Building theories on top of, and not independent of, statistical models: The case of the p-factor

Watts, Ashley L.; Lane, Sean P.; Bonifay, Wes; Steinley, Douglas; Meyer, Francisco A. C.

doi:10.31234/osf.io/3vsey

Cited by 20 publications

(47 citation statements)

References 38 publications

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“…We believe our findings raise important questions regarding the statistical and ontological status of the p-factor, the latent structure of psychopathology, and comorbidity more generally (Watts et al, 2019(Watts et al, , 2020.…”

Section: Discussionmentioning

confidence: 87%

“…1 Most research presumes the p-factor is substantive and causal, with proposed mechanisms including: poor emotional control (Carver et al, 2017), negative emotionality (Brandes et al, 2019;Tackett et al, 2013), and disordered thought (Caspi et al, 2014). Few researchers have considered whether a general factor of psychopathology arises from a method artifact (c.f., Caspi & Moffit, 2018;Watts et al, 2020).…”

Section: Undiagnosed Cases and The P-factormentioning

confidence: 99%

“…Note that we refer to the theoretical construct as the p-factor and its statistical instantiation as the general factor of psychopathology to avoid conflating theoretical and statistical models(Watts et al, 2019(Watts et al, , 2020.…”

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confidence: 99%

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Spurious empirical support for the p-factor arises with the inclusion of undiagnosed cases

Al¹,

Meyer²,

Greene³

et al. 2021

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Background: The p-factor is thought to cause a positive manifold in psychopathology data, and many researchers presume that it is a substantive mechanism as opposed to a methodological artifact. Limited research suggests that including completely undiagnosed cases (i.e., cases without a single diagnosis) affects the dimensionality of psychological constructs, so we examined whether empirical support for the p-factor arises with their inclusion in structural models of psychopathology.Methods: We drew on data from three large, nationally representative samples of US community members, the: National Epidemiologic Study on Alcohol and Related Conditions Wave 1 (N=43,093), National Comorbidity Survey (N=5,877), and National Comorbidity Survey- Replication (N=5,692). We systematically culled undiagnosed cases from the data in increments of 10% and re-estimated structural models of psychopathology.Results: The correlation between latent externalizing and internalizing factors varied considerably as a function of the proportion of undiagnosed cases, with the correlation dropping to zero or even slightly negative when all undiagnosed cases were excluded. Also, as undiagnosed cases were removed, general factors of psychopathology explained less variance in psychopathology and weakened in terms of the extent to which it was well-represented by its indicators.Conclusions: Including undiagnosed cases in structural models of psychopathology induces a homogeneous, unidimensional structure. Ultimately, our findings raise questions about the nature of the p-factor, including whether it reflects a methodological artifact or arises due to the inclusion of cases with absence of diagnosed psychopathology.

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Section: Discussionmentioning

confidence: 87%

Section: Undiagnosed Cases and The P-factormentioning

confidence: 99%

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Spurious empirical support for the p-factor arises with the inclusion of undiagnosed cases

Al¹,

Meyer²,

Greene³

et al. 2021

Preprint

Self Cite

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“…Our proposed antidote is the adoption of methodological and analytic procedures that are more likely to detect multidimensionality, should it exist. Though there may be certain scenarios in which it is useful to extract a unidimensional factor of a given construct, this should not be confused with evidence that the construct itself is unidimensional (Watts et al, 2020).…”

Section: Parsing Heterogeneity Of Psychiatric Disordersmentioning

confidence: 99%

Does crude measurement contribute to observed unidimensionality of psychological constructs? An example with DSM-5 alcohol use disorder

Watts¹,

Boness²,

Stevens³

et al. 2020

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Psychiatric diagnoses are complex, multifaceted phenomena that are associated with profound heterogeneity and comorbidity. Despite the heterogeneity of psychiatric diagnoses, most are generally considered unitary dimensions. We argue that certain measurement practices, especially using too few indicators per construct, preclude the detection of meaningful multidimensionality. We demonstrate the implications of crude measurement for detecting multidimensionality within constructs using alcohol use disorder (AUD) as an example. To do so, we used a large sample of college heavy drinkers (N=909) for whom AUD symptomology was thoroughly assessed (87 items) to examine multidimensionality in AUD using a blend of confirmatory factor analysis, exploratory factor analysis, and hierarchical clustering. A unidimensional model of AUD fit the data poorly and was outperformed by a multidimensional model in terms of variance explained in theoretically-relevant external criteria. We converged on a hierarchically-organized model of AUD with three broad, transcriterial dimensions that reflected Tolerance, Withdrawal, and Loss of Control. In addition to introducing a hierarchical model of AUD, we offer that thorough, improved assessment of psychiatric diagnoses paired with serious consideration of alternative, multidimensional structures can move past the deadlock of unidimensional representations of psychiatric disorders.

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“…It has been theorized that the p factor reflects a general syndrome of psychopathology that involves unspecific elevation on numerous symptom characteristics (e.g., negative emotionality, low self-esteem, impulsivity, cognitive dysregulation, social withdrawal). Other researchers suggested that the p factor could instead reflect an index of symptomatic distress which is linked to negative self-evaluation, thus explaining why individuals report a greater degree of symptomatic complaints in general (Smith et al, 2020;Watts et al, 2020a). Still others proposed that the p factor reflects vicious causal cycles of mutually reinforcing symptoms (e.g., Bringmann & Eronen, 2018).…”

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confidence: 99%

Mapping established psychopathology scales onto the Hierarchical Taxonomy of Psychopathology (HiTOP)

Wendt¹,

Jankowsky²,

Zimmermann³

et al. 2021

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The hierarchical taxonomy of psychopathology (HiTOP) organizes phenotypes of mental disorder in a hierarchy from narrower symptoms to broader patterns of dysfunction. Little is known about how well traditional self-report questionnaires of psychopathology from the pre-HiTOP era are covered by the current HiTOP working model. We derived a comprehensive 64-item measure of symptom clusters (HiTOP-Map) from established measures (i.e., 685 items within 72 scales) using a sample of community participants and outpatients (N = 909) in a multistep construction process: First, content validity was rated for structuring the item pool. Second, exploratory factor analyses identified 16 symptom clusters. Third, a metaheuristic (ant colony optimization) compiled 64 prototypical items to cover the breadth across all clusters. Bass-ackwards analyses of HiTOP-Map showed a close correspondence to the current HiTOP working model. HiTOP-Map covered the original scales well (median r = .71), indicating that established self-report questionnaires of psychopathology are efficiently summarized by HiTOP.

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Building theories on top of, and not independent of, statistical models: The case of the p-factor

Abstract: This manuscript does not include an abstract.

Cited by 20 publications

References 38 publications

Spurious empirical support for the p-factor arises with the inclusion of undiagnosed cases

Spurious empirical support for the p-factor arises with the inclusion of undiagnosed cases

Does crude measurement contribute to observed unidimensionality of psychological constructs? An example with DSM-5 alcohol use disorder

Mapping established psychopathology scales onto the Hierarchical Taxonomy of Psychopathology (HiTOP)

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