA ALEXANDROVA AND DANIEL M. HAYBRON
could provide a theoretically motivated basis for developing affect-based
well-being instruments.
We do not deny that PANAS is useful or exhibits some desirable statis-
tical properties, and perhaps it does provide a reasonable, if somewhat
opaque, metric of well-being. As before, our purpose is not to critique a par-
ticular measure so much as to illustrate how practices of construct valida-
tion can be seriously inadequate given the ease with which they can fail
to attend seriously to theoretical concerns. While we have not tried to doc-
ument the extent of the problem and have focused mainly on illustrating the
risks, that there is some problem should be uncontroversial. The risks, we
think, are not infrequently realized if only because the examples discussed
here, the SWLS and PANAS, are very popular. The problem here resembles
a complaint often lodged against philosophers’conceptual or linguistic anal-
yses, namely, a heavy reliance on the investigators’hunches or intuitions,
without adequate attention to the theoretical motivation, or lack thereof, for
reaching a certain view. This is not just a hazard for philosophers.
5. What Is to Be Done?.It is understandable that social scientists, like
other researchers, will want to focus their efforts where their competence
and interests are greatest. The theory-avoidant status quo has developed in
psychology owing to its operationalist heritage, which was key to its estab-
lishment as a ‘hard’science; even today, psychologists insist that although
building substantive theories of subjective well-being is a worthy enterprise,
they are not trained to do so and it is safer to tread close to the easily observ-
able and reproducible results of psychometrics. Any proposal for reform
should respect the fact that this status quo is unlikely to change in any deep
ways. But correlation mongering is no substitute for theory, and so we urge
that construct validation protocols also assess the normative validity of mea-
sures. The normative validity of a measure of, say, happiness, is the extent to
which this measure respects the importance of happiness for well-being,
since well-being is the ultimate object of concern for the scientific project
in question. We conceive of normative validity as a fourth condition on Im-
plicit Logic in addition to the three existing ones: a measure Mmust respect
what is important about construct C. Just as philosophers relying on empir-
ical assumptions are increasingly expected to engage with the relevant sci-
entific literatures, so too should empirical researchers attend to the literature
that bears on the key philosophical assumptions they are making.
We are under no illusions that this is a lot to ask of scientists whose iden-
tity often enough consists in not being philosophers. Besides, the very ques-
tion of normative validity can be a genuinely difficult one—philosophers do
disagree about the importance of, say, life satisfaction for well-being. Never-
theless, a scholarly convention to discuss normative validity at least briefly
in articles on validation would go some way toward flagging this issue. At
IS CONSTRUCT VALIDATION VALID? 1107
the very least, if there is no theory of well-being according to which the con-
struct in question is important, that should count against a measure.
The science of well-being makes no pretense of being value-free in one
clear sense: well-being is a value worth understanding and pursuing. The
eager and successful policy engagement of the prominent figures in this
field attests to this therapeutic mission. From this point of view our proposal
is quite tame—we merely try to show how the measurement and validation
practices of the science of well-being can catch up to the already-existing
normative ambition.
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