On hierarchically‐informed measures of psychopathology
Authors: Kristian E. Markon
Journal: World Psychiatry (2021)

Lahey et al1 provide an overview of superordinate structural models of psychopathology, focusing on the role of a general psychopathology factor within them. The authors review differences between hierarchical and higher-order accounts, structural findings from family and longitudinal designs, theories about the nature of the structures, and remaining questions and tasks facing research and clinical communities. They argue that these communities are “hampered by the current absence of a comprehensive dimensional measure of psychopathology that includes all symptoms”, and suggest that “we cannot comprehensively define the dimensions of psychopathology until we can study all of the symptoms that define the universe of psychopathology at the same time and in the same way”. Although there are certainly pressing needs for psychopathology measures, is it true that there is an absence of comprehensive dimensional measures of psychopathology? What would it mean to “include all symptoms”? Is an optimal measure one in which different symptoms of psychopathology are quantified in the same way? What would an ideal measure look like? A truly comprehensive measure of psychopathology might be unattainable, given that measurement needs evolve with fields and will always change. However, reasonable proxies for this ideal arguably do exist – the Achenbach System of Empirically Based Assessment (ASEBA)2 is an important example. On the other hand, one can point to a myriad of domain-specific measures, such as measures of mood, somatic symptoms, or thought disorder, which have been carefully constructed to represent the phenomena they purport to measure, although they may be circumscribed in depth or breadth, or otherwise limited in some way. These measures are not comprehensive individually but, jointly assembled, they usually cover their respective terrain well. Many studies of superordinate psychopathology structure are reasonably based on these types of measures, consolidated into wholes. As research on superordinate structure progresses, it is important to ask what, exactly, the field needs in a comprehensive measure. A hierarchical model such as a bifactor model typically differs from alternatives in that its general factor either has direct expression in indicators (i.e., there are “pure” indicators of the general factor) or its expression is not otherwise mediated through specific factors (i.e., relationships between indicators of different specific factors cannot be explained in terms of general factor relationships)3. The former case suggests that pure indicators of the general factor could be developed, possibly in the process of testing hypothetical underlying mechanisms of that factor, such as those enumerated by Lahey et al (i.e., variables such as negative emotion, cognitive ability, impulsive response to emotions, disordered thinking, or various neurobiological factors). The latter case, perhaps encountered through an eventual failure to identify pure indicators, might suggest more systemic factors at play, and an emergent, rather than correspondent, general factor where very detailed processes acting across specific factors dynamically relate4. A comprehensive measure is arguably­ inseparable from an underlying set of hy­potheses or assumptions, implicit or explic­it, about the nature of its constituent factors and how they manifest in indicators. The development or consolidation of such a mea­sure, by extension, would probably have greatest utility if those assumptions are systematically evaluated as part of the design. It remains to be seen how much the specific origins of a measure affect structural-causal modeling of indicators, even though the need for a single origin seems to be an implicit assumption of many current superordinate measurement efforts. For example, if two research groups were to independently develop two measures of the same psychopathology variable, how much would conclusions about the structure of that variable differ depending on which measure was used? Independently developed measures of psychopathology are commonly used together in structural studies, and few questions have been raised about method effects outside of major variables such as type of respondent or time of assessment. What are the limits? How much do unspecified method effects associated with origin, such as unarticulated patterns of item construction, affect results? Is there anything significant about sharing a common origin beyond the associated hypotheses underlying their construction? Does focusing on a comprehensive measure inhibit competitive evaluation of measurement and structural theories? It is also important to consider whether, in a comprehensive measure, “all of the symptoms that define the universe of psychopathology” would ideally be assessed “at the same time and in the same way”. It is possible that different types of psychopathology might be best assessed in different ways, either due to the nature of the psychopathology, or due to issues pertaining to insight, unreliable recall, reliable or unreliable situational variability, or other variables. Disorders with cognitive ability deficits might be assessed differently than mood disorders, for example; delusions or narcissistic psychopathology might be optimally assessed by different informants than would be used to assess distress; it might be that a general factor impels a different measurement approach than specific factors. At some level, it is unclear if an exhaustive set of symptoms could be defined. One must consider the possibility that idiographic assessment components would be necessary to achieve comprehensiveness. In that case, one might essentially have different measures for each individual, an extreme case of symptoms not being assessed at the same time or in the same way. One interesting problem raised by the hierarchical or bifactor model is how to conceptualize severity of psychopathology symptoms, especially in a clinical context. One classic conception of severity is in terms of risk conferred by a symptom for some significant sequelae or outcomes, such as death, serious injury, hospitalization, or significant loss. Another common, more recent conceptualization of severity is found in item response theory, in which severity is framed in terms of level of psychopathology required to endorse a symptom. Typically, in unidimensional item response theory models, endorsement of more symptoms points to greater levels of an underlying psychopathology dimension and therefore severity. However, in ­hierarchical models, endorsement of more symptoms might reflect something etiologically distinct. That is, as an individual endorses more and more diverse symptoms, the most appropriate inference might not be that the individual is elevated on many specific factors, but rather that he/she is elevated on a general factor, with distinct etiology, reflecting a different set of pathologies. This shifts perspective on the meaning of diverse symptom endorsement somewhat, away from severity per se or multifactoriality. Development and use of hierarchical comprehensive measures should ideally address these types of issues in how they are scored, and how responses to the measures are interpreted.