How Robust Is the p Factor? Using Multitrait-Multimethod Modeling to Inform the Meaning of General Factors of Youth Psychopathology

Ashley L. Watts, Bridget A. Makol, Isabella M. Palumbo, Andres De Los Reyes, Thomas M. Olino, Robert D. Latzman, Colin G. DeYoung, Phillip K. Wood, Kenneth J. Sher

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


We used multitrait-multimethod (MTMM) modeling to examine general factors of psychopathology in three samples of youths (Ns = 2,119, 303, and 592) for whom three informants reported on the youth’s psychopathology (e.g., child, parent, teacher). Empirical support for the p-factor diminished in multi-informant models compared with mono-informant models: The correlation between externalizing and internalizing factors decreased, and the general factor in bifactor models essentially reflected externalizing. Widely used MTMM-informed approaches for modeling multi-informant data cannot distinguish between competing interpretations of the patterns of effects we observed, including that the p factor reflects, in part, evaluative consistency bias or that psychopathology manifests differently across contexts (e.g., home vs. school). Ultimately, support for the p factor may be stronger in mono-informant designs, although it does not entirely vanish in multi-informant models. Instead, the general factor of psychopathology in any given mono-informant model likely reflects a complex mix of variances, some substantive and some methodological.

Original languageEnglish (US)
Pages (from-to)640-661
Number of pages22
JournalClinical Psychological Science
Issue number4
StatePublished - Jul 2022

Bibliographical note

Funding Information:
We thank Christopher Hopwood and two other reviewers for their feedback on this article. Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study ( ), which is held in the National Institute of Mental Health Data Archive, and from a limited-access data set provided by the Child Mind Institute Biobank, Healthy Brain Network ( ). The ABCD Study is a multisite, longitudinal study designed to recruit more than 10,000 children ages 9 to 10 and follow them more than 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health (NIH) and additional federal partners under National Institute on Drug Abuse Grants U01-DA041022, U01-DA041028, U01-DA041048, U01-DA041089, U01-DA041106, U01-DA041117, U01-DA041120, U01-DA041134, U01-DA041148, U01-DA041156, U01-DA041174, U24-DA041123, U24-DA041147, U01-DA041093, and U01-DA041025. A full list of supporters is available at . A listing of participating sites and a complete listing of the study investigators can be found at . ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in analysis or writing of this article. The ABCD data repository grows and changes over time. The ABCD data used in this report came from doi:10.15154/1503209. This article reflects the views of the authors and may not necessarily reflect the opinions or views of the NIH, the ABCD consortium investigators, or the Child Mind Institute.

Publisher Copyright:
© The Author(s) 2021.


  • general factor of psychopathology
  • multi-informant psychopathology structures
  • multitrait-multimethod modeling
  • p factor

PubMed: MeSH publication types

  • Journal Article


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