Co-occurrence of psychiatric disorders is welldocumented. Recent quantitative efforts have moved toward an understanding of this phenomenon, with the 'general psychopathology' or p-factor model emerging as the most prominent characterization. Over the past decade, bifactor model analysis has become increasingly popular as a statistical approach to describe common/shared and unique elements in psychopathology. However, recent work has highlighted potential problems with common approaches to evaluating and interpreting bifactor models. Here, we argue that, when properly applied and interpreted, bifactor models can be useful for answering some important questions in psychology and psychiatry research. We review problems with evaluating bifactor models based on global model fit statistics. We then describe more valid approaches to evaluating bifactor models and highlight three types of research questions for which bifactor models are wellsuited to answer. We also discuss the utility and limits of bifactor applications in genetic and neurobiological research. We close by comparing advantages and disadvantages of bifactor models to other analytic approaches and noting that no statistical model is a panacea to rectify limitations of the research design used to gather data. 1 Sometimes, group factors are called "specific factors." However, "specific factor" more correctly refers to an item's reliable (non-error) variance that is not shared with other items (5).

Co-occurrence of psychiatric disorders is well-documented. Recent quantitative efforts have moved toward an understanding of this phenomenon, with the ‘general psychopathology’ or p-factor model emerging as the most prominent characterization. Over the past decade, bifactor model analysis has become increasingly popular as a statistical approach to describe common/shared and unique elements in psychopathology. However, recent work has highlighted potential problems with common approaches to evaluating and interpreting bifactor models. Here, we argue that, when properly applied and interpreted, bifactor models can be useful for answering some important questions in psychology and psychiatry research. We review problems with evaluating bifactor models based on global model fit statistics. We then describe more valid approaches to evaluating bifactor models and highlight three types of research questions for which bifactor models are well-suited to answer. We also discuss the utility and limits of bifactor applications in genetic and neurobiological research. We close by comparing advantages and disadvantages of bifactor models to other analytic approaches and noting that no statistical model is a panacea to rectify limitations of the research design used to gather data.

BackgroundParental characteristics and practices predict borderline personality disorder (BPD) symptoms in children. However, it is difficult to disentangle whether these effects are genetically or environmentally mediated. The present study examines the contributions of genetic and environmental influences by comparing the effects of familial risk factors (i.e. parental psychopathology and borderline traits, maladaptive parenting, marital discord) on child BPD traits in genetically related (biological) and non-related (adoptive) families.MethodsData are from 409 adoptive and 208 biological families who participated in the Siblings Interaction and Behavior Study (SIBS) and 580 twin families the Minnesota Twin Family Study (MTFS). Parent characteristics and practices included parental psychopathology (measured via structured clinical interviews), parental BPD traits, parenting behaviors, and marital discord. A series of multi-level regression models were estimated to examine the relationship of familial risk factors to child BPD traits and to test whether children's adoptive status moderated the association.ResultsSymptom counts of parents' conduct disorder, adult antisocial behavior, nicotine, alcohol, and illicit drug dependence, and paternal BPD traits substantially predicted child BPD traits only in biological offspring, implying genetic transmission. Maternal BPD traits and both maternal and paternal conflict, lack of regard, and lack of involvement predicted offspring BPD traits regardless of the adoptive status, implying environmental transmission.ConclusionsParental externalizing psychopathology and father's BPD traits contribute genetic risk for offspring BPD traits, but mothers' BPD traits and parents' poor parenting constitute environmental risks for the development of these offspring traits.

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