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What are psychological constructs? On the nature and statistical modeling of emotions, intelligence, personality traits and mental disorders

What are psychological constructs? On the nature and statistical
modelling of emotions, intelligence, personality traits and mental
Eiko I. Fried
Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
ARTICLE HISTORY Received 25 February 2017; Accepted 10 March 2017
Many scholars have raised two related questions: what are psychological constructs (PCs) such as
cognitions, emotions, attitudes, personality characteristics and intelligence? And how are they best
modelled statistically? This commentary provides (1) an overview of common theories and statistical
models, (2) connects these two domains and (3) discusses how the recently proposed framework
pragmatic nihilism (Peters & Crutzen, 2017) fits in.
For this overview, I use an inclusive definition of the term psychological constructthat also
encompasses mental disorders, similar to Cronbach and Meehl (1955). This is consistent with
recent efforts such as the research domain criteria (RDoC) that aim to refine such constructs (Cuthbert
& Kozak, 2013), and is relevant given many recent discussions on the nature of psychopathology.
Psychological kinds
Four common accounts have been put forward: PCs are natural, social, practical or complex kinds.
Natural kinds
Natural kinds are unchanging and ahistorical entities that exist whether or not they are recognised as
such. They have intrinsic properties that establish a natural set of kind members, making them the
thing they are. The element gold, for instance, has 79 protons, and everything with 79 protons is
gold; this internal feature is necessary and sufficient to define kind membership. In psychology,
basic or primary emotions such as fear, anger or disgust are often seen as natural kinds (Barrett,
2006). Among others, Ekman and Cordaro (2011) suggested that emotions are discrete [and] can
be distinguished fundamentally from one another(p. 364). Mental disorders are a second
example of PCs. In recent years, they have been described increasingly as brain disorders (Insel
et al., 2010), and the search for biological markers presupposes that these disorders exist as
natural kinds that can in principle be discovered; that is, the notion of brain disorders assumes a
realist ontology about mental disorders. Personality characteristics have also been conceptualised
as natural kinds by, among others, McCrae and Costa (e.g., McCrae et al., 2004; cf., Mõttus & Allerhand,
2017, for a detailed discussion).
Social kinds
The idea that psychological kinds are socially constructed socially agreed upon definitions is more
common in the social sciences. Emotions, personality domains or mental disorders do not carve
nature at its joints: they are produced, not discovered. Berger and Luckmanns seminal book on the
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topic states that all knowledge, even the most basic insight, is derived and maintained by social inter-
actions (1966). As an example for PCs, Szasz posited that all mental disorders are socially constructed
(e.g., Szasz, 1961; cf., Hacking, 1999, for a more nuanced perspective).
Practical kinds
Instrumentalists such as James, Dewey and Zachar understand PCs as practical kinds, a position
referred to as pragmatic nominalism. From this perspective, emotions (Zachar & Bartlett, 2001), per-
sonality characteristics (Zachar, 2002) or mental disorders (Kendler, Zachar, & Craver, 2011) should be
judged in terms of practical scientific success, not whether they correspond to an independent
reality. Above anything, constructs should be useful. Socioeconomic status (SES) hardly represents
arealentity in a metaphysical sense, but provides important insights because it predicts adverse
social and health outcomes such as injury, poverty, morbidity and mortality. Instrumentalists argue
that unlike social kinds, practical kinds are not just made up: the goal is to identify scientifically
useful categories. Many PCs such as emotions (Zachar & Bartlett, 2001) or mental disorders (Zachar
& Kendler, 2007) have been discussed as practical kinds.
Complex kinds
Boyd (1991,1999) understood biological species as homeostatic property clusters (HPC). Biological fea-
tures often occur together in nature because the presence of one property tends to favour the pres-
ence of another, forming clusters. This means that beavers are aggregations of features that reliably
occur together because of underlying causal processes. The same can be argued for depression:
symptoms co-occur because the presence of one likely leads to others. Relationships between prop-
erties are probabilistic and not deterministic, and imperfect aggregations of properties are common:
not all beavers have identical biological features, and not all depressed patients have the same symp-
toms. In this aspect, HPCs are different from natural kinds (which have necessary and sufficient fea-
tures), but like natural kinds, they are non-arbitrary. Recent work suggests that a wide range of PCs
may best be understood as HPCs: mental disorders (Borsboom, 2017; Kendler et al., 2011), personality
characteristics (Mõttus & Allerhand, 2017), attitudes (Dalege et al., 2016) and intelligence (van der
Maas et al., 2006; van der Maas, Kan, Marsman, & Stevenson, 2017).
Statistical models
The question what a PC is guides the type of statistical model that is appropriate. Three common
models are described, along with their relationships to psychological kinds. Since constructs in
Figure 1. Schematic visualization of three types of statistical models for psychological constructs (PC). Left: reflective model where
the latent variable (thick border) PCis the common cause for the 10 observed indicators 110 (thin borders). Centre: formative
model where the latent variable is constructed from the indicators. Right: network model where the co-occurrence of all observed
items is due to causal processes; there is no latent variable, and self-loops indicate that items cause each other over time.
psychology are usually unobserved, they are referred to as latent variables in the statistical literature.
The three models are summarised in Figure 1.
Reflective models
Conceptualising a PC as a natural kind implies a clear causal model: the latent variable causes a set of
observable indicators, and a persons position on the latent variable can be inferred by measuring
these indicators (Figure 1, left). To assess mathematical intelligence, for instance, a battery of math
items (e.g., what is 4 + 4) can be used. For extraversion and schizophrenia, researchers can
measure extraversion items (e.g., I like going to parties) and schizophrenia symptoms (e.g., I hear
voices), respectively. Statistically, such models are called reflective latent variable models, because
the items reflect the manifestation of an underlying PC (Edwards & Bagozzi, 2000). While the use
of reflective models follows from a natural kind perspective, not all reflective models represent
natural kinds. Being rich can be understood to be socially constructed: some societies attribute mon-
etary value to pieces of paper or digits on bank accounts, while others attribute value to oddly shaped
wooden sticks. It is possible to infer if someone is affluent (the latent variable) via observable indi-
cators such as the quality of clothing, the frequency of vacations, or by counting how many oddly
shaped sticks a person carries.
Formative models
The formative model is the causal opposite (Figure 1, centre): indicators determine the latent variable
(Edwards & Bagozzi, 2000). I used SES as an example for a practical kind, and it fits the bill of a com-
posite variable. SES is constructed via indicators such as occupation, education and income: if a
persons income increases, so does the SES, but it is not possible to increase a persons income by
changing the latent variable. The opposite holds for reflective models: changing mathematical intel-
ligence in the brain (if possible) would change a persons performance on math questions, but inter-
fering with a persons ability to do well on a test (e.g., lack of sleep) does not diminish the persons
actual mathematical ability. Note that such interventions have been proposed as a way to determine
whether a PC is real(Hacking, 1983): if researchers feel comfortable manipulating it in an experiment
to investigate something less known, it can be considered real.
Network models
From the perspective of HPC, extraversion items do not co-occur because of an underlying PC they
are related because they influence each other (Mõttus & Allerhand, 2017)(Figure 1, right). Likewise,
mental disorders such as schizophrenia or psychosis can be conceptualised as clusters of symptoms
with causal interactions and vicious circles that can form stable systems (Borsboom, 2017; Cramer,
Waldorp, van der Maas, & Borsboom, 2010; Fried et al., 2016), rather than as passive indicators of a
brain disorder. Instead of modelling PCs as a reflective or formative latent variable, network
models for cross-sectional (Epskamp, Borsboom, & Fried, 2017) and longitudinal (Bringmann et al.,
2013) data have been developed recently that allow for modelling complex systems of observable
items. It has also been suggested that the environment plays an important role in such networks,
and that it may lead to stable networks (Mõttus & Allerhand, 2017; van der Maas et al., 2017).
Pragmatic nihilism
A major shortcoming of the empirical literature on PCs is the lack of clarity regarding how researchers
understand what they study. For personality traits and mental disorders, for instance, researchers pre-
dominantly use reflective latent variable models, but they remain largely silent about what extraver-
sionor neuroticismfactors are, or what a cognitive depressionfactor is with symptoms like
132 E. I. FRIED
worthlessness, hopelessness, guilt and pessimism. Do these latent variables cause their indicators,
and what is their ontology?
The question what psychological kinds are is also at the core of Peters and Crutzens(2017) idea of
pragmatic nihilism (from here on P&C). The authors do not refer to the large corpus of literature on
the topic, and I hope the brief overview above is helpful to embed their ideas into prior work.
The first tenet of pragmatic nihilism is that PCs are useful metaphors in understanding and mod-
elling the world, but need not actually exist. This resembles a practical kinds view: pragmatic nomin-
alists have long argued that realnessdoes not add anything relevant to a construct (Fine, 1984), and
that constructs should be, above all, useful. Second, pragmatic nihilism pertains to definition,
measurement and operationalisation of constructs: how researchers define them determines how
they should be measured. This calls for attention to operationalisation: if PCs are not necessarily
real, and the goal is to create useful metaphors rather than to discover truths, the operationalisation
of a construct defines it. Depression rating scales, for instance, differ considerably in symptom content
(Fried, 2017), and P&C would likely argue that these different scales measure somewhat different
depressions. This focus seems to call for formative instead of reflective latent variable models,
which contrasts with the widespread use of exploratory and confirmatory (i.e., reflective) factor
models in the psychological literature. Third, P&C argue that cognition and emotion [] exist as
emergent properties in a distributed network of neurons, but they cannot be pinpointed physically
in peoples brains. This raises the question whether the authors understand PCs as identical to their
neurological realisations, or whether they supervene on neurological processes (cf., Kievit et al., 2011).
My main concern is this: there is nothing better than a true theory. Discovering truths about the
universe, such as the table of elements, has facilitated and in many cases enabled scientific pro-
gress. If there is nothing real about psychological processes, psychology can only ever describe, but
never understand, which may greatly limit prediction and intervention. Im not quite ready to give up
on the notion that psychology can discover truths, and find the idea that all PCs are made-up dis-
heartening but thats what you get messing about with nihilism, I suppose.
I would like to extend my sincerest thanks to Denny Borsboom for comments on a prior version of this paper, and to the
editor for soliciting this commentary.
Disclosure statement
No potential conflict of interest was reported by the author(s).
This work was supported by European Research Council [ERC Consolidator Grant no. 647209].
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Network analysis is an emerging field for the study of psychopathology that considers constructs as arising from the interactions among their constituents. Pairwise effects among psychological components are often investigated by using this framework. Few studies have applied Bayesian networks, models that include directed interactions to perform causal inference on psychological constructs. Directed graphical models may be less straightforward to interpret in case the construct at hand does not contain symptoms but instead psychometric items from self-report measures. However, they may be useful in validating specific research questions that arise while using standard pairwise network models. In this study, we use Bayesian networks to investigate a well-known psychological construct, empathy from the Interpersonal Reactivity Index, in large two samples of 1973 university students from Belgium. Overall, our results support the hypotheses emphasizing empathic concern (i.e., sympathy) as causally important in the construct of empathy, and overall attribute the primacy of emotional components of empathy over their intellectual counterparts. Bayesian networks help researchers identify the plausible causal relationships in psychometric data, to gain new insight on the psychological construct under examination, help generate new hypotheses and provide evidence relevant to old ones.
... The intelligence quotient (IQ) is considered to be the best predictor of educational career and professional success (Kiese-Himmel 2016; Stumpf & Perleth 2019). The IQ distribution has a normalized mean of 100 and a pre-defined SD of 15 (McCall From the age of 6 years, measured intelligence is a relatively stable predictor of learning skills in primary school, and thereby of great pragmatic value (Fried 2017). For long-term predictions, measured intelligence from the age of 9 or 10 years is considered sufficiently valid (Kiese-Himmel 2012). ...
Objectives: Intelligence as a construct of cognitive abilities is the basis of knowledge and skill acquisition and the main predictor of academic achievement. As a broad construct, it is usually divided into subdomains, such as nonverbal and verbal intelligence. Verbal intelligence is one domain of intelligence but is not synonymous with specific linguistic abilities like grammar proficiency. We aim to address the general expectation that early cochlear implantation enables children who are hard of hearing to develop comprehensively, including with respect to verbal intelligence. The primary purpose of this study is to trace the longitudinal development of verbal and nonverbal intelligence in children with cochlear implants (CIs). Design: Sixteen children with congenital hearing loss who received unilateral or bilateral implants and completed at least two intelligence assessments around the age of school entrance were included in the study. The first assessment was performed around 3 years after CI fitting (chronological age range: 3.93 to 7.03 years). The second assessment was performed approximately 2 years after the first assessment. To analyze verbal and nonverbal IQ in conjunction and across children at different ages, we used corresponding standardized and normalized tests from the same test family (Wechsler Preschool and Primary Scale of Intelligence and/or Wechsler Intelligence Scale for Children). Results: Regarding longitudinal development, both verbal and nonverbal IQ increased, but verbal IQ increased more substantially over time. At the time of the second measurement, verbal and nonverbal IQ were on a comparable level. Nevertheless, we also observed strong inter-individual differences. The duration between both assessments was significantly associated with verbal IQ at the second measurement time point and thus with verbal IQ gain over time. Education mode (regular vs. special kindergarten/school) was significantly correlated with nonverbal IQ at the second assessment time point. Conclusions: The results, despite the small sample size, clearly suggest that children with CIs can achieve intellectual abilities comparable to those of their normal-hearing peers by around the third year after initial CI fitting, and they continue to improve over the following 2 years. We recommend further research focusing on verbal IQ assessed around the age of school entrance to be used as a predictor for further development and for the establishment of an individual educational program.
... To be included, studies needed to assess the target behaviour and at least one psychological or contextual variable through EMAs, and to have reported at least one within-or between-person predictor-behaviour association. In this review, we defined psychological variables as emergent properties of a distributed network of neurons, including cognitions (e.g., beliefs, attitudes, goals), emotions (e.g., negative affect, cravings) and processes operating on these (e.g., self-regulation, learning), which are linked to behaviour (Fried, 2017). We defined contextual variables as any potential environmental (i.e., social or physical) influences on behaviour, including the presence of other people, weather, or the availability of unhealthy foods/tobacco/alcohol. ...
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Ecological Momentary Assessment (EMA) involves repeated, real-time sampling of health behaviours in context. We present the state-of-knowledge in EMA research focused on five key health behaviours (physical activity and sedentary behaviour, dietary behaviour, alcohol consumption, tobacco smoking, sexual health), summarising theoretical (e.g., psychological and contextual predictors) and methodological aspects (e.g., study characteristics, EMA adherence). We searched Ovid MEDLINE, Embase, PsycINFO and Web of Science until February 2021. We included studies focused on any of the aforementioned health behaviours in adult, non-clinical populations that assessed ≥1 psychological/contextual predictor and reported a predictor-behaviour association. A narrative synthesis and random-effects meta-analyses of EMA adherence were conducted. We included 633 studies. The median study duration was 14 days. The most frequently assessed predictors were ‘negative feeling states’ (21%) and ‘motivation and goals’ (16.5%). The pooled percentage of EMA adherence was high at 81.4% (95% CI = 80.0%, 82.8%, k=348) and did not differ by target behaviour but was somewhat higher in student (vs. general) samples, when EMAs were delivered via mobile phones (vs. handheld devices), and when event contingent (vs. fixed) sampling was used. This review showcases how the EMA method has been applied to improve understanding and prediction of health behaviours in context.
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Unlabelled: The past two decades have established that people generally have insight into their personalities, but less is known about how and why self-knowledge might vary between individuals. Using the Realistic Accuracy Model as a framework, we investigate whether some people make better "targets" of self-perception by behaving more consistently in everyday life, and whether these differences have benefits for psychological adjustment. Methods: Using data from the Electronically Activated Recorder (EAR, n=286), we indexed self-knowledge as the link between self-reports of personality and actual daily behaviour measured over one week. We then tested if consistency in daily behaviour as well as psychological adjustment predicted stronger self-knowledge. Results: We found that behaving more consistently in everyday life was associated with more accurate self-reports, but that psychological adjustment was not. Conclusion: Analogous to interpersonal perception, self-knowledge of personality might be affected by "target-side" factors, like the quality of information provided through one's behaviour. However, unlike being a good target of interpersonal perception, self-knowledge does not seem to be related to psychological adjustment.
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‘Responsible drinking’ remains a poorly defined construct despite decades of use among diverse stakeholders including industry, academics, governmental agencies, and addiction advocacy groups. To move the field closer to a consensus definition of responsible drinking that is useful for educational and research purposes, we describe five primary barriers that discourage the construction of a shared definition of responsible drinking. These barriers include the lack of foundational empirical evidence, the social construction of the term, the possibility that different targets require different definitions, the political implications of responsible drinking, and the possibility that there is no safe level of alcohol consumption. We conclude this article by offering suggestions to overcome these barriers through further research.
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Cronbach’s (1957) famous division of scientific psychology into two disciplines is still actual for the fields of cognition (general mechanisms) and intelligence (dimensionality of individual differences). The welcome integration of the two fields requires the construction of mechanistic models of cognition and cognitive development that explain key phenomena in individual differences research. In this paper we argue that network modeling is a promising approach to integrate the processes of cognitive development and (developing) intelligence into one unified theory. Network models are defined mathematically, describe mechanisms on the level of the individual, and are able to explain positive correlations among intelligence subtest scores - the empirical basis for the well-known g-factor - as well as more complex factorial structures. Links between network modeling, factor modeling and item response theory allow for a common metric, encompassing both discrete and continuous characteristics, for cognitive development and intelligence.
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In recent years, the network approach to psychopathology has been advanced as an alternative way of conceptualizing mental disorders. In this approach, mental disorders arise from direct interactions between symptoms. Although the network approach has led to many novel methodologies and substantive applications, it has not yet been fully articulated as a scientific theory of mental disorders. The present paper aims to develop such a theory, by postulating a limited set of theoretical principles regarding the structure and dynamics of symptom networks. At the heart of the theory lies the notion that symptoms of psychopathology are causally connected through myriads of biological, psychological and societal mechanisms. If these causal relations are sufficiently strong, symptoms can generate a level of feedback that renders them self-sustaining. In this case, the network can get stuck in a disorder state. The network theory holds that this is a general feature of mental disorders, which can therefore be understood as alternative stable states of strongly connected symptom networks. This idea naturally leads to a comprehensive model of psychopathology, encompassing a common explanatory model for mental disorders, as well as novel definitions of associated concepts such as mental health, resilience, vulnerability and liability. In addition, the network theory has direct implications for how to understand diagnosis and treatment, and suggests a clear agenda for future research in psychiatry and associated disciplines.
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Health psychology aims to explain and change a wide variety of behaviours, and to this end has developed a plethora of theories. Several attempts have been undertaken to build integrative theories, and some even strive for a Theory of Everything. We argue against these efforts, arguing that instead, adopting a stance that may be called ‘pragmatic nihilism’ is more fruitful in the endeavour to understand and change specific health behaviours. The first tenet of pragmatic nihilism is that psychological variables, those defined in our health psychology theories, are usefully considered as metaphors rather than referring to entities that exist in the mind. As a consequence, the second tenet emphasizes theories’ definitions and guidelines for the operationalisation of those variables. The third tenet of pragmatic nihilism is that each operationalisation represents a cross section of a variety of dimensions, such as behavioural specificity and duration of the behaviour, and most importantly, psychological aggregation level. Any operationalisation thus represents a number of implicit or explicit choices regarding these dimensions. These three tenets of pragmatic nihilism have two implications. First, they provide a foundation that enables integrating theories in a more flexible and accurate manner than made possible by integrative theories. Second, this perspective emphasizes the importance of operationalisations, underlining the importance of investing in the careful development of measurement instruments, and thorough and extensive reporting of the specifics and performance on those measurement instruments as well as disclosure of the instruments themselves.
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This chapter deals with one of the most pervasive personality-related phenomenon: the coalescence of tendencies for specific thoughts, feelings and behaviors (characteristics) into broader patterns—traits. Two possible explanations are discussed. The more established explanation is that certain characteristics tend to co-exist because they reflect a common underlying cause. A more recent explanation is that they may also hang together because of having direct causal links between them—some characteristics can contribute to, or inhibit, others. However, the chapter offers a more general, mathematically formalized framework, which can, in fact, merge the to explanations. Furthermore, this framework can be used to represent both processes within individuals and individual differences, with the latter emerging from the former. This means potential for a formal bridge between two branches of personality psychology—the social cognitive and trait approaches. Some empirical findings will be reviewed that are consistent with the proposed framework.
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Purpose: The network perspective on psychopathology understands mental disorders as complex networks of interacting symptoms. Despite its recent debut, with conceptual foundations in 2008 and empirical foundations in 2010, the framework has received considerable attention and recognition in the last years. Methods: This paper provides a review of all empirical network studies published between 2010 and 2016 and discusses them according to three main themes: comorbidity, prediction, and clinical intervention. Results: Pertaining to comorbidity, the network approach provides a powerful new framework to explain why certain disorders may co-occur more often than others. For prediction, studies have consistently found that symptom networks of people with mental disorders show different characteristics than that of healthy individuals, and preliminary evidence suggests that networks of healthy people show early warning signals before shifting into disordered states. For intervention, centrality—a metric that measures how connected and clinically relevant a symptom is in a network—is the most commonly studied topic, and numerous studies have suggested that targeting the most central symptoms may offer novel therapeutic strategies. Conclusions: We sketch future directions for the network approach pertaining to both clinical and methodological research, and conclude that network analysis has yielded important insights and may provide an important inroad towards personalized medicine by investigating the net- work structures of individual patients.
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The usage of psychological networks that conceptualize behavior as a complex interplay of psychological and other components has gained increasing popularity in various research fields. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online. Electronic supplementary material The online version of this article (doi:10.3758/s13428-017-0862-1) contains supplementary material, which is available to authorized users.
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Scores on cognitive tasks used in intelligence tests correlate positively with each other, that is, they display a positive manifold of correlations. The positive manifold is often explained by positing a dominant latent variable, the g factor, associated with a single quantitative cognitive or biological process or capacity. In this article, a new explanation of the positive manifold based on a dynamical model is proposed, in which reciprocal causation or mutualism plays a central role. It is shown that the positive manifold emerges purely by positive beneficial interactions between cognitive processes during development. A single underlying g factor plays no role in the model. The model offers explanations of important findings in intelligence research, such as the hierarchical factor structure of intelligence, the low predictability of intelligence from early childhood performance, the integration/differentiation effect, the increase in heritability of g, and the Jensen effect, and is consistent with current explanations of the Flynn effect.
Background: Depression severity is assessed in numerous research disciplines, ranging from the social sciences to genetics, and used as a dependent variable, predictor, covariate, or to enroll participants. The routine practice is to assess depression severity with one particular depression scale, and draw conclusions about depression in general, relying on the assumption that scales are interchangeable measures of depression. The present paper investigates to which degree 7 common depression scales differ in their item content and generalizability. Methods: A content analysis is carried out to determine symptom overlap among the 7 scales via the Jaccard index (0=no overlap, 1=full overlap). Per scale, rates of idiosyncratic symptoms, and rates of specific vs. compound symptoms, are computed. Results: The 7 instruments encompass 52 disparate symptoms. Mean overlap among all scales is low (0.36), mean overlap of each scale with all others ranges from 0.27 to 0.40, overlap among individual scales from 0.26 to 0.61. Symptoms feature across a mean of 3 scales, 40% of the symptoms appear in only a single scale, 12% across all instruments. Scales differ regarding their rates of idiosyncratic symptoms (0-33%) and compound symptoms (22-90%). Limitations: Future studies analyzing more and different scales will be required to obtain a better estimate of the number of depression symptoms; the present content analysis was carried out conservatively and likely underestimates heterogeneity across the 7 scales. Conclusion: The substantial heterogeneity of the depressive syndrome and low overlap among scales may lead to research results idiosyncratic to particular scales used, posing a threat to the replicability and generalizability of depression research. Implications and future research opportunities are discussed.