Reliability and validity of bifactor models of dimensional psychopathology in youth.

心理学 CBCL公司 精神病理学 相关性 可靠性(半导体) 因子分析 临床心理学 统计 数学 几何学 量子力学 物理 功率(物理)
作者
Maurício Scopel Hoffmann,Tyler M. Moore,Luiza Kvitko Axelrud,Nim Tottenham,Xi‐Nian Zuo,Luís Augusto Rohde,Michael Peter Milham,Theodore Daniel Satterthwaite,Giovanni Abrahão Salum
出处
期刊:Journal of psychopathology and clinical science [American Psychological Association]
卷期号:131 (4): 407-421 被引量:38
标识
DOI:10.1037/abn0000749
摘要

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. We aimed to 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) searched for the published item-level 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 the p- and attention factors predicted daily life symptoms' impact in all models. Models were broadly invariant across different characteristics. P-factors were highly correlated across models (r = .88 to .99) and homotypic specific factors were highly correlated. These results suggest that regardless of item selection and strategy to compose CBCL bifactor models, they assess very similar constructs. Taken together, our results support the robustness of the p-factor across distinct bifactor models and studies of distinct characteristics. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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