Harmonizing bifactor models of psychopathology between distinct assessment instruments: Reliability, measurement invariance, and authenticity

Model configuration is important for mental health data harmonization. We provide a method to investigate the performance of different bifactor model configurations to harmonize different instruments.

[1]  R. Krueger,et al.  Seven reasons why binary diagnostic categories should be replaced with empirically sounder and less stigmatizing dimensions , 2022, JCPP advances.

[2]  M. Milham,et al.  Heterogeneity in caregiving-related early adversity: Creating stable dimensions and subtypes , 2022, Development and Psychopathology.

[3]  J. Turner,et al.  The Enhancing NeuroImaging Genetics through Meta‐Analysis Consortium: 10 Years of Global Collaborations in Human Brain Mapping , 2021, Human brain mapping.

[4]  M. Milham,et al.  Reliability and Validity of Bifactor Models of Dimensional Psychopathology in Youth from three Continents , 2021, medRxiv.

[5]  D. Watson,et al.  Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): II. Externalizing superspectrum , 2021, World psychiatry : official journal of the World Psychiatric Association.

[6]  R. Krueger,et al.  Unraveling the Optimum Latent Structure of Attention-Deficit/Hyperactivity Disorder: Evidence Supporting ICD and HiTOP Frameworks , 2021, Frontiers in Psychiatry.

[7]  D. Watson,et al.  The Hierarchical Taxonomy of Psychopathology (HiTOP): A Quantitative Nosology Based on Consensus of Evidence. , 2021, Annual review of clinical psychology.

[8]  A. Goodman,et al.  Psychological distress from early adulthood to early old age: evidence from the 1946, 1958 and 1970 British birth cohorts , 2021, Psychological Medicine.

[9]  D. Watson,et al.  What Is the General Factor of Psychopathology? Consistency of the p Factor Across Samples , 2020, Assessment.

[10]  Ashley L. Watts,et al.  Building Theories on Top of, and Not Independent of, Statistical Models: The Case of the p-factor , 2020, Psychological inquiry.

[11]  P. Fonagy,et al.  Changes in the adult consequences of adolescent mental ill-health: findings from the 1958 and 1970 British birth cohorts , 2020, Psychological Medicine.

[12]  Xi-Nian Zuo,et al.  Cohort Profile: Chinese Color Nest Project , 2020 .

[13]  A. Shabalin,et al.  General v. specific vulnerabilities: polygenic risk scores and higher-order psychopathology dimensions in the Adolescent Brain Cognitive Development (ABCD) Study , 2020, Psychological Medicine.

[14]  Malerie G. McDowell,et al.  Criterion Validity and Relationships between Alternative Hierarchical Dimensional Models of General and Specific Psychopathology , 2020, bioRxiv.

[15]  A. Caspi,et al.  Longitudinal Assessment of Mental Health Disorders and Comorbidities Across 4 Decades Among Participants in the Dunedin Birth Cohort Study , 2020, JAMA network open.

[16]  C. Sripada,et al.  The General Factor of Psychopathology in the Adolescent Brain Cognitive Development (ABCD) Study: A Comparison of Alternative Modeling Approaches , 2020, Clinical psychological science : a journal of the Association for Psychological Science.

[17]  David C. Glahn,et al.  Extensions of Multiple-Group Item Response Theory Alignment: Application to Psychiatric Phenotypes in an International Genomics Consortium , 2020, Educational and psychological measurement.

[18]  Gregory T. Smith,et al.  The General Factor of Psychopathology. , 2020, Annual review of clinical psychology.

[19]  Brenton M. Wiernik,et al.  Appropriate Use of Bifactor Analysis in Psychopathology Research: Appreciating Benefits and Limitations , 2020, Biological Psychiatry.

[20]  G. Ploubidis,et al.  A longitudinal examination of the measurement equivalence of mental health assessments in two British birth cohorts , 2019, Longitudinal and Life Course Studies.

[21]  J. Ormel,et al.  The wide‐ranging life outcome correlates of a general psychopathology factor in adolescent psychopathology , 2019, Personality and mental health.

[22]  P. Fonagy,et al.  Evaluating Bifactor Models of Psychopathology Using Model-Based Reliability Indices , 2019 .

[23]  Adon F. G. Rosen,et al.  Evidence for Dissociable Linkage of Dimensions of Psychopathology to Brain Structure in Youths. , 2019, The American journal of psychiatry.

[24]  C. Greenwood,et al.  General psychopathology, internalising and externalising in children and functional outcomes in late adolescence , 2019, Journal of child psychology and psychiatry, and allied disciplines.

[25]  Adon F. G. Rosen,et al.  S12. Dimensions of Psychopathology are Dissociably Linked to Brain Structure in Youth , 2019, Biological Psychiatry.

[26]  R. Plomin,et al.  The p factor: genetic analyses support a general dimension of psychopathology in childhood and adolescence , 2019, bioRxiv.

[27]  M. Eid,et al.  Giving G a Meaning: An Application of the Bifactor-(S-1) Approach to Realize a More Symptom-Oriented Modeling of the Beck Depression Inventory–II , 2018, Assessment.

[28]  Avshalom Caspi,et al.  All for One and One for All: Mental Disorders in One Dimension. , 2018, The American journal of psychiatry.

[29]  Joshua F. Wiley,et al.  MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus , 2018, Structural equation modeling : a multidisciplinary journal.

[30]  P. Vidal-Ribas,et al.  Should Clinicians Split or Lump Psychiatric Symptoms? The Structure of Psychopathology in Two Large Pediatric Clinical Samples from England and Norway , 2017, Child Psychiatry & Human Development.

[31]  J. Belsky,et al.  Developmental stability of general and specific factors of psychopathology from early childhood to adolescence: dynamic mutualism or p‐differentiation? , 2017, Journal of child psychology and psychiatry, and allied disciplines.

[32]  Russell T. Shinohara,et al.  Common and Dissociable Regional Cerebral Blood Flow Differences Associate with Dimensions of Psychopathology Across Categorical Diagnoses , 2017, Molecular Psychiatry.

[33]  Natan Vega Potler,et al.  An open resource for transdiagnostic research in pediatric mental health and learning disorders , 2017, Scientific Data.

[34]  S. Reise,et al.  Applying Bifactor Statistical Indices in the Evaluation of Psychological Measures , 2016, Journal of personality assessment.

[35]  Efstathios D. Gennatas,et al.  Common and Dissociable Mechanisms of Executive System Dysfunction Across Psychiatric Disorders in Youth. , 2016, The American journal of psychiatry.

[36]  Mark A. Elliott,et al.  The Philadelphia Neurodevelopmental Cohort: A publicly available resource for the study of normal and abnormal brain development in youth , 2016, NeuroImage.

[37]  Kosha Ruparel,et al.  The Philadelphia Neurodevelopmental Cohort: constructing a deep phenotyping collaborative. , 2015, Journal of child psychology and psychiatry, and allied disciplines.

[38]  G. Salum,et al.  High risk cohort study for psychiatric disorders in childhood: rationale, design, methods and preliminary results , 2015, International journal of methods in psychiatric research.

[39]  Thomas E. Nichols,et al.  The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data , 2014, Brain Imaging and Behavior.

[40]  Margaret D. King,et al.  The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry , 2012, Front. Neurosci..

[41]  T. Insel,et al.  Wesleyan University From the SelectedWorks of Charles A . Sanislow , Ph . D . 2010 Research Domain Criteria ( RDoC ) : Toward a New Classification Framework for Research on Mental Disorders , 2018 .

[42]  F. Chen Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance , 2007 .

[43]  Gerome Breen,et al.  Psychiatric Genomics: An Update and an Agenda , 2017, bioRxiv.

[44]  David M. Dueber Bifactor Indices Calculator: A Microsoft Excel-Based Tool to Calculate Various Indices Relevant to Bifactor CFA Models , 2017 .

[45]  Susan Bachman,et al.  ► Agenda , 2016, Geriatrie et psychologie neuropsychiatrie du vieillissement.

[46]  R. Barkley History of ADHD. , 2015 .

[47]  Bengt,et al.  Latent Variable Analysis With Categorical Outcomes : Multiple-Group And Growth Modeling In Mplus , 2002 .

[48]  T. Achenbach Manual for ASEBA School-Age Forms & Profiles , 2001 .

[49]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[50]  T. Achenbach,et al.  The classification of children's psychiatric symptoms: a factor-analytic study. , 1966, Psychological monographs.