They demonstrated that a \"Big Everything\" factor accounted for considerable variance across these domains, but not cognitive functioning; further, they cautioned against overinterpreting these results, highlighting limitations of factor analytic approaches and bifactor models in determining the structure of psychological constructs. Recently, [23] proposed a similar model, with a single overarching factor explaining correlations among all cognitive and psychopathology indicators, beyond what is explained by \"g\" and \"p\". They termed this super-ordinate factor \"c\" for \"cereb function\", which places equal emphasis on cognitive and clinical aspects of \"c\". Notably, [PERSON] et al. [23] did not estimate the \"c\" factor in a data set, but rather presented a theory and framework for how \"c\" could be modeled and provided suggestions for how that model could be validated. Here, we take that further step and estimate and validate a \"c\" factor in a large, well-studied data set, the Philadelphia Neurodevelopmental Cohort [13]. Configuration of the c-factor model is \"tri-factor\" [24] in that each variable loads on the following three factors simultaneously:  
1. Its own specific factor (e.g., verbal memory test on the \"Memory\" factor);
2. The general factor comprising variables in its super-domain (e.g., verbal memory test on the \"g\" factor);
3. The c-factor comprising all variables. Both cognitive and clinical variables have been conceptualized as bifactor [25] configurations, the \"p\" factor [20] and \"g\" factor [18; 26], respectively, and the c-factor combines those two bifactor models into a single model with a super-ordinate factor encompassing both \"g\" and \"p\". The model tests whether the apparent hierarchical structures of psychopathology and cognition might not \"top out\" at \"p\" and \"g\", but rather these might themselves be sub-factors in a yet higher-order phenomenon termed \"c\". Tri-factor modeling was developed to address a fundamental limitation in analyzing multi-informat data, where traditional approaches struggled to separate shared variance across raters from unique rater perspectives and trait-specific components. This methodological advancement is particularly valuable when researchers need to model three distinct sources of variation simultaneously, and unlike bifactor models that can partition variance into only two components, tri-factor models allow researchers to examine how much variance in observed scores is attributable to a general factor, a second general factor, and specific content domains, providing a more nuanced understanding of complex measurement structures. This makes tri-factor modeling an excellent fit for the current study, where we have exactly three sources of variance to explain. Parsing levels of cerebral phenomena, from the specific (e.g., depression or episodic memory) to the super-domain (\"p\", \"g\", and maybe others to be uncovered) to the highest-order (\"c\"), is an essential step toward precision psychiatry [27]. As pointed out by [PERSON] et al. [23], establishing a c factor \"may transform many areas of research and clinical practice...[and] help to reveal the respective biological and psychosocial determinants that can be tackled using personalized and evidence-based treatments.\" (p. 15). The goals of the present study were to, (1) in a large community sample, model the relationships among cognitive and psychopathology indicators using a tri-factor structural measurement model, (2) examine how factor scores calculated from this model relate to external demographic and biological criteria, and (3) evaluate whether the c factor helps predict functional outcome in a follow-up sample. Before describing the study, it is worth noting that factor analysis in psychiatric research presents several methodological challenges that warrant careful consideration, and some of these are further elaborated in the Discussion.