A New Story on the Multidimensionality of the MSPSS: Validity of the Internal Structure through Bifactor ESEM

The internal structure of the Multidimensional Scale of Perceived Social Support (MSPSS) in adolescents has been evaluated with some factorial analysis methodologies but not with bifactor exploratory structural equation modeling (ESEM), and possibly the inconsistency in the internal structure was dependent on these approaches. The objective of the study was to update evidence regarding its internal structure of MSPSS, by means of a detailed examination of its multidimensionality The participants were 460 adolescents from an educational institution in the Callao region, Lima, Peru. The structure was modeled using unidimensional, three-factor and bifactor models with confirmatory factor analysis (CFA) and ESEM approaches. The models showed good levels of fit, with the exception of the unidimensional model; however, the multidimensionality indicators supported the superiority of the bifactor ESEM. In contrast, the general factor was not strong enough, and the interfactorial correlations were substantially lower. It is concluded that the MSPSS can be interpreted by independent but moderately correlated factors, and there is possible systematic variance that potentially prevented the identification of a general factor.

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

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.

On the Meaning of the “P Factor” in Symmetrical Bifactor Models of Psychopathology: Recommendations for Future Research From the Bifactor-(S−1) Perspective

Symmetrical bifactor models are frequently applied to diverse symptoms of psychopathology to identify a general P factor. This factor is assumed to mark shared liability across all psychopathology dimensions and mental disorders. Despite their popularity, however, symmetrical bifactor models of P often yield anomalous results, including but not limited to nonsignificant or negative specific factor variances and nonsignificant or negative factor loadings. To date, these anomalies have often been treated as nuisances to be explained away. In this article, we demonstrate why these anomalies alter the substantive meaning of P such that it (a) does not reflect general liability to psychopathology and (b) differs in meaning across studies. We then describe an alternative modeling framework, the bifactor-( S−1) approach. This method avoids anomalous results, provides a framework for explaining unexpected findings in published symmetrical bifactor studies, and yields a well-defined general factor that can be compared across studies when researchers hypothesize what construct they consider “transdiagnostically meaningful” and measure it directly. We present an empirical example to illustrate these points and provide concrete recommendations to help researchers decide for or against specific variants of bifactor structure.

A-96 Stability of ImPACT’s Latent Structure from Baseline to Post Concussion Assessment

Objective ImPACT is commonly used for sport-concussion management. Baseline and post-concussion tests serve as within-athlete comparisons for return-to-play decision-making. Previous literature has questioned whether ImPACT’s five composites accurately represent the internal structure of its cognitive scores. A recent alternative four-factor structure has strong confirmatory evidence for baseline scores (Maietta et al., doi:10.1037/pas0001014). The present study examined the stability of these constructs post-concussion. Method The current study utilized a case-matched design (age, sex, sport category) to select a sample of 3560 high school athletes’ baseline (n = 1780) and post-concussion (n = 1780) assessments. Multi-group CFA of first-order, hierarchical, and bifactor models was conducted to assess measurement invariance (configural, metric, scalar, and residual invariance) between baseline and post-concussion samples. Change in comparative fit indices was interpreted as the primary indicator of model invariance. Results ImPACT’s five composite structure, as well as the hierarchical and bifactor models, exhibited inadequate fit to the baseline and post-concussion data. The four-factor model demonstrated superior fit in the baseline sample and good fit in the post-concussion sample. The four-factor structure demonstrated invariance across injury status (baseline to post-concussion). Conclusion Given that ImPACT’s scores are used for return-to-play decision-making, it is important that they are psychometrically sound. Recent literature suggests that ImPACT’s five composites are not an adequate representation of the cognitive constructs. Findings support validity of the four-factor structure despite injury status, suggesting these cognitive constructs are assessable at both pre- and post-concussion. Additional research is needed to determine implications of these findings for tracking cognitive change following sport-related concussion and making return-to-play decisions.

Stress reactivity and social cognition in pure and co-occurring early childhood relational bullying and victimization

This study tested the independent effects and interactions of sympathetic nervous system reactivity and hostile attribution biases (HAB) in predicting change in pure and co-occurring relational bullying and victimization experiences over one year. Co-occurring and pure relational bullying and victimization experiences were measured using a dimensional bifactor model, aiming to address methodological limitations of categorical approaches, using data from 300 preschoolers (Mage = 44.70 months, SD = 4.38). Factor scores were then saved and used in nested path analyses with a subset of participants (n = 81) to test main study hypotheses regarding effects of HAB and skin conductance level reactivity (SCL-R). Bifactor models provided good fit to the data at two independent time points. HAB and SCL-R interacted to predict increases in co-occurring relational bullying/victimization with evidence for over- and underarousal pathways.

The Role of General and Specific Cognitive Abilities in Predicting Performance of Three Occupations: Evidence from Bifactor Models

Cognitive abilities are related to job performance. However, there is less agreement about the relative contribution of general versus specific cognitive abilities to job performance. Similarly, it is not clear how cognitive abilities operate in the context of complex occupations. This study assessed the role of cognitive abilities on the performance of three aviation-related jobs: flying, navigation, and air battle management (ABM). Correlated-factor and bifactor models were used to draw a conclusion about the predictive relations between cognitive abilities and job performance. Overall, the importance of particular cognitive abilities tends to vary across the three occupations, and each occupation has different sets of essential abilities. Importantly, the interplay of general versus specific abilities is different across occupations, and some specific abilities also show substantial predictive power.

Issues in Estimating Interpretable Lower Order Factors in Second-Order Hierarchical Models: Commentary on Clark et al. (2021)

In a previous issue of Clinical Psychological Science, Clark and colleagues asserted that lower order factors in second-order models are comparable with specific factors in bifactor models when residualized on the general factor. Modeling simulated data demonstrated that residualized lower order factors are correlated with bifactor-specific factors only to the extent that factor loadings are proportional. Modeling actual data with violations of proportionality showed that specific and residualized lower order factors are not always highly correlated and have differential correlations with criterion variables even when both models fit acceptably. Because proportionality constraints limit only second-order models, bifactor models should be the first option for hierarchical modeling.

Going “Up” to Move Forward: S-1 Bifactor Models and the Study of Neurocognitive Abilities in Psychopathology

Recently, structural models of psychopathology, that address the diagnostic stability and comorbidity issues of the traditional nosological approach, have dominated much of the psychopathology literature. Structural approaches have given rise to the p-factor, which is claimed to reflect an individual’s propensity toward all common psychopathological symptoms. Neurocognitive abilities are argued to be important to the development and maintenance of a wide range of disorders, and have been suggested as an important driver of the p-factor. However, recent evidence argues against p being an interpretable substantive construct, limiting conclusions that can be drawn from associations between p, the specific factors of a psychopathology model, and neurocognitive abilities. Here, we argue for the use of the S-1 bifactor approach, where the general factor is defined by neurocognitive abilities, to explore the association between neurocognitive performance and a wide range of psychopathological symptoms. We use simulation techniques to give examples of how S-1 bifactor models can be used to examine this relationship, and how the results can be interpreted.

Reliability and Validity of Bifactor Models of Dimensional Psychopathology in Youth from three Continents

Bifactor models are a promising strategy to parse general from specific aspects of psychopathology in youth. Currently, there are multiple configurations of bifactor models originating from different theoretical and empirical perspectives. Our aim is to identify and test the reliability, validity, measurement invariance, and the correlation of different bifactor models of psychopathology using the Child Behavior Checklist (CBCL). We used data from the Reproducible Brain Charts (RBC) initiative (N=7,011, ages 5 to 22 years, 40.2% females). Factor models were tested using the baseline data. To address our aim, we a) mapped the published bifactor models using the CBCL; b) tested their global model fit; c) calculated model-based reliability indices. d) tested associations with symptoms' impact in everyday life; e) tested measurement invariance across many characteristics and f) analyzed the observed factor correlation across the models. We found 11 bifactor models ranging from 39 to 116 items. Their global model fit was broadly similar. Factor determinacy and H index were acceptable for the p-factors, internalizing, externalizing and somatic specific factors in most models. However, only p- and attention factors were predictors of symptoms' impact in all models. Models were broadly invariant across different characteristics. P-factors were highly correlated across models (r = 0.88 to 0.99). Homotypic specific factors were also highly correlated. Regardless of item selection and strategy to compose CBCL bifactor models, results suggest that they all assess very similar constructs. Our results provide support for the robustness of the bifactor of psychopathology and distinct study characteristics.